diff --git a/.env.test b/.env.test index 4ef1b503c..44662728d 100644 --- a/.env.test +++ b/.env.test @@ -21,7 +21,6 @@ OPENROUTER_API_KEY=fake-openrouter-key AWS_ACCESS_KEY_ID=fake-aws-access-key AWS_SECRET_ACCESS_KEY=fake-aws-secret-key AWS_DEFAULT_REGION=us-east-1 -AWS_REGION_NAME=us-east-1 # ----------------------------------------------------------------------------- # Azure OpenAI Configuration diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 04284c7fe..1b3000647 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -1,8 +1,6 @@ name: Publish to PyPI on: - repository_dispatch: - types: [deployment-tests-passed] workflow_dispatch: inputs: release_tag: @@ -20,11 +18,8 @@ jobs: - name: Determine release tag id: release run: | - # Priority: workflow_dispatch input > repository_dispatch payload > default branch if [ -n "${{ inputs.release_tag }}" ]; then echo "tag=${{ inputs.release_tag }}" >> $GITHUB_OUTPUT - elif [ -n "${{ github.event.client_payload.release_tag }}" ]; then - echo "tag=${{ github.event.client_payload.release_tag }}" >> $GITHUB_OUTPUT else echo "tag=" >> $GITHUB_OUTPUT fi diff --git a/.github/workflows/trigger-deployment-tests.yml b/.github/workflows/trigger-deployment-tests.yml deleted file mode 100644 index eaad490a5..000000000 --- a/.github/workflows/trigger-deployment-tests.yml +++ /dev/null @@ -1,18 +0,0 @@ -name: Trigger Deployment Tests - -on: - release: - types: [published] - -jobs: - trigger: - name: Trigger deployment tests - runs-on: ubuntu-latest - steps: - - name: Trigger deployment tests - uses: peter-evans/repository-dispatch@v3 - with: - token: ${{ secrets.CREWAI_DEPLOYMENTS_PAT }} - repository: ${{ secrets.CREWAI_DEPLOYMENTS_REPOSITORY }} - event-type: crewai-release - client-payload: '{"release_tag": "${{ github.event.release.tag_name }}", "release_name": "${{ github.event.release.name }}"}' diff --git a/.gitignore b/.gitignore index 53164cfdc..785c2c299 100644 --- a/.gitignore +++ b/.gitignore @@ -27,3 +27,6 @@ conceptual_plan.md build_image chromadb-*.lock .claude +.crewai/memory +blogs/* +secrets/* diff --git a/conftest.py b/conftest.py index 50392e10d..9b2c7c5c4 100644 --- a/conftest.py +++ b/conftest.py @@ -11,7 +11,12 @@ from typing import Any from dotenv import load_dotenv import pytest from vcr.request import Request # type: ignore[import-untyped] -import vcr.stubs.httpx_stubs as httpx_stubs # type: ignore[import-untyped] + + +try: + import vcr.stubs.httpx_stubs as httpx_stubs # type: ignore[import-untyped] +except ModuleNotFoundError: + import vcr.stubs.httpcore_stubs as httpx_stubs # type: ignore[import-untyped] env_test_path = Path(__file__).parent / ".env.test" diff --git a/docs/docs.json b/docs/docs.json index 161d6d5ff..84eed2947 100644 --- a/docs/docs.json +++ b/docs/docs.json @@ -54,449 +54,922 @@ } ] }, - "tabs": [ + "versions": [ { - "tab": "Home", - "icon": "house", - "groups": [ + "version": "v1.10.1", + "default": true, + "tabs": [ { - "group": "Welcome", - "pages": [ - "index" - ] - } - ] - }, - { - "tab": "Documentation", - "icon": "book-open", - "groups": [ - { - "group": "Get Started", - "pages": [ - "en/introduction", - "en/installation", - "en/quickstart" - ] - }, - { - "group": "Guides", - "pages": [ + "tab": "Home", + "icon": "house", + "groups": [ { - "group": "Strategy", - "icon": "compass", + "group": "Welcome", "pages": [ - "en/guides/concepts/evaluating-use-cases" - ] - }, - { - "group": "Agents", - "icon": "user", - "pages": [ - "en/guides/agents/crafting-effective-agents" - ] - }, - { - "group": "Crews", - "icon": "users", - "pages": [ - "en/guides/crews/first-crew" - ] - }, - { - "group": "Flows", - "icon": "code-branch", - "pages": [ - "en/guides/flows/first-flow", - "en/guides/flows/mastering-flow-state" - ] - }, - { - "group": "Coding Tools", - "icon": "terminal", - "pages": [ - "en/guides/coding-tools/agents-md" - ] - }, - { - "group": "Advanced", - "icon": "gear", - "pages": [ - "en/guides/advanced/customizing-prompts", - "en/guides/advanced/fingerprinting" + "index" ] } ] }, { - "group": "Core Concepts", - "pages": [ - "en/concepts/agents", - "en/concepts/tasks", - "en/concepts/crews", - "en/concepts/flows", - "en/concepts/production-architecture", - "en/concepts/knowledge", - "en/concepts/llms", - "en/concepts/files", - "en/concepts/processes", - "en/concepts/collaboration", - "en/concepts/training", - "en/concepts/memory", - "en/concepts/reasoning", - "en/concepts/planning", - "en/concepts/testing", - "en/concepts/cli", - "en/concepts/tools", - "en/concepts/event-listener" - ] - }, - { - "group": "MCP Integration", - "pages": [ - "en/mcp/overview", - "en/mcp/dsl-integration", - "en/mcp/stdio", - "en/mcp/sse", - "en/mcp/streamable-http", - "en/mcp/multiple-servers", - "en/mcp/security" - ] - }, - { - "group": "Tools", - "pages": [ - "en/tools/overview", + "tab": "Documentation", + "icon": "book-open", + "groups": [ { - "group": "File & Document", - "icon": "folder-open", + "group": "Get Started", "pages": [ - "en/tools/file-document/overview", - "en/tools/file-document/filereadtool", - "en/tools/file-document/filewritetool", - "en/tools/file-document/pdfsearchtool", - "en/tools/file-document/docxsearchtool", - "en/tools/file-document/mdxsearchtool", - "en/tools/file-document/xmlsearchtool", - "en/tools/file-document/txtsearchtool", - "en/tools/file-document/jsonsearchtool", - "en/tools/file-document/csvsearchtool", - "en/tools/file-document/directorysearchtool", - "en/tools/file-document/directoryreadtool", - "en/tools/file-document/ocrtool", - "en/tools/file-document/pdf-text-writing-tool" + "en/introduction", + "en/installation", + "en/quickstart" ] }, { - "group": "Web Scraping & Browsing", - "icon": "globe", + "group": "Guides", "pages": [ - "en/tools/web-scraping/overview", - "en/tools/web-scraping/scrapewebsitetool", - "en/tools/web-scraping/scrapeelementfromwebsitetool", - "en/tools/web-scraping/scrapflyscrapetool", - "en/tools/web-scraping/seleniumscrapingtool", - "en/tools/web-scraping/scrapegraphscrapetool", - "en/tools/web-scraping/spidertool", - "en/tools/web-scraping/browserbaseloadtool", - "en/tools/web-scraping/hyperbrowserloadtool", - "en/tools/web-scraping/stagehandtool", - "en/tools/web-scraping/firecrawlcrawlwebsitetool", - "en/tools/web-scraping/firecrawlscrapewebsitetool", - "en/tools/web-scraping/oxylabsscraperstool", - "en/tools/web-scraping/brightdata-tools" + { + "group": "Strategy", + "icon": "compass", + "pages": [ + "en/guides/concepts/evaluating-use-cases" + ] + }, + { + "group": "Agents", + "icon": "user", + "pages": [ + "en/guides/agents/crafting-effective-agents" + ] + }, + { + "group": "Crews", + "icon": "users", + "pages": [ + "en/guides/crews/first-crew" + ] + }, + { + "group": "Flows", + "icon": "code-branch", + "pages": [ + "en/guides/flows/first-flow", + "en/guides/flows/mastering-flow-state" + ] + }, + { + "group": "Coding Tools", + "icon": "terminal", + "pages": [ + "en/guides/coding-tools/agents-md" + ] + }, + { + "group": "Advanced", + "icon": "gear", + "pages": [ + "en/guides/advanced/customizing-prompts", + "en/guides/advanced/fingerprinting" + ] + }, + { + "group": "Migration", + "icon": "shuffle", + "pages": [ + "en/guides/migration/migrating-from-langgraph" + ] + } ] }, { - "group": "Search & Research", - "icon": "magnifying-glass", + "group": "Core Concepts", "pages": [ - "en/tools/search-research/overview", - "en/tools/search-research/serperdevtool", - "en/tools/search-research/bravesearchtool", - "en/tools/search-research/exasearchtool", - "en/tools/search-research/linkupsearchtool", - "en/tools/search-research/githubsearchtool", - "en/tools/search-research/websitesearchtool", - "en/tools/search-research/codedocssearchtool", - "en/tools/search-research/youtubechannelsearchtool", - "en/tools/search-research/youtubevideosearchtool", - "en/tools/search-research/tavilysearchtool", - "en/tools/search-research/tavilyextractortool", - "en/tools/search-research/arxivpapertool", - "en/tools/search-research/serpapi-googlesearchtool", - "en/tools/search-research/serpapi-googleshoppingtool", - "en/tools/search-research/databricks-query-tool" + "en/concepts/agents", + "en/concepts/tasks", + "en/concepts/crews", + "en/concepts/flows", + "en/concepts/production-architecture", + "en/concepts/knowledge", + "en/concepts/llms", + "en/concepts/files", + "en/concepts/processes", + "en/concepts/collaboration", + "en/concepts/training", + "en/concepts/memory", + "en/concepts/reasoning", + "en/concepts/planning", + "en/concepts/testing", + "en/concepts/cli", + "en/concepts/tools", + "en/concepts/event-listener" ] }, { - "group": "Database & Data", - "icon": "database", + "group": "MCP Integration", "pages": [ - "en/tools/database-data/overview", - "en/tools/database-data/mysqltool", - "en/tools/database-data/pgsearchtool", - "en/tools/database-data/snowflakesearchtool", - "en/tools/database-data/nl2sqltool", - "en/tools/database-data/qdrantvectorsearchtool", - "en/tools/database-data/weaviatevectorsearchtool", - "en/tools/database-data/mongodbvectorsearchtool", - "en/tools/database-data/singlestoresearchtool" + "en/mcp/overview", + "en/mcp/dsl-integration", + "en/mcp/stdio", + "en/mcp/sse", + "en/mcp/streamable-http", + "en/mcp/multiple-servers", + "en/mcp/security" ] }, { - "group": "AI & Machine Learning", - "icon": "brain", + "group": "Tools", "pages": [ - "en/tools/ai-ml/overview", - "en/tools/ai-ml/dalletool", - "en/tools/ai-ml/visiontool", - "en/tools/ai-ml/aimindtool", - "en/tools/ai-ml/llamaindextool", - "en/tools/ai-ml/langchaintool", - "en/tools/ai-ml/ragtool", - "en/tools/ai-ml/codeinterpretertool" + "en/tools/overview", + { + "group": "File & Document", + "icon": "folder-open", + "pages": [ + "en/tools/file-document/overview", + "en/tools/file-document/filereadtool", + "en/tools/file-document/filewritetool", + "en/tools/file-document/pdfsearchtool", + "en/tools/file-document/docxsearchtool", + "en/tools/file-document/mdxsearchtool", + "en/tools/file-document/xmlsearchtool", + "en/tools/file-document/txtsearchtool", + "en/tools/file-document/jsonsearchtool", + "en/tools/file-document/csvsearchtool", + "en/tools/file-document/directorysearchtool", + "en/tools/file-document/directoryreadtool", + "en/tools/file-document/ocrtool", + "en/tools/file-document/pdf-text-writing-tool" + ] + }, + { + "group": "Web Scraping & Browsing", + "icon": "globe", + "pages": [ + "en/tools/web-scraping/overview", + "en/tools/web-scraping/scrapewebsitetool", + "en/tools/web-scraping/scrapeelementfromwebsitetool", + "en/tools/web-scraping/scrapflyscrapetool", + "en/tools/web-scraping/seleniumscrapingtool", + "en/tools/web-scraping/scrapegraphscrapetool", + "en/tools/web-scraping/spidertool", + "en/tools/web-scraping/browserbaseloadtool", + "en/tools/web-scraping/hyperbrowserloadtool", + "en/tools/web-scraping/stagehandtool", + "en/tools/web-scraping/firecrawlcrawlwebsitetool", + "en/tools/web-scraping/firecrawlscrapewebsitetool", + "en/tools/web-scraping/oxylabsscraperstool", + "en/tools/web-scraping/brightdata-tools" + ] + }, + { + "group": "Search & Research", + "icon": "magnifying-glass", + "pages": [ + "en/tools/search-research/overview", + "en/tools/search-research/serperdevtool", + "en/tools/search-research/bravesearchtool", + "en/tools/search-research/exasearchtool", + "en/tools/search-research/linkupsearchtool", + "en/tools/search-research/githubsearchtool", + "en/tools/search-research/websitesearchtool", + "en/tools/search-research/codedocssearchtool", + "en/tools/search-research/youtubechannelsearchtool", + "en/tools/search-research/youtubevideosearchtool", + "en/tools/search-research/tavilysearchtool", + "en/tools/search-research/tavilyextractortool", + "en/tools/search-research/arxivpapertool", + "en/tools/search-research/serpapi-googlesearchtool", + "en/tools/search-research/serpapi-googleshoppingtool", + "en/tools/search-research/databricks-query-tool" + ] + }, + { + "group": "Database & Data", + "icon": "database", + "pages": [ + "en/tools/database-data/overview", + "en/tools/database-data/mysqltool", + "en/tools/database-data/pgsearchtool", + "en/tools/database-data/snowflakesearchtool", + "en/tools/database-data/nl2sqltool", + "en/tools/database-data/qdrantvectorsearchtool", + "en/tools/database-data/weaviatevectorsearchtool", + "en/tools/database-data/mongodbvectorsearchtool", + "en/tools/database-data/singlestoresearchtool" + ] + }, + { + "group": "AI & Machine Learning", + "icon": "brain", + "pages": [ + "en/tools/ai-ml/overview", + "en/tools/ai-ml/dalletool", + "en/tools/ai-ml/visiontool", + "en/tools/ai-ml/aimindtool", + "en/tools/ai-ml/llamaindextool", + "en/tools/ai-ml/langchaintool", + "en/tools/ai-ml/ragtool", + "en/tools/ai-ml/codeinterpretertool" + ] + }, + { + "group": "Cloud & Storage", + "icon": "cloud", + "pages": [ + "en/tools/cloud-storage/overview", + "en/tools/cloud-storage/s3readertool", + "en/tools/cloud-storage/s3writertool", + "en/tools/cloud-storage/bedrockkbretriever" + ] + }, + { + "group": "Integrations", + "icon": "plug", + "pages": [ + "en/tools/integration/overview", + "en/tools/integration/bedrockinvokeagenttool", + "en/tools/integration/crewaiautomationtool", + "en/tools/integration/mergeagenthandlertool" + ] + }, + { + "group": "Automation", + "icon": "bolt", + "pages": [ + "en/tools/automation/overview", + "en/tools/automation/apifyactorstool", + "en/tools/automation/composiotool", + "en/tools/automation/multiontool", + "en/tools/automation/zapieractionstool" + ] + } ] }, { - "group": "Cloud & Storage", - "icon": "cloud", + "group": "Observability", "pages": [ - "en/tools/cloud-storage/overview", - "en/tools/cloud-storage/s3readertool", - "en/tools/cloud-storage/s3writertool", - "en/tools/cloud-storage/bedrockkbretriever" + "en/observability/tracing", + "en/observability/overview", + "en/observability/arize-phoenix", + "en/observability/braintrust", + "en/observability/datadog", + "en/observability/galileo", + "en/observability/langdb", + "en/observability/langfuse", + "en/observability/langtrace", + "en/observability/maxim", + "en/observability/mlflow", + "en/observability/neatlogs", + "en/observability/openlit", + "en/observability/opik", + "en/observability/patronus-evaluation", + "en/observability/portkey", + "en/observability/weave", + "en/observability/truefoundry" ] }, { - "group": "Integrations", - "icon": "plug", + "group": "Learn", "pages": [ - "en/tools/integration/overview", - "en/tools/integration/bedrockinvokeagenttool", - "en/tools/integration/crewaiautomationtool", - "en/tools/integration/mergeagenthandlertool" + "en/learn/overview", + "en/learn/llm-selection-guide", + "en/learn/conditional-tasks", + "en/learn/coding-agents", + "en/learn/create-custom-tools", + "en/learn/custom-llm", + "en/learn/custom-manager-agent", + "en/learn/customizing-agents", + "en/learn/dalle-image-generation", + "en/learn/force-tool-output-as-result", + "en/learn/hierarchical-process", + "en/learn/human-input-on-execution", + "en/learn/human-in-the-loop", + "en/learn/human-feedback-in-flows", + "en/learn/kickoff-async", + "en/learn/kickoff-for-each", + "en/learn/llm-connections", + "en/learn/multimodal-agents", + "en/learn/replay-tasks-from-latest-crew-kickoff", + "en/learn/sequential-process", + "en/learn/using-annotations", + "en/learn/execution-hooks", + "en/learn/llm-hooks", + "en/learn/tool-hooks" ] }, { - "group": "Automation", - "icon": "bolt", + "group": "Telemetry", "pages": [ - "en/tools/automation/overview", - "en/tools/automation/apifyactorstool", - "en/tools/automation/composiotool", - "en/tools/automation/multiontool", - "en/tools/automation/zapieractionstool" + "en/telemetry" ] } ] }, { - "group": "Observability", - "pages": [ - "en/observability/tracing", - "en/observability/overview", - "en/observability/arize-phoenix", - "en/observability/braintrust", - "en/observability/datadog", - "en/observability/galileo", - "en/observability/langdb", - "en/observability/langfuse", - "en/observability/langtrace", - "en/observability/maxim", - "en/observability/mlflow", - "en/observability/neatlogs", - "en/observability/openlit", - "en/observability/opik", - "en/observability/patronus-evaluation", - "en/observability/portkey", - "en/observability/weave", - "en/observability/truefoundry" + "tab": "AMP", + "icon": "briefcase", + "groups": [ + { + "group": "Getting Started", + "pages": [ + "en/enterprise/introduction" + ] + }, + { + "group": "Build", + "pages": [ + "en/enterprise/features/automations", + "en/enterprise/features/crew-studio", + "en/enterprise/features/marketplace", + "en/enterprise/features/agent-repositories", + "en/enterprise/features/tools-and-integrations", + "en/enterprise/features/pii-trace-redactions" + ] + }, + { + "group": "Operate", + "pages": [ + "en/enterprise/features/traces", + "en/enterprise/features/webhook-streaming", + "en/enterprise/features/hallucination-guardrail", + "en/enterprise/features/flow-hitl-management" + ] + }, + { + "group": "Manage", + "pages": [ + "en/enterprise/features/rbac" + ] + }, + { + "group": "Integration Docs", + "pages": [ + "en/enterprise/integrations/asana", + "en/enterprise/integrations/box", + "en/enterprise/integrations/clickup", + "en/enterprise/integrations/github", + "en/enterprise/integrations/gmail", + "en/enterprise/integrations/google_calendar", + "en/enterprise/integrations/google_contacts", + "en/enterprise/integrations/google_docs", + "en/enterprise/integrations/google_drive", + "en/enterprise/integrations/google_sheets", + "en/enterprise/integrations/google_slides", + "en/enterprise/integrations/hubspot", + "en/enterprise/integrations/jira", + "en/enterprise/integrations/linear", + "en/enterprise/integrations/microsoft_excel", + "en/enterprise/integrations/microsoft_onedrive", + "en/enterprise/integrations/microsoft_outlook", + "en/enterprise/integrations/microsoft_sharepoint", + "en/enterprise/integrations/microsoft_teams", + "en/enterprise/integrations/microsoft_word", + "en/enterprise/integrations/notion", + "en/enterprise/integrations/salesforce", + "en/enterprise/integrations/shopify", + "en/enterprise/integrations/slack", + "en/enterprise/integrations/stripe", + "en/enterprise/integrations/zendesk" + ] + }, + { + "group": "Triggers", + "pages": [ + "en/enterprise/guides/automation-triggers", + "en/enterprise/guides/gmail-trigger", + "en/enterprise/guides/google-calendar-trigger", + "en/enterprise/guides/google-drive-trigger", + "en/enterprise/guides/outlook-trigger", + "en/enterprise/guides/onedrive-trigger", + "en/enterprise/guides/microsoft-teams-trigger", + "en/enterprise/guides/slack-trigger", + "en/enterprise/guides/hubspot-trigger", + "en/enterprise/guides/salesforce-trigger", + "en/enterprise/guides/zapier-trigger" + ] + }, + { + "group": "How-To Guides", + "pages": [ + "en/enterprise/guides/build-crew", + "en/enterprise/guides/prepare-for-deployment", + "en/enterprise/guides/deploy-to-amp", + "en/enterprise/guides/private-package-registry", + "en/enterprise/guides/kickoff-crew", + "en/enterprise/guides/update-crew", + "en/enterprise/guides/enable-crew-studio", + "en/enterprise/guides/capture_telemetry_logs", + "en/enterprise/guides/azure-openai-setup", + "en/enterprise/guides/tool-repository", + "en/enterprise/guides/react-component-export", + "en/enterprise/guides/team-management", + "en/enterprise/guides/human-in-the-loop", + "en/enterprise/guides/webhook-automation" + ] + }, + { + "group": "Resources", + "pages": [ + "en/enterprise/resources/frequently-asked-questions" + ] + } ] }, { - "group": "Learn", - "pages": [ - "en/learn/overview", - "en/learn/llm-selection-guide", - "en/learn/conditional-tasks", - "en/learn/coding-agents", - "en/learn/create-custom-tools", - "en/learn/custom-llm", - "en/learn/custom-manager-agent", 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[ + "en/examples/example", + "en/examples/cookbooks" + ] + } + ] + }, + { + "tab": "Changelog", + "icon": "clock", + "groups": [ + { + "group": "Release Notes", + "pages": [ + "en/changelog" + ] + } ] } ] }, { - "tab": "AMP", - "icon": "briefcase", - "groups": [ + "version": "v1.10.0", + "tabs": [ { - "group": "Getting Started", - "pages": [ - "en/enterprise/introduction" + "tab": "Home", + "icon": "house", + "groups": [ + { + "group": "Welcome", + "pages": [ + "index" + ] + } ] }, { - "group": "Build", - "pages": [ - "en/enterprise/features/automations", - "en/enterprise/features/crew-studio", - "en/enterprise/features/marketplace", - "en/enterprise/features/agent-repositories", - "en/enterprise/features/tools-and-integrations", - "en/enterprise/features/pii-trace-redactions" + "tab": "Documentation", + "icon": "book-open", + "groups": [ + { + "group": "Get Started", + "pages": [ + "en/introduction", + "en/installation", + "en/quickstart" + ] + }, + { + "group": "Guides", + "pages": [ 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"pt-BR/tools/integration/bedrockinvokeagenttool", + "pt-BR/tools/integration/crewaiautomationtool" + ] + }, + { + "group": "Automação", + "icon": "bolt", + "pages": [ + "pt-BR/tools/automation/overview", + "pt-BR/tools/automation/apifyactorstool", + "pt-BR/tools/automation/composiotool", + "pt-BR/tools/automation/multiontool" + ] + } ] }, { - "group": "Cloud & Armazenamento", - "icon": "cloud", + "group": "Observabilidade", "pages": [ - "pt-BR/tools/cloud-storage/overview", - "pt-BR/tools/cloud-storage/s3readertool", - "pt-BR/tools/cloud-storage/s3writertool", - "pt-BR/tools/cloud-storage/bedrockkbretriever" + "pt-BR/observability/tracing", + "pt-BR/observability/overview", + "pt-BR/observability/arize-phoenix", + "pt-BR/observability/braintrust", + "pt-BR/observability/datadog", + "pt-BR/observability/galileo", + "pt-BR/observability/langdb", + "pt-BR/observability/langfuse", + "pt-BR/observability/langtrace", + "pt-BR/observability/maxim", + "pt-BR/observability/mlflow", + 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"pt-BR/observability/mlflow", - "pt-BR/observability/openlit", - "pt-BR/observability/opik", - "pt-BR/observability/patronus-evaluation", - "pt-BR/observability/portkey", - "pt-BR/observability/weave", - "pt-BR/observability/truefoundry" + "tab": "AMP", + "icon": "briefcase", + "groups": [ + { + "group": "Começando", + "pages": [ + "pt-BR/enterprise/introduction" + ] + }, + { + "group": "Construir", + "pages": [ + "pt-BR/enterprise/features/automations", + "pt-BR/enterprise/features/crew-studio", + "pt-BR/enterprise/features/marketplace", + "pt-BR/enterprise/features/agent-repositories", + "pt-BR/enterprise/features/tools-and-integrations", + "pt-BR/enterprise/features/pii-trace-redactions" + ] + }, + { + "group": "Operar", + "pages": [ + "pt-BR/enterprise/features/traces", + "pt-BR/enterprise/features/webhook-streaming", + "pt-BR/enterprise/features/hallucination-guardrail", + "pt-BR/enterprise/features/flow-hitl-management" + ] + }, + { + "group": "Gerenciar", + "pages": [ + "pt-BR/enterprise/features/rbac" + ] + }, + { + "group": "Documentação de Integração", + "pages": [ + "pt-BR/enterprise/integrations/asana", + "pt-BR/enterprise/integrations/box", + "pt-BR/enterprise/integrations/clickup", + "pt-BR/enterprise/integrations/github", + "pt-BR/enterprise/integrations/gmail", + "pt-BR/enterprise/integrations/google_calendar", + "pt-BR/enterprise/integrations/google_contacts", + "pt-BR/enterprise/integrations/google_docs", + "pt-BR/enterprise/integrations/google_drive", + "pt-BR/enterprise/integrations/google_sheets", + "pt-BR/enterprise/integrations/google_slides", + "pt-BR/enterprise/integrations/hubspot", + "pt-BR/enterprise/integrations/jira", + "pt-BR/enterprise/integrations/linear", + "pt-BR/enterprise/integrations/microsoft_excel", + "pt-BR/enterprise/integrations/microsoft_onedrive", + "pt-BR/enterprise/integrations/microsoft_outlook", + "pt-BR/enterprise/integrations/microsoft_sharepoint", + "pt-BR/enterprise/integrations/microsoft_teams", + "pt-BR/enterprise/integrations/microsoft_word", + "pt-BR/enterprise/integrations/notion", + "pt-BR/enterprise/integrations/salesforce", + "pt-BR/enterprise/integrations/shopify", + "pt-BR/enterprise/integrations/slack", + "pt-BR/enterprise/integrations/stripe", + "pt-BR/enterprise/integrations/zendesk" + ] + }, + { + "group": "Guias", + "pages": [ + "pt-BR/enterprise/guides/build-crew", + "pt-BR/enterprise/guides/prepare-for-deployment", + "pt-BR/enterprise/guides/deploy-to-amp", + "pt-BR/enterprise/guides/private-package-registry", + "pt-BR/enterprise/guides/kickoff-crew", + "pt-BR/enterprise/guides/update-crew", + "pt-BR/enterprise/guides/enable-crew-studio", + "pt-BR/enterprise/guides/azure-openai-setup", + "pt-BR/enterprise/guides/tool-repository", + "pt-BR/enterprise/guides/react-component-export", + "pt-BR/enterprise/guides/team-management", + "pt-BR/enterprise/guides/human-in-the-loop", + "pt-BR/enterprise/guides/webhook-automation" + ] + }, + { + "group": "Triggers", + "pages": [ + "pt-BR/enterprise/guides/automation-triggers", + "pt-BR/enterprise/guides/gmail-trigger", + "pt-BR/enterprise/guides/google-calendar-trigger", + "pt-BR/enterprise/guides/google-drive-trigger", + "pt-BR/enterprise/guides/outlook-trigger", + "pt-BR/enterprise/guides/onedrive-trigger", + "pt-BR/enterprise/guides/microsoft-teams-trigger", + "pt-BR/enterprise/guides/slack-trigger", + "pt-BR/enterprise/guides/hubspot-trigger", + "pt-BR/enterprise/guides/salesforce-trigger", + "pt-BR/enterprise/guides/zapier-trigger" + ] + }, + { + "group": "Recursos", + "pages": [ + "pt-BR/enterprise/resources/frequently-asked-questions" + ] + } ] }, { - "group": "Aprenda", - "pages": [ - "pt-BR/learn/overview", - "pt-BR/learn/llm-selection-guide", - "pt-BR/learn/conditional-tasks", - "pt-BR/learn/coding-agents", - "pt-BR/learn/create-custom-tools", - "pt-BR/learn/custom-llm", - "pt-BR/learn/custom-manager-agent", - "pt-BR/learn/customizing-agents", - "pt-BR/learn/dalle-image-generation", - "pt-BR/learn/force-tool-output-as-result", - "pt-BR/learn/hierarchical-process", - "pt-BR/learn/human-input-on-execution", - "pt-BR/learn/human-in-the-loop", - "pt-BR/learn/human-feedback-in-flows", - "pt-BR/learn/kickoff-async", - "pt-BR/learn/kickoff-for-each", - "pt-BR/learn/llm-connections", - "pt-BR/learn/multimodal-agents", - "pt-BR/learn/replay-tasks-from-latest-crew-kickoff", - "pt-BR/learn/sequential-process", - "pt-BR/learn/using-annotations", - "pt-BR/learn/execution-hooks", - "pt-BR/learn/llm-hooks", - "pt-BR/learn/tool-hooks" + "tab": "Referência da API", + "icon": "magnifying-glass", + "groups": [ + { + "group": "Começando", + "pages": [ + "pt-BR/api-reference/introduction", + "pt-BR/api-reference/inputs", + "pt-BR/api-reference/kickoff", + "pt-BR/api-reference/resume", + "pt-BR/api-reference/status" + ] + } ] }, { - "group": "Telemetria", - "pages": [ - "pt-BR/telemetry" + "tab": "Exemplos", + "icon": "code", + "groups": [ + { + "group": "Exemplos", + "pages": [ + "pt-BR/examples/example", + "pt-BR/examples/cookbooks" + ] + } + ] + }, + { + "tab": "Notas de Versão", + "icon": "clock", + "groups": [ + { + "group": "Notas de Versão", + "pages": [ + "pt-BR/changelog" + ] + } ] } ] }, { - "tab": "AMP", - "icon": "briefcase", - "groups": [ + "version": "v1.10.0", + "tabs": [ { - "group": "Começando", - "pages": [ - "pt-BR/enterprise/introduction" + "tab": "Início", + "icon": "house", + "groups": [ + { + "group": "Bem-vindo", + "pages": [ + "pt-BR/index" + ] + } ] }, { - "group": "Construir", - "pages": [ - "pt-BR/enterprise/features/automations", - "pt-BR/enterprise/features/crew-studio", - "pt-BR/enterprise/features/marketplace", - "pt-BR/enterprise/features/agent-repositories", - "pt-BR/enterprise/features/tools-and-integrations", - "pt-BR/enterprise/features/pii-trace-redactions" + "tab": "Documentação", + "icon": "book-open", + "groups": [ + { + "group": "Começando", + "pages": [ + "pt-BR/introduction", + "pt-BR/installation", + "pt-BR/quickstart" + ] + }, + { + "group": "Guias", + "pages": [ + { + "group": "Estratégia", + "icon": "compass", + "pages": [ + "pt-BR/guides/concepts/evaluating-use-cases" + ] + }, + { + "group": "Agentes", + "icon": "user", + "pages": [ + "pt-BR/guides/agents/crafting-effective-agents" + ] + }, + { + "group": "Crews", + "icon": "users", + "pages": [ + "pt-BR/guides/crews/first-crew" + ] + }, + { + "group": "Flows", + "icon": "code-branch", + "pages": [ + "pt-BR/guides/flows/first-flow", + "pt-BR/guides/flows/mastering-flow-state" + ] + }, + { + "group": "Avançado", + "icon": "gear", + "pages": [ + "pt-BR/guides/advanced/customizing-prompts", + "pt-BR/guides/advanced/fingerprinting" + ] + }, + { + "group": "Migração", + "icon": "shuffle", + "pages": [ + "pt-BR/guides/migration/migrating-from-langgraph" + ] + } + ] + }, + { + "group": "Conceitos-Chave", + "pages": [ + "pt-BR/concepts/agents", + "pt-BR/concepts/tasks", + "pt-BR/concepts/crews", + "pt-BR/concepts/flows", + 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"pt-BR/tools/file-document/docxsearchtool", + "pt-BR/tools/file-document/mdxsearchtool", + "pt-BR/tools/file-document/xmlsearchtool", + "pt-BR/tools/file-document/txtsearchtool", + "pt-BR/tools/file-document/jsonsearchtool", + "pt-BR/tools/file-document/csvsearchtool", + "pt-BR/tools/file-document/directorysearchtool", + "pt-BR/tools/file-document/directoryreadtool" + ] + }, + { + "group": "Web Scraping & Navegação", + "icon": "globe", + "pages": [ + "pt-BR/tools/web-scraping/overview", + "pt-BR/tools/web-scraping/scrapewebsitetool", + "pt-BR/tools/web-scraping/scrapeelementfromwebsitetool", + "pt-BR/tools/web-scraping/scrapflyscrapetool", + "pt-BR/tools/web-scraping/seleniumscrapingtool", + "pt-BR/tools/web-scraping/scrapegraphscrapetool", + "pt-BR/tools/web-scraping/spidertool", + "pt-BR/tools/web-scraping/browserbaseloadtool", + "pt-BR/tools/web-scraping/hyperbrowserloadtool", + "pt-BR/tools/web-scraping/stagehandtool", + "pt-BR/tools/web-scraping/firecrawlcrawlwebsitetool", + "pt-BR/tools/web-scraping/firecrawlscrapewebsitetool", + "pt-BR/tools/web-scraping/oxylabsscraperstool" + ] + }, + { + "group": "Pesquisa", + "icon": "magnifying-glass", + "pages": [ + "pt-BR/tools/search-research/overview", + "pt-BR/tools/search-research/serperdevtool", + "pt-BR/tools/search-research/bravesearchtool", + "pt-BR/tools/search-research/exasearchtool", + "pt-BR/tools/search-research/linkupsearchtool", + "pt-BR/tools/search-research/githubsearchtool", + "pt-BR/tools/search-research/websitesearchtool", + "pt-BR/tools/search-research/codedocssearchtool", + "pt-BR/tools/search-research/youtubechannelsearchtool", + "pt-BR/tools/search-research/youtubevideosearchtool" + ] + }, + { + "group": "Dados", + "icon": "database", + "pages": [ + "pt-BR/tools/database-data/overview", + "pt-BR/tools/database-data/mysqltool", + "pt-BR/tools/database-data/pgsearchtool", + "pt-BR/tools/database-data/snowflakesearchtool", + "pt-BR/tools/database-data/nl2sqltool", + "pt-BR/tools/database-data/qdrantvectorsearchtool", + "pt-BR/tools/database-data/weaviatevectorsearchtool" + ] + }, + { + "group": "IA & Machine Learning", + "icon": "brain", + "pages": [ + "pt-BR/tools/ai-ml/overview", + "pt-BR/tools/ai-ml/dalletool", + "pt-BR/tools/ai-ml/visiontool", + "pt-BR/tools/ai-ml/aimindtool", + "pt-BR/tools/ai-ml/llamaindextool", + "pt-BR/tools/ai-ml/langchaintool", + "pt-BR/tools/ai-ml/ragtool", + "pt-BR/tools/ai-ml/codeinterpretertool" + ] + }, + { + "group": "Cloud & Armazenamento", + "icon": "cloud", + "pages": [ + "pt-BR/tools/cloud-storage/overview", + "pt-BR/tools/cloud-storage/s3readertool", + "pt-BR/tools/cloud-storage/s3writertool", + "pt-BR/tools/cloud-storage/bedrockkbretriever" + ] + }, + { + "group": "Integrations", + "icon": "plug", + "pages": [ + "pt-BR/tools/integration/overview", + "pt-BR/tools/integration/bedrockinvokeagenttool", + "pt-BR/tools/integration/crewaiautomationtool" + ] + }, + { + "group": "Automação", + "icon": "bolt", + "pages": [ + "pt-BR/tools/automation/overview", + "pt-BR/tools/automation/apifyactorstool", + "pt-BR/tools/automation/composiotool", + "pt-BR/tools/automation/multiontool" + ] + } + ] + }, + { + "group": "Observabilidade", + "pages": [ + "pt-BR/observability/tracing", + "pt-BR/observability/overview", + "pt-BR/observability/arize-phoenix", + "pt-BR/observability/braintrust", + "pt-BR/observability/datadog", + "pt-BR/observability/galileo", + "pt-BR/observability/langdb", + "pt-BR/observability/langfuse", + "pt-BR/observability/langtrace", + "pt-BR/observability/maxim", + "pt-BR/observability/mlflow", + "pt-BR/observability/openlit", + "pt-BR/observability/opik", + "pt-BR/observability/patronus-evaluation", + "pt-BR/observability/portkey", + "pt-BR/observability/weave", + "pt-BR/observability/truefoundry" + ] + }, + { + "group": "Aprenda", + "pages": [ + "pt-BR/learn/overview", + "pt-BR/learn/llm-selection-guide", + "pt-BR/learn/conditional-tasks", + "pt-BR/learn/coding-agents", + "pt-BR/learn/create-custom-tools", + "pt-BR/learn/custom-llm", + "pt-BR/learn/custom-manager-agent", + "pt-BR/learn/customizing-agents", + "pt-BR/learn/dalle-image-generation", + "pt-BR/learn/force-tool-output-as-result", + "pt-BR/learn/hierarchical-process", + "pt-BR/learn/human-input-on-execution", + "pt-BR/learn/human-in-the-loop", + "pt-BR/learn/human-feedback-in-flows", + "pt-BR/learn/kickoff-async", + "pt-BR/learn/kickoff-for-each", + "pt-BR/learn/llm-connections", + "pt-BR/learn/multimodal-agents", + "pt-BR/learn/replay-tasks-from-latest-crew-kickoff", + "pt-BR/learn/sequential-process", + "pt-BR/learn/using-annotations", + "pt-BR/learn/execution-hooks", + "pt-BR/learn/llm-hooks", + "pt-BR/learn/tool-hooks" + ] + }, + { + "group": "Telemetria", + "pages": [ + "pt-BR/telemetry" + ] + } ] }, { - "group": "Operar", - "pages": [ - "pt-BR/enterprise/features/traces", - "pt-BR/enterprise/features/webhook-streaming", - "pt-BR/enterprise/features/hallucination-guardrail", - "pt-BR/enterprise/features/flow-hitl-management" + "tab": "AMP", + "icon": "briefcase", + "groups": [ + { + "group": "Começando", + "pages": [ + "pt-BR/enterprise/introduction" + ] + }, + { + "group": "Construir", + "pages": [ + "pt-BR/enterprise/features/automations", + "pt-BR/enterprise/features/crew-studio", + "pt-BR/enterprise/features/marketplace", + "pt-BR/enterprise/features/agent-repositories", + "pt-BR/enterprise/features/tools-and-integrations", + "pt-BR/enterprise/features/pii-trace-redactions" + ] + }, + { + "group": "Operar", + "pages": [ + "pt-BR/enterprise/features/traces", + "pt-BR/enterprise/features/webhook-streaming", + "pt-BR/enterprise/features/hallucination-guardrail", + "pt-BR/enterprise/features/flow-hitl-management" + ] + }, + { + "group": "Gerenciar", + "pages": [ + "pt-BR/enterprise/features/rbac" + ] + }, + { + "group": "Documentação de Integração", + "pages": [ + "pt-BR/enterprise/integrations/asana", + "pt-BR/enterprise/integrations/box", + "pt-BR/enterprise/integrations/clickup", + "pt-BR/enterprise/integrations/github", + "pt-BR/enterprise/integrations/gmail", + "pt-BR/enterprise/integrations/google_calendar", + "pt-BR/enterprise/integrations/google_contacts", + "pt-BR/enterprise/integrations/google_docs", + "pt-BR/enterprise/integrations/google_drive", + "pt-BR/enterprise/integrations/google_sheets", + "pt-BR/enterprise/integrations/google_slides", + "pt-BR/enterprise/integrations/hubspot", + "pt-BR/enterprise/integrations/jira", + "pt-BR/enterprise/integrations/linear", + "pt-BR/enterprise/integrations/microsoft_excel", + "pt-BR/enterprise/integrations/microsoft_onedrive", + "pt-BR/enterprise/integrations/microsoft_outlook", + "pt-BR/enterprise/integrations/microsoft_sharepoint", + "pt-BR/enterprise/integrations/microsoft_teams", + "pt-BR/enterprise/integrations/microsoft_word", + "pt-BR/enterprise/integrations/notion", + "pt-BR/enterprise/integrations/salesforce", + "pt-BR/enterprise/integrations/shopify", + "pt-BR/enterprise/integrations/slack", + "pt-BR/enterprise/integrations/stripe", + "pt-BR/enterprise/integrations/zendesk" + ] + }, + { + "group": "Guias", + "pages": [ + "pt-BR/enterprise/guides/build-crew", + "pt-BR/enterprise/guides/prepare-for-deployment", + "pt-BR/enterprise/guides/deploy-to-amp", + "pt-BR/enterprise/guides/private-package-registry", + "pt-BR/enterprise/guides/kickoff-crew", + "pt-BR/enterprise/guides/update-crew", + "pt-BR/enterprise/guides/enable-crew-studio", + "pt-BR/enterprise/guides/azure-openai-setup", + "pt-BR/enterprise/guides/tool-repository", + "pt-BR/enterprise/guides/react-component-export", + "pt-BR/enterprise/guides/team-management", + "pt-BR/enterprise/guides/human-in-the-loop", + "pt-BR/enterprise/guides/webhook-automation" + ] + }, + { + "group": "Triggers", + "pages": [ + "pt-BR/enterprise/guides/automation-triggers", + "pt-BR/enterprise/guides/gmail-trigger", + "pt-BR/enterprise/guides/google-calendar-trigger", + "pt-BR/enterprise/guides/google-drive-trigger", + "pt-BR/enterprise/guides/outlook-trigger", + "pt-BR/enterprise/guides/onedrive-trigger", + "pt-BR/enterprise/guides/microsoft-teams-trigger", + "pt-BR/enterprise/guides/slack-trigger", + "pt-BR/enterprise/guides/hubspot-trigger", + "pt-BR/enterprise/guides/salesforce-trigger", + "pt-BR/enterprise/guides/zapier-trigger" + ] + }, + { + "group": "Recursos", + "pages": [ + "pt-BR/enterprise/resources/frequently-asked-questions" + ] + } ] }, { - "group": "Gerenciar", - "pages": [ - "pt-BR/enterprise/features/rbac" + "tab": "Referência da API", + "icon": "magnifying-glass", + "groups": [ + { + "group": "Começando", + "pages": [ + "pt-BR/api-reference/introduction", + "pt-BR/api-reference/inputs", + "pt-BR/api-reference/kickoff", + "pt-BR/api-reference/resume", + "pt-BR/api-reference/status" + ] + } ] }, { - "group": "Documentação de Integração", - "pages": [ - "pt-BR/enterprise/integrations/asana", - "pt-BR/enterprise/integrations/box", - "pt-BR/enterprise/integrations/clickup", - "pt-BR/enterprise/integrations/github", - "pt-BR/enterprise/integrations/gmail", - "pt-BR/enterprise/integrations/google_calendar", - "pt-BR/enterprise/integrations/google_contacts", - "pt-BR/enterprise/integrations/google_docs", - "pt-BR/enterprise/integrations/google_drive", - "pt-BR/enterprise/integrations/google_sheets", - "pt-BR/enterprise/integrations/google_slides", - "pt-BR/enterprise/integrations/hubspot", - "pt-BR/enterprise/integrations/jira", - "pt-BR/enterprise/integrations/linear", - "pt-BR/enterprise/integrations/microsoft_excel", - "pt-BR/enterprise/integrations/microsoft_onedrive", - "pt-BR/enterprise/integrations/microsoft_outlook", - "pt-BR/enterprise/integrations/microsoft_sharepoint", - "pt-BR/enterprise/integrations/microsoft_teams", - "pt-BR/enterprise/integrations/microsoft_word", - "pt-BR/enterprise/integrations/notion", - "pt-BR/enterprise/integrations/salesforce", - "pt-BR/enterprise/integrations/shopify", - "pt-BR/enterprise/integrations/slack", - "pt-BR/enterprise/integrations/stripe", - "pt-BR/enterprise/integrations/zendesk" + "tab": "Exemplos", + "icon": "code", + "groups": [ + { + "group": "Exemplos", + "pages": [ + "pt-BR/examples/example", + "pt-BR/examples/cookbooks" + ] + } ] }, { - "group": "Guias", - "pages": [ - "pt-BR/enterprise/guides/build-crew", - "pt-BR/enterprise/guides/prepare-for-deployment", - "pt-BR/enterprise/guides/deploy-to-amp", - "pt-BR/enterprise/guides/kickoff-crew", - "pt-BR/enterprise/guides/update-crew", - "pt-BR/enterprise/guides/enable-crew-studio", - "pt-BR/enterprise/guides/azure-openai-setup", - "pt-BR/enterprise/guides/tool-repository", - "pt-BR/enterprise/guides/react-component-export", - "pt-BR/enterprise/guides/team-management", - "pt-BR/enterprise/guides/human-in-the-loop", - "pt-BR/enterprise/guides/webhook-automation" - ] - }, - { - "group": "Triggers", - "pages": [ - "pt-BR/enterprise/guides/automation-triggers", - "pt-BR/enterprise/guides/gmail-trigger", - "pt-BR/enterprise/guides/google-calendar-trigger", - "pt-BR/enterprise/guides/google-drive-trigger", - "pt-BR/enterprise/guides/outlook-trigger", - "pt-BR/enterprise/guides/onedrive-trigger", - "pt-BR/enterprise/guides/microsoft-teams-trigger", - "pt-BR/enterprise/guides/slack-trigger", - "pt-BR/enterprise/guides/hubspot-trigger", - "pt-BR/enterprise/guides/salesforce-trigger", - "pt-BR/enterprise/guides/zapier-trigger" - ] - }, - { - "group": "Recursos", - "pages": [ - "pt-BR/enterprise/resources/frequently-asked-questions" - ] - } - ] - }, - { - "tab": "Referência da API", - "icon": "magnifying-glass", - "groups": [ - { - "group": "Começando", - "pages": [ - "pt-BR/api-reference/introduction", - "pt-BR/api-reference/inputs", - "pt-BR/api-reference/kickoff", - "pt-BR/api-reference/resume", - "pt-BR/api-reference/status" - ] - } - ] - }, - { - "tab": "Exemplos", - "icon": "code", - "groups": [ - { - "group": "Exemplos", - "pages": [ - "pt-BR/examples/example", - "pt-BR/examples/cookbooks" - ] - } - ] - }, - { - "tab": "Notas de Versão", - "icon": "clock", - "groups": [ - { - "group": "Notas de Versão", - "pages": [ - "pt-BR/changelog" + "tab": "Notas de Versão", + "icon": "clock", + "groups": [ + { + "group": "Notas de Versão", + "pages": [ + "pt-BR/changelog" + ] + } ] } ] @@ -982,439 +1906,902 @@ } ] }, - "tabs": [ + "versions": [ { - "tab": "홈", - "icon": "house", - "groups": [ + "version": "v1.10.1", + "default": true, + "tabs": [ { - "group": "환영합니다", - "pages": [ - "ko/index" - ] - } - ] - }, - { - "tab": "기술 문서", - "icon": "book-open", - "groups": [ - { - "group": "시작 안내", - "pages": [ - "ko/introduction", - "ko/installation", - "ko/quickstart" - ] - }, - { - "group": "가이드", - "pages": [ + "tab": "홈", + "icon": "house", + "groups": [ { - "group": "전략", - "icon": "compass", + "group": "환영합니다", "pages": [ - "ko/guides/concepts/evaluating-use-cases" - ] - }, - { - "group": "에이전트 (Agents)", - "icon": "user", - "pages": [ - "ko/guides/agents/crafting-effective-agents" - ] - }, - { - "group": "크루 (Crews)", - "icon": "users", - "pages": [ - "ko/guides/crews/first-crew" - ] - }, - { - "group": "플로우 (Flows)", - "icon": "code-branch", - "pages": [ - "ko/guides/flows/first-flow", - "ko/guides/flows/mastering-flow-state" - ] - }, - { - "group": "고급", - "icon": "gear", - "pages": [ - "ko/guides/advanced/customizing-prompts", - "ko/guides/advanced/fingerprinting" + "ko/index" ] } ] }, { - "group": "핵심 개념", - "pages": [ - "ko/concepts/agents", - "ko/concepts/tasks", - "ko/concepts/crews", - "ko/concepts/flows", - "ko/concepts/production-architecture", - "ko/concepts/knowledge", - "ko/concepts/llms", - "ko/concepts/files", - "ko/concepts/processes", - "ko/concepts/collaboration", - "ko/concepts/training", - "ko/concepts/memory", - "ko/concepts/reasoning", - "ko/concepts/planning", - "ko/concepts/testing", - "ko/concepts/cli", - "ko/concepts/tools", - "ko/concepts/event-listener" - ] - }, - { - "group": "MCP 통합", - "pages": [ - "ko/mcp/overview", - "ko/mcp/dsl-integration", - "ko/mcp/stdio", - "ko/mcp/sse", - "ko/mcp/streamable-http", - "ko/mcp/multiple-servers", - "ko/mcp/security" - ] - }, - { - "group": "도구 (Tools)", - "pages": [ - "ko/tools/overview", + "tab": "기술 문서", + "icon": "book-open", + "groups": [ { - "group": "파일 & 문서", - "icon": "folder-open", + "group": "시작 안내", "pages": [ - "ko/tools/file-document/overview", - "ko/tools/file-document/filereadtool", - "ko/tools/file-document/filewritetool", - "ko/tools/file-document/pdfsearchtool", - "ko/tools/file-document/docxsearchtool", - "ko/tools/file-document/mdxsearchtool", - "ko/tools/file-document/xmlsearchtool", - "ko/tools/file-document/txtsearchtool", - "ko/tools/file-document/jsonsearchtool", - "ko/tools/file-document/csvsearchtool", - "ko/tools/file-document/directorysearchtool", - "ko/tools/file-document/directoryreadtool", - "ko/tools/file-document/ocrtool", - "ko/tools/file-document/pdf-text-writing-tool" + "ko/introduction", + "ko/installation", + "ko/quickstart" ] }, { - "group": "웹 스크래핑 & 브라우징", - "icon": "globe", + "group": "가이드", "pages": [ - "ko/tools/web-scraping/overview", - "ko/tools/web-scraping/scrapewebsitetool", - "ko/tools/web-scraping/scrapeelementfromwebsitetool", - "ko/tools/web-scraping/scrapflyscrapetool", - "ko/tools/web-scraping/seleniumscrapingtool", - "ko/tools/web-scraping/scrapegraphscrapetool", - "ko/tools/web-scraping/spidertool", - "ko/tools/web-scraping/browserbaseloadtool", - "ko/tools/web-scraping/hyperbrowserloadtool", - "ko/tools/web-scraping/stagehandtool", - "ko/tools/web-scraping/firecrawlcrawlwebsitetool", - "ko/tools/web-scraping/firecrawlscrapewebsitetool", - "ko/tools/web-scraping/oxylabsscraperstool", - "ko/tools/web-scraping/brightdata-tools" + { + "group": "전략", + "icon": "compass", + "pages": [ + "ko/guides/concepts/evaluating-use-cases" + ] + }, + { + "group": "에이전트 (Agents)", + "icon": "user", + "pages": [ + "ko/guides/agents/crafting-effective-agents" + ] + }, + { + "group": "크루 (Crews)", + "icon": "users", + "pages": [ + "ko/guides/crews/first-crew" + ] + }, + { + "group": "플로우 (Flows)", + "icon": "code-branch", + "pages": [ + "ko/guides/flows/first-flow", + "ko/guides/flows/mastering-flow-state" + ] + }, + { + "group": "고급", + "icon": "gear", + "pages": [ + "ko/guides/advanced/customizing-prompts", + "ko/guides/advanced/fingerprinting" + ] + }, + { + "group": "마이그레이션", + "icon": "shuffle", + "pages": [ + "ko/guides/migration/migrating-from-langgraph" + ] + } ] }, { - "group": "검색 및 연구", - "icon": "magnifying-glass", + "group": "핵심 개념", "pages": [ - "ko/tools/search-research/overview", - "ko/tools/search-research/serperdevtool", - "ko/tools/search-research/bravesearchtool", - "ko/tools/search-research/exasearchtool", - "ko/tools/search-research/linkupsearchtool", - "ko/tools/search-research/githubsearchtool", - "ko/tools/search-research/websitesearchtool", - "ko/tools/search-research/codedocssearchtool", - "ko/tools/search-research/youtubechannelsearchtool", - "ko/tools/search-research/youtubevideosearchtool", - "ko/tools/search-research/tavilysearchtool", - "ko/tools/search-research/tavilyextractortool", - "ko/tools/search-research/arxivpapertool", - "ko/tools/search-research/serpapi-googlesearchtool", - "ko/tools/search-research/serpapi-googleshoppingtool", - "ko/tools/search-research/databricks-query-tool" + "ko/concepts/agents", + "ko/concepts/tasks", + "ko/concepts/crews", + "ko/concepts/flows", + "ko/concepts/production-architecture", + "ko/concepts/knowledge", + "ko/concepts/llms", + "ko/concepts/files", + "ko/concepts/processes", + "ko/concepts/collaboration", + "ko/concepts/training", + "ko/concepts/memory", + "ko/concepts/reasoning", + "ko/concepts/planning", + "ko/concepts/testing", + "ko/concepts/cli", + "ko/concepts/tools", + "ko/concepts/event-listener" ] }, { - "group": "데이터베이스 & 데이터", - "icon": "database", + "group": "MCP 통합", "pages": [ - "ko/tools/database-data/overview", - "ko/tools/database-data/mysqltool", - "ko/tools/database-data/pgsearchtool", - "ko/tools/database-data/snowflakesearchtool", - "ko/tools/database-data/nl2sqltool", - "ko/tools/database-data/qdrantvectorsearchtool", - "ko/tools/database-data/weaviatevectorsearchtool", - "ko/tools/database-data/mongodbvectorsearchtool", - "ko/tools/database-data/singlestoresearchtool" + "ko/mcp/overview", + "ko/mcp/dsl-integration", + "ko/mcp/stdio", + "ko/mcp/sse", + "ko/mcp/streamable-http", + "ko/mcp/multiple-servers", + "ko/mcp/security" ] }, { - "group": "인공지능 & 머신러닝", - "icon": "brain", + "group": "도구 (Tools)", "pages": [ - "ko/tools/ai-ml/overview", - "ko/tools/ai-ml/dalletool", - "ko/tools/ai-ml/visiontool", - "ko/tools/ai-ml/aimindtool", - "ko/tools/ai-ml/llamaindextool", - "ko/tools/ai-ml/langchaintool", - "ko/tools/ai-ml/ragtool", - "ko/tools/ai-ml/codeinterpretertool" + "ko/tools/overview", + { + "group": "파일 & 문서", + "icon": "folder-open", + "pages": [ + "ko/tools/file-document/overview", + "ko/tools/file-document/filereadtool", + "ko/tools/file-document/filewritetool", + "ko/tools/file-document/pdfsearchtool", + "ko/tools/file-document/docxsearchtool", + "ko/tools/file-document/mdxsearchtool", + "ko/tools/file-document/xmlsearchtool", + "ko/tools/file-document/txtsearchtool", + "ko/tools/file-document/jsonsearchtool", + "ko/tools/file-document/csvsearchtool", + "ko/tools/file-document/directorysearchtool", + "ko/tools/file-document/directoryreadtool", + "ko/tools/file-document/ocrtool", + "ko/tools/file-document/pdf-text-writing-tool" + ] + }, + { + "group": "웹 스크래핑 & 브라우징", + "icon": "globe", + "pages": [ + "ko/tools/web-scraping/overview", + "ko/tools/web-scraping/scrapewebsitetool", + "ko/tools/web-scraping/scrapeelementfromwebsitetool", + "ko/tools/web-scraping/scrapflyscrapetool", + "ko/tools/web-scraping/seleniumscrapingtool", + "ko/tools/web-scraping/scrapegraphscrapetool", + "ko/tools/web-scraping/spidertool", + "ko/tools/web-scraping/browserbaseloadtool", + "ko/tools/web-scraping/hyperbrowserloadtool", + "ko/tools/web-scraping/stagehandtool", + "ko/tools/web-scraping/firecrawlcrawlwebsitetool", + "ko/tools/web-scraping/firecrawlscrapewebsitetool", + "ko/tools/web-scraping/oxylabsscraperstool", + "ko/tools/web-scraping/brightdata-tools" + ] + }, + { + "group": "검색 및 연구", + "icon": "magnifying-glass", + "pages": [ + "ko/tools/search-research/overview", + "ko/tools/search-research/serperdevtool", + "ko/tools/search-research/bravesearchtool", + "ko/tools/search-research/exasearchtool", + "ko/tools/search-research/linkupsearchtool", + "ko/tools/search-research/githubsearchtool", + "ko/tools/search-research/websitesearchtool", + "ko/tools/search-research/codedocssearchtool", + "ko/tools/search-research/youtubechannelsearchtool", + "ko/tools/search-research/youtubevideosearchtool", + "ko/tools/search-research/tavilysearchtool", + "ko/tools/search-research/tavilyextractortool", + "ko/tools/search-research/arxivpapertool", + "ko/tools/search-research/serpapi-googlesearchtool", + "ko/tools/search-research/serpapi-googleshoppingtool", + "ko/tools/search-research/databricks-query-tool" + ] + }, + { + "group": "데이터베이스 & 데이터", + "icon": "database", + "pages": [ + "ko/tools/database-data/overview", + "ko/tools/database-data/mysqltool", + "ko/tools/database-data/pgsearchtool", + "ko/tools/database-data/snowflakesearchtool", + "ko/tools/database-data/nl2sqltool", + "ko/tools/database-data/qdrantvectorsearchtool", + "ko/tools/database-data/weaviatevectorsearchtool", + "ko/tools/database-data/mongodbvectorsearchtool", + "ko/tools/database-data/singlestoresearchtool" + ] + }, + { + "group": "인공지능 & 머신러닝", + "icon": "brain", + "pages": [ + "ko/tools/ai-ml/overview", + "ko/tools/ai-ml/dalletool", + "ko/tools/ai-ml/visiontool", + "ko/tools/ai-ml/aimindtool", + "ko/tools/ai-ml/llamaindextool", + "ko/tools/ai-ml/langchaintool", + "ko/tools/ai-ml/ragtool", + "ko/tools/ai-ml/codeinterpretertool" + ] + }, + { + "group": "클라우드 & 스토리지", + "icon": "cloud", + "pages": [ + "ko/tools/cloud-storage/overview", + "ko/tools/cloud-storage/s3readertool", + "ko/tools/cloud-storage/s3writertool", + "ko/tools/cloud-storage/bedrockkbretriever" + ] + }, + { + "group": "Integrations", + "icon": "plug", + "pages": [ + "ko/tools/integration/overview", + "ko/tools/integration/bedrockinvokeagenttool", + "ko/tools/integration/crewaiautomationtool" + ] + }, + { + "group": "자동화", + "icon": "bolt", + "pages": [ + "ko/tools/automation/overview", + "ko/tools/automation/apifyactorstool", + "ko/tools/automation/composiotool", + "ko/tools/automation/multiontool", + "ko/tools/automation/zapieractionstool" + ] + } ] }, { - "group": "클라우드 & 스토리지", - "icon": "cloud", + "group": "Observability", "pages": [ - "ko/tools/cloud-storage/overview", - "ko/tools/cloud-storage/s3readertool", - "ko/tools/cloud-storage/s3writertool", - "ko/tools/cloud-storage/bedrockkbretriever" + "ko/observability/tracing", + "ko/observability/overview", + "ko/observability/arize-phoenix", + "ko/observability/braintrust", + "ko/observability/datadog", + "ko/observability/galileo", + "ko/observability/langdb", + "ko/observability/langfuse", + "ko/observability/langtrace", + "ko/observability/maxim", + "ko/observability/mlflow", + "ko/observability/neatlogs", + "ko/observability/openlit", + "ko/observability/opik", + "ko/observability/patronus-evaluation", + "ko/observability/portkey", + "ko/observability/weave" ] }, { - "group": "Integrations", - "icon": "plug", + "group": "학습", "pages": [ - "ko/tools/integration/overview", - "ko/tools/integration/bedrockinvokeagenttool", - "ko/tools/integration/crewaiautomationtool" + "ko/learn/overview", + "ko/learn/llm-selection-guide", + "ko/learn/conditional-tasks", + "ko/learn/coding-agents", + "ko/learn/create-custom-tools", + "ko/learn/custom-llm", + "ko/learn/custom-manager-agent", + "ko/learn/customizing-agents", + "ko/learn/dalle-image-generation", + "ko/learn/force-tool-output-as-result", + "ko/learn/hierarchical-process", + "ko/learn/human-input-on-execution", + "ko/learn/human-in-the-loop", + "ko/learn/human-feedback-in-flows", + "ko/learn/kickoff-async", + "ko/learn/kickoff-for-each", + "ko/learn/llm-connections", + "ko/learn/multimodal-agents", + "ko/learn/replay-tasks-from-latest-crew-kickoff", + "ko/learn/sequential-process", + "ko/learn/using-annotations", + "ko/learn/execution-hooks", + "ko/learn/llm-hooks", + "ko/learn/tool-hooks" ] }, { - "group": "자동화", - "icon": "bolt", + "group": "Telemetry", "pages": [ - "ko/tools/automation/overview", - "ko/tools/automation/apifyactorstool", - "ko/tools/automation/composiotool", - "ko/tools/automation/multiontool", - "ko/tools/automation/zapieractionstool" + "ko/telemetry" ] } ] }, { - "group": "Observability", - "pages": [ - "ko/observability/tracing", - "ko/observability/overview", - "ko/observability/arize-phoenix", - "ko/observability/braintrust", - "ko/observability/datadog", - "ko/observability/galileo", - "ko/observability/langdb", - "ko/observability/langfuse", - "ko/observability/langtrace", - "ko/observability/maxim", - "ko/observability/mlflow", - "ko/observability/neatlogs", - "ko/observability/openlit", - "ko/observability/opik", - "ko/observability/patronus-evaluation", - "ko/observability/portkey", - "ko/observability/weave" + "tab": "엔터프라이즈", + "icon": "briefcase", + "groups": [ + { + "group": "시작 안내", + "pages": [ + "ko/enterprise/introduction" + ] + }, + { + "group": "빌드", + "pages": [ + "ko/enterprise/features/automations", + "ko/enterprise/features/crew-studio", + "ko/enterprise/features/marketplace", + "ko/enterprise/features/agent-repositories", + "ko/enterprise/features/tools-and-integrations", + "ko/enterprise/features/pii-trace-redactions" + ] + }, + { + "group": "운영", + "pages": [ + "ko/enterprise/features/traces", + "ko/enterprise/features/webhook-streaming", + "ko/enterprise/features/hallucination-guardrail", + "ko/enterprise/features/flow-hitl-management" + ] + }, + { + "group": "관리", + "pages": [ + "ko/enterprise/features/rbac" + ] + }, + { + "group": "통합 문서", + "pages": [ + "ko/enterprise/integrations/asana", + "ko/enterprise/integrations/box", + "ko/enterprise/integrations/clickup", + "ko/enterprise/integrations/github", + "ko/enterprise/integrations/gmail", + "ko/enterprise/integrations/google_calendar", + "ko/enterprise/integrations/google_contacts", + "ko/enterprise/integrations/google_docs", + "ko/enterprise/integrations/google_drive", + "ko/enterprise/integrations/google_sheets", + "ko/enterprise/integrations/google_slides", + "ko/enterprise/integrations/hubspot", + "ko/enterprise/integrations/jira", + "ko/enterprise/integrations/linear", + "ko/enterprise/integrations/microsoft_excel", + "ko/enterprise/integrations/microsoft_onedrive", + "ko/enterprise/integrations/microsoft_outlook", + "ko/enterprise/integrations/microsoft_sharepoint", + "ko/enterprise/integrations/microsoft_teams", + "ko/enterprise/integrations/microsoft_word", + "ko/enterprise/integrations/notion", + "ko/enterprise/integrations/salesforce", + "ko/enterprise/integrations/shopify", + "ko/enterprise/integrations/slack", + "ko/enterprise/integrations/stripe", + "ko/enterprise/integrations/zendesk" + ] + }, + { + "group": "How-To Guides", + "pages": [ + "ko/enterprise/guides/build-crew", + "ko/enterprise/guides/prepare-for-deployment", + "ko/enterprise/guides/deploy-to-amp", + "ko/enterprise/guides/private-package-registry", + "ko/enterprise/guides/kickoff-crew", + "ko/enterprise/guides/update-crew", + "ko/enterprise/guides/enable-crew-studio", + "ko/enterprise/guides/azure-openai-setup", + "ko/enterprise/guides/tool-repository", + "ko/enterprise/guides/react-component-export", + "ko/enterprise/guides/team-management", + "ko/enterprise/guides/human-in-the-loop", + "ko/enterprise/guides/webhook-automation" + ] + }, + { + "group": "트리거", + "pages": [ + "ko/enterprise/guides/automation-triggers", + "ko/enterprise/guides/gmail-trigger", + "ko/enterprise/guides/google-calendar-trigger", + "ko/enterprise/guides/google-drive-trigger", + "ko/enterprise/guides/outlook-trigger", + "ko/enterprise/guides/onedrive-trigger", + "ko/enterprise/guides/microsoft-teams-trigger", + "ko/enterprise/guides/slack-trigger", + "ko/enterprise/guides/hubspot-trigger", + "ko/enterprise/guides/salesforce-trigger", + "ko/enterprise/guides/zapier-trigger" + ] + }, + { + "group": "학습 자원", + "pages": [ + "ko/enterprise/resources/frequently-asked-questions" + ] + } ] }, { - "group": "학습", - "pages": [ - "ko/learn/overview", - "ko/learn/llm-selection-guide", - "ko/learn/conditional-tasks", - "ko/learn/coding-agents", - "ko/learn/create-custom-tools", - "ko/learn/custom-llm", - "ko/learn/custom-manager-agent", - "ko/learn/customizing-agents", - "ko/learn/dalle-image-generation", - "ko/learn/force-tool-output-as-result", - "ko/learn/hierarchical-process", - "ko/learn/human-input-on-execution", - "ko/learn/human-in-the-loop", - "ko/learn/human-feedback-in-flows", - "ko/learn/kickoff-async", - "ko/learn/kickoff-for-each", - "ko/learn/llm-connections", - "ko/learn/multimodal-agents", - "ko/learn/replay-tasks-from-latest-crew-kickoff", - "ko/learn/sequential-process", - "ko/learn/using-annotations", - "ko/learn/execution-hooks", - "ko/learn/llm-hooks", - "ko/learn/tool-hooks" + "tab": "API 레퍼런스", + "icon": "magnifying-glass", + "groups": [ + { + "group": "시작 안내", + "pages": [ + "ko/api-reference/introduction", + "ko/api-reference/inputs", + "ko/api-reference/kickoff", + "ko/api-reference/resume", + "ko/api-reference/status" + ] + } ] }, { - "group": "Telemetry", - "pages": [ - "ko/telemetry" + "tab": "예시", + "icon": "code", + "groups": [ + { + "group": "예시", + "pages": [ + "ko/examples/example", + "ko/examples/cookbooks" + ] + } + ] + }, + { + "tab": "변경 로그", + "icon": "clock", + "groups": [ + { + "group": "릴리스 노트", + "pages": [ + "ko/changelog" + ] + } ] } ] }, { - "tab": "엔터프라이즈", - "icon": "briefcase", - "groups": [ + "version": "v1.10.0", + "tabs": [ { - "group": "시작 안내", - "pages": [ - "ko/enterprise/introduction" + "tab": "홈", + "icon": "house", + "groups": [ + { + "group": "환영합니다", + "pages": [ + "ko/index" + ] + } ] }, { - "group": "빌드", - "pages": [ - "ko/enterprise/features/automations", - "ko/enterprise/features/crew-studio", - "ko/enterprise/features/marketplace", - "ko/enterprise/features/agent-repositories", - "ko/enterprise/features/tools-and-integrations", - "ko/enterprise/features/pii-trace-redactions" + "tab": "기술 문서", + "icon": "book-open", + "groups": [ + { + "group": "시작 안내", + "pages": [ + "ko/introduction", + "ko/installation", + "ko/quickstart" + ] + }, + { + "group": "가이드", + "pages": [ + { + "group": "전략", + "icon": "compass", + "pages": [ + "ko/guides/concepts/evaluating-use-cases" + ] + }, + { + "group": "에이전트 (Agents)", + "icon": "user", + "pages": [ + "ko/guides/agents/crafting-effective-agents" + ] + }, + { + "group": "크루 (Crews)", + "icon": "users", + "pages": [ + "ko/guides/crews/first-crew" + ] + }, + { + "group": "플로우 (Flows)", + "icon": "code-branch", + "pages": [ + "ko/guides/flows/first-flow", + "ko/guides/flows/mastering-flow-state" + ] + }, + { + "group": "고급", + "icon": "gear", + "pages": [ + "ko/guides/advanced/customizing-prompts", + "ko/guides/advanced/fingerprinting" + ] + }, + { + "group": "마이그레이션", + "icon": "shuffle", + "pages": [ + "ko/guides/migration/migrating-from-langgraph" + ] + } + ] + }, + { + "group": "핵심 개념", + "pages": [ + "ko/concepts/agents", + "ko/concepts/tasks", + "ko/concepts/crews", + "ko/concepts/flows", + "ko/concepts/production-architecture", + "ko/concepts/knowledge", + "ko/concepts/llms", + "ko/concepts/files", + "ko/concepts/processes", + "ko/concepts/collaboration", + "ko/concepts/training", + "ko/concepts/memory", + "ko/concepts/reasoning", + "ko/concepts/planning", + "ko/concepts/testing", + "ko/concepts/cli", + "ko/concepts/tools", + "ko/concepts/event-listener" + ] + }, + { + "group": "MCP 통합", + "pages": [ + "ko/mcp/overview", + "ko/mcp/dsl-integration", + "ko/mcp/stdio", + "ko/mcp/sse", + "ko/mcp/streamable-http", + "ko/mcp/multiple-servers", + "ko/mcp/security" + ] + }, + { + "group": "도구 (Tools)", + "pages": [ + "ko/tools/overview", + { + "group": "파일 & 문서", + "icon": "folder-open", + "pages": [ + "ko/tools/file-document/overview", + "ko/tools/file-document/filereadtool", + "ko/tools/file-document/filewritetool", + "ko/tools/file-document/pdfsearchtool", + "ko/tools/file-document/docxsearchtool", + "ko/tools/file-document/mdxsearchtool", + "ko/tools/file-document/xmlsearchtool", + "ko/tools/file-document/txtsearchtool", + "ko/tools/file-document/jsonsearchtool", + "ko/tools/file-document/csvsearchtool", + "ko/tools/file-document/directorysearchtool", + "ko/tools/file-document/directoryreadtool", + "ko/tools/file-document/ocrtool", + "ko/tools/file-document/pdf-text-writing-tool" + ] + }, + { + "group": "웹 스크래핑 & 브라우징", + "icon": "globe", + "pages": [ + "ko/tools/web-scraping/overview", + "ko/tools/web-scraping/scrapewebsitetool", + "ko/tools/web-scraping/scrapeelementfromwebsitetool", + "ko/tools/web-scraping/scrapflyscrapetool", + "ko/tools/web-scraping/seleniumscrapingtool", + "ko/tools/web-scraping/scrapegraphscrapetool", + "ko/tools/web-scraping/spidertool", + "ko/tools/web-scraping/browserbaseloadtool", + "ko/tools/web-scraping/hyperbrowserloadtool", + "ko/tools/web-scraping/stagehandtool", + "ko/tools/web-scraping/firecrawlcrawlwebsitetool", + "ko/tools/web-scraping/firecrawlscrapewebsitetool", + "ko/tools/web-scraping/oxylabsscraperstool", + "ko/tools/web-scraping/brightdata-tools" + ] + }, + { + "group": "검색 및 연구", + "icon": "magnifying-glass", + "pages": [ + "ko/tools/search-research/overview", + "ko/tools/search-research/serperdevtool", + "ko/tools/search-research/bravesearchtool", + "ko/tools/search-research/exasearchtool", + "ko/tools/search-research/linkupsearchtool", + "ko/tools/search-research/githubsearchtool", + "ko/tools/search-research/websitesearchtool", + "ko/tools/search-research/codedocssearchtool", + "ko/tools/search-research/youtubechannelsearchtool", + "ko/tools/search-research/youtubevideosearchtool", + "ko/tools/search-research/tavilysearchtool", + "ko/tools/search-research/tavilyextractortool", + "ko/tools/search-research/arxivpapertool", + "ko/tools/search-research/serpapi-googlesearchtool", + "ko/tools/search-research/serpapi-googleshoppingtool", + "ko/tools/search-research/databricks-query-tool" + ] + }, + { + "group": "데이터베이스 & 데이터", + "icon": "database", + "pages": [ + "ko/tools/database-data/overview", + "ko/tools/database-data/mysqltool", + "ko/tools/database-data/pgsearchtool", + "ko/tools/database-data/snowflakesearchtool", + "ko/tools/database-data/nl2sqltool", + "ko/tools/database-data/qdrantvectorsearchtool", + "ko/tools/database-data/weaviatevectorsearchtool", + "ko/tools/database-data/mongodbvectorsearchtool", + "ko/tools/database-data/singlestoresearchtool" + ] + }, + { + "group": "인공지능 & 머신러닝", + "icon": "brain", + "pages": [ + "ko/tools/ai-ml/overview", + "ko/tools/ai-ml/dalletool", + "ko/tools/ai-ml/visiontool", + "ko/tools/ai-ml/aimindtool", + "ko/tools/ai-ml/llamaindextool", + "ko/tools/ai-ml/langchaintool", + "ko/tools/ai-ml/ragtool", + "ko/tools/ai-ml/codeinterpretertool" + ] + }, + { + "group": "클라우드 & 스토리지", + "icon": "cloud", + "pages": [ + "ko/tools/cloud-storage/overview", + "ko/tools/cloud-storage/s3readertool", + "ko/tools/cloud-storage/s3writertool", + "ko/tools/cloud-storage/bedrockkbretriever" + ] + }, + { + "group": "Integrations", + "icon": "plug", + "pages": [ + "ko/tools/integration/overview", + "ko/tools/integration/bedrockinvokeagenttool", + "ko/tools/integration/crewaiautomationtool" + ] + }, + { + "group": "자동화", + "icon": "bolt", + "pages": [ + "ko/tools/automation/overview", + "ko/tools/automation/apifyactorstool", + "ko/tools/automation/composiotool", + "ko/tools/automation/multiontool", + "ko/tools/automation/zapieractionstool" + ] + } + ] + }, + { + "group": "Observability", + "pages": [ + "ko/observability/tracing", + "ko/observability/overview", + "ko/observability/arize-phoenix", + "ko/observability/braintrust", + "ko/observability/datadog", + "ko/observability/galileo", + "ko/observability/langdb", + "ko/observability/langfuse", + "ko/observability/langtrace", + "ko/observability/maxim", + "ko/observability/mlflow", + "ko/observability/neatlogs", + "ko/observability/openlit", + "ko/observability/opik", + "ko/observability/patronus-evaluation", + "ko/observability/portkey", + "ko/observability/weave" + ] + }, + { + "group": "학습", + "pages": [ + "ko/learn/overview", + "ko/learn/llm-selection-guide", + "ko/learn/conditional-tasks", + "ko/learn/coding-agents", + "ko/learn/create-custom-tools", + "ko/learn/custom-llm", + "ko/learn/custom-manager-agent", + "ko/learn/customizing-agents", + "ko/learn/dalle-image-generation", + "ko/learn/force-tool-output-as-result", + "ko/learn/hierarchical-process", + "ko/learn/human-input-on-execution", + "ko/learn/human-in-the-loop", + "ko/learn/human-feedback-in-flows", + "ko/learn/kickoff-async", + "ko/learn/kickoff-for-each", + "ko/learn/llm-connections", + "ko/learn/multimodal-agents", + "ko/learn/replay-tasks-from-latest-crew-kickoff", + "ko/learn/sequential-process", + "ko/learn/using-annotations", + "ko/learn/execution-hooks", + "ko/learn/llm-hooks", + "ko/learn/tool-hooks" + ] + }, + { + "group": "Telemetry", + "pages": [ + "ko/telemetry" + ] + } ] }, { - "group": "운영", - "pages": [ - "ko/enterprise/features/traces", - "ko/enterprise/features/webhook-streaming", - "ko/enterprise/features/hallucination-guardrail", - "ko/enterprise/features/flow-hitl-management" + "tab": "엔터프라이즈", + "icon": "briefcase", + "groups": [ + { + "group": "시작 안내", + "pages": [ + "ko/enterprise/introduction" + ] + }, + { + "group": "빌드", + "pages": [ + "ko/enterprise/features/automations", + "ko/enterprise/features/crew-studio", + "ko/enterprise/features/marketplace", + "ko/enterprise/features/agent-repositories", + "ko/enterprise/features/tools-and-integrations", + "ko/enterprise/features/pii-trace-redactions" + ] + }, + { + "group": "운영", + "pages": [ + "ko/enterprise/features/traces", + "ko/enterprise/features/webhook-streaming", + "ko/enterprise/features/hallucination-guardrail", + "ko/enterprise/features/flow-hitl-management" + ] + }, + { + "group": "관리", + "pages": [ + "ko/enterprise/features/rbac" + ] + }, + { + "group": "통합 문서", + "pages": [ + "ko/enterprise/integrations/asana", + "ko/enterprise/integrations/box", + "ko/enterprise/integrations/clickup", + "ko/enterprise/integrations/github", + "ko/enterprise/integrations/gmail", + "ko/enterprise/integrations/google_calendar", + "ko/enterprise/integrations/google_contacts", + "ko/enterprise/integrations/google_docs", + "ko/enterprise/integrations/google_drive", + "ko/enterprise/integrations/google_sheets", + "ko/enterprise/integrations/google_slides", + "ko/enterprise/integrations/hubspot", + "ko/enterprise/integrations/jira", + "ko/enterprise/integrations/linear", + "ko/enterprise/integrations/microsoft_excel", + "ko/enterprise/integrations/microsoft_onedrive", + "ko/enterprise/integrations/microsoft_outlook", + "ko/enterprise/integrations/microsoft_sharepoint", + "ko/enterprise/integrations/microsoft_teams", + "ko/enterprise/integrations/microsoft_word", + "ko/enterprise/integrations/notion", + "ko/enterprise/integrations/salesforce", + "ko/enterprise/integrations/shopify", + "ko/enterprise/integrations/slack", + "ko/enterprise/integrations/stripe", + "ko/enterprise/integrations/zendesk" + ] + }, + { + "group": "How-To Guides", + "pages": [ + "ko/enterprise/guides/build-crew", + "ko/enterprise/guides/prepare-for-deployment", + "ko/enterprise/guides/deploy-to-amp", + "ko/enterprise/guides/private-package-registry", + "ko/enterprise/guides/kickoff-crew", + "ko/enterprise/guides/update-crew", + "ko/enterprise/guides/enable-crew-studio", + "ko/enterprise/guides/azure-openai-setup", + "ko/enterprise/guides/tool-repository", + "ko/enterprise/guides/react-component-export", + "ko/enterprise/guides/team-management", + "ko/enterprise/guides/human-in-the-loop", + "ko/enterprise/guides/webhook-automation" + ] + }, + { + "group": "트리거", + "pages": [ + "ko/enterprise/guides/automation-triggers", + "ko/enterprise/guides/gmail-trigger", + "ko/enterprise/guides/google-calendar-trigger", + "ko/enterprise/guides/google-drive-trigger", + "ko/enterprise/guides/outlook-trigger", + "ko/enterprise/guides/onedrive-trigger", + "ko/enterprise/guides/microsoft-teams-trigger", + "ko/enterprise/guides/slack-trigger", + "ko/enterprise/guides/hubspot-trigger", + "ko/enterprise/guides/salesforce-trigger", + "ko/enterprise/guides/zapier-trigger" + ] + }, + { + "group": "학습 자원", + "pages": [ + "ko/enterprise/resources/frequently-asked-questions" + ] + } ] }, { - "group": "관리", - "pages": [ - "ko/enterprise/features/rbac" + "tab": "API 레퍼런스", + "icon": "magnifying-glass", + "groups": [ + { + "group": "시작 안내", + "pages": [ + "ko/api-reference/introduction", + "ko/api-reference/inputs", + "ko/api-reference/kickoff", + "ko/api-reference/resume", + "ko/api-reference/status" + ] + } ] }, { - "group": "통합 문서", - "pages": [ - "ko/enterprise/integrations/asana", - "ko/enterprise/integrations/box", - "ko/enterprise/integrations/clickup", - "ko/enterprise/integrations/github", - "ko/enterprise/integrations/gmail", - "ko/enterprise/integrations/google_calendar", - "ko/enterprise/integrations/google_contacts", - "ko/enterprise/integrations/google_docs", - "ko/enterprise/integrations/google_drive", - "ko/enterprise/integrations/google_sheets", - "ko/enterprise/integrations/google_slides", - "ko/enterprise/integrations/hubspot", - "ko/enterprise/integrations/jira", - "ko/enterprise/integrations/linear", - "ko/enterprise/integrations/microsoft_excel", - "ko/enterprise/integrations/microsoft_onedrive", - "ko/enterprise/integrations/microsoft_outlook", - "ko/enterprise/integrations/microsoft_sharepoint", - "ko/enterprise/integrations/microsoft_teams", - "ko/enterprise/integrations/microsoft_word", - "ko/enterprise/integrations/notion", - "ko/enterprise/integrations/salesforce", - "ko/enterprise/integrations/shopify", - "ko/enterprise/integrations/slack", - "ko/enterprise/integrations/stripe", - "ko/enterprise/integrations/zendesk" + "tab": "예시", + "icon": "code", + "groups": [ + { + "group": "예시", + "pages": [ + "ko/examples/example", + "ko/examples/cookbooks" + ] + } ] }, { - "group": "How-To Guides", - "pages": [ - "ko/enterprise/guides/build-crew", - "ko/enterprise/guides/prepare-for-deployment", - "ko/enterprise/guides/deploy-to-amp", - "ko/enterprise/guides/kickoff-crew", - "ko/enterprise/guides/update-crew", - "ko/enterprise/guides/enable-crew-studio", - "ko/enterprise/guides/azure-openai-setup", - "ko/enterprise/guides/tool-repository", - "ko/enterprise/guides/react-component-export", - "ko/enterprise/guides/team-management", - "ko/enterprise/guides/human-in-the-loop", - "ko/enterprise/guides/webhook-automation" - ] - }, - { - "group": "트리거", - "pages": [ - "ko/enterprise/guides/automation-triggers", - "ko/enterprise/guides/gmail-trigger", - "ko/enterprise/guides/google-calendar-trigger", - "ko/enterprise/guides/google-drive-trigger", - "ko/enterprise/guides/outlook-trigger", - "ko/enterprise/guides/onedrive-trigger", - "ko/enterprise/guides/microsoft-teams-trigger", - "ko/enterprise/guides/slack-trigger", - "ko/enterprise/guides/hubspot-trigger", - "ko/enterprise/guides/salesforce-trigger", - "ko/enterprise/guides/zapier-trigger" - ] - }, - { - "group": "학습 자원", - "pages": [ - "ko/enterprise/resources/frequently-asked-questions" - ] - } - ] - }, - { - "tab": "API 레퍼런스", - "icon": "magnifying-glass", - "groups": [ - { - "group": "시작 안내", - "pages": [ - "ko/api-reference/introduction", - "ko/api-reference/inputs", - "ko/api-reference/kickoff", - "ko/api-reference/resume", - "ko/api-reference/status" - ] - } - ] - }, - { - "tab": "예시", - "icon": "code", - "groups": [ - { - "group": "예시", - "pages": [ - "ko/examples/example", - "ko/examples/cookbooks" - ] - } - ] - }, - { - "tab": "변경 로그", - "icon": "clock", - "groups": [ - { - "group": "릴리스 노트", - "pages": [ - "ko/changelog" + "tab": "변경 로그", + "icon": "clock", + "groups": [ + { + "group": "릴리스 노트", + "pages": [ + "ko/changelog" + ] + } ] } ] @@ -1578,4 +2965,4 @@ "reddit": "https://www.reddit.com/r/crewAIInc/" } } -} \ No newline at end of file +} diff --git a/docs/en/changelog.mdx b/docs/en/changelog.mdx index ce074e466..b73204e73 100644 --- a/docs/en/changelog.mdx +++ b/docs/en/changelog.mdx @@ -4,6 +4,138 @@ description: "Product updates, improvements, and bug fixes for CrewAI" icon: "clock" mode: "wide" --- + + ## v1.10.1 + + [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1) + + ## What's Changed + + ### Features + - Upgrade Gemini GenAI + + ### Bug Fixes + - Adjust executor listener value to avoid recursion + - Group parallel function response parts in a single Content object in Gemini + - Surface thought output from thinking models in Gemini + - Load MCP and platform tools when agent tools are None + - Support Jupyter environments with running event loops in A2A + - Use anonymous ID for ephemeral traces + - Conditionally pass plus header + - Skip signal handler registration in non-main threads for telemetry + - Inject tool errors as observations and resolve name collisions + - Upgrade pypdf from 4.x to 6.7.4 to resolve Dependabot alerts + - Resolve critical and high Dependabot security alerts + + ### Documentation + - Sync Composio tool documentation across locales + + ## Contributors + + @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96 + + + + + ## v1.10.1a1 + + [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1) + + ## What's Changed + + ### Features + - Implement asynchronous invocation support in step callback methods + - Implement lazy loading for heavy dependencies in Memory module + + ### Documentation + - Update changelog and version for v1.10.0 + + ### Refactoring + - Refactor step callback methods to support asynchronous invocation + - Refactor to implement lazy loading for heavy dependencies in Memory module + + ### Bug Fixes + - Fix branch for release notes + + ## Contributors + + @greysonlalonde, @joaomdmoura + + + + + ## v1.10.1a1 + + [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1) + + ## What's Changed + + ### Refactoring + - Refactor step callback methods to support asynchronous invocation + - Implement lazy loading for heavy dependencies in Memory module + + ### Documentation + - Update changelog and version for v1.10.0 + + ### Bug Fixes + - Make branch for release notes + + ## Contributors + + @greysonlalonde, @joaomdmoura + + + + + ## v1.10.0 + + [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.0) + + ## What's Changed + + ### Features + - Enhance MCP tool resolution and related events + - Update lancedb version and add lance-namespace packages + - Enhance JSON argument parsing and validation in CrewAgentExecutor and BaseTool + - Migrate CLI HTTP client from requests to httpx + - Add versioned documentation + - Add yanked detection for version notes + - Implement user input handling in Flows + - Enhance HITL self-loop functionality in human feedback integration tests + - Add started_event_id and set in eventbus + - Auto update tools.specs + + ### Bug Fixes + - Validate tool kwargs even when empty to prevent cryptic TypeError + - Preserve null types in tool parameter schemas for LLM + - Map output_pydantic/output_json to native structured output + - Ensure callbacks are ran/awaited if promise + - Capture method name in exception context + - Preserve enum type in router result; improve types + - Fix cyclic flows silently breaking when persistence ID is passed in inputs + - Correct CLI flag format from --skip-provider to --skip_provider + - Ensure OpenAI tool call stream is finalized + - Resolve complex schema $ref pointers in MCP tools + - Enforce additionalProperties=false in schemas + - Reject reserved script names for crew folders + - Resolve race condition in guardrail event emission test + + ### Documentation + - Add litellm dependency note for non-native LLM providers + - Clarify NL2SQL security model and hardening guidance + - Add 96 missing actions across 9 integrations + + ### Refactoring + - Refactor crew to provider + - Extract HITL to provider pattern + - Improve hook typing and registration + + ## Contributors + + @dependabot[bot], @github-actions[bot], @github-code-quality[bot], @greysonlalonde, @heitorado, @hobostay, @joaomdmoura, @johnvan7, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha, @mplachta, @nicoferdi96, @theCyberTech, @thiagomoretto, @vinibrsl + + + ## v1.9.0 diff --git a/docs/en/concepts/flows.mdx b/docs/en/concepts/flows.mdx index f0335177d..defbd3e01 100644 --- a/docs/en/concepts/flows.mdx +++ b/docs/en/concepts/flows.mdx @@ -975,6 +975,79 @@ result = streaming.result Learn more about streaming in the [Streaming Flow Execution](/en/learn/streaming-flow-execution) guide. +## Memory in Flows + +Every Flow automatically has access to CrewAI's unified [Memory](/concepts/memory) system. You can store, recall, and extract memories directly inside any flow method using three built-in convenience methods. + +### Built-in Methods + +| Method | Description | +| :--- | :--- | +| `self.remember(content, **kwargs)` | Store content in memory. Accepts optional `scope`, `categories`, `metadata`, `importance`. | +| `self.recall(query, **kwargs)` | Retrieve relevant memories. Accepts optional `scope`, `categories`, `limit`, `depth`. | +| `self.extract_memories(content)` | Break raw text into discrete, self-contained memory statements. | + +A default `Memory()` instance is created automatically when the Flow initializes. You can also pass a custom one: + +```python +from crewai.flow.flow import Flow +from crewai import Memory + +custom_memory = Memory( + recency_weight=0.5, + recency_half_life_days=7, + embedder={"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}}, +) + +flow = MyFlow(memory=custom_memory) +``` + +### Example: Research and Analyze Flow + +```python +from crewai.flow.flow import Flow, listen, start + + +class ResearchAnalysisFlow(Flow): + @start() + def gather_data(self): + # Simulate research findings + findings = ( + "PostgreSQL handles 10k concurrent connections with connection pooling. " + "MySQL caps at around 5k. MongoDB scales horizontally but adds complexity." + ) + + # Extract atomic facts and remember each one + memories = self.extract_memories(findings) + for mem in memories: + self.remember(mem, scope="/research/databases") + + return findings + + @listen(gather_data) + def analyze(self, raw_findings): + # Recall relevant past research (from this run or previous runs) + past = self.recall("database performance and scaling", limit=10, depth="shallow") + + context_lines = [f"- {m.record.content}" for m in past] + context = "\n".join(context_lines) if context_lines else "No prior context." + + return { + "new_findings": raw_findings, + "prior_context": context, + "total_memories": len(past), + } + + +flow = ResearchAnalysisFlow() +result = flow.kickoff() +print(result) +``` + +Because memory persists across runs (backed by LanceDB on disk), the `analyze` step will recall findings from previous executions too -- enabling flows that learn and accumulate knowledge over time. + +See the [Memory documentation](/concepts/memory) for details on scopes, slices, composite scoring, embedder configuration, and more. + ### Using the CLI Starting from version 0.103.0, you can run flows using the `crewai run` command: diff --git a/docs/en/concepts/llms.mdx b/docs/en/concepts/llms.mdx index 88ac16e88..98bfbeb23 100644 --- a/docs/en/concepts/llms.mdx +++ b/docs/en/concepts/llms.mdx @@ -106,6 +106,15 @@ There are different places in CrewAI code where you can specify the model to use + + CrewAI provides native SDK integrations for OpenAI, Anthropic, Google (Gemini API), Azure, and AWS Bedrock — no extra install needed beyond the provider-specific extras (e.g. `uv add "crewai[openai]"`). + + All other providers are powered by **LiteLLM**. If you plan to use any of them, add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` + + ## Provider Configuration Examples CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities. @@ -275,6 +284,11 @@ In this section, you'll find detailed examples that help you select, configure, | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Text, Image | Text | | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Text | Text | | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Text | Text | + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -470,7 +484,7 @@ In this section, you'll find detailed examples that help you select, configure, To get an Express mode API key: - New Google Cloud users: Get an [express mode API key](https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey) - Existing Google Cloud users: Get a [Google Cloud API key bound to a service account](https://cloud.google.com/docs/authentication/api-keys) - + For more details, see the [Vertex AI Express mode documentation](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey). @@ -571,6 +585,11 @@ In this section, you'll find detailed examples that help you select, configure, | gemini-1.5-flash | 1M tokens | Balanced multimodal model, good for most tasks | | gemini-1.5-flash-8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks | | gemini-1.5-pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration | + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -652,6 +671,7 @@ In this section, you'll find detailed examples that help you select, configure, # Optional AWS_SESSION_TOKEN= # For temporary credentials AWS_DEFAULT_REGION= # Defaults to us-east-1 + AWS_REGION_NAME= # Alternative configuration for backwards compatibility with LiteLLM. Defaults to us-east-1 ``` **Basic Usage:** @@ -695,6 +715,7 @@ In this section, you'll find detailed examples that help you select, configure, - `AWS_SECRET_ACCESS_KEY`: AWS secret key (required) - `AWS_SESSION_TOKEN`: AWS session token for temporary credentials (optional) - `AWS_DEFAULT_REGION`: AWS region (defaults to `us-east-1`) + - `AWS_REGION_NAME`: AWS region (defaults to `us-east-1`). Alternative configuration for backwards compatibility with LiteLLM **Features:** - Native tool calling support via Converse API @@ -764,6 +785,11 @@ In this section, you'll find detailed examples that help you select, configure, model="sagemaker/" ) ``` + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -779,6 +805,11 @@ In this section, you'll find detailed examples that help you select, configure, temperature=0.7 ) ``` + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -865,6 +896,11 @@ In this section, you'll find detailed examples that help you select, configure, | rakuten/rakutenai-7b-instruct | 1,024 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. | | rakuten/rakutenai-7b-chat | 1,024 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. | | baichuan-inc/baichuan2-13b-chat | 4,096 tokens | Support Chinese and English chat, coding, math, instruction following, solving quizzes | + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -905,6 +941,11 @@ In this section, you'll find detailed examples that help you select, configure, # ... ``` + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -926,6 +967,11 @@ In this section, you'll find detailed examples that help you select, configure, | Llama 3.1 70B/8B | 131,072 tokens | High-performance, large context tasks | | Llama 3.2 Series | 8,192 tokens | General-purpose tasks | | Mixtral 8x7B | 32,768 tokens | Balanced performance and context | + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -948,6 +994,11 @@ In this section, you'll find detailed examples that help you select, configure, base_url="https://api.watsonx.ai/v1" ) ``` + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -961,6 +1012,11 @@ In this section, you'll find detailed examples that help you select, configure, base_url="http://localhost:11434" ) ``` + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -976,6 +1032,11 @@ In this section, you'll find detailed examples that help you select, configure, temperature=0.7 ) ``` + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -991,6 +1052,11 @@ In this section, you'll find detailed examples that help you select, configure, base_url="https://api.perplexity.ai/" ) ``` + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -1005,6 +1071,11 @@ In this section, you'll find detailed examples that help you select, configure, model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct" ) ``` + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -1028,6 +1099,11 @@ In this section, you'll find detailed examples that help you select, configure, | Llama 3.2 Series | 8,192 tokens | General-purpose, multimodal tasks | | Llama 3.3 70B | Up to 131,072 tokens | High-performance and output quality | | Qwen2 familly | 8,192 tokens | High-performance and output quality | + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -1053,6 +1129,11 @@ In this section, you'll find detailed examples that help you select, configure, - Good balance of speed and quality - Support for long context windows + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -1075,6 +1156,11 @@ In this section, you'll find detailed examples that help you select, configure, - openrouter/deepseek/deepseek-r1 - openrouter/deepseek/deepseek-chat + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` @@ -1097,6 +1183,11 @@ In this section, you'll find detailed examples that help you select, configure, - Competitive pricing - Good balance of speed and quality + + **Note:** This provider uses LiteLLM. Add it as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` diff --git a/docs/en/concepts/memory.mdx b/docs/en/concepts/memory.mdx index 7639d873e..954d5efe6 100644 --- a/docs/en/concepts/memory.mdx +++ b/docs/en/concepts/memory.mdx @@ -1,1261 +1,878 @@ --- title: Memory -description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities. +description: Leveraging the unified memory system in CrewAI to enhance agent capabilities. icon: database mode: "wide" --- ## Overview -The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **two distinct memory approaches** that serve different use cases: +CrewAI provides a **unified memory system** -- a single `Memory` class that replaces separate short-term, long-term, entity, and external memory types with one intelligent API. Memory uses an LLM to analyze content when saving (inferring scope, categories, and importance) and supports adaptive-depth recall with composite scoring that blends semantic similarity, recency, and importance. -1. **Basic Memory System** - Built-in short-term, long-term, and entity memory -2. **External Memory** - Standalone external memory providers +You can use memory four ways: **standalone** (scripts, notebooks), **with Crews**, **with Agents**, or **inside Flows**. -## Memory System Components +## Quick Start -| Component | Description | -| :------------------- | :---------------------------------------------------------------------------------------------------------------------- | -| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.| -| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. | -| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. | -| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, `ExternalMemory` and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. | - -## 1. Basic Memory System (Recommended) - -The simplest and most commonly used approach. Enable memory for your crew with a single parameter: - -### Quick Start ```python -from crewai import Crew, Agent, Task, Process +from crewai import Memory -# Enable basic memory system +memory = Memory() + +# Store -- the LLM infers scope, categories, and importance +memory.remember("We decided to use PostgreSQL for the user database.") + +# Retrieve -- results ranked by composite score (semantic + recency + importance) +matches = memory.recall("What database did we choose?") +for m in matches: + print(f"[{m.score:.2f}] {m.record.content}") + +# Tune scoring for a fast-moving project +memory = Memory(recency_weight=0.5, recency_half_life_days=7) + +# Forget +memory.forget(scope="/project/old") + +# Explore the self-organized scope tree +print(memory.tree()) +print(memory.info("/")) +``` + +## Four Ways to Use Memory + +### Standalone + +Use memory in scripts, notebooks, CLI tools, or as a standalone knowledge base -- no agents or crews required. + +```python +from crewai import Memory + +memory = Memory() + +# Build up knowledge +memory.remember("The API rate limit is 1000 requests per minute.") +memory.remember("Our staging environment uses port 8080.") +memory.remember("The team agreed to use feature flags for all new releases.") + +# Later, recall what you need +matches = memory.recall("What are our API limits?", limit=5) +for m in matches: + print(f"[{m.score:.2f}] {m.record.content}") + +# Extract atomic facts from a longer text +raw = """Meeting notes: We decided to migrate from MySQL to PostgreSQL +next quarter. The budget is $50k. Sarah will lead the migration.""" + +facts = memory.extract_memories(raw) +# ["Migration from MySQL to PostgreSQL planned for next quarter", +# "Database migration budget is $50k", +# "Sarah will lead the database migration"] + +for fact in facts: + memory.remember(fact) +``` + +### With Crews + +Pass `memory=True` for default settings, or pass a configured `Memory` instance for custom behavior. + +```python +from crewai import Crew, Agent, Task, Process, Memory + +# Option 1: Default memory crew = Crew( - agents=[...], - tasks=[...], + agents=[researcher, writer], + tasks=[research_task, writing_task], process=Process.sequential, - memory=True, # Enables short-term, long-term, and entity memory - verbose=True -) -``` - -### How It Works -- **Short-Term Memory**: Uses ChromaDB with RAG for current context -- **Long-Term Memory**: Uses SQLite3 to store task results across sessions -- **Entity Memory**: Uses RAG to track entities (people, places, concepts) -- **Storage Location**: Platform-specific location via `appdirs` package -- **Custom Storage Directory**: Set `CREWAI_STORAGE_DIR` environment variable - -## Storage Location Transparency - - -**Understanding Storage Locations**: CrewAI uses platform-specific directories to store memory and knowledge files following OS conventions. Understanding these locations helps with production deployments, backups, and debugging. - - -### Where CrewAI Stores Files - -By default, CrewAI uses the `appdirs` library to determine storage locations following platform conventions. Here's exactly where your files are stored: - -#### Default Storage Locations by Platform - -**macOS:** -``` -~/Library/Application Support/CrewAI/{project_name}/ -├── knowledge/ # Knowledge base ChromaDB files -├── short_term_memory/ # Short-term memory ChromaDB files -├── long_term_memory/ # Long-term memory ChromaDB files -├── entities/ # Entity memory ChromaDB files -└── long_term_memory_storage.db # SQLite database -``` - -**Linux:** -``` -~/.local/share/CrewAI/{project_name}/ -├── knowledge/ -├── short_term_memory/ -├── long_term_memory/ -├── entities/ -└── long_term_memory_storage.db -``` - -**Windows:** -``` -C:\Users\{username}\AppData\Local\CrewAI\{project_name}\ -├── knowledge\ -├── short_term_memory\ -├── long_term_memory\ -├── entities\ -└── long_term_memory_storage.db -``` - -### Finding Your Storage Location - -To see exactly where CrewAI is storing files on your system: - -```python -from crewai.utilities.paths import db_storage_path -import os - -# Get the base storage path -storage_path = db_storage_path() -print(f"CrewAI storage location: {storage_path}") - -# List all CrewAI storage directories -if os.path.exists(storage_path): - print("\nStored files and directories:") - for item in os.listdir(storage_path): - item_path = os.path.join(storage_path, item) - if os.path.isdir(item_path): - print(f"📁 {item}/") - # Show ChromaDB collections - if os.path.exists(item_path): - for subitem in os.listdir(item_path): - print(f" └── {subitem}") - else: - print(f"📄 {item}") -else: - print("No CrewAI storage directory found yet.") -``` - -### Controlling Storage Locations - -#### Option 1: Environment Variable (Recommended) -```python -import os -from crewai import Crew - -# Set custom storage location -os.environ["CREWAI_STORAGE_DIR"] = "./my_project_storage" - -# All memory and knowledge will now be stored in ./my_project_storage/ -crew = Crew( - agents=[...], - tasks=[...], - memory=True -) -``` - -#### Option 2: Custom Storage Paths -```python -import os -from crewai import Crew -from crewai.memory import LongTermMemory -from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage - -# Configure custom storage location -custom_storage_path = "./storage" -os.makedirs(custom_storage_path, exist_ok=True) - -crew = Crew( memory=True, - long_term_memory=LongTermMemory( - storage=LTMSQLiteStorage( - db_path=f"{custom_storage_path}/memory.db" - ) - ) -) -``` - -#### Option 3: Project-Specific Storage -```python -import os -from pathlib import Path - -# Store in project directory -project_root = Path(__file__).parent -storage_dir = project_root / "crewai_storage" - -os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir) - -# Now all storage will be in your project directory -``` - -### Embedding Provider Defaults - - -**Default Embedding Provider**: CrewAI defaults to OpenAI embeddings for consistency and reliability. You can easily customize this to match your LLM provider or use local embeddings. - - -#### Understanding Default Behavior -```python -# When using Claude as your LLM... -from crewai import Agent, LLM - -agent = Agent( - role="Analyst", - goal="Analyze data", - backstory="Expert analyst", - llm=LLM(provider="anthropic", model="claude-3-sonnet") # Using Claude + verbose=True, ) -# CrewAI will use OpenAI embeddings by default for consistency -# You can easily customize this to match your preferred provider -``` - -#### Customizing Embedding Providers -```python -from crewai import Crew - -# Option 1: Match your LLM provider +# Option 2: Custom memory with tuned scoring +memory = Memory( + recency_weight=0.4, + semantic_weight=0.4, + importance_weight=0.2, + recency_half_life_days=14, +) crew = Crew( - agents=[agent], - tasks=[task], - memory=True, - embedder={ - "provider": "anthropic", # Match your LLM provider - "config": { - "api_key": "your-anthropic-key", - "model": "text-embedding-3-small" - } - } -) - -# Option 2: Use local embeddings (no external API calls) -crew = Crew( - agents=[agent], - tasks=[task], - memory=True, - embedder={ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } + agents=[researcher, writer], + tasks=[research_task, writing_task], + memory=memory, ) ``` -### Debugging Storage Issues +When `memory=True`, the crew creates a default `Memory()` and passes the crew's `embedder` configuration through automatically. All agents in the crew share the crew's memory unless an agent has its own. + +After each task, the crew automatically extracts discrete facts from the task output and stores them. Before each task, the agent recalls relevant context from memory and injects it into the task prompt. + +### With Agents + +Agents can use the crew's shared memory (default) or receive a scoped view for private context. -#### Check Storage Permissions ```python -import os -from crewai.utilities.paths import db_storage_path +from crewai import Agent, Memory -storage_path = db_storage_path() -print(f"Storage path: {storage_path}") -print(f"Path exists: {os.path.exists(storage_path)}") -print(f"Is writable: {os.access(storage_path, os.W_OK) if os.path.exists(storage_path) else 'Path does not exist'}") +memory = Memory() -# Create with proper permissions -if not os.path.exists(storage_path): - os.makedirs(storage_path, mode=0o755, exist_ok=True) - print(f"Created storage directory: {storage_path}") +# Researcher gets a private scope -- only sees /agent/researcher +researcher = Agent( + role="Researcher", + goal="Find and analyze information", + backstory="Expert researcher with attention to detail", + memory=memory.scope("/agent/researcher"), +) + +# Writer uses crew shared memory (no agent-level memory set) +writer = Agent( + role="Writer", + goal="Produce clear, well-structured content", + backstory="Experienced technical writer", + # memory not set -- uses crew._memory when crew has memory enabled +) ``` -#### Inspect ChromaDB Collections +This pattern gives the researcher private findings while the writer reads from the shared crew memory. + +### With Flows + +Every Flow has built-in memory. Use `self.remember()`, `self.recall()`, and `self.extract_memories()` inside any flow method. + ```python -import chromadb -from crewai.utilities.paths import db_storage_path +from crewai.flow.flow import Flow, listen, start -# Connect to CrewAI's ChromaDB -storage_path = db_storage_path() -chroma_path = os.path.join(storage_path, "knowledge") +class ResearchFlow(Flow): + @start() + def gather_data(self): + findings = "PostgreSQL handles 10k concurrent connections. MySQL caps at 5k." + self.remember(findings, scope="/research/databases") + return findings -if os.path.exists(chroma_path): - client = chromadb.PersistentClient(path=chroma_path) - collections = client.list_collections() - - print("ChromaDB Collections:") - for collection in collections: - print(f" - {collection.name}: {collection.count()} documents") -else: - print("No ChromaDB storage found") + @listen(gather_data) + def write_report(self, findings): + # Recall past research to provide context + past = self.recall("database performance benchmarks") + context = "\n".join(f"- {m.record.content}" for m in past) + return f"Report:\nNew findings: {findings}\nPrevious context:\n{context}" ``` -#### Reset Storage (Debugging) +See the [Flows documentation](/concepts/flows) for more on memory in Flows. + + +## Hierarchical Scopes + +### What Scopes Are + +Memories are organized into a hierarchical tree of scopes, similar to a filesystem. Each scope is a path like `/`, `/project/alpha`, or `/agent/researcher/findings`. + +``` +/ + /company + /company/engineering + /company/product + /project + /project/alpha + /project/beta + /agent + /agent/researcher + /agent/writer +``` + +Scopes provide **context-dependent memory** -- when you recall within a scope, you only search that branch of the tree, which improves both precision and performance. + +### How Scope Inference Works + +When you call `remember()` without specifying a scope, the LLM analyzes the content and the existing scope tree, then suggests the best placement. If no existing scope fits, it creates a new one. Over time, the scope tree grows organically from the content itself -- you don't need to design a schema upfront. + ```python -from crewai import Crew +memory = Memory() -# Reset all memory storage -crew = Crew(agents=[...], tasks=[...], memory=True) +# LLM infers scope from content +memory.remember("We chose PostgreSQL for the user database.") +# -> might be placed under /project/decisions or /engineering/database -# Reset specific memory types -crew.reset_memories(command_type='short') # Short-term memory -crew.reset_memories(command_type='long') # Long-term memory -crew.reset_memories(command_type='entity') # Entity memory -crew.reset_memories(command_type='knowledge') # Knowledge storage +# You can also specify scope explicitly +memory.remember("Sprint velocity is 42 points", scope="/team/metrics") ``` -### Production Best Practices +### Visualizing the Scope Tree -1. **Set `CREWAI_STORAGE_DIR`** to a known location in production for better control -2. **Choose explicit embedding providers** to match your LLM setup -3. **Monitor storage directory size** for large-scale deployments -4. **Include storage directories** in your backup strategy -5. **Set appropriate file permissions** (0o755 for directories, 0o644 for files) -6. **Use project-relative paths** for containerized deployments - -### Common Storage Issues - -**"ChromaDB permission denied" errors:** -```bash -# Fix permissions -chmod -R 755 ~/.local/share/CrewAI/ -``` - -**"Database is locked" errors:** ```python -# Ensure only one CrewAI instance accesses storage -import fcntl -import os +print(memory.tree()) +# / (15 records) +# /project (8 records) +# /project/alpha (5 records) +# /project/beta (3 records) +# /agent (7 records) +# /agent/researcher (4 records) +# /agent/writer (3 records) -storage_path = db_storage_path() -lock_file = os.path.join(storage_path, ".crewai.lock") - -with open(lock_file, 'w') as f: - fcntl.flock(f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) - # Your CrewAI code here +print(memory.info("/project/alpha")) +# ScopeInfo(path='/project/alpha', record_count=5, +# categories=['architecture', 'database'], +# oldest_record=datetime(...), newest_record=datetime(...), +# child_scopes=[]) ``` -**Storage not persisting between runs:** +### MemoryScope: Subtree Views + +A `MemoryScope` restricts all operations to a branch of the tree. The agent or code using it can only see and write within that subtree. + ```python -# Verify storage location is consistent -import os -print("CREWAI_STORAGE_DIR:", os.getenv("CREWAI_STORAGE_DIR")) -print("Current working directory:", os.getcwd()) -print("Computed storage path:", db_storage_path()) +memory = Memory() + +# Create a scope for a specific agent +agent_memory = memory.scope("/agent/researcher") + +# Everything is relative to /agent/researcher +agent_memory.remember("Found three relevant papers on LLM memory.") +# -> stored under /agent/researcher + +agent_memory.recall("relevant papers") +# -> searches only under /agent/researcher + +# Narrow further with subscope +project_memory = agent_memory.subscope("project-alpha") +# -> /agent/researcher/project-alpha ``` -## Custom Embedder Configuration +### Best Practices for Scope Design -CrewAI supports multiple embedding providers to give you flexibility in choosing the best option for your use case. Here's a comprehensive guide to configuring different embedding providers for your memory system. +- **Start flat, let the LLM organize.** Don't over-engineer your scope hierarchy upfront. Begin with `memory.remember(content)` and let the LLM's scope inference create structure as content accumulates. -### Why Choose Different Embedding Providers? +- **Use `/{entity_type}/{identifier}` patterns.** Natural hierarchies emerge from patterns like `/project/alpha`, `/agent/researcher`, `/company/engineering`, `/customer/acme-corp`. -- **Cost Optimization**: Local embeddings (Ollama) are free after initial setup -- **Privacy**: Keep your data local with Ollama or use your preferred cloud provider -- **Performance**: Some models work better for specific domains or languages -- **Consistency**: Match your embedding provider with your LLM provider -- **Compliance**: Meet specific regulatory or organizational requirements +- **Scope by concern, not by data type.** Use `/project/alpha/decisions` rather than `/decisions/project/alpha`. This keeps related content together. -### OpenAI Embeddings (Default) +- **Keep depth shallow (2-3 levels).** Deeply nested scopes become too sparse. `/project/alpha/architecture` is good; `/project/alpha/architecture/decisions/databases/postgresql` is too deep. -OpenAI provides reliable, high-quality embeddings that work well for most use cases. +- **Use explicit scopes when you know, let the LLM infer when you don't.** If you're storing a known project decision, pass `scope="/project/alpha/decisions"`. If you're storing freeform agent output, omit the scope and let the LLM figure it out. + +### Use Case Examples + +**Multi-project team:** +```python +memory = Memory() +# Each project gets its own branch +memory.remember("Using microservices architecture", scope="/project/alpha/architecture") +memory.remember("GraphQL API for client apps", scope="/project/beta/api") + +# Recall across all projects +memory.recall("API design decisions") + +# Or within a specific project +memory.recall("API design", scope="/project/beta") +``` + +**Per-agent private context with shared knowledge:** +```python +memory = Memory() + +# Researcher has private findings +researcher_memory = memory.scope("/agent/researcher") + +# Writer can read from both its own scope and shared company knowledge +writer_view = memory.slice( + scopes=["/agent/writer", "/company/knowledge"], + read_only=True, +) +``` + +**Customer support (per-customer context):** +```python +memory = Memory() + +# Each customer gets isolated context +memory.remember("Prefers email communication", scope="/customer/acme-corp") +memory.remember("On enterprise plan, 50 seats", scope="/customer/acme-corp") + +# Shared product docs are accessible to all agents +memory.remember("Rate limit is 1000 req/min on enterprise plan", scope="/product/docs") +``` + + +## Memory Slices + +### What Slices Are + +A `MemorySlice` is a view across multiple, possibly disjoint scopes. Unlike a scope (which restricts to one subtree), a slice lets you recall from several branches simultaneously. + +### When to Use Slices vs Scopes + +- **Scope**: Use when an agent or code block should be restricted to a single subtree. Example: an agent that only sees `/agent/researcher`. +- **Slice**: Use when you need to combine context from multiple branches. Example: an agent that reads from its own scope plus shared company knowledge. + +### Read-Only Slices + +The most common pattern: give an agent read access to multiple branches without letting it write to shared areas. + +```python +memory = Memory() + +# Agent can recall from its own scope AND company knowledge, +# but cannot write to company knowledge +agent_view = memory.slice( + scopes=["/agent/researcher", "/company/knowledge"], + read_only=True, +) + +matches = agent_view.recall("company security policies", limit=5) +# Searches both /agent/researcher and /company/knowledge, merges and ranks results + +agent_view.remember("new finding") # Raises PermissionError (read-only) +``` + +### Read-Write Slices + +When read-only is disabled, you can write to any of the included scopes, but you must specify which scope explicitly. + +```python +view = memory.slice(scopes=["/team/alpha", "/team/beta"], read_only=False) + +# Must specify scope when writing +view.remember("Cross-team decision", scope="/team/alpha", categories=["decisions"]) +``` + + +## Composite Scoring + +Recall results are ranked by a weighted combination of three signals: + +``` +composite = semantic_weight * similarity + recency_weight * decay + importance_weight * importance +``` + +Where: +- **similarity** = `1 / (1 + distance)` from the vector index (0 to 1) +- **decay** = `0.5^(age_days / half_life_days)` -- exponential decay (1.0 for today, 0.5 at half-life) +- **importance** = the record's importance score (0 to 1), set at encoding time + +Configure these directly on the `Memory` constructor: + +```python +# Sprint retrospective: favor recent memories, short half-life +memory = Memory( + recency_weight=0.5, + semantic_weight=0.3, + importance_weight=0.2, + recency_half_life_days=7, +) + +# Architecture knowledge base: favor important memories, long half-life +memory = Memory( + recency_weight=0.1, + semantic_weight=0.5, + importance_weight=0.4, + recency_half_life_days=180, +) +``` + +Each `MemoryMatch` includes a `match_reasons` list so you can see why a result ranked where it did (e.g. `["semantic", "recency", "importance"]`). + + +## LLM Analysis Layer + +Memory uses the LLM in three ways: + +1. **On save** -- When you omit scope, categories, or importance, the LLM analyzes the content and suggests scope, categories, importance, and metadata (entities, dates, topics). +2. **On recall** -- For deep/auto recall, the LLM analyzes the query (keywords, time hints, suggested scopes, complexity) to guide retrieval. +3. **Extract memories** -- `extract_memories(content)` breaks raw text (e.g. task output) into discrete memory statements. Agents use this before calling `remember()` on each statement so that atomic facts are stored instead of one large blob. + +All analysis degrades gracefully on LLM failure -- see [Failure Behavior](#failure-behavior). + + +## Memory Consolidation + +When saving new content, the encoding pipeline automatically checks for similar existing records in storage. If the similarity is above `consolidation_threshold` (default 0.85), the LLM decides what to do: + +- **keep** -- The existing record is still accurate and not redundant. +- **update** -- The existing record should be updated with new information (LLM provides the merged content). +- **delete** -- The existing record is outdated, superseded, or contradicted. +- **insert_new** -- Whether the new content should also be inserted as a separate record. + +This prevents duplicates from accumulating. For example, if you save "CrewAI ensures reliable operation" three times, consolidation recognizes the duplicates and keeps only one record. + +### Intra-batch Dedup + +When using `remember_many()`, items within the same batch are compared against each other before hitting storage. If two items have cosine similarity >= `batch_dedup_threshold` (default 0.98), the later one is silently dropped. This catches exact or near-exact duplicates within a single batch without any LLM calls (pure vector math). + +```python +# Only 2 records are stored (the third is a near-duplicate of the first) +memory.remember_many([ + "CrewAI supports complex workflows.", + "Python is a great language.", + "CrewAI supports complex workflows.", # dropped by intra-batch dedup +]) +``` + + +## Non-blocking Saves + +`remember_many()` is **non-blocking** -- it submits the encoding pipeline to a background thread and returns immediately. This means the agent can continue to the next task while memories are being saved. + +```python +# Returns immediately -- save happens in background +memory.remember_many(["Fact A.", "Fact B.", "Fact C."]) + +# recall() automatically waits for pending saves before searching +matches = memory.recall("facts") # sees all 3 records +``` + +### Read Barrier + +Every `recall()` call automatically calls `drain_writes()` before searching, ensuring the query always sees the latest persisted records. This is transparent -- you never need to think about it. + +### Crew Shutdown + +When a crew finishes, `kickoff()` drains all pending memory saves in its `finally` block, so no saves are lost even if the crew completes while background saves are in flight. + +### Standalone Usage + +For scripts or notebooks where there's no crew lifecycle, call `drain_writes()` or `close()` explicitly: + +```python +memory = Memory() +memory.remember_many(["Fact A.", "Fact B."]) + +# Option 1: Wait for pending saves +memory.drain_writes() + +# Option 2: Drain and shut down the background pool +memory.close() +``` + + +## Source and Privacy + +Every memory record can carry a `source` tag for provenance tracking and a `private` flag for access control. + +### Source Tracking + +The `source` parameter identifies where a memory came from: + +```python +# Tag memories with their origin +memory.remember("User prefers dark mode", source="user:alice") +memory.remember("System config updated", source="admin") +memory.remember("Agent found a bug", source="agent:debugger") + +# Recall only memories from a specific source +matches = memory.recall("user preferences", source="user:alice") +``` + +### Private Memories + +Private memories are only visible to recall when the `source` matches: + +```python +# Store a private memory +memory.remember("Alice's API key is sk-...", source="user:alice", private=True) + +# This recall sees the private memory (source matches) +matches = memory.recall("API key", source="user:alice") + +# This recall does NOT see it (different source) +matches = memory.recall("API key", source="user:bob") + +# Admin access: see all private records regardless of source +matches = memory.recall("API key", include_private=True) +``` + +This is particularly useful in multi-user or enterprise deployments where different users' memories should be isolated. + + +## RecallFlow (Deep Recall) + +`recall()` supports two depths: + +- **`depth="shallow"`** -- Direct vector search with composite scoring. Fast (~200ms), no LLM calls. +- **`depth="deep"` (default)** -- Runs a multi-step RecallFlow: query analysis, scope selection, parallel vector search, confidence-based routing, and optional recursive exploration when confidence is low. + +**Smart LLM skip**: Queries shorter than `query_analysis_threshold` (default 200 characters) skip the LLM query analysis entirely, even in deep mode. Short queries like "What database do we use?" are already good search phrases -- the LLM analysis adds little value. This saves ~1-3s per recall for typical short queries. Only longer queries (e.g. full task descriptions) go through LLM distillation into targeted sub-queries. + +```python +# Shallow: pure vector search, no LLM +matches = memory.recall("What did we decide?", limit=10, depth="shallow") + +# Deep (default): intelligent retrieval with LLM analysis for long queries +matches = memory.recall( + "Summarize all architecture decisions from this quarter", + limit=10, + depth="deep", +) +``` + +The confidence thresholds that control the RecallFlow router are configurable: + +```python +memory = Memory( + confidence_threshold_high=0.9, # Only synthesize when very confident + confidence_threshold_low=0.4, # Explore deeper more aggressively + exploration_budget=2, # Allow up to 2 exploration rounds + query_analysis_threshold=200, # Skip LLM for queries shorter than this +) +``` + + +## Embedder Configuration + +Memory needs an embedding model to convert text into vectors for semantic search. You can configure this in three ways. + +### Passing to Memory Directly + +```python +from crewai import Memory + +# As a config dict +memory = Memory(embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}}) + +# As a pre-built callable +from crewai.rag.embeddings.factory import build_embedder +embedder = build_embedder({"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}}) +memory = Memory(embedder=embedder) +``` + +### Via Crew Embedder Config + +When using `memory=True`, the crew's `embedder` config is passed through: ```python from crewai import Crew -# Basic OpenAI configuration (uses environment OPENAI_API_KEY) crew = Crew( agents=[...], tasks=[...], memory=True, - embedder={ - "provider": "openai", - "config": { - "model_name": "text-embedding-3-small" # or "text-embedding-3-large" - } - } -) - -# Advanced OpenAI configuration -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": "your-openai-api-key", # Optional: override env var - "model_name": "text-embedding-3-large", - "dimensions": 1536, # Optional: reduce dimensions for smaller storage - "organization_id": "your-org-id" # Optional: for organization accounts - } - } + embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}}, ) ``` -### Azure OpenAI Embeddings - -For enterprise users with Azure OpenAI deployments. +### Provider Examples + + ```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", # Use openai provider for Azure - "config": { - "api_key": "your-azure-api-key", - "api_base": "https://your-resource.openai.azure.com/", - "api_type": "azure", - "api_version": "2023-05-15", - "model_name": "text-embedding-3-small", - "deployment_id": "your-deployment-name" # Azure deployment name - } - } -) -``` - -### Google AI Embeddings - -Use Google's text embedding models for integration with Google Cloud services. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "google-generativeai", - "config": { - "api_key": "your-google-api-key", - "model_name": "gemini-embedding-001" # or "text-embedding-005", "text-multilingual-embedding-002" - } - } -) -``` - -### Vertex AI Embeddings - -For Google Cloud users with Vertex AI access. Supports both legacy and new embedding models with automatic SDK selection. - - -**Deprecation Notice:** Legacy models (`textembedding-gecko*`) use the deprecated `vertexai.language_models` SDK which will be removed after June 24, 2026. Consider migrating to newer models like `gemini-embedding-001`. See the [Google migration guide](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/deprecations/genai-vertexai-sdk) for details. - - -```python -# Recommended: Using new models with google-genai SDK -crew = Crew( - memory=True, - embedder={ - "provider": "google-vertex", - "config": { - "project_id": "your-gcp-project-id", - "location": "us-central1", - "model_name": "gemini-embedding-001", # or "text-embedding-005", "text-multilingual-embedding-002" - "task_type": "RETRIEVAL_DOCUMENT", # Optional - "output_dimensionality": 768 # Optional - } - } -) - -# Using API key authentication (Exp) -crew = Crew( - memory=True, - embedder={ - "provider": "google-vertex", - "config": { - "api_key": "your-google-api-key", - "model_name": "gemini-embedding-001" - } - } -) - -# Legacy models (backwards compatible, emits deprecation warning) -crew = Crew( - memory=True, - embedder={ - "provider": "google-vertex", - "config": { - "project_id": "your-gcp-project-id", - "region": "us-central1", # or "location" (region is deprecated) - "model_name": "textembedding-gecko" # Legacy model - } - } -) -``` - -**Available models:** -- **New SDK models** (recommended): `gemini-embedding-001`, `text-embedding-005`, `text-multilingual-embedding-002` -- **Legacy models** (deprecated): `textembedding-gecko`, `textembedding-gecko@001`, `textembedding-gecko-multilingual` - -### Ollama Embeddings (Local) - -Run embeddings locally for privacy and cost savings. - -```python -# First, install and run Ollama locally, then pull an embedding model: -# ollama pull mxbai-embed-large - -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": { - "model": "mxbai-embed-large", # or "nomic-embed-text" - "url": "http://localhost:11434/api/embeddings" # Default Ollama URL - } - } -) - -# For custom Ollama installations -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": { - "model": "mxbai-embed-large", - "url": "http://your-ollama-server:11434/api/embeddings" - } - } -) -``` - -### Cohere Embeddings - -Use Cohere's embedding models for multilingual support. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "cohere", - "config": { - "api_key": "your-cohere-api-key", - "model_name": "embed-english-v3.0" # or "embed-multilingual-v3.0" - } - } -) -``` - -### VoyageAI Embeddings - -High-performance embeddings optimized for retrieval tasks. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "voyageai", - "config": { - "api_key": "your-voyage-api-key", - "model": "voyage-3", # or "voyage-3-lite", "voyage-code-3" - "input_type": "document" # or "query" - } - } -) -``` - -### AWS Bedrock Embeddings - -For AWS users with Bedrock access. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "bedrock", - "config": { - "aws_access_key_id": "your-access-key", - "aws_secret_access_key": "your-secret-key", - "region_name": "us-east-1", - "model": "amazon.titan-embed-text-v1" - } - } -) -``` - -### Hugging Face Embeddings - -Use open-source models from Hugging Face. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "huggingface", - "config": { - "api_key": "your-hf-token", # Optional for public models - "model": "sentence-transformers/all-MiniLM-L6-v2" - } - } -) -``` - -### IBM Watson Embeddings - -For IBM Cloud users. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "watson", - "config": { - "api_key": "your-watson-api-key", - "url": "your-watson-instance-url", - "model": "ibm/slate-125m-english-rtrvr" - } - } -) -``` - -### Mem0 Provider - -Short-Term Memory and Entity Memory both supports a tight integration with both Mem0 OSS and Mem0 Client as a provider. Here is how you can use Mem0 as a provider. - -```python -from crewai.memory.short_term.short_term_memory import ShortTermMemory -from crewai.memory.entity_entity_memory import EntityMemory - -mem0_oss_embedder_config = { - "provider": "mem0", - "config": { - "user_id": "john", - "local_mem0_config": { - "vector_store": {"provider": "qdrant","config": {"host": "localhost", "port": 6333}}, - "llm": {"provider": "openai","config": {"api_key": "your-api-key", "model": "gpt-4"}}, - "embedder": {"provider": "openai","config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}} - }, - "infer": True # Optional defaults to True - }, - } - - -mem0_client_embedder_config = { - "provider": "mem0", - "config": { - "user_id": "john", - "org_id": "my_org_id", # Optional - "project_id": "my_project_id", # Optional - "api_key": "custom-api-key" # Optional - overrides env var - "run_id": "my_run_id", # Optional - for short-term memory - "includes": "include1", # Optional - "excludes": "exclude1", # Optional - "infer": True # Optional defaults to True - "custom_categories": new_categories # Optional - custom categories for user memory - }, - } - - -short_term_memory_mem0_oss = ShortTermMemory(embedder_config=mem0_oss_embedder_config) # Short Term Memory with Mem0 OSS -short_term_memory_mem0_client = ShortTermMemory(embedder_config=mem0_client_embedder_config) # Short Term Memory with Mem0 Client -entity_memory_mem0_oss = EntityMemory(embedder_config=mem0_oss_embedder_config) # Entity Memory with Mem0 OSS -entity_memory_mem0_client = EntityMemory(embedder_config=mem0_client_embedder_config) # Short Term Memory with Mem0 Client - -crew = Crew( - memory=True, - short_term_memory=short_term_memory_mem0_oss, # or short_term_memory_mem0_client - entity_memory=entity_memory_mem0_oss # or entity_memory_mem0_client -) -``` - -### Choosing the Right Embedding Provider - -When selecting an embedding provider, consider factors like performance, privacy, cost, and integration needs. -Below is a comparison to help you decide: - -| Provider | Best For | Pros | Cons | -| -------------- | ------------------------------ | --------------------------------- | ------------------------- | -| **OpenAI** | General use, high reliability | High quality, widely tested | Paid service, API key required | -| **Ollama** | Privacy-focused, cost savings | Free, runs locally, fully private | Requires local installation/setup | -| **Google AI** | Integration in Google ecosystem| Strong performance, good support | Google account required | -| **Azure OpenAI** | Enterprise & compliance needs| Enterprise-grade features, security | More complex setup process | -| **Cohere** | Multilingual content handling | Excellent language support | More niche use cases | -| **VoyageAI** | Information retrieval & search | Optimized for retrieval tasks | Relatively new provider | -| **Mem0** | Per-user personalization | Search-optimized embeddings | Paid service, API key required | - - -### Environment Variable Configuration - -For security, store API keys in environment variables: - -```python -import os - -# Set environment variables -os.environ["OPENAI_API_KEY"] = "your-openai-key" -os.environ["GOOGLE_API_KEY"] = "your-google-key" -os.environ["COHERE_API_KEY"] = "your-cohere-key" - -# Use without exposing keys in code -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "model": "text-embedding-3-small" - # API key automatically loaded from environment - } - } -) -``` - -### Testing Different Embedding Providers - -Compare embedding providers for your specific use case: - -```python -from crewai import Crew -from crewai.utilities.paths import db_storage_path - -# Test different providers with the same data -providers_to_test = [ - { - "name": "OpenAI", - "config": { - "provider": "openai", - "config": {"model": "text-embedding-3-small"} - } - }, - { - "name": "Ollama", - "config": { - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } - } -] - -for provider in providers_to_test: - print(f"\nTesting {provider['name']} embeddings...") - - # Create crew with specific embedder - crew = Crew( - agents=[...], - tasks=[...], - memory=True, - embedder=provider['config'] - ) - - # Run your test and measure performance - result = crew.kickoff() - print(f"{provider['name']} completed successfully") -``` - -### Troubleshooting Embedding Issues - -**Model not found errors:** -```python -# Verify model availability -from crewai.rag.embeddings.configurator import EmbeddingConfigurator - -configurator = EmbeddingConfigurator() -try: - embedder = configurator.configure_embedder({ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - }) - print("Embedder configured successfully") -except Exception as e: - print(f"Configuration error: {e}") -``` - -**API key issues:** -```python -import os - -# Check if API keys are set -required_keys = ["OPENAI_API_KEY", "GOOGLE_API_KEY", "COHERE_API_KEY"] -for key in required_keys: - if os.getenv(key): - print(f"✅ {key} is set") - else: - print(f"❌ {key} is not set") -``` - -**Performance comparison:** -```python -import time - -def test_embedding_performance(embedder_config, test_text="This is a test document"): - start_time = time.time() - - crew = Crew( - agents=[...], - tasks=[...], - memory=True, - embedder=embedder_config - ) - - # Simulate memory operation - crew.kickoff() - - end_time = time.time() - return end_time - start_time - -# Compare performance -openai_time = test_embedding_performance({ +memory = Memory(embedder={ "provider": "openai", - "config": {"model": "text-embedding-3-small"} + "config": { + "model_name": "text-embedding-3-small", + # "api_key": "sk-...", # or set OPENAI_API_KEY env var + }, }) +``` + -ollama_time = test_embedding_performance({ + +```python +memory = Memory(embedder={ "provider": "ollama", - "config": {"model": "mxbai-embed-large"} + "config": { + "model_name": "mxbai-embed-large", + "url": "http://localhost:11434/api/embeddings", + }, }) - -print(f"OpenAI: {openai_time:.2f}s") -print(f"Ollama: {ollama_time:.2f}s") ``` + -### Entity Memory batching behavior - -Entity Memory supports batching when saving multiple entities at once. When you pass a list of `EntityMemoryItem`, the system: - -- Emits a single MemorySaveStartedEvent with `entity_count` -- Saves each entity internally, collecting any partial errors -- Emits MemorySaveCompletedEvent with aggregate metadata (saved count, errors) -- Raises a partial-save exception if some entities failed (includes counts) - -This improves performance and observability when writing many entities in one operation. - -## 2. External Memory -External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing. - -### Basic External Memory with Mem0 + ```python -import os -from crewai import Agent, Crew, Process, Task -from crewai.memory.external.external_memory import ExternalMemory - -# Create external memory instance with local Mem0 Configuration -external_memory = ExternalMemory( - embedder_config={ - "provider": "mem0", - "config": { - "user_id": "john", - "local_mem0_config": { - "vector_store": { - "provider": "qdrant", - "config": {"host": "localhost", "port": 6333} - }, - "llm": { - "provider": "openai", - "config": {"api_key": "your-api-key", "model": "gpt-4"} - }, - "embedder": { - "provider": "openai", - "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"} - } - }, - "infer": True # Optional defaults to True - }, - } -) - -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory, # Separate from basic memory - process=Process.sequential, - verbose=True -) +memory = Memory(embedder={ + "provider": "azure", + "config": { + "deployment_id": "your-embedding-deployment", + "api_key": "your-azure-api-key", + "api_base": "https://your-resource.openai.azure.com", + "api_version": "2024-02-01", + }, +}) ``` + -### Advanced External Memory with Mem0 Client -When using Mem0 Client, you can customize the memory configuration further, by using parameters like 'includes', 'excludes', 'custom_categories', 'infer' and 'run_id' (this is only for short-term memory). -You can find more details in the [Mem0 documentation](https://docs.mem0.ai/). + +```python +memory = Memory(embedder={ + "provider": "google-generativeai", + "config": { + "model_name": "gemini-embedding-001", + # "api_key": "...", # or set GOOGLE_API_KEY env var + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "google-vertex", + "config": { + "model_name": "gemini-embedding-001", + "project_id": "your-gcp-project-id", + "location": "us-central1", + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "cohere", + "config": { + "model_name": "embed-english-v3.0", + # "api_key": "...", # or set COHERE_API_KEY env var + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "voyageai", + "config": { + "model": "voyage-3", + # "api_key": "...", # or set VOYAGE_API_KEY env var + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "amazon-bedrock", + "config": { + "model_name": "amazon.titan-embed-text-v1", + # Uses default AWS credentials (boto3 session) + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "huggingface", + "config": { + "model_name": "sentence-transformers/all-MiniLM-L6-v2", + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "jina", + "config": { + "model_name": "jina-embeddings-v2-base-en", + # "api_key": "...", # or set JINA_API_KEY env var + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "watsonx", + "config": { + "model_id": "ibm/slate-30m-english-rtrvr", + "api_key": "your-watsonx-api-key", + "project_id": "your-project-id", + "url": "https://us-south.ml.cloud.ibm.com", + }, +}) +``` + + + +```python +# Pass any callable that takes a list of strings and returns a list of vectors +def my_embedder(texts: list[str]) -> list[list[float]]: + # Your embedding logic here + return [[0.1, 0.2, ...] for _ in texts] + +memory = Memory(embedder=my_embedder) +``` + + + +### Provider Reference + +| Provider | Key | Typical Model | Notes | +| :--- | :--- | :--- | :--- | +| OpenAI | `openai` | `text-embedding-3-small` | Default. Set `OPENAI_API_KEY`. | +| Ollama | `ollama` | `mxbai-embed-large` | Local, no API key needed. | +| Azure OpenAI | `azure` | `text-embedding-ada-002` | Requires `deployment_id`. | +| Google AI | `google-generativeai` | `gemini-embedding-001` | Set `GOOGLE_API_KEY`. | +| Google Vertex | `google-vertex` | `gemini-embedding-001` | Requires `project_id`. | +| Cohere | `cohere` | `embed-english-v3.0` | Strong multilingual support. | +| VoyageAI | `voyageai` | `voyage-3` | Optimized for retrieval. | +| AWS Bedrock | `amazon-bedrock` | `amazon.titan-embed-text-v1` | Uses boto3 credentials. | +| Hugging Face | `huggingface` | `all-MiniLM-L6-v2` | Local sentence-transformers. | +| Jina | `jina` | `jina-embeddings-v2-base-en` | Set `JINA_API_KEY`. | +| IBM WatsonX | `watsonx` | `ibm/slate-30m-english-rtrvr` | Requires `project_id`. | +| Sentence Transformer | `sentence-transformer` | `all-MiniLM-L6-v2` | Local, no API key. | +| Custom | `custom` | -- | Requires `embedding_callable`. | + + +## LLM Configuration + +Memory uses an LLM for save analysis (scope, categories, importance inference), consolidation decisions, and deep recall query analysis. You can configure which model to use. ```python -import os -from crewai import Agent, Crew, Process, Task -from crewai.memory.external.external_memory import ExternalMemory +from crewai import Memory, LLM -new_categories = [ - {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"}, - {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"}, - {"personal_information": "Basic information about the user including name, preferences, and personality traits"} -] +# Default: gpt-4o-mini +memory = Memory() -os.environ["MEM0_API_KEY"] = "your-api-key" +# Use a different OpenAI model +memory = Memory(llm="gpt-4o") -# Create external memory instance with Mem0 Client -external_memory = ExternalMemory( - embedder_config={ - "provider": "mem0", - "config": { - "user_id": "john", - "org_id": "my_org_id", # Optional - "project_id": "my_project_id", # Optional - "api_key": "custom-api-key" # Optional - overrides env var - "run_id": "my_run_id", # Optional - for short-term memory - "includes": "include1", # Optional - "excludes": "exclude1", # Optional - "infer": True # Optional defaults to True - "custom_categories": new_categories # Optional - custom categories for user memory - }, - } -) +# Use Anthropic +memory = Memory(llm="anthropic/claude-3-haiku-20240307") -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory, # Separate from basic memory - process=Process.sequential, - verbose=True -) +# Use Ollama for fully local/private analysis +memory = Memory(llm="ollama/llama3.2") + +# Use Google Gemini +memory = Memory(llm="gemini/gemini-2.0-flash") + +# Pass a pre-configured LLM instance with custom settings +llm = LLM(model="gpt-4o", temperature=0) +memory = Memory(llm=llm) ``` -### Custom Storage Implementation +The LLM is initialized **lazily** -- it's only created when first needed. This means `Memory()` never fails at construction time, even if API keys aren't set. Errors only surface when the LLM is actually called (e.g. when saving without explicit scope/categories, or during deep recall). + +For fully offline/private operation, use a local model for both the LLM and embedder: + ```python -from crewai.memory.external.external_memory import ExternalMemory -from crewai.memory.storage.interface import Storage - -class CustomStorage(Storage): - def __init__(self): - self.memories = [] - - def save(self, value, metadata=None, agent=None): - self.memories.append({ - "value": value, - "metadata": metadata, - "agent": agent - }) - - def search(self, query, limit=10, score_threshold=0.5): - # Implement your search logic here - return [m for m in self.memories if query.lower() in str(m["value"]).lower()] - - def reset(self): - self.memories = [] - -# Use custom storage -external_memory = ExternalMemory(storage=CustomStorage()) - -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory +memory = Memory( + llm="ollama/llama3.2", + embedder={"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}}, ) ``` -## 🧠 Memory System Comparison -| **Category** | **Feature** | **Basic Memory** | **External Memory** | -|---------------------|------------------------|-----------------------------|------------------------------| -| **Ease of Use** | Setup Complexity | Simple | Moderate | -| | Integration | Built-in (contextual) | Standalone | -| **Persistence** | Storage | Local files | Custom / Mem0 | -| | Cross-session Support | ✅ | ✅ | -| **Personalization** | User-specific Memory | ❌ | ✅ | -| | Custom Providers | Limited | Any provider | -| **Use Case Fit** | Recommended For | Most general use cases | Specialized / custom needs | +## Storage Backend + +- **Default**: LanceDB, stored under `./.crewai/memory` (or `$CREWAI_STORAGE_DIR/memory` if the env var is set, or the path you pass as `storage="path/to/dir"`). +- **Custom backend**: Implement the `StorageBackend` protocol (see `crewai.memory.storage.backend`) and pass an instance to `Memory(storage=your_backend)`. -## Supported Embedding Providers +## Discovery + +Inspect the scope hierarchy, categories, and records: -### OpenAI (Default) ```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": {"model": "text-embedding-3-small"} - } -) +memory.tree() # Formatted tree of scopes and record counts +memory.tree("/project", max_depth=2) # Subtree view +memory.info("/project") # ScopeInfo: record_count, categories, oldest/newest +memory.list_scopes("/") # Immediate child scopes +memory.list_categories() # Category names and counts +memory.list_records(scope="/project/alpha", limit=20) # Records in a scope, newest first ``` -### Ollama -```python -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } -) -``` -### Google AI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "google-generativeai", - "config": { - "api_key": "your-api-key", - "model_name": "gemini-embedding-001" - } - } -) -``` +## Failure Behavior -### Azure OpenAI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": "your-api-key", - "api_base": "https://your-resource.openai.azure.com/", - "api_version": "2023-05-15", - "model_name": "text-embedding-3-small" - } - } -) -``` +If the LLM fails during analysis (network error, rate limit, invalid response), memory degrades gracefully: -### Vertex AI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "vertexai", - "config": { - "project_id": "your-project-id", - "region": "your-region", - "api_key": "your-api-key", - "model_name": "textembedding-gecko" - } - } -) -``` +- **Save analysis** -- A warning is logged and the memory is still stored with default scope `/`, empty categories, and importance `0.5`. +- **Extract memories** -- The full content is stored as a single memory so nothing is dropped. +- **Query analysis** -- Recall falls back to simple scope selection and vector search so you still get results. -## Security Best Practices +No exception is raised for these analysis failures; only storage or embedder failures will raise. -### Environment Variables -```python -import os -from crewai import Crew -# Store sensitive data in environment variables -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": os.getenv("OPENAI_API_KEY"), - "model": "text-embedding-3-small" - } - } -) -``` +## Privacy Note -### Storage Security -```python -import os -from crewai import Crew -from crewai.memory import LongTermMemory -from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage +Memory content is sent to the configured LLM for analysis (scope/categories/importance on save, query analysis and optional deep recall). For sensitive data, use a local LLM (e.g. Ollama) or ensure your provider meets your compliance requirements. -# Use secure storage paths -storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage") -os.makedirs(storage_path, mode=0o700, exist_ok=True) # Restricted permissions - -crew = Crew( - memory=True, - long_term_memory=LongTermMemory( - storage=LTMSQLiteStorage( - db_path=f"{storage_path}/memory.db" - ) - ) -) -``` - -## Troubleshooting - -### Common Issues - -**Memory not persisting between sessions?** -- Check `CREWAI_STORAGE_DIR` environment variable -- Ensure write permissions to storage directory -- Verify memory is enabled with `memory=True` - -**Mem0 authentication errors?** -- Verify `MEM0_API_KEY` environment variable is set -- Check API key permissions on Mem0 dashboard -- Ensure `mem0ai` package is installed - -**High memory usage with large datasets?** -- Consider using External Memory with custom storage -- Implement pagination in custom storage search methods -- Use smaller embedding models for reduced memory footprint - -### Performance Tips - -- Use `memory=True` for most use cases (simplest and fastest) -- Only use User Memory if you need user-specific persistence -- Consider External Memory for high-scale or specialized requirements -- Choose smaller embedding models for faster processing -- Set appropriate search limits to control memory retrieval size - -## Benefits of Using CrewAI's Memory System - -- 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks. -- 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences. -- 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights. ## Memory Events -CrewAI's event system provides powerful insights into memory operations. By leveraging memory events, you can monitor, debug, and optimize your memory system's performance and behavior. - -### Available Memory Events - -CrewAI emits the following memory-related events: +All memory operations emit events with `source_type="unified_memory"`. You can listen for timing, errors, and content. | Event | Description | Key Properties | | :---- | :---------- | :------------- | -| **MemoryQueryStartedEvent** | Emitted when a memory query begins | `query`, `limit`, `score_threshold` | -| **MemoryQueryCompletedEvent** | Emitted when a memory query completes successfully | `query`, `results`, `limit`, `score_threshold`, `query_time_ms` | -| **MemoryQueryFailedEvent** | Emitted when a memory query fails | `query`, `limit`, `score_threshold`, `error` | -| **MemorySaveStartedEvent** | Emitted when a memory save operation begins | `value`, `metadata`, `agent_role` | -| **MemorySaveCompletedEvent** | Emitted when a memory save operation completes successfully | `value`, `metadata`, `agent_role`, `save_time_ms` | -| **MemorySaveFailedEvent** | Emitted when a memory save operation fails | `value`, `metadata`, `agent_role`, `error` | -| **MemoryRetrievalStartedEvent** | Emitted when memory retrieval for a task prompt starts | `task_id` | -| **MemoryRetrievalCompletedEvent** | Emitted when memory retrieval completes successfully | `task_id`, `memory_content`, `retrieval_time_ms` | +| **MemoryQueryStartedEvent** | Query begins | `query`, `limit` | +| **MemoryQueryCompletedEvent** | Query succeeds | `query`, `results`, `query_time_ms` | +| **MemoryQueryFailedEvent** | Query fails | `query`, `error` | +| **MemorySaveStartedEvent** | Save begins | `value`, `metadata` | +| **MemorySaveCompletedEvent** | Save succeeds | `value`, `save_time_ms` | +| **MemorySaveFailedEvent** | Save fails | `value`, `error` | +| **MemoryRetrievalStartedEvent** | Agent retrieval starts | `task_id` | +| **MemoryRetrievalCompletedEvent** | Agent retrieval done | `task_id`, `memory_content`, `retrieval_time_ms` | -### Practical Applications - -#### 1. Memory Performance Monitoring - -Track memory operation timing to optimize your application: +Example: monitor query time: ```python -from crewai.events import ( - BaseEventListener, - MemoryQueryCompletedEvent, - MemorySaveCompletedEvent -) -import time - -class MemoryPerformanceMonitor(BaseEventListener): - def __init__(self): - super().__init__() - self.query_times = [] - self.save_times = [] +from crewai.events import BaseEventListener, MemoryQueryCompletedEvent +class MemoryMonitor(BaseEventListener): def setup_listeners(self, crewai_event_bus): @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_memory_query_completed(source, event: MemoryQueryCompletedEvent): - self.query_times.append(event.query_time_ms) - print(f"Memory query completed in {event.query_time_ms:.2f}ms. Query: '{event.query}'") - print(f"Average query time: {sum(self.query_times)/len(self.query_times):.2f}ms") - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_memory_save_completed(source, event: MemorySaveCompletedEvent): - self.save_times.append(event.save_time_ms) - print(f"Memory save completed in {event.save_time_ms:.2f}ms") - print(f"Average save time: {sum(self.save_times)/len(self.save_times):.2f}ms") - -# Create an instance of your listener -memory_monitor = MemoryPerformanceMonitor() + def on_done(source, event): + if getattr(event, "source_type", None) == "unified_memory": + print(f"Query '{event.query}' completed in {event.query_time_ms:.0f}ms") ``` -#### 2. Memory Content Logging -Log memory operations for debugging and insights: +## Troubleshooting +**Memory not persisting?** +- Ensure the storage path is writable (default `./.crewai/memory`). Pass `storage="./your_path"` to use a different directory, or set the `CREWAI_STORAGE_DIR` environment variable. +- When using a crew, confirm `memory=True` or `memory=Memory(...)` is set. + +**Slow recall?** +- Use `depth="shallow"` for routine agent context. Reserve `depth="deep"` for complex queries. +- Increase `query_analysis_threshold` to skip LLM analysis for more queries. + +**LLM analysis errors in logs?** +- Memory still saves/recalls with safe defaults. Check API keys, rate limits, and model availability if you want full LLM analysis. + +**Background save errors in logs?** +- Memory saves run in a background thread. Errors are emitted as `MemorySaveFailedEvent` but don't crash the agent. Check logs for the root cause (usually LLM or embedder connection issues). + +**Concurrent write conflicts?** +- LanceDB operations are serialized with a shared lock and retried automatically on conflict. This handles multiple `Memory` instances pointing at the same database (e.g. agent memory + crew memory). No action needed. + +**Browse memory from the terminal:** +```bash +crewai memory # Opens the TUI browser +crewai memory --storage-path ./my_memory # Point to a specific directory +``` + +**Reset memory (e.g. for tests):** ```python -from crewai.events import ( - BaseEventListener, - MemorySaveStartedEvent, - MemoryQueryStartedEvent, - MemoryRetrievalCompletedEvent -) -import logging - -# Configure logging -logger = logging.getLogger('memory_events') - -class MemoryLogger(BaseEventListener): - def setup_listeners(self, crewai_event_bus): - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_memory_save_started(source, event: MemorySaveStartedEvent): - if event.agent_role: - logger.info(f"Agent '{event.agent_role}' saving memory: {event.value[:50]}...") - else: - logger.info(f"Saving memory: {event.value[:50]}...") - - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_memory_query_started(source, event: MemoryQueryStartedEvent): - logger.info(f"Memory query started: '{event.query}' (limit: {event.limit})") - - @crewai_event_bus.on(MemoryRetrievalCompletedEvent) - def on_memory_retrieval_completed(source, event: MemoryRetrievalCompletedEvent): - if event.task_id: - logger.info(f"Memory retrieved for task {event.task_id} in {event.retrieval_time_ms:.2f}ms") - else: - logger.info(f"Memory retrieved in {event.retrieval_time_ms:.2f}ms") - logger.debug(f"Memory content: {event.memory_content}") - -# Create an instance of your listener -memory_logger = MemoryLogger() +crew.reset_memories(command_type="memory") # Resets unified memory +# Or on a Memory instance: +memory.reset() # All scopes +memory.reset(scope="/project/old") # Only that subtree ``` -#### 3. Error Tracking and Notifications -Capture and respond to memory errors: +## Configuration Reference -```python -from crewai.events import ( - BaseEventListener, - MemorySaveFailedEvent, - MemoryQueryFailedEvent -) -import logging -from typing import Optional +All configuration is passed as keyword arguments to `Memory(...)`. Every parameter has a sensible default. -# Configure logging -logger = logging.getLogger('memory_errors') - -class MemoryErrorTracker(BaseEventListener): - def __init__(self, notify_email: Optional[str] = None): - super().__init__() - self.notify_email = notify_email - self.error_count = 0 - - def setup_listeners(self, crewai_event_bus): - @crewai_event_bus.on(MemorySaveFailedEvent) - def on_memory_save_failed(source, event: MemorySaveFailedEvent): - self.error_count += 1 - agent_info = f"Agent '{event.agent_role}'" if event.agent_role else "Unknown agent" - error_message = f"Memory save failed: {event.error}. {agent_info}" - logger.error(error_message) - - if self.notify_email and self.error_count % 5 == 0: - self._send_notification(error_message) - - @crewai_event_bus.on(MemoryQueryFailedEvent) - def on_memory_query_failed(source, event: MemoryQueryFailedEvent): - self.error_count += 1 - error_message = f"Memory query failed: {event.error}. Query: '{event.query}'" - logger.error(error_message) - - if self.notify_email and self.error_count % 5 == 0: - self._send_notification(error_message) - - def _send_notification(self, message): - # Implement your notification system (email, Slack, etc.) - print(f"[NOTIFICATION] Would send to {self.notify_email}: {message}") - -# Create an instance of your listener -error_tracker = MemoryErrorTracker(notify_email="admin@example.com") -``` - -### Integrating with Analytics Platforms - -Memory events can be forwarded to analytics and monitoring platforms to track performance metrics, detect anomalies, and visualize memory usage patterns: - -```python -from crewai.events import ( - BaseEventListener, - MemoryQueryCompletedEvent, - MemorySaveCompletedEvent -) - -class MemoryAnalyticsForwarder(BaseEventListener): - def __init__(self, analytics_client): - super().__init__() - self.client = analytics_client - - def setup_listeners(self, crewai_event_bus): - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_memory_query_completed(source, event: MemoryQueryCompletedEvent): - # Forward query metrics to analytics platform - self.client.track_metric({ - "event_type": "memory_query", - "query": event.query, - "duration_ms": event.query_time_ms, - "result_count": len(event.results) if hasattr(event.results, "__len__") else 0, - "timestamp": event.timestamp - }) - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_memory_save_completed(source, event: MemorySaveCompletedEvent): - # Forward save metrics to analytics platform - self.client.track_metric({ - "event_type": "memory_save", - "agent_role": event.agent_role, - "duration_ms": event.save_time_ms, - "timestamp": event.timestamp - }) -``` - -### Best Practices for Memory Event Listeners - -1. **Keep handlers lightweight**: Avoid complex processing in event handlers to prevent performance impacts -2. **Use appropriate logging levels**: Use INFO for normal operations, DEBUG for details, ERROR for issues -3. **Batch metrics when possible**: Accumulate metrics before sending to external systems -4. **Handle exceptions gracefully**: Ensure your event handlers don't crash due to unexpected data -5. **Consider memory consumption**: Be mindful of storing large amounts of event data - -## Conclusion - -Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, -you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability. +| Parameter | Default | Description | +| :--- | :--- | :--- | +| `llm` | `"gpt-4o-mini"` | LLM for analysis (model name or `BaseLLM` instance). | +| `storage` | `"lancedb"` | Storage backend (`"lancedb"`, a path string, or a `StorageBackend` instance). | +| `embedder` | `None` (OpenAI default) | Embedder (config dict, callable, or `None` for default OpenAI). | +| `recency_weight` | `0.3` | Weight for recency in composite score. | +| `semantic_weight` | `0.5` | Weight for semantic similarity in composite score. | +| `importance_weight` | `0.2` | Weight for importance in composite score. | +| `recency_half_life_days` | `30` | Days for recency score to halve (exponential decay). | +| `consolidation_threshold` | `0.85` | Similarity above which consolidation is triggered on save. Set to `1.0` to disable. | +| `consolidation_limit` | `5` | Max existing records to compare during consolidation. | +| `default_importance` | `0.5` | Importance assigned when not provided and LLM analysis is skipped. | +| `batch_dedup_threshold` | `0.98` | Cosine similarity for dropping near-duplicates within a `remember_many()` batch. | +| `confidence_threshold_high` | `0.8` | Recall confidence above which results are returned directly. | +| `confidence_threshold_low` | `0.5` | Recall confidence below which deeper exploration is triggered. | +| `complex_query_threshold` | `0.7` | For complex queries, explore deeper below this confidence. | +| `exploration_budget` | `1` | Number of LLM-driven exploration rounds during deep recall. | +| `query_analysis_threshold` | `200` | Queries shorter than this (in characters) skip LLM analysis during deep recall. | diff --git a/docs/en/enterprise/features/flow-hitl-management.mdx b/docs/en/enterprise/features/flow-hitl-management.mdx index c0b1fa957..36eb4325c 100644 --- a/docs/en/enterprise/features/flow-hitl-management.mdx +++ b/docs/en/enterprise/features/flow-hitl-management.mdx @@ -38,22 +38,21 @@ CrewAI Enterprise provides a comprehensive Human-in-the-Loop (HITL) management s Configure human review checkpoints within your Flows using the `@human_feedback` decorator. When execution reaches a review point, the system pauses, notifies the assignee via email, and waits for a response. ```python -from crewai.flow.flow import Flow, start, listen +from crewai.flow.flow import Flow, start, listen, or_ from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult class ContentApprovalFlow(Flow): @start() def generate_content(self): - # AI generates content return "Generated marketing copy for Q1 campaign..." - @listen(generate_content) @human_feedback( message="Please review this content for brand compliance:", emit=["approved", "rejected", "needs_revision"], ) - def review_content(self, content): - return content + @listen(or_("generate_content", "needs_revision")) + def review_content(self): + return "Marketing copy for review..." @listen("approved") def publish_content(self, result: HumanFeedbackResult): @@ -62,10 +61,6 @@ class ContentApprovalFlow(Flow): @listen("rejected") def archive_content(self, result: HumanFeedbackResult): print(f"Content rejected. Reason: {result.feedback}") - - @listen("needs_revision") - def revise_content(self, result: HumanFeedbackResult): - print(f"Revision requested: {result.feedback}") ``` For complete implementation details, see the [Human Feedback in Flows](/en/learn/human-feedback-in-flows) guide. diff --git a/docs/en/enterprise/guides/deploy-to-amp.mdx b/docs/en/enterprise/guides/deploy-to-amp.mdx index 31ff8ca20..c0309c0b6 100644 --- a/docs/en/enterprise/guides/deploy-to-amp.mdx +++ b/docs/en/enterprise/guides/deploy-to-amp.mdx @@ -177,6 +177,11 @@ You need to push your crew to a GitHub repository. If you haven't created a crew ![Set Environment Variables](/images/enterprise/set-env-variables.png) + + Using private Python packages? You'll need to add your registry credentials here too. + See [Private Package Registries](/en/enterprise/guides/private-package-registry) for the required variables. + + diff --git a/docs/en/enterprise/guides/prepare-for-deployment.mdx b/docs/en/enterprise/guides/prepare-for-deployment.mdx index fe09e5319..3e472e4e6 100644 --- a/docs/en/enterprise/guides/prepare-for-deployment.mdx +++ b/docs/en/enterprise/guides/prepare-for-deployment.mdx @@ -256,6 +256,12 @@ Before deployment, ensure you have: 1. **LLM API keys** ready (OpenAI, Anthropic, Google, etc.) 2. **Tool API keys** if using external tools (Serper, etc.) + + If your project depends on packages from a **private PyPI registry**, you'll also need to configure + registry authentication credentials as environment variables. See the + [Private Package Registries](/en/enterprise/guides/private-package-registry) guide for details. + + Test your project locally with the same environment variables before deploying to catch configuration issues early. diff --git a/docs/en/enterprise/guides/private-package-registry.mdx b/docs/en/enterprise/guides/private-package-registry.mdx new file mode 100644 index 000000000..feb521436 --- /dev/null +++ b/docs/en/enterprise/guides/private-package-registry.mdx @@ -0,0 +1,263 @@ +--- +title: "Private Package Registries" +description: "Install private Python packages from authenticated PyPI registries in CrewAI AMP" +icon: "lock" +mode: "wide" +--- + + + This guide covers how to configure your CrewAI project to install Python packages + from private PyPI registries (Azure DevOps Artifacts, GitHub Packages, GitLab, AWS CodeArtifact, etc.) + when deploying to CrewAI AMP. + + +## When You Need This + +If your project depends on internal or proprietary Python packages hosted on a private registry +rather than the public PyPI, you'll need to: + +1. Tell UV **where** to find the package (an index URL) +2. Tell UV **which** packages come from that index (a source mapping) +3. Provide **credentials** so UV can authenticate during install + +CrewAI AMP uses [UV](https://docs.astral.sh/uv/) for dependency resolution and installation. +UV supports authenticated private registries through `pyproject.toml` configuration combined +with environment variables for credentials. + +## Step 1: Configure pyproject.toml + +Three pieces work together in your `pyproject.toml`: + +### 1a. Declare the dependency + +Add the private package to your `[project.dependencies]` like any other dependency: + +```toml +[project] +dependencies = [ + "crewai[tools]>=0.100.1,<1.0.0", + "my-private-package>=1.2.0", +] +``` + +### 1b. Define the index + +Register your private registry as a named index under `[[tool.uv.index]]`: + +```toml +[[tool.uv.index]] +name = "my-private-registry" +url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/" +explicit = true +``` + + + The `name` field is important — UV uses it to construct the environment variable names + for authentication (see [Step 2](#step-2-set-authentication-credentials) below). + + Setting `explicit = true` means UV won't search this index for every package — only the + ones you explicitly map to it in `[tool.uv.sources]`. This avoids unnecessary queries + against your private registry and protects against dependency confusion attacks. + + +### 1c. Map the package to the index + +Tell UV which packages should be resolved from your private index using `[tool.uv.sources]`: + +```toml +[tool.uv.sources] +my-private-package = { index = "my-private-registry" } +``` + +### Complete example + +```toml +[project] +name = "my-crew-project" +version = "0.1.0" +requires-python = ">=3.10,<=3.13" +dependencies = [ + "crewai[tools]>=0.100.1,<1.0.0", + "my-private-package>=1.2.0", +] + +[tool.crewai] +type = "crew" + +[[tool.uv.index]] +name = "my-private-registry" +url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/" +explicit = true + +[tool.uv.sources] +my-private-package = { index = "my-private-registry" } +``` + +After updating `pyproject.toml`, regenerate your lock file: + +```bash +uv lock +``` + + + Always commit the updated `uv.lock` along with your `pyproject.toml` changes. + The lock file is required for deployment — see [Prepare for Deployment](/en/enterprise/guides/prepare-for-deployment). + + +## Step 2: Set Authentication Credentials + +UV authenticates against private indexes using environment variables that follow a naming convention +based on the index name you defined in `pyproject.toml`: + +``` +UV_INDEX_{UPPER_NAME}_USERNAME +UV_INDEX_{UPPER_NAME}_PASSWORD +``` + +Where `{UPPER_NAME}` is your index name converted to **uppercase** with **hyphens replaced by underscores**. + +For example, an index named `my-private-registry` uses: + +| Variable | Value | +|----------|-------| +| `UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME` | Your registry username or token name | +| `UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD` | Your registry password or token/PAT | + + + These environment variables **must** be added via the CrewAI AMP **Environment Variables** settings — + either globally or at the deployment level. They cannot be set in `.env` files or hardcoded in your project. + + See [Setting Environment Variables in AMP](#setting-environment-variables-in-amp) below. + + +## Registry Provider Reference + +The table below shows the index URL format and credential values for common registry providers. +Replace placeholder values with your actual organization and feed details. + +| Provider | Index URL | Username | Password | +|----------|-----------|----------|----------| +| **Azure DevOps Artifacts** | `https://pkgs.dev.azure.com/{org}/_packaging/{feed}/pypi/simple/` | Any non-empty string (e.g. `token`) | Personal Access Token (PAT) with Packaging Read scope | +| **GitHub Packages** | `https://pypi.pkg.github.com/{owner}/simple/` | GitHub username | Personal Access Token (classic) with `read:packages` scope | +| **GitLab Package Registry** | `https://gitlab.com/api/v4/projects/{project_id}/packages/pypi/simple/` | `__token__` | Project or Personal Access Token with `read_api` scope | +| **AWS CodeArtifact** | Use the URL from `aws codeartifact get-repository-endpoint` | `aws` | Token from `aws codeartifact get-authorization-token` | +| **Google Artifact Registry** | `https://{region}-python.pkg.dev/{project}/{repo}/simple/` | `_json_key_base64` | Base64-encoded service account key | +| **JFrog Artifactory** | `https://{instance}.jfrog.io/artifactory/api/pypi/{repo}/simple/` | Username or email | API key or identity token | +| **Self-hosted (devpi, Nexus, etc.)** | Your registry's simple API URL | Registry username | Registry password | + + + For **AWS CodeArtifact**, the authorization token expires periodically. + You'll need to refresh the `UV_INDEX_*_PASSWORD` value when it expires. + Consider automating this in your CI/CD pipeline. + + +## Setting Environment Variables in AMP + +Private registry credentials must be configured as environment variables in CrewAI AMP. +You have two options: + + + + 1. Log in to [CrewAI AMP](https://app.crewai.com) + 2. Navigate to your automation + 3. Open the **Environment Variables** tab + 4. Add each variable (`UV_INDEX_*_USERNAME` and `UV_INDEX_*_PASSWORD`) with its value + + See the [Deploy to AMP — Set Environment Variables](/en/enterprise/guides/deploy-to-amp#set-environment-variables) step for details. + + + Add the variables to your local `.env` file before running `crewai deploy create`. + The CLI will securely transfer them to the platform: + + ```bash + # .env + OPENAI_API_KEY=sk-... + UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token + UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat-here + ``` + + ```bash + crewai deploy create + ``` + + + + + **Never** commit credentials to your repository. Use AMP environment variables for all secrets. + The `.env` file should be listed in `.gitignore`. + + +To update credentials on an existing deployment, see [Update Your Crew — Environment Variables](/en/enterprise/guides/update-crew). + +## How It All Fits Together + +When CrewAI AMP builds your automation, the resolution flow works like this: + + + + AMP pulls your repository and reads `pyproject.toml` and `uv.lock`. + + + UV reads `[tool.uv.sources]` to determine which index each package should come from. + + + For each private index, UV looks up `UV_INDEX_{NAME}_USERNAME` and `UV_INDEX_{NAME}_PASSWORD` + from the environment variables you configured in AMP. + + + UV downloads and installs all packages — both public (from PyPI) and private (from your registry). + + + Your crew or flow starts with all dependencies available. + + + +## Troubleshooting + +### Authentication Errors During Build + +**Symptom**: Build fails with `401 Unauthorized` or `403 Forbidden` when resolving a private package. + +**Check**: +- The `UV_INDEX_*` environment variable names match your index name exactly (uppercased, hyphens → underscores) +- Credentials are set in AMP environment variables, not just in a local `.env` +- Your token/PAT has the required read permissions for the package feed +- The token hasn't expired (especially relevant for AWS CodeArtifact) + +### Package Not Found + +**Symptom**: `No matching distribution found for my-private-package`. + +**Check**: +- The index URL in `pyproject.toml` ends with `/simple/` +- The `[tool.uv.sources]` entry maps the correct package name to the correct index name +- The package is actually published to your private registry +- Run `uv lock` locally with the same credentials to verify resolution works + +### Lock File Conflicts + +**Symptom**: `uv lock` fails or produces unexpected results after adding a private index. + +**Solution**: Set the credentials locally and regenerate: + +```bash +export UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token +export UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat +uv lock +``` + +Then commit the updated `uv.lock`. + +## Related Guides + + + + Verify project structure and dependencies before deploying. + + + Deploy your crew or flow and configure environment variables. + + + Update environment variables and push changes to a running deployment. + + diff --git a/docs/en/guides/migration/migrating-from-langgraph.mdx b/docs/en/guides/migration/migrating-from-langgraph.mdx new file mode 100644 index 000000000..192aa53e4 --- /dev/null +++ b/docs/en/guides/migration/migrating-from-langgraph.mdx @@ -0,0 +1,518 @@ +--- +title: "Moving from LangGraph to CrewAI: A Practical Guide for Engineers" +description: If you already have built with LangGraph, learn how to quickly port your projects to CrewAI +icon: switch +mode: "wide" +--- + +You've built agents with LangGraph. You've wrestled with `StateGraph`, wired up conditional edges, and debugged state dictionaries at 2 AM. It works — but somewhere along the way, you started wondering if there's a better path to production. + +There is. **CrewAI Flows** gives you the same power — event-driven orchestration, conditional routing, shared state — with dramatically less boilerplate and a mental model that maps cleanly to how you actually think about multi-step AI workflows. + +This article walks through the core concepts side by side, shows real code comparisons, and demonstrates why CrewAI Flows is the framework you'll want to reach for next. + +--- + +## The Mental Model Shift + +LangGraph asks you to think in **graphs**: nodes, edges, and state dictionaries. Every workflow is a directed graph where you explicitly wire transitions between computation steps. It's powerful, but the abstraction carries overhead — especially when your workflow is fundamentally sequential with a few decision points. + +CrewAI Flows asks you to think in **events**: methods that start things, methods that listen for results, and methods that route execution. The topology of your workflow emerges from decorator annotations rather than explicit graph construction. This isn't just syntactic sugar — it changes how you design, read, and maintain your pipelines. + +Here's the core mapping: + +| LangGraph Concept | CrewAI Flows Equivalent | +| --- | --- | +| `StateGraph` class | `Flow` class | +| `add_node()` | Methods decorated with `@start`, `@listen` | +| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators | +| `TypedDict` state | Pydantic `BaseModel` state | +| `START` / `END` constants | `@start()` decorator / natural method return | +| `graph.compile()` | `flow.kickoff()` | +| Checkpointer / persistence | Built-in memory (LanceDB-backed) | + +Let's see what this looks like in practice. + +--- + +## Demo 1: A Simple Sequential Pipeline + +Imagine you're building a pipeline that takes a topic, researches it, writes a summary, and formats the output. Here's how each framework handles it. + +### LangGraph Approach + +```python +from typing import TypedDict +from langgraph.graph import StateGraph, START, END + +class ResearchState(TypedDict): + topic: str + raw_research: str + summary: str + formatted_output: str + +def research_topic(state: ResearchState) -> dict: + # Call an LLM or search API + result = llm.invoke(f"Research the topic: {state['topic']}") + return {"raw_research": result} + +def write_summary(state: ResearchState) -> dict: + result = llm.invoke( + f"Summarize this research:\n{state['raw_research']}" + ) + return {"summary": result} + +def format_output(state: ResearchState) -> dict: + result = llm.invoke( + f"Format this summary as a polished article section:\n{state['summary']}" + ) + return {"formatted_output": result} + +# Build the graph +graph = StateGraph(ResearchState) +graph.add_node("research", research_topic) +graph.add_node("summarize", write_summary) +graph.add_node("format", format_output) + +graph.add_edge(START, "research") +graph.add_edge("research", "summarize") +graph.add_edge("summarize", "format") +graph.add_edge("format", END) + +# Compile and run +app = graph.compile() +result = app.invoke({"topic": "quantum computing advances in 2026"}) +print(result["formatted_output"]) +``` + +You define functions, register them as nodes, and manually wire every transition. For a simple sequence like this, there's a lot of ceremony. + +### CrewAI Flows Approach + +```python +from crewai import LLM, Agent, Crew, Process, Task +from crewai.flow.flow import Flow, listen, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class ResearchState(BaseModel): + topic: str = "" + raw_research: str = "" + summary: str = "" + formatted_output: str = "" + +class ResearchFlow(Flow[ResearchState]): + @start() + def research_topic(self): + # Option 1: Direct LLM call + result = llm.call(f"Research the topic: {self.state.topic}") + self.state.raw_research = result + return result + + @listen(research_topic) + def write_summary(self, research_output): + # Option 2: A single agent + summarizer = Agent( + role="Research Summarizer", + goal="Produce concise, accurate summaries of research content", + backstory="You are an expert at distilling complex research into clear, " + "digestible summaries.", + llm=llm, + verbose=True, + ) + result = summarizer.kickoff( + f"Summarize this research:\n{self.state.raw_research}" + ) + self.state.summary = str(result) + return self.state.summary + + @listen(write_summary) + def format_output(self, summary_output): + # Option 3: a complete crew (with one or more agents) + formatter = Agent( + role="Content Formatter", + goal="Transform research summaries into polished, publication-ready article sections", + backstory="You are a skilled editor with expertise in structuring and " + "presenting technical content for a general audience.", + llm=llm, + verbose=True, + ) + format_task = Task( + description=f"Format this summary as a polished article section:\n{self.state.summary}", + expected_output="A well-structured, polished article section ready for publication.", + agent=formatter, + ) + crew = Crew( + agents=[formatter], + tasks=[format_task], + process=Process.sequential, + verbose=True, + ) + result = crew.kickoff() + self.state.formatted_output = str(result) + return self.state.formatted_output + +# Run the flow +flow = ResearchFlow() +flow.state.topic = "quantum computing advances in 2026" +result = flow.kickoff() +print(flow.state.formatted_output) + +``` + +Notice what's different: no graph construction, no edge wiring, no compile step. The execution order is declared right where the logic lives. `@start()` marks the entry point, and `@listen(method_name)` chains steps together. The state is a proper Pydantic model with type safety, validation, and IDE auto-completion. + +--- + +## Demo 2: Conditional Routing + +This is where things get interesting. Say you're building a content pipeline that routes to different processing paths based on the type of content detected. + +### LangGraph Approach + +```python +from typing import TypedDict, Literal +from langgraph.graph import StateGraph, START, END + +class ContentState(TypedDict): + input_text: str + content_type: str + result: str + +def classify_content(state: ContentState) -> dict: + content_type = llm.invoke( + f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}" + ) + return {"content_type": content_type.strip().lower()} + +def process_technical(state: ContentState) -> dict: + result = llm.invoke(f"Process as technical doc:\n{state['input_text']}") + return {"result": result} + +def process_creative(state: ContentState) -> dict: + result = llm.invoke(f"Process as creative writing:\n{state['input_text']}") + return {"result": result} + +def process_business(state: ContentState) -> dict: + result = llm.invoke(f"Process as business content:\n{state['input_text']}") + return {"result": result} + +# Routing function +def route_content(state: ContentState) -> Literal["technical", "creative", "business"]: + return state["content_type"] + +# Build the graph +graph = StateGraph(ContentState) +graph.add_node("classify", classify_content) +graph.add_node("technical", process_technical) +graph.add_node("creative", process_creative) +graph.add_node("business", process_business) + +graph.add_edge(START, "classify") +graph.add_conditional_edges( + "classify", + route_content, + { + "technical": "technical", + "creative": "creative", + "business": "business", + } +) +graph.add_edge("technical", END) +graph.add_edge("creative", END) +graph.add_edge("business", END) + +app = graph.compile() +result = app.invoke({"input_text": "Explain how TCP handshakes work"}) +``` + +You need a separate routing function, explicit conditional edge mapping, and termination edges for every branch. The routing logic is decoupled from the node that produces the routing decision. + +### CrewAI Flows Approach + +```python +from crewai import LLM, Agent +from crewai.flow.flow import Flow, listen, router, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class ContentState(BaseModel): + input_text: str = "" + content_type: str = "" + result: str = "" + +class ContentFlow(Flow[ContentState]): + @start() + def classify_content(self): + self.state.content_type = ( + llm.call( + f"Classify this content as 'technical', 'creative', or 'business':\n" + f"{self.state.input_text}" + ) + .strip() + .lower() + ) + return self.state.content_type + + @router(classify_content) + def route_content(self, classification): + if classification == "technical": + return "process_technical" + elif classification == "creative": + return "process_creative" + else: + return "process_business" + + @listen("process_technical") + def handle_technical(self): + agent = Agent( + role="Technical Writer", + goal="Produce clear, accurate technical documentation", + backstory="You are an expert technical writer who specializes in " + "explaining complex technical concepts precisely.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as technical doc:\n{self.state.input_text}") + ) + + @listen("process_creative") + def handle_creative(self): + agent = Agent( + role="Creative Writer", + goal="Craft engaging and imaginative creative content", + backstory="You are a talented creative writer with a flair for " + "compelling storytelling and vivid expression.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as creative writing:\n{self.state.input_text}") + ) + + @listen("process_business") + def handle_business(self): + agent = Agent( + role="Business Writer", + goal="Produce professional, results-oriented business content", + backstory="You are an experienced business writer who communicates " + "strategy and value clearly to professional audiences.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as business content:\n{self.state.input_text}") + ) + +flow = ContentFlow() +flow.state.input_text = "Explain how TCP handshakes work" +flow.kickoff() +print(flow.state.result) + +``` + +The `@router()` decorator turns a method into a decision point. It returns a string that matches a listener — no mapping dictionaries, no separate routing functions. The branching logic reads like a Python `if` statement because it *is* one. + +--- + +## Demo 3: Integrating AI Agent Crews into Flows + +Here's where CrewAI's real power shines. Flows aren't just for chaining LLM calls — they orchestrate full **Crews** of autonomous agents. This is something LangGraph simply doesn't have a native equivalent for. + +```python +from crewai import Agent, Task, Crew +from crewai.flow.flow import Flow, listen, start +from pydantic import BaseModel + +class ArticleState(BaseModel): + topic: str = "" + research: str = "" + draft: str = "" + final_article: str = "" + +class ArticleFlow(Flow[ArticleState]): + + @start() + def run_research_crew(self): + """A full Crew of agents handles research.""" + researcher = Agent( + role="Senior Research Analyst", + goal=f"Produce comprehensive research on: {self.state.topic}", + backstory="You're a veteran analyst known for thorough, " + "well-sourced research reports.", + llm="gpt-4o" + ) + + research_task = Task( + description=f"Research '{self.state.topic}' thoroughly. " + "Cover key trends, data points, and expert opinions.", + expected_output="A detailed research brief with sources.", + agent=researcher + ) + + crew = Crew(agents=[researcher], tasks=[research_task]) + result = crew.kickoff() + self.state.research = result.raw + return result.raw + + @listen(run_research_crew) + def run_writing_crew(self, research_output): + """A different Crew handles writing.""" + writer = Agent( + role="Technical Writer", + goal="Write a compelling article based on provided research.", + backstory="You turn complex research into engaging, clear prose.", + llm="gpt-4o" + ) + + editor = Agent( + role="Senior Editor", + goal="Review and polish articles for publication quality.", + backstory="20 years of editorial experience at top tech publications.", + llm="gpt-4o" + ) + + write_task = Task( + description=f"Write an article based on this research:\n{self.state.research}", + expected_output="A well-structured draft article.", + agent=writer + ) + + edit_task = Task( + description="Review, fact-check, and polish the draft article.", + expected_output="A publication-ready article.", + agent=editor + ) + + crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task]) + result = crew.kickoff() + self.state.final_article = result.raw + return result.raw + +# Run the full pipeline +flow = ArticleFlow() +flow.state.topic = "The Future of Edge AI" +flow.kickoff() +print(flow.state.final_article) +``` + +This is the key insight: **Flows provide the orchestration layer, and Crews provide the intelligence layer.** Each step in a Flow can spin up a full team of collaborating agents, each with their own roles, goals, and tools. You get structured, predictable control flow *and* autonomous agent collaboration — the best of both worlds. + +In LangGraph, achieving something similar means manually implementing agent communication protocols, tool-calling loops, and delegation logic inside your node functions. It's possible, but it's plumbing you're building from scratch every time. + +--- + +## Demo 4: Parallel Execution and Synchronization + +Real-world pipelines often need to fan out work and join the results. CrewAI Flows handles this elegantly with `and_` and `or_` operators. + +```python +from crewai import LLM +from crewai.flow.flow import Flow, and_, listen, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class AnalysisState(BaseModel): + topic: str = "" + market_data: str = "" + tech_analysis: str = "" + competitor_intel: str = "" + final_report: str = "" + +class ParallelAnalysisFlow(Flow[AnalysisState]): + @start() + def start_method(self): + pass + + @listen(start_method) + def gather_market_data(self): + # Your agentic or deterministic code + pass + + @listen(start_method) + def run_tech_analysis(self): + # Your agentic or deterministic code + pass + + @listen(start_method) + def gather_competitor_intel(self): + # Your agentic or deterministic code + pass + + @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel)) + def synthesize_report(self): + # Your agentic or deterministic code + pass + +flow = ParallelAnalysisFlow() +flow.state.topic = "AI-powered developer tools" +flow.kickoff() + +``` + +Multiple `@start()` decorators fire in parallel. The `and_()` combinator on the `@listen` decorator ensures `synthesize_report` only executes after *all three* upstream methods complete. There's also `or_()` for when you want to proceed as soon as *any* upstream task finishes. + +In LangGraph, you'd need to build a fan-out/fan-in pattern with parallel branches, a synchronization node, and careful state merging — all wired explicitly through edges. + +--- + +## Why CrewAI Flows for Production + +Beyond cleaner syntax, Flows deliver several production-critical advantages: + +**Built-in state persistence.** Flow state is backed by LanceDB, meaning your workflows can survive crashes, be resumed, and accumulate knowledge across runs. LangGraph requires you to configure a separate checkpointer. + +**Type-safe state management.** Pydantic models give you validation, serialization, and IDE support out of the box. LangGraph's `TypedDict` states don't validate at runtime. + +**First-class agent orchestration.** Crews are a native primitive. You define agents with roles, goals, backstories, and tools — and they collaborate autonomously within the structured envelope of a Flow. No need to reinvent multi-agent coordination. + +**Simpler mental model.** Decorators declare intent. `@start` means "begin here." `@listen(x)` means "run after x." `@router(x)` means "decide where to go after x." The code reads like the workflow it describes. + +**CLI integration.** Run flows with `crewai run`. No separate compilation step, no graph serialization. Your Flow is a Python class, and it runs like one. + +--- + +## Migration Cheat Sheet + +If you're sitting on a LangGraph codebase and want to move to CrewAI Flows, here's a practical conversion guide: + +1. **Map your state.** Convert your `TypedDict` to a Pydantic `BaseModel`. Add default values for all fields. +2. **Convert nodes to methods.** Each `add_node` function becomes a method on your `Flow` subclass. Replace `state["field"]` reads with `self.state.field`. +3. **Replace edges with decorators.** Your `add_edge(START, "first_node")` becomes `@start()` on the first method. Sequential `add_edge("a", "b")` becomes `@listen(a)` on method `b`. +4. **Replace conditional edges with `@router`.** Your routing function and `add_conditional_edges()` mapping become a single `@router()` method that returns a route string. +5. **Replace compile + invoke with kickoff.** Drop `graph.compile()`. Call `flow.kickoff()` instead. +6. **Consider where Crews fit.** Any node where you have complex multi-step agent logic is a candidate for extraction into a Crew. This is where you'll see the biggest quality improvement. + +--- + +## Getting Started + +Install CrewAI and scaffold a new Flow project: + +```bash +pip install crewai +crewai create flow my_first_flow +cd my_first_flow +``` + +This generates a project structure with a ready-to-edit Flow class, configuration files, and a `pyproject.toml` with `type = "flow"` already set. Run it with: + +```bash +crewai run +``` + +From there, add your agents, wire up your listeners, and ship it. + +--- + +## Final Thoughts + +LangGraph taught the ecosystem that AI workflows need structure. That was an important lesson. But CrewAI Flows takes that lesson and delivers it in a form that's faster to write, easier to read, and more powerful in production — especially when your workflows involve multiple collaborating agents. + +If you're building anything beyond a single-agent chain, give Flows a serious look. The decorator-driven model, native Crew integration, and built-in state management mean you'll spend less time on plumbing and more time on the problems that matter. + +Start with `crewai create flow`. You won't look back. diff --git a/docs/en/learn/human-feedback-in-flows.mdx b/docs/en/learn/human-feedback-in-flows.mdx index 60588657a..0c3792bca 100644 --- a/docs/en/learn/human-feedback-in-flows.mdx +++ b/docs/en/learn/human-feedback-in-flows.mdx @@ -73,6 +73,8 @@ When this flow runs, it will: | `default_outcome` | `str` | No | Outcome to use if no feedback provided. Must be in `emit` | | `metadata` | `dict` | No | Additional data for enterprise integrations | | `provider` | `HumanFeedbackProvider` | No | Custom provider for async/non-blocking feedback. See [Async Human Feedback](#async-human-feedback-non-blocking) | +| `learn` | `bool` | No | Enable HITL learning: distill lessons from feedback and pre-review future output. Default `False`. See [Learning from Feedback](#learning-from-feedback) | +| `learn_limit` | `int` | No | Max past lessons to recall for pre-review. Default `5` | ### Basic Usage (No Routing) @@ -96,33 +98,43 @@ def handle_feedback(self, result): When you specify `emit`, the decorator becomes a router. The human's free-form feedback is interpreted by an LLM and collapsed into one of the specified outcomes: ```python Code -@start() -@human_feedback( - message="Do you approve this content for publication?", - emit=["approved", "rejected", "needs_revision"], - llm="gpt-4o-mini", - default_outcome="needs_revision", -) -def review_content(self): - return "Draft blog post content here..." +from crewai.flow.flow import Flow, start, listen, or_ +from crewai.flow.human_feedback import human_feedback -@listen("approved") -def publish(self, result): - print(f"Publishing! User said: {result.feedback}") +class ReviewFlow(Flow): + @start() + def generate_content(self): + return "Draft blog post content here..." -@listen("rejected") -def discard(self, result): - print(f"Discarding. Reason: {result.feedback}") + @human_feedback( + message="Do you approve this content for publication?", + emit=["approved", "rejected", "needs_revision"], + llm="gpt-4o-mini", + default_outcome="needs_revision", + ) + @listen(or_("generate_content", "needs_revision")) + def review_content(self): + return "Draft blog post content here..." -@listen("needs_revision") -def revise(self, result): - print(f"Revising based on: {result.feedback}") + @listen("approved") + def publish(self, result): + print(f"Publishing! User said: {result.feedback}") + + @listen("rejected") + def discard(self, result): + print(f"Discarding. Reason: {result.feedback}") ``` +When the human says something like "needs more detail", the LLM collapses that to `"needs_revision"`, which triggers `review_content` again via `or_()` — creating a revision loop. The loop continues until the outcome is `"approved"` or `"rejected"`. + The LLM uses structured outputs (function calling) when available to guarantee the response is one of your specified outcomes. This makes routing reliable and predictable. + +A `@start()` method only runs once at the beginning of the flow. If you need a revision loop, separate the start method from the review method and use `@listen(or_("trigger", "revision_outcome"))` on the review method to enable the self-loop. + + ## HumanFeedbackResult The `HumanFeedbackResult` dataclass contains all information about a human feedback interaction: @@ -186,127 +198,183 @@ Each `HumanFeedbackResult` is appended to `human_feedback_history`, so multiple ## Complete Example: Content Approval Workflow -Here's a full example implementing a content review and approval workflow: +Here's a full example implementing a content review and approval workflow with a revision loop: ```python Code -from crewai.flow.flow import Flow, start, listen +from crewai.flow.flow import Flow, start, listen, or_ from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult from pydantic import BaseModel class ContentState(BaseModel): - topic: str = "" draft: str = "" - final_content: str = "" revision_count: int = 0 + status: str = "pending" class ContentApprovalFlow(Flow[ContentState]): - """A flow that generates content and gets human approval.""" + """A flow that generates content and loops until the human approves.""" @start() - def get_topic(self): - self.state.topic = input("What topic should I write about? ") - return self.state.topic - - @listen(get_topic) - def generate_draft(self, topic): - # In real use, this would call an LLM - self.state.draft = f"# {topic}\n\nThis is a draft about {topic}..." + def generate_draft(self): + self.state.draft = "# AI Safety\n\nThis is a draft about AI Safety..." return self.state.draft - @listen(generate_draft) @human_feedback( - message="Please review this draft. Reply 'approved', 'rejected', or provide revision feedback:", + message="Please review this draft. Approve, reject, or describe what needs changing:", emit=["approved", "rejected", "needs_revision"], llm="gpt-4o-mini", default_outcome="needs_revision", ) - def review_draft(self, draft): - return draft + @listen(or_("generate_draft", "needs_revision")) + def review_draft(self): + self.state.revision_count += 1 + return f"{self.state.draft} (v{self.state.revision_count})" @listen("approved") def publish_content(self, result: HumanFeedbackResult): - self.state.final_content = result.output - print("\n✅ Content approved and published!") - print(f"Reviewer comment: {result.feedback}") + self.state.status = "published" + print(f"Content approved and published! Reviewer said: {result.feedback}") return "published" @listen("rejected") def handle_rejection(self, result: HumanFeedbackResult): - print("\n❌ Content rejected") - print(f"Reason: {result.feedback}") + self.state.status = "rejected" + print(f"Content rejected. Reason: {result.feedback}") return "rejected" - @listen("needs_revision") - def revise_content(self, result: HumanFeedbackResult): - self.state.revision_count += 1 - print(f"\n📝 Revision #{self.state.revision_count} requested") - print(f"Feedback: {result.feedback}") - # In a real flow, you might loop back to generate_draft - # For this example, we just acknowledge - return "revision_requested" - - -# Run the flow flow = ContentApprovalFlow() result = flow.kickoff() -print(f"\nFlow completed. Revisions requested: {flow.state.revision_count}") +print(f"\nFlow completed. Status: {flow.state.status}, Reviews: {flow.state.revision_count}") ``` ```text Output -What topic should I write about? AI Safety +================================================== +OUTPUT FOR REVIEW: +================================================== +# AI Safety + +This is a draft about AI Safety... (v1) +================================================== + +Please review this draft. Approve, reject, or describe what needs changing: +(Press Enter to skip, or type your feedback) + +Your feedback: Needs more detail on alignment research ================================================== OUTPUT FOR REVIEW: ================================================== # AI Safety -This is a draft about AI Safety... +This is a draft about AI Safety... (v2) ================================================== -Please review this draft. Reply 'approved', 'rejected', or provide revision feedback: +Please review this draft. Approve, reject, or describe what needs changing: (Press Enter to skip, or type your feedback) Your feedback: Looks good, approved! -✅ Content approved and published! -Reviewer comment: Looks good, approved! +Content approved and published! Reviewer said: Looks good, approved! -Flow completed. Revisions requested: 0 +Flow completed. Status: published, Reviews: 2 ``` +The key pattern is `@listen(or_("generate_draft", "needs_revision"))` — the review method listens to both the initial trigger and its own revision outcome, creating a self-loop that repeats until the human approves or rejects. + ## Combining with Other Decorators -The `@human_feedback` decorator works with other flow decorators. Place it as the innermost decorator (closest to the function): +The `@human_feedback` decorator works with `@start()`, `@listen()`, and `or_()`. Both decorator orderings work — the framework propagates attributes in both directions — but the recommended patterns are: ```python Code -# Correct: @human_feedback is innermost (closest to the function) +# One-shot review at the start of a flow (no self-loop) @start() -@human_feedback(message="Review this:") +@human_feedback(message="Review this:", emit=["approved", "rejected"], llm="gpt-4o-mini") def my_start_method(self): return "content" +# Linear review on a listener (no self-loop) @listen(other_method) -@human_feedback(message="Review this too:") +@human_feedback(message="Review this too:", emit=["good", "bad"], llm="gpt-4o-mini") def my_listener(self, data): return f"processed: {data}" + +# Self-loop: review that can loop back for revisions +@human_feedback(message="Approve or revise?", emit=["approved", "revise"], llm="gpt-4o-mini") +@listen(or_("upstream_method", "revise")) +def review_with_loop(self): + return "content for review" ``` - -Place `@human_feedback` as the innermost decorator (last/closest to the function) so it wraps the method directly and can capture the return value before passing to the flow system. - +### Self-loop pattern + +To create a revision loop, the review method must listen to **both** an upstream trigger and its own revision outcome using `or_()`: + +```python Code +@start() +def generate(self): + return "initial draft" + +@human_feedback( + message="Approve or request changes?", + emit=["revise", "approved"], + llm="gpt-4o-mini", + default_outcome="approved", +) +@listen(or_("generate", "revise")) +def review(self): + return "content" + +@listen("approved") +def publish(self): + return "published" +``` + +When the outcome is `"revise"`, the flow routes back to `review` (because it listens to `"revise"` via `or_()`). When the outcome is `"approved"`, the flow continues to `publish`. This works because the flow engine exempts routers from the "fire once" rule, allowing them to re-execute on each loop iteration. + +### Chained routers + +A listener triggered by one router's outcome can itself be a router: + +```python Code +@start() +def generate(self): + return "draft content" + +@human_feedback(message="First review:", emit=["approved", "rejected"], llm="gpt-4o-mini") +@listen("generate") +def first_review(self): + return "draft content" + +@human_feedback(message="Final review:", emit=["publish", "hold"], llm="gpt-4o-mini") +@listen("approved") +def final_review(self, prev): + return "final content" + +@listen("publish") +def on_publish(self, prev): + return "published" + +@listen("hold") +def on_hold(self, prev): + return "held for later" +``` + +### Limitations + +- **`@start()` methods run once**: A `@start()` method cannot self-loop. If you need a revision cycle, use a separate `@start()` method as the entry point and put the `@human_feedback` on a `@listen()` method. +- **No `@start()` + `@listen()` on the same method**: This is a Flow framework constraint. A method is either a start point or a listener, not both. ## Best Practices ### 1. Write Clear Request Messages -The `request` parameter is what the human sees. Make it actionable: +The `message` parameter is what the human sees. Make it actionable: ```python Code # ✅ Good - clear and actionable @@ -514,9 +582,9 @@ class ContentPipeline(Flow): @start() @human_feedback( message="Approve this content for publication?", - emit=["approved", "rejected", "needs_revision"], + emit=["approved", "rejected"], llm="gpt-4o-mini", - default_outcome="needs_revision", + default_outcome="rejected", provider=SlackNotificationProvider("#content-reviews"), ) def generate_content(self): @@ -532,11 +600,6 @@ class ContentPipeline(Flow): print(f"Archived. Reason: {result.feedback}") return {"status": "archived"} - @listen("needs_revision") - def queue_revision(self, result): - print(f"Queued for revision: {result.feedback}") - return {"status": "revision_needed"} - # Starting the flow (will pause and wait for Slack response) def start_content_pipeline(): @@ -576,6 +639,64 @@ If you're using an async web framework (FastAPI, aiohttp, Slack Bolt async mode) 5. **Automatic persistence**: State is automatically saved when `HumanFeedbackPending` is raised and uses `SQLiteFlowPersistence` by default 6. **Custom persistence**: Pass a custom persistence instance to `from_pending()` if needed +## Learning from Feedback + +The `learn=True` parameter enables a feedback loop between human reviewers and the memory system. When enabled, the system progressively improves its outputs by learning from past human corrections. + +### How It Works + +1. **After feedback**: The LLM extracts generalizable lessons from the output + feedback and stores them in memory with `source="hitl"`. If the feedback is just approval (e.g. "looks good"), nothing is stored. +2. **Before next review**: Past HITL lessons are recalled from memory and applied by the LLM to improve the output before the human sees it. + +Over time, the human sees progressively better pre-reviewed output because each correction informs future reviews. + +### Example + +```python Code +class ArticleReviewFlow(Flow): + @start() + def generate_article(self): + return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw + + @human_feedback( + message="Review this article draft:", + emit=["approved", "needs_revision"], + llm="gpt-4o-mini", + learn=True, # enable HITL learning + ) + @listen(or_("generate_article", "needs_revision")) + def review_article(self): + return self.last_human_feedback.output if self.last_human_feedback else "article draft" + + @listen("approved") + def publish(self): + print(f"Publishing: {self.last_human_feedback.output}") +``` + +**First run**: The human sees the raw output and says "Always include citations for factual claims." The lesson is distilled and stored in memory. + +**Second run**: The system recalls the citation lesson, pre-reviews the output to add citations, then shows the improved version. The human's job shifts from "fix everything" to "catch what the system missed." + +### Configuration + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `learn` | `False` | Enable HITL learning | +| `learn_limit` | `5` | Max past lessons to recall for pre-review | + +### Key Design Decisions + +- **Same LLM for everything**: The `llm` parameter on the decorator is shared by outcome collapsing, lesson distillation, and pre-review. No need to configure multiple models. +- **Structured output**: Both distillation and pre-review use function calling with Pydantic models when the LLM supports it, falling back to text parsing otherwise. +- **Non-blocking storage**: Lessons are stored via `remember_many()` which runs in a background thread -- the flow continues immediately. +- **Graceful degradation**: If the LLM fails during distillation, nothing is stored. If it fails during pre-review, the raw output is shown. Neither failure blocks the flow. +- **No scope/categories needed**: When storing lessons, only `source` is passed. The encoding pipeline infers scope, categories, and importance automatically. + + +`learn=True` requires the Flow to have memory available. Flows get memory automatically by default, but if you've disabled it with `_skip_auto_memory`, HITL learning will be silently skipped. + + + ## Related Documentation - [Flows Overview](/en/concepts/flows) - Learn about CrewAI Flows @@ -583,3 +704,4 @@ If you're using an async web framework (FastAPI, aiohttp, Slack Bolt async mode) - [Flow Persistence](/en/concepts/flows#persistence) - Persisting flow state - [Routing with @router](/en/concepts/flows#router) - More about conditional routing - [Human Input on Execution](/en/learn/human-input-on-execution) - Task-level human input +- [Memory](/en/concepts/memory) - The unified memory system used by HITL learning diff --git a/docs/en/learn/llm-connections.mdx b/docs/en/learn/llm-connections.mdx index daedc21a2..2b7a5d278 100644 --- a/docs/en/learn/llm-connections.mdx +++ b/docs/en/learn/llm-connections.mdx @@ -7,7 +7,7 @@ mode: "wide" ## Connect CrewAI to LLMs -CrewAI uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface. +CrewAI connects to LLMs through native SDK integrations for the most popular providers (OpenAI, Anthropic, Google Gemini, Azure, and AWS Bedrock), and uses LiteLLM as a flexible fallback for all other providers. By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set. @@ -41,6 +41,14 @@ LiteLLM supports a wide range of providers, including but not limited to: For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers). + + To use any provider not covered by a native integration, add LiteLLM as a dependency to your project: + ```bash + uv add 'crewai[litellm]' + ``` + Native providers (OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock) use their own SDK extras — see the [Provider Configuration Examples](/en/concepts/llms#provider-configuration-examples). + + ## Changing the LLM To use a different LLM with your CrewAI agents, you have several options: diff --git a/docs/en/observability/tracing.mdx b/docs/en/observability/tracing.mdx index 5663e22ba..ce620946a 100644 --- a/docs/en/observability/tracing.mdx +++ b/docs/en/observability/tracing.mdx @@ -35,7 +35,7 @@ Visit [app.crewai.com](https://app.crewai.com) and create your free account. Thi If you haven't already, install CrewAI with the CLI tools: ```bash -uv add crewai[tools] +uv add 'crewai[tools]' ``` Then authenticate your CLI with your CrewAI AMP account: diff --git a/docs/en/tools/automation/composiotool.mdx b/docs/en/tools/automation/composiotool.mdx index b8edbc253..9613aeb19 100644 --- a/docs/en/tools/automation/composiotool.mdx +++ b/docs/en/tools/automation/composiotool.mdx @@ -18,77 +18,46 @@ Composio is an integration platform that allows you to connect your AI agents to To incorporate Composio tools into your project, follow the instructions below: ```shell -pip install composio-crewai +pip install composio composio-crewai pip install crewai ``` -After the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`. Get your Composio API key from [here](https://app.composio.dev) +After the installation is complete, set your Composio API key as `COMPOSIO_API_KEY`. Get your Composio API key from [here](https://platform.composio.dev) ## Example The following example demonstrates how to initialize the tool and execute a github action: -1. Initialize Composio toolset +1. Initialize Composio with CrewAI Provider ```python Code -from composio_crewai import ComposioToolSet, App, Action +from composio_crewai import ComposioProvider +from composio import Composio from crewai import Agent, Task, Crew -toolset = ComposioToolSet() +composio = Composio(provider=ComposioProvider()) ``` -2. Connect your GitHub account +2. Create a new Composio Session and retrieve the tools -```shell CLI -composio add github -``` -```python Code -request = toolset.initiate_connection(app=App.GITHUB) -print(f"Open this URL to authenticate: {request.redirectUrl}") +```python +session = composio.create( + user_id="your-user-id", + toolkits=["gmail", "github"] # optional, default is all toolkits +) +tools = session.tools() ``` +Read more about sessions and user management [here](https://docs.composio.dev/docs/configuring-sessions) -3. Get Tools +3. Authenticating users manually -- Retrieving all the tools from an app (not recommended for production): +Composio automatically authenticates the users during the agent chat session. However, you can also authenticate the user manually by calling the `authorize` method. ```python Code -tools = toolset.get_tools(apps=[App.GITHUB]) +connection_request = session.authorize("github") +print(f"Open this URL to authenticate: {connection_request.redirect_url}") ``` -- Filtering tools based on tags: -```python Code -tag = "users" - -filtered_action_enums = toolset.find_actions_by_tags( - App.GITHUB, - tags=[tag], -) - -tools = toolset.get_tools(actions=filtered_action_enums) -``` - -- Filtering tools based on use case: -```python Code -use_case = "Star a repository on GitHub" - -filtered_action_enums = toolset.find_actions_by_use_case( - App.GITHUB, use_case=use_case, advanced=False -) - -tools = toolset.get_tools(actions=filtered_action_enums) -``` -Set `advanced` to True to get actions for complex use cases - -- Using specific tools: - -In this demo, we will use the `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` action from the GitHub app. -```python Code -tools = toolset.get_tools( - actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER] -) -``` -Learn more about filtering actions [here](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions) - 4. Define agent ```python Code @@ -116,4 +85,4 @@ crew = Crew(agents=[crewai_agent], tasks=[task]) crew.kickoff() ``` -* More detailed list of tools can be found [here](https://app.composio.dev) +* More detailed list of tools can be found [here](https://docs.composio.dev/toolkits) diff --git a/docs/ko/changelog.mdx b/docs/ko/changelog.mdx index 8b3b4da28..10030ef7a 100644 --- a/docs/ko/changelog.mdx +++ b/docs/ko/changelog.mdx @@ -4,6 +4,138 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정" icon: "clock" mode: "wide" --- + + ## v1.10.1 + + [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1) + + ## 변경 사항 + + ### 기능 + - Gemini GenAI 업그레이드 + + ### 버그 수정 + - 재귀를 피하기 위해 실행기 리스너 값을 조정 + - Gemini에서 병렬 함수 응답 부분을 단일 Content 객체로 그룹화 + - Gemini에서 사고 모델의 사고 출력을 표시 + - 에이전트 도구가 None일 때 MCP 및 플랫폼 도구 로드 + - A2A에서 실행 이벤트 루프가 있는 Jupyter 환경 지원 + - 일시적인 추적을 위해 익명 ID 사용 + - 조건부로 플러스 헤더 전달 + - 원격 측정을 위해 비주 스레드에서 신호 처리기 등록 건너뛰기 + - 도구 오류를 관찰로 주입하고 이름 충돌 해결 + - Dependabot 경고를 해결하기 위해 pypdf를 4.x에서 6.7.4로 업그레이드 + - 심각 및 높은 Dependabot 보안 경고 해결 + + ### 문서 + - Composio 도구 문서를 지역별로 동기화 + + ## 기여자 + + @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96 + + + + + ## v1.10.1a1 + + [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1) + + ## 변경 사항 + + ### 기능 + - 단계 콜백 메서드에서 비동기 호출 지원 구현 + - 메모리 모듈의 무거운 의존성에 대한 지연 로딩 구현 + + ### 문서 + - v1.10.0에 대한 변경 로그 및 버전 업데이트 + + ### 리팩토링 + - 비동기 호출을 지원하기 위해 단계 콜백 메서드 리팩토링 + - 메모리 모듈의 무거운 의존성에 대한 지연 로딩을 구현하기 위해 리팩토링 + + ### 버그 수정 + - 릴리스 노트의 분기 수정 + + ## 기여자 + + @greysonlalonde, @joaomdmoura + + + + + ## v1.10.1a1 + + [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1) + + ## 변경 사항 + + ### 리팩토링 + - 비동기 호출을 지원하기 위해 단계 콜백 메서드 리팩토링 + - 메모리 모듈의 무거운 의존성에 대해 지연 로딩 구현 + + ### 문서화 + - v1.10.0에 대한 변경 로그 및 버전 업데이트 + + ### 버그 수정 + - 릴리스 노트를 위한 브랜치 생성 + + ## 기여자 + + @greysonlalonde, @joaomdmoura + + + + + ## v1.10.0 + + [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.0) + + ## 변경 사항 + + ### 기능 + - MCP 도구 해상도 및 관련 이벤트 개선 + - lancedb 버전 업데이트 및 lance-namespace 패키지 추가 + - CrewAgentExecutor 및 BaseTool에서 JSON 인수 파싱 및 검증 개선 + - CLI HTTP 클라이언트를 requests에서 httpx로 마이그레이션 + - 버전화된 문서 추가 + - 버전 노트에 대한 yanked 감지 추가 + - Flows에서 사용자 입력 처리 구현 + - 인간 피드백 통합 테스트에서 HITL 자기 루프 기능 개선 + - eventbus에 started_event_id 추가 및 설정 + - tools.specs 자동 업데이트 + + ### 버그 수정 + - 빈 경우에도 도구 kwargs를 검증하여 모호한 TypeError 방지 + - LLM을 위한 도구 매개변수 스키마에서 null 타입 유지 + - output_pydantic/output_json을 네이티브 구조화된 출력으로 매핑 + - 약속이 있는 경우 콜백이 실행/대기되도록 보장 + - 예외 컨텍스트에서 메서드 이름 캡처 + - 라우터 결과에서 enum 타입 유지; 타입 개선 + - 입력으로 지속성 ID가 전달될 때 조용히 깨지는 순환 흐름 수정 + - CLI 플래그 형식을 --skip-provider에서 --skip_provider로 수정 + - OpenAI 도구 호출 스트림이 완료되도록 보장 + - MCP 도구에서 복잡한 스키마 $ref 포인터 해결 + - 스키마에서 additionalProperties=false 강제 적용 + - 크루 폴더에 대해 예약된 스크립트 이름 거부 + - 가드레일 이벤트 방출 테스트에서 경쟁 조건 해결 + + ### 문서 + - 비네이티브 LLM 공급자를 위한 litellm 종속성 노트 추가 + - NL2SQL 보안 모델 및 강화 지침 명확화 + - 9개 통합에서 96개의 누락된 작업 추가 + + ### 리팩토링 + - crew를 provider로 리팩토링 + - HITL을 provider 패턴으로 추출 + - 훅 타이핑 및 등록 개선 + + ## 기여자 + + @dependabot[bot], @github-actions[bot], @github-code-quality[bot], @greysonlalonde, @heitorado, @hobostay, @joaomdmoura, @johnvan7, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha, @mplachta, @nicoferdi96, @theCyberTech, @thiagomoretto, @vinibrsl + + + ## v1.9.0 diff --git a/docs/ko/concepts/llms.mdx b/docs/ko/concepts/llms.mdx index 84b30f5a9..77e71d518 100644 --- a/docs/ko/concepts/llms.mdx +++ b/docs/ko/concepts/llms.mdx @@ -105,6 +105,15 @@ CrewAI 코드 내에는 사용할 모델을 지정할 수 있는 여러 위치 + + CrewAI는 OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock에 대해 네이티브 SDK 통합을 제공합니다 — 제공자별 extras(예: `uv add "crewai[openai]"`) 외에 추가 설치가 필요하지 않습니다. + + 그 외 모든 제공자는 **LiteLLM**을 통해 지원됩니다. 이를 사용하려면 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` + + ## 공급자 구성 예시 CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양한 LLM 공급자를 지원합니다. @@ -214,6 +223,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | 텍스트, 이미지 | 텍스트 | | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | 텍스트 | 텍스트 | | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | 텍스트 | 텍스트 | + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -354,6 +368,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 | gemini-1.5-flash | 1M 토큰 | 밸런스 잡힌 멀티모달 모델, 대부분의 작업에 적합 | | gemini-1.5-flash-8B | 1M 토큰 | 가장 빠르고, 비용 효율적, 고빈도 작업에 적합 | | gemini-1.5-pro | 2M 토큰 | 최고의 성능, 논리적 추론, 코딩, 창의적 협업 등 다양한 추론 작업에 적합 | + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -439,6 +458,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 model="sagemaker/" ) ``` + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -454,6 +478,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 temperature=0.7 ) ``` + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -540,6 +569,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 | rakuten/rakutenai-7b-instruct | 1,024 토큰 | 언어 이해, 추론, 텍스트 생성이 탁월한 최첨단 LLM | | rakuten/rakutenai-7b-chat | 1,024 토큰 | 언어 이해, 추론, 텍스트 생성이 탁월한 최첨단 LLM | | baichuan-inc/baichuan2-13b-chat | 4,096 토큰 | 중국어 및 영어 대화, 코딩, 수학, 지시 따르기, 퀴즈 풀이 지원 | + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -580,6 +614,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 # ... ``` + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -601,6 +640,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 | Llama 3.1 70B/8B| 131,072 토큰 | 고성능, 대용량 문맥 작업 | | Llama 3.2 Series| 8,192 토큰 | 범용 작업 | | Mixtral 8x7B | 32,768 토큰 | 성능과 문맥의 균형 | + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -623,6 +667,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 base_url="https://api.watsonx.ai/v1" ) ``` + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -636,6 +685,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 base_url="http://localhost:11434" ) ``` + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -651,6 +705,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 temperature=0.7 ) ``` + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -666,6 +725,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 base_url="https://api.perplexity.ai/" ) ``` + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -680,6 +744,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct" ) ``` + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -703,6 +772,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 | Llama 3.2 Series| 8,192 토큰 | 범용, 멀티모달 작업 | | Llama 3.3 70B | 최대 131,072 토큰 | 고성능, 높은 출력 품질 | | Qwen2 familly | 8,192 토큰 | 고성능, 높은 출력 품질 | + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -728,6 +802,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 - 속도와 품질의 우수한 밸런스 - 긴 컨텍스트 윈도우 지원 + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -750,6 +829,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 - openrouter/deepseek/deepseek-r1 - openrouter/deepseek/deepseek-chat + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` @@ -772,6 +856,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양 - 경쟁력 있는 가격 - 속도와 품질의 우수한 밸런스 + + **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` diff --git a/docs/ko/concepts/memory.mdx b/docs/ko/concepts/memory.mdx index 23a98e7fe..ea4463eea 100644 --- a/docs/ko/concepts/memory.mdx +++ b/docs/ko/concepts/memory.mdx @@ -1,1159 +1,878 @@ --- title: 메모리 -description: CrewAI 프레임워크에서 메모리 시스템을 활용하여 에이전트의 역량을 강화합니다. +description: CrewAI의 통합 메모리 시스템을 활용하여 에이전트 역량을 강화합니다. icon: database mode: "wide" --- ## 개요 -CrewAI 프레임워크는 AI 에이전트의 역량을 크게 향상시키기 위해 설계된 정교한 메모리 시스템을 제공합니다. CrewAI는 서로 다른 용도에 맞는 **세 가지 구별되는 메모리 접근 방식**을 제공합니다: +CrewAI는 **통합 메모리 시스템**을 제공합니다 -- 단기, 장기, 엔터티, 외부 메모리 유형을 하나의 지능형 API인 단일 `Memory` 클래스로 대체합니다. 메모리는 저장 시 LLM을 사용하여 콘텐츠를 분석하고(범위, 카테고리, 중요도 추론) 의미 유사도, 최신성, 중요도를 혼합한 복합 점수로 적응형 깊이 recall을 지원합니다. -1. **기본 메모리 시스템** - 내장 단기, 장기, 엔터티 메모리 -2. **외부 메모리** - 독립적인 외부 메모리 제공자 +메모리를 네 가지 방법으로 사용할 수 있습니다: **독립 실행**(스크립트, 노트북), **Crew와 함께**, **에이전트와 함께**, 또는 **Flow 내부에서**. -## 메모리 시스템 구성 요소 +## 빠른 시작 -| 구성 요소 | 설명 | -| :------------------- | :---------------------------------------------------------------------------------------------------------------------- | -| **Short-Term Memory**| 최근 상호작용과 결과를 `RAG`를 사용하여 임시로 저장하며, 에이전트가 현재 실행 중인 컨텍스트와 관련된 정보를 기억하고 활용할 수 있도록 합니다. | -| **Long-Term Memory** | 과거 실행에서 얻은 귀중한 인사이트와 학습 내용을 보존하여 에이전트가 시간이 지남에 따라 지식을 구축하고 개선할 수 있게 합니다. | -| **Entity Memory** | 작업 중에 접한 엔터티(사람, 장소, 개념)에 대한 정보를 포착하고 조직하여 더 깊은 이해와 관계 매핑을 지원합니다. 엔터티 정보 저장을 위해 `RAG`를 사용합니다. | -| **Contextual Memory**| `ShortTermMemory`, `LongTermMemory`, `ExternalMemory`, `EntityMemory`를 결합하여 상호작용의 컨텍스트를 유지해줌으로써, 일련의 작업 또는 대화 전반에 걸쳐 에이전트의 응답 일관성과 관련성을 높입니다. | - -## 1. 기본 메모리 시스템 (권장) - -가장 단순하고 일반적으로 사용되는 방법입니다. 한 가지 파라미터로 crew의 memory를 활성화할 수 있습니다: - -### 빠른 시작 ```python -from crewai import Crew, Agent, Task, Process +from crewai import Memory -# Enable basic memory system +memory = Memory() + +# 저장 -- LLM이 scope, categories, importance를 추론 +memory.remember("We decided to use PostgreSQL for the user database.") + +# 검색 -- 복합 점수(의미 + 최신성 + 중요도)로 결과 순위 매기기 +matches = memory.recall("What database did we choose?") +for m in matches: + print(f"[{m.score:.2f}] {m.record.content}") + +# 빠르게 변하는 프로젝트를 위한 점수 조정 +memory = Memory(recency_weight=0.5, recency_half_life_days=7) + +# 삭제 +memory.forget(scope="/project/old") + +# 자동 구성된 scope 트리 탐색 +print(memory.tree()) +print(memory.info("/")) +``` + +## 메모리를 사용하는 네 가지 방법 + +### 독립 실행 + +스크립트, 노트북, CLI 도구 또는 독립 지식 베이스로 메모리를 사용합니다 -- 에이전트나 crew가 필요하지 않습니다. + +```python +from crewai import Memory + +memory = Memory() + +# 지식 구축 +memory.remember("The API rate limit is 1000 requests per minute.") +memory.remember("Our staging environment uses port 8080.") +memory.remember("The team agreed to use feature flags for all new releases.") + +# 나중에 필요한 것을 recall +matches = memory.recall("What are our API limits?", limit=5) +for m in matches: + print(f"[{m.score:.2f}] {m.record.content}") + +# 긴 텍스트에서 원자적 사실 추출 +raw = """Meeting notes: We decided to migrate from MySQL to PostgreSQL +next quarter. The budget is $50k. Sarah will lead the migration.""" + +facts = memory.extract_memories(raw) +# ["Migration from MySQL to PostgreSQL planned for next quarter", +# "Database migration budget is $50k", +# "Sarah will lead the database migration"] + +for fact in facts: + memory.remember(fact) +``` + +### Crew와 함께 사용 + +기본 설정은 `memory=True`를 전달하고, 사용자 정의 동작은 설정된 `Memory` 인스턴스를 전달합니다. + +```python +from crewai import Crew, Agent, Task, Process, Memory + +# 옵션 1: 기본 메모리 crew = Crew( - agents=[...], - tasks=[...], + agents=[researcher, writer], + tasks=[research_task, writing_task], process=Process.sequential, - memory=True, # Enables short-term, long-term, and entity memory - verbose=True -) -``` - -### 작동 방식 -- **단기 메모리**: 현재 컨텍스트를 위해 ChromaDB와 RAG 사용 -- **장기 메모리**: 세션 간의 작업 결과를 저장하기 위해 SQLite3 사용 -- **엔티티 메모리**: 엔티티(사람, 장소, 개념)를 추적하기 위해 RAG 사용 -- **저장 위치**: `appdirs` 패키지를 통한 플랫폼별 위치 -- **사용자 지정 저장 디렉터리**: `CREWAI_STORAGE_DIR` 환경 변수 설정 - -## 저장 위치 투명성 - - -**저장 위치 이해하기**: CrewAI는 운영 체제의 관례에 따라 메모리와 knowledge 파일을 저장하기 위해 플랫폼별 디렉토리를 사용합니다. 이러한 위치를 이해하면 프로덕션 배포, 백업, 디버깅에 도움이 됩니다. - - -### CrewAI가 파일을 저장하는 위치 - -기본적으로 CrewAI는 플랫폼 규칙을 따르기 위해 `appdirs` 라이브러리를 사용하여 저장 위치를 결정합니다. 파일이 실제로 저장되는 위치는 다음과 같습니다: - -#### 플랫폼별 기본 저장 위치 - -**macOS:** -``` -~/Library/Application Support/CrewAI/{project_name}/ -├── knowledge/ # Knowledge base ChromaDB files -├── short_term_memory/ # Short-term memory ChromaDB files -├── long_term_memory/ # Long-term memory ChromaDB files -├── entities/ # Entity memory ChromaDB files -└── long_term_memory_storage.db # SQLite database -``` - -**Linux:** -``` -~/.local/share/CrewAI/{project_name}/ -├── knowledge/ -├── short_term_memory/ -├── long_term_memory/ -├── entities/ -└── long_term_memory_storage.db -``` - -**Windows:** -``` -C:\Users\{username}\AppData\Local\CrewAI\{project_name}\ -├── knowledge\ -├── short_term_memory\ -├── long_term_memory\ -├── entities\ -└── long_term_memory_storage.db -``` - -### 저장 위치 찾기 - -CrewAI가 시스템에 파일을 저장하는 위치를 정확히 확인하려면: - -```python -from crewai.utilities.paths import db_storage_path -import os - -# Get the base storage path -storage_path = db_storage_path() -print(f"CrewAI storage location: {storage_path}") - -# List all CrewAI storage directories -if os.path.exists(storage_path): - print("\nStored files and directories:") - for item in os.listdir(storage_path): - item_path = os.path.join(storage_path, item) - if os.path.isdir(item_path): - print(f"📁 {item}/") - # Show ChromaDB collections - if os.path.exists(item_path): - for subitem in os.listdir(item_path): - print(f" └── {subitem}") - else: - print(f"📄 {item}") -else: - print("No CrewAI storage directory found yet.") -``` - -### 저장 위치 제어 - -#### 옵션 1: 환경 변수 (권장) -```python -import os -from crewai import Crew - -# Set custom storage location -os.environ["CREWAI_STORAGE_DIR"] = "./my_project_storage" - -# All memory and knowledge will now be stored in ./my_project_storage/ -crew = Crew( - agents=[...], - tasks=[...], - memory=True -) -``` - -#### 옵션 2: 사용자 지정 저장 경로 -```python -import os -from crewai import Crew -from crewai.memory import LongTermMemory -from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage - -# Configure custom storage location -custom_storage_path = "./storage" -os.makedirs(custom_storage_path, exist_ok=True) - -crew = Crew( memory=True, - long_term_memory=LongTermMemory( - storage=LTMSQLiteStorage( - db_path=f"{custom_storage_path}/memory.db" - ) - ) -) -``` - -#### 옵션 3: 프로젝트별 스토리지 -```python -import os -from pathlib import Path - -# Store in project directory -project_root = Path(__file__).parent -storage_dir = project_root / "crewai_storage" - -os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir) - -# Now all storage will be in your project directory -``` - -### 임베딩 제공자 기본값 - - -**기본 임베딩 제공자**: CrewAI는 일관성과 신뢰성을 위해 기본적으로 OpenAI 임베딩을 사용합니다. 이를 쉽게 사용자 맞춤화하여 LLM 제공자에 맞추거나 로컬 임베딩을 사용할 수 있습니다. - - -#### 기본 동작 이해하기 -```python -# When using Claude as your LLM... -from crewai import Agent, LLM - -agent = Agent( - role="Analyst", - goal="Analyze data", - backstory="Expert analyst", - llm=LLM(provider="anthropic", model="claude-3-sonnet") # Using Claude + verbose=True, ) -# CrewAI will use OpenAI embeddings by default for consistency -# You can easily customize this to match your preferred provider -``` - -#### 임베딩 공급자 사용자 지정 -```python -from crewai import Crew - -# Option 1: Match your LLM provider +# 옵션 2: 조정된 점수가 있는 사용자 정의 메모리 +memory = Memory( + recency_weight=0.4, + semantic_weight=0.4, + importance_weight=0.2, + recency_half_life_days=14, +) crew = Crew( - agents=[agent], - tasks=[task], - memory=True, - embedder={ - "provider": "anthropic", # Match your LLM provider - "config": { - "api_key": "your-anthropic-key", - "model": "text-embedding-3-small" - } - } -) - -# Option 2: Use local embeddings (no external API calls) -crew = Crew( - agents=[agent], - tasks=[task], - memory=True, - embedder={ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } + agents=[researcher, writer], + tasks=[research_task, writing_task], + memory=memory, ) ``` -### 스토리지 문제 디버깅 +`memory=True`일 때 crew는 기본 `Memory()`를 생성하고 crew의 `embedder` 설정을 자동으로 전달합니다. crew의 모든 에이전트는 자체 메모리가 없는 한 crew의 메모리를 공유합니다. + +각 작업 후 crew는 자동으로 작업 출력에서 개별 사실을 추출하여 저장합니다. 각 작업 전에 에이전트는 메모리에서 관련 컨텍스트를 recall하여 작업 프롬프트에 주입합니다. + +### 에이전트와 함께 사용 + +에이전트는 crew의 공유 메모리(기본값)를 사용하거나 비공개 컨텍스트를 위한 범위 지정 뷰를 받을 수 있습니다. -#### 스토리지 권한 확인 ```python -import os -from crewai.utilities.paths import db_storage_path +from crewai import Agent, Memory -storage_path = db_storage_path() -print(f"Storage path: {storage_path}") -print(f"Path exists: {os.path.exists(storage_path)}") -print(f"Is writable: {os.access(storage_path, os.W_OK) if os.path.exists(storage_path) else 'Path does not exist'}") +memory = Memory() -# Create with proper permissions -if not os.path.exists(storage_path): - os.makedirs(storage_path, mode=0o755, exist_ok=True) - print(f"Created storage directory: {storage_path}") +# 연구원은 비공개 scope를 받음 -- /agent/researcher만 볼 수 있음 +researcher = Agent( + role="Researcher", + goal="Find and analyze information", + backstory="Expert researcher with attention to detail", + memory=memory.scope("/agent/researcher"), +) + +# 작성자는 crew 공유 메모리 사용 (에이전트 수준 메모리 미설정) +writer = Agent( + role="Writer", + goal="Produce clear, well-structured content", + backstory="Experienced technical writer", + # memory 미설정 -- crew에 메모리가 활성화되면 crew._memory 사용 +) ``` -#### ChromaDB 컬렉션 검사하기 +이 패턴은 연구원에게 비공개 발견을 제공하면서 작성자는 crew 공유 메모리에서 읽습니다. + +### Flow와 함께 사용 + +모든 Flow에는 내장 메모리가 있습니다. 모든 flow 메서드 내부에서 `self.remember()`, `self.recall()`, `self.extract_memories()`를 사용하세요. + ```python -import chromadb -from crewai.utilities.paths import db_storage_path +from crewai.flow.flow import Flow, listen, start -# Connect to CrewAI's ChromaDB -storage_path = db_storage_path() -chroma_path = os.path.join(storage_path, "knowledge") +class ResearchFlow(Flow): + @start() + def gather_data(self): + findings = "PostgreSQL handles 10k concurrent connections. MySQL caps at 5k." + self.remember(findings, scope="/research/databases") + return findings -if os.path.exists(chroma_path): - client = chromadb.PersistentClient(path=chroma_path) - collections = client.list_collections() - - print("ChromaDB Collections:") - for collection in collections: - print(f" - {collection.name}: {collection.count()} documents") -else: - print("No ChromaDB storage found") + @listen(gather_data) + def write_report(self, findings): + # 컨텍스트를 제공하기 위해 과거 연구 recall + past = self.recall("database performance benchmarks") + context = "\n".join(f"- {m.record.content}" for m in past) + return f"Report:\nNew findings: {findings}\nPrevious context:\n{context}" ``` -#### 스토리지 리셋 (디버깅) +Flow에서의 메모리에 대한 자세한 내용은 [Flows 문서](/concepts/flows)를 참조하세요. + + +## 계층적 범위(Scopes) + +### 범위란 무엇인가 + +메모리는 파일 시스템과 유사한 계층적 scope 트리로 구성됩니다. 각 scope는 `/`, `/project/alpha` 또는 `/agent/researcher/findings`와 같은 경로입니다. + +``` +/ + /company + /company/engineering + /company/product + /project + /project/alpha + /project/beta + /agent + /agent/researcher + /agent/writer +``` + +범위는 **컨텍스트 의존적 메모리**를 제공합니다 -- 범위 내에서 recall하면 해당 트리 분기만 검색하여 정밀도와 성능을 모두 향상시킵니다. + +### 범위 추론 작동 방식 + +`remember()` 호출 시 scope를 지정하지 않으면 LLM이 콘텐츠와 기존 scope 트리를 분석한 후 최적의 배치를 제안합니다. 적합한 기존 scope가 없으면 새로 생성합니다. 시간이 지남에 따라 scope 트리는 콘텐츠 자체에서 유기적으로 성장합니다 -- 미리 스키마를 설계할 필요가 없습니다. + ```python -from crewai import Crew +memory = Memory() -# Reset all memory storage -crew = Crew(agents=[...], tasks=[...], memory=True) +# LLM이 콘텐츠에서 scope 추론 +memory.remember("We chose PostgreSQL for the user database.") +# -> /project/decisions 또는 /engineering/database 아래에 배치될 수 있음 -# Reset specific memory types -crew.reset_memories(command_type='short') # 단기 메모리 -crew.reset_memories(command_type='long') # 장기 메모리 -crew.reset_memories(command_type='entity') # 엔티티 메모리 -crew.reset_memories(command_type='knowledge') # 지식 스토리지 +# scope를 명시적으로 지정할 수도 있음 +memory.remember("Sprint velocity is 42 points", scope="/team/metrics") ``` -### 프로덕션 모범 사례 +### 범위 트리 시각화 -1. **`CREWAI_STORAGE_DIR`**를 프로덕션 환경에서 제어가 쉬운 경로로 설정하세요. -2. **명시적인 임베딩 공급자**를 선택하여 LLM 설정과 일치시키세요. -3. **스토리지 디렉토리 크기를 모니터링**하여 대규모 배포에 대비하세요. -4. **스토리지 디렉토리**를 백업 전략에 포함하세요. -5. **적절한 파일 권한**을 설정하세요 (디렉토리는 0o755, 파일은 0o644). -6. **컨테이너화된 배포**를 위해 프로젝트 상대 경로를 사용하세요. - -### 일반적인 스토리지 문제 - -**"ChromaDB permission denied" 오류:** -```bash -# Fix permissions -chmod -R 755 ~/.local/share/CrewAI/ -``` - -**"Database is locked" 오류:** ```python -# Ensure only one CrewAI instance accesses storage -import fcntl -import os +print(memory.tree()) +# / (15 records) +# /project (8 records) +# /project/alpha (5 records) +# /project/beta (3 records) +# /agent (7 records) +# /agent/researcher (4 records) +# /agent/writer (3 records) -storage_path = db_storage_path() -lock_file = os.path.join(storage_path, ".crewai.lock") - -with open(lock_file, 'w') as f: - fcntl.flock(f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) - # Your CrewAI code here +print(memory.info("/project/alpha")) +# ScopeInfo(path='/project/alpha', record_count=5, +# categories=['architecture', 'database'], +# oldest_record=datetime(...), newest_record=datetime(...), +# child_scopes=[]) ``` -**실행 간 스토리지가 유지되지 않는 문제:** +### MemoryScope: 하위 트리 뷰 + +`MemoryScope`는 모든 연산을 트리의 한 분기로 제한합니다. 이를 사용하는 에이전트나 코드는 해당 하위 트리 내에서만 보고 쓸 수 있습니다. + ```python -# Verify storage location is consistent -import os -print("CREWAI_STORAGE_DIR:", os.getenv("CREWAI_STORAGE_DIR")) -print("Current working directory:", os.getcwd()) -print("Computed storage path:", db_storage_path()) +memory = Memory() + +# 특정 에이전트를 위한 scope 생성 +agent_memory = memory.scope("/agent/researcher") + +# 모든 것이 /agent/researcher 기준으로 상대적 +agent_memory.remember("Found three relevant papers on LLM memory.") +# -> /agent/researcher 아래에 저장 + +agent_memory.recall("relevant papers") +# -> /agent/researcher 아래에서만 검색 + +# subscope로 더 좁히기 +project_memory = agent_memory.subscope("project-alpha") +# -> /agent/researcher/project-alpha ``` -## 커스텀 임베더 설정 +### 범위 설계 모범 사례 -CrewAI는 다양한 임베딩 공급자를 지원하여 사용 사례에 가장 적합한 옵션을 선택할 수 있는 유연성을 제공합니다. 메모리 시스템에 사용할 수 있는 다양한 임베딩 공급자를 설정하는 방법에 대한 종합적인 가이드를 아래에 제공합니다. +- **평평하게 시작하고 LLM이 구성하게 하세요.** 범위 계층 구조를 미리 과도하게 설계하지 마세요. `memory.remember(content)`로 시작하고 콘텐츠가 축적됨에 따라 LLM의 scope 추론이 구조를 만들게 하세요. -### 왜 서로 다른 임베딩 제공업체를 선택해야 할까요? +- **`/{엔터티_유형}/{식별자}` 패턴을 사용하세요.** `/project/alpha`, `/agent/researcher`, `/company/engineering`, `/customer/acme-corp` 같은 패턴에서 자연스러운 계층 구조가 나타납니다. -- **비용 최적화**: 로컬 임베딩(Ollama)은 초기 설정 후 무료입니다 -- **프라이버시**: Ollama를 사용하여 데이터를 로컬에 보관하거나 선호하는 클라우드 제공업체를 사용할 수 있습니다 -- **성능**: 일부 모델은 특정 도메인이나 언어에 더 잘 작동합니다 -- **일관성**: 임베딩 제공업체와 LLM 제공업체를 맞출 수 있습니다 -- **컴플라이언스**: 특정 규제 또는 조직 요구사항을 충족할 수 있습니다 +- **데이터 유형이 아닌 관심사별로 scope를 지정하세요.** `/decisions/project/alpha` 대신 `/project/alpha/decisions`를 사용하세요. 이렇게 하면 관련 콘텐츠가 함께 유지됩니다. -### OpenAI 임베딩 (기본값) +- **깊이를 얕게 유지하세요 (2-3 수준).** 깊이 중첩된 scope는 너무 희소해집니다. `/project/alpha/architecture`는 좋지만 `/project/alpha/architecture/decisions/databases/postgresql`은 너무 깊습니다. -OpenAI는 대부분의 사용 사례에 잘 작동하는 신뢰할 수 있고 고품질의 임베딩을 제공합니다. +- **알 때는 명시적 scope를, 모를 때는 LLM 추론을 사용하세요.** 알려진 프로젝트 결정을 저장할 때는 `scope="/project/alpha/decisions"`를 전달하세요. 자유 형식 에이전트 출력을 저장할 때는 scope를 생략하고 LLM이 결정하게 하세요. + +### 사용 사례 예시 + +**다중 프로젝트 팀:** +```python +memory = Memory() +# 각 프로젝트가 자체 분기를 가짐 +memory.remember("Using microservices architecture", scope="/project/alpha/architecture") +memory.remember("GraphQL API for client apps", scope="/project/beta/api") + +# 모든 프로젝트에서 recall +memory.recall("API design decisions") + +# 특정 프로젝트 내에서만 +memory.recall("API design", scope="/project/beta") +``` + +**공유 지식과 에이전트별 비공개 컨텍스트:** +```python +memory = Memory() + +# 연구원은 비공개 발견을 가짐 +researcher_memory = memory.scope("/agent/researcher") + +# 작성자는 자체 scope와 공유 회사 지식에서 읽을 수 있음 +writer_view = memory.slice( + scopes=["/agent/writer", "/company/knowledge"], + read_only=True, +) +``` + +**고객 지원 (고객별 컨텍스트):** +```python +memory = Memory() + +# 각 고객이 격리된 컨텍스트를 가짐 +memory.remember("Prefers email communication", scope="/customer/acme-corp") +memory.remember("On enterprise plan, 50 seats", scope="/customer/acme-corp") + +# 공유 제품 문서는 모든 에이전트가 접근 가능 +memory.remember("Rate limit is 1000 req/min on enterprise plan", scope="/product/docs") +``` + + +## 메모리 슬라이스 + +### 슬라이스란 무엇인가 + +`MemorySlice`는 여러 개의 분리된 scope에 대한 뷰입니다. 하나의 하위 트리로 제한하는 scope와 달리, 슬라이스는 여러 분기에서 동시에 recall할 수 있게 합니다. + +### 슬라이스 vs 범위 사용 시기 + +- **범위(Scope)**: 에이전트나 코드 블록을 단일 하위 트리로 제한해야 할 때 사용. 예: `/agent/researcher`만 보는 에이전트. +- **슬라이스(Slice)**: 여러 분기의 컨텍스트를 결합해야 할 때 사용. 예: 자체 scope와 공유 회사 지식에서 읽는 에이전트. + +### 읽기 전용 슬라이스 + +가장 일반적인 패턴: 에이전트에게 여러 분기에 대한 읽기 액세스를 제공하되 공유 영역에 쓰지 못하게 합니다. + +```python +memory = Memory() + +# 에이전트는 자체 scope와 회사 지식에서 recall 가능, +# 하지만 회사 지식에 쓸 수 없음 +agent_view = memory.slice( + scopes=["/agent/researcher", "/company/knowledge"], + read_only=True, +) + +matches = agent_view.recall("company security policies", limit=5) +# /agent/researcher와 /company/knowledge 모두에서 검색, 결과 병합 및 순위 매기기 + +agent_view.remember("new finding") # PermissionError 발생 (읽기 전용) +``` + +### 읽기/쓰기 슬라이스 + +읽기 전용이 비활성화되면 포함된 scope 중 어디에든 쓸 수 있지만, 어떤 scope인지 명시적으로 지정해야 합니다. + +```python +view = memory.slice(scopes=["/team/alpha", "/team/beta"], read_only=False) + +# 쓸 때 scope를 반드시 지정 +view.remember("Cross-team decision", scope="/team/alpha", categories=["decisions"]) +``` + + +## 복합 점수(Composite Scoring) + +Recall 결과는 세 가지 신호의 가중 조합으로 순위가 매겨집니다: + +``` +composite = semantic_weight * similarity + recency_weight * decay + importance_weight * importance +``` + +여기서: +- **similarity** = 벡터 인덱스에서 `1 / (1 + distance)` (0에서 1) +- **decay** = `0.5^(age_days / half_life_days)` -- 지수 감쇠 (오늘은 1.0, 반감기에서 0.5) +- **importance** = 레코드의 중요도 점수 (0에서 1), 인코딩 시 설정 + +`Memory` 생성자에서 직접 설정합니다: + +```python +# 스프린트 회고: 최근 메모리 선호, 짧은 반감기 +memory = Memory( + recency_weight=0.5, + semantic_weight=0.3, + importance_weight=0.2, + recency_half_life_days=7, +) + +# 아키텍처 지식 베이스: 중요한 메모리 선호, 긴 반감기 +memory = Memory( + recency_weight=0.1, + semantic_weight=0.5, + importance_weight=0.4, + recency_half_life_days=180, +) +``` + +각 `MemoryMatch`에는 결과가 해당 위치에 순위된 이유를 볼 수 있는 `match_reasons` 목록이 포함됩니다 (예: `["semantic", "recency", "importance"]`). + + +## LLM 분석 레이어 + +메모리는 LLM을 세 가지 방식으로 사용합니다: + +1. **저장 시** -- scope, categories, importance를 생략하면 LLM이 콘텐츠를 분석하여 scope, categories, importance, 메타데이터(엔터티, 날짜, 주제)를 제안합니다. +2. **recall 시** -- deep/auto recall의 경우 LLM이 쿼리(키워드, 시간 힌트, 제안 scope, 복잡도)를 분석하여 검색을 안내합니다. +3. **메모리 추출** -- `extract_memories(content)`는 원시 텍스트(예: 작업 출력)를 개별 메모리 문장으로 나눕니다. 에이전트는 각 문장에 `remember()`를 호출하기 전에 이를 사용하여 하나의 큰 블록 대신 원자적 사실이 저장되도록 합니다. + +모든 분석은 LLM 실패 시 우아하게 저하됩니다 -- [오류 시 동작](#오류-시-동작)을 참조하세요. + + +## 메모리 통합 + +새 콘텐츠를 저장할 때 인코딩 파이프라인은 자동으로 스토리지에서 유사한 기존 레코드를 확인합니다. 유사도가 `consolidation_threshold`(기본값 0.85) 이상이면 LLM이 처리 방법을 결정합니다: + +- **keep** -- 기존 레코드가 여전히 정확하고 중복이 아닙니다. +- **update** -- 기존 레코드를 새 정보로 업데이트해야 합니다 (LLM이 병합된 콘텐츠를 제공). +- **delete** -- 기존 레코드가 오래되었거나, 대체되었거나, 모순됩니다. +- **insert_new** -- 새 콘텐츠를 별도의 레코드로 삽입해야 하는지 여부. + +이를 통해 중복이 축적되는 것을 방지합니다. 예를 들어, "CrewAI ensures reliable operation"을 세 번 저장하면 통합이 중복을 인식하고 하나의 레코드만 유지합니다. + +### 배치 내 중복 제거 + +`remember_many()`를 사용할 때 동일 배치 내의 항목은 스토리지에 도달하기 전에 서로 비교됩니다. 두 항목의 코사인 유사도가 `batch_dedup_threshold`(기본값 0.98) 이상이면 나중 항목이 자동으로 삭제됩니다. 이는 LLM 호출 없이 순수 벡터 연산으로 단일 배치 내의 정확하거나 거의 정확한 중복을 잡아냅니다. + +```python +# 2개의 레코드만 저장됨 (세 번째는 첫 번째의 거의 중복) +memory.remember_many([ + "CrewAI supports complex workflows.", + "Python is a great language.", + "CrewAI supports complex workflows.", # 배치 내 중복 제거로 삭제 +]) +``` + + +## 비차단 저장 + +`remember_many()`는 **비차단**입니다 -- 인코딩 파이프라인을 백그라운드 스레드에 제출하고 즉시 반환합니다. 이는 메모리가 저장되는 동안 에이전트가 다음 작업을 계속할 수 있음을 의미합니다. + +```python +# 즉시 반환 -- 저장은 백그라운드에서 발생 +memory.remember_many(["Fact A.", "Fact B.", "Fact C."]) + +# recall()은 검색 전에 보류 중인 저장을 자동으로 대기 +matches = memory.recall("facts") # 3개 레코드 모두 확인 가능 +``` + +### 읽기 배리어 + +모든 `recall()` 호출은 검색 전에 자동으로 `drain_writes()`를 호출하여 쿼리가 항상 최신 저장된 레코드를 볼 수 있도록 합니다. 이는 투명하게 작동하므로 별도로 신경 쓸 필요가 없습니다. + +### Crew 종료 + +crew가 완료되면 `kickoff()`는 `finally` 블록에서 보류 중인 모든 메모리 저장을 드레인하므로, 백그라운드 저장이 진행 중인 상태에서 crew가 완료되더라도 저장이 손실되지 않습니다. + +### 독립 실행 사용 + +crew 수명 주기가 없는 스크립트나 노트북에서는 `drain_writes()` 또는 `close()`를 명시적으로 호출하세요: + +```python +memory = Memory() +memory.remember_many(["Fact A.", "Fact B."]) + +# 옵션 1: 보류 중인 저장 대기 +memory.drain_writes() + +# 옵션 2: 드레인 후 백그라운드 풀 종료 +memory.close() +``` + + +## 출처 및 개인정보 + +모든 메모리 레코드는 출처 추적을 위한 `source` 태그와 접근 제어를 위한 `private` 플래그를 가질 수 있습니다. + +### 출처 추적 + +`source` 매개변수는 메모리의 출처를 식별합니다: + +```python +# 메모리에 출처 태그 지정 +memory.remember("User prefers dark mode", source="user:alice") +memory.remember("System config updated", source="admin") +memory.remember("Agent found a bug", source="agent:debugger") + +# 특정 출처의 메모리만 recall +matches = memory.recall("user preferences", source="user:alice") +``` + +### 비공개 메모리 + +비공개 메모리는 `source`가 일치할 때만 recall에서 볼 수 있습니다: + +```python +# 비공개 메모리 저장 +memory.remember("Alice's API key is sk-...", source="user:alice", private=True) + +# 이 recall은 비공개 메모리를 볼 수 있음 (source 일치) +matches = memory.recall("API key", source="user:alice") + +# 이 recall은 볼 수 없음 (다른 source) +matches = memory.recall("API key", source="user:bob") + +# 관리자 액세스: source에 관계없이 모든 비공개 레코드 보기 +matches = memory.recall("API key", include_private=True) +``` + +이는 서로 다른 사용자의 메모리가 격리되어야 하는 다중 사용자 또는 엔터프라이즈 배포에서 특히 유용합니다. + + +## RecallFlow (딥 Recall) + +`recall()`은 두 가지 깊이를 지원합니다: + +- **`depth="shallow"`** -- 복합 점수를 사용한 직접 벡터 검색. 빠름 (~200ms), LLM 호출 없음. +- **`depth="deep"` (기본값)** -- 다단계 RecallFlow 실행: 쿼리 분석, scope 선택, 병렬 벡터 검색, 신뢰도 기반 라우팅, 신뢰도가 낮을 때 선택적 재귀 탐색. + +**스마트 LLM 건너뛰기**: `query_analysis_threshold`(기본값 200자)보다 짧은 쿼리는 deep 모드에서도 LLM 쿼리 분석을 완전히 건너뜁니다. "What database do we use?"와 같은 짧은 쿼리는 이미 좋은 검색 구문이므로 LLM 분석이 큰 가치를 더하지 않습니다. 이를 통해 일반적인 짧은 쿼리에서 recall당 ~1-3초를 절약합니다. 긴 쿼리(예: 전체 작업 설명)만 대상 하위 쿼리로의 LLM 분석을 거칩니다. + +```python +# Shallow: 순수 벡터 검색, LLM 없음 +matches = memory.recall("What did we decide?", limit=10, depth="shallow") + +# Deep (기본값): 긴 쿼리에 대한 LLM 분석을 포함한 지능형 검색 +matches = memory.recall( + "Summarize all architecture decisions from this quarter", + limit=10, + depth="deep", +) +``` + +RecallFlow 라우터를 제어하는 신뢰도 임계값은 설정 가능합니다: + +```python +memory = Memory( + confidence_threshold_high=0.9, # 매우 확신할 때만 합성 + confidence_threshold_low=0.4, # 더 적극적으로 깊이 탐색 + exploration_budget=2, # 최대 2라운드 탐색 허용 + query_analysis_threshold=200, # 이보다 짧은 쿼리는 LLM 건너뛰기 +) +``` + + +## Embedder 설정 + +메모리는 의미 검색을 위해 텍스트를 벡터로 변환하는 임베딩 모델이 필요합니다. 세 가지 방법으로 설정할 수 있습니다. + +### Memory에 직접 전달 + +```python +from crewai import Memory + +# 설정 dict로 +memory = Memory(embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}}) + +# 사전 구축된 callable로 +from crewai.rag.embeddings.factory import build_embedder +embedder = build_embedder({"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}}) +memory = Memory(embedder=embedder) +``` + +### Crew Embedder 설정으로 + +`memory=True` 사용 시 crew의 `embedder` 설정이 전달됩니다: ```python from crewai import Crew -# Basic OpenAI configuration (uses environment OPENAI_API_KEY) crew = Crew( agents=[...], tasks=[...], memory=True, - embedder={ - "provider": "openai", - "config": { - "model": "text-embedding-3-small" # or "text-embedding-3-large" - } - } -) - -# Advanced OpenAI configuration -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": "your-openai-api-key", # Optional: override env var - "model": "text-embedding-3-large", - "dimensions": 1536, # Optional: reduce dimensions for smaller storage - "organization_id": "your-org-id" # Optional: for organization accounts - } - } + embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}}, ) ``` -### Azure OpenAI 임베딩 - -Azure OpenAI 배포를 사용하는 엔터프라이즈 사용자용. +### 제공자 예시 + + ```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", # Use openai provider for Azure - "config": { - "api_key": "your-azure-api-key", - "api_base": "https://your-resource.openai.azure.com/", - "api_type": "azure", - "api_version": "2023-05-15", - "model": "text-embedding-3-small", - "deployment_id": "your-deployment-name" # Azure deployment name - } - } -) -``` - -### Google AI 임베딩 - -Google의 텍스트 임베딩 모델을 사용하여 Google Cloud 서비스와 연동할 수 있습니다. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "google", - "config": { - "api_key": "your-google-api-key", - "model": "text-embedding-004" # or "text-embedding-preview-0409" - } - } -) -``` - -### Vertex AI 임베딩 - -Vertex AI 액세스 권한이 있는 Google Cloud 사용자용. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "vertexai", - "config": { - "project_id": "your-gcp-project-id", - "region": "us-central1", # 또는 원하는 리전 - "api_key": "your-service-account-key", - "model_name": "textembedding-gecko" - } - } -) -``` - -### Ollama 임베딩 (로컬) - -개인 정보 보호 및 비용 절감을 위해 임베딩을 로컬에서 실행하세요. - -```python -# 먼저 Ollama를 로컬에 설치하고 실행한 다음, 임베딩 모델을 pull 합니다: -# ollama pull mxbai-embed-large - -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": { - "model": "mxbai-embed-large", # 또는 "nomic-embed-text" - "url": "http://localhost:11434/api/embeddings" # 기본 Ollama URL - } - } -) - -# 사용자 지정 Ollama 설치의 경우 -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": { - "model": "mxbai-embed-large", - "url": "http://your-ollama-server:11434/api/embeddings" - } - } -) -``` - -### Cohere 임베딩 - -Cohere의 임베딩 모델을 사용하여 다국어 지원을 제공합니다. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "cohere", - "config": { - "api_key": "your-cohere-api-key", - "model": "embed-english-v3.0" # or "embed-multilingual-v3.0" - } - } -) -``` - -### VoyageAI 임베딩 - -검색 작업에 최적화된 고성능 임베딩입니다. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "voyageai", - "config": { - "api_key": "your-voyage-api-key", - "model": "voyage-large-2", # or "voyage-code-2" for code - "input_type": "document" # or "query" - } - } -) -``` - -### AWS Bedrock 임베딩 - -Bedrock 액세스 권한이 있는 AWS 사용자용. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "bedrock", - "config": { - "aws_access_key_id": "your-access-key", - "aws_secret_access_key": "your-secret-key", - "region_name": "us-east-1", - "model": "amazon.titan-embed-text-v1" - } - } -) -``` - -### Hugging Face 임베딩 - -Hugging Face의 오픈 소스 모델을 사용합니다. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "huggingface", - "config": { - "api_key": "your-hf-token", # Optional for public models - "model": "sentence-transformers/all-MiniLM-L6-v2" - } - } -) -``` - -### IBM Watson 임베딩 - -IBM Cloud 사용자를 위한 안내입니다. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "watson", - "config": { - "api_key": "your-watson-api-key", - "url": "your-watson-instance-url", - "model": "ibm/slate-125m-english-rtrvr" - } - } -) -``` - -### 적합한 임베딩 제공업체 선택하기 - -| 제공업체 | 최적 용도 | 장점 | 단점 | -|:---------|:----------|:------|:------| -| **OpenAI** | 일반적인 사용, 신뢰성 | 높은 품질, 잘 검증됨 | 비용, API 키 필요 | -| **Ollama** | 프라이버시, 비용 절감 | 무료, 로컬, 프라이빗 | 로컬 설정 필요 | -| **Google AI** | Google 생태계 | 좋은 성능 | Google 계정 필요 | -| **Azure OpenAI** | 엔터프라이즈, 컴플라이언스 | 엔터프라이즈 기능 | 복잡한 설정 | -| **Cohere** | 다국어 콘텐츠 | 뛰어난 언어 지원 | 특수한 사용 사례 | -| **VoyageAI** | 검색 작업 | 검색에 최적화됨 | 신규 제공업체 | - -### 환경 변수 설정 - -보안을 위해 API 키를 환경 변수에 저장하세요: - -```python -import os - -# Set environment variables -os.environ["OPENAI_API_KEY"] = "your-openai-key" -os.environ["GOOGLE_API_KEY"] = "your-google-key" -os.environ["COHERE_API_KEY"] = "your-cohere-key" - -# Use without exposing keys in code -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "model": "text-embedding-3-small" - # API key automatically loaded from environment - } - } -) -``` - -### 다양한 임베딩 제공자 테스트하기 - -특정 사용 사례에 맞게 임베딩 제공자를 비교하세요: - -```python -from crewai import Crew -from crewai.utilities.paths import db_storage_path - -# Test different providers with the same data -providers_to_test = [ - { - "name": "OpenAI", - "config": { - "provider": "openai", - "config": {"model": "text-embedding-3-small"} - } - }, - { - "name": "Ollama", - "config": { - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } - } -] - -for provider in providers_to_test: - print(f"\nTesting {provider['name']} embeddings...") - - # Create crew with specific embedder - crew = Crew( - agents=[...], - tasks=[...], - memory=True, - embedder=provider['config'] - ) - - # Run your test and measure performance - result = crew.kickoff() - print(f"{provider['name']} completed successfully") -``` - -### 임베딩 문제 해결 - -**모델을 찾을 수 없음 오류:** -```python -# Verify model availability -from crewai.rag.embeddings.configurator import EmbeddingConfigurator - -configurator = EmbeddingConfigurator() -try: - embedder = configurator.configure_embedder({ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - }) - print("Embedder configured successfully") -except Exception as e: - print(f"Configuration error: {e}") -``` - -**API 키 문제:** -```python -import os - -# Check if API keys are set -required_keys = ["OPENAI_API_KEY", "GOOGLE_API_KEY", "COHERE_API_KEY"] -for key in required_keys: - if os.getenv(key): - print(f"✅ {key} is set") - else: - print(f"❌ {key} is not set") -``` - -**성능 비교:** -```python -import time - -def test_embedding_performance(embedder_config, test_text="This is a test document"): - start_time = time.time() - - crew = Crew( - agents=[...], - tasks=[...], - memory=True, - embedder=embedder_config - ) - - # Simulate memory operation - crew.kickoff() - - end_time = time.time() - return end_time - start_time - -# Compare performance -openai_time = test_embedding_performance({ +memory = Memory(embedder={ "provider": "openai", - "config": {"model": "text-embedding-3-small"} + "config": { + "model_name": "text-embedding-3-small", + # "api_key": "sk-...", # 또는 OPENAI_API_KEY 환경 변수 설정 + }, }) +``` + -ollama_time = test_embedding_performance({ + +```python +memory = Memory(embedder={ "provider": "ollama", - "config": {"model": "mxbai-embed-large"} + "config": { + "model_name": "mxbai-embed-large", + "url": "http://localhost:11434/api/embeddings", + }, }) - -print(f"OpenAI: {openai_time:.2f}s") -print(f"Ollama: {ollama_time:.2f}s") ``` + -## 2. 외부 메모리 -외부 메모리는 crew의 내장 메모리와 독립적으로 작동하는 독립형 메모리 시스템을 제공합니다. 이는 특화된 메모리 공급자나 응용 프로그램 간 메모리 공유에 이상적입니다. - -### Mem0를 사용한 기본 외부 메모리 + ```python -import os -from crewai import Agent, Crew, Process, Task -from crewai.memory.external.external_memory import ExternalMemory - -# 로컬 Mem0 구성으로 외부 메모리 인스턴스 생성 -external_memory = ExternalMemory( - embedder_config={ - "provider": "mem0", - "config": { - "user_id": "john", - "local_mem0_config": { - "vector_store": { - "provider": "qdrant", - "config": {"host": "localhost", "port": 6333} - }, - "llm": { - "provider": "openai", - "config": {"api_key": "your-api-key", "model": "gpt-4"} - }, - "embedder": { - "provider": "openai", - "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"} - } - }, - "infer": True # Optional defaults to True - }, - } -) - -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory, # 기본 메모리와 분리됨 - process=Process.sequential, - verbose=True -) +memory = Memory(embedder={ + "provider": "azure", + "config": { + "deployment_id": "your-embedding-deployment", + "api_key": "your-azure-api-key", + "api_base": "https://your-resource.openai.azure.com", + "api_version": "2024-02-01", + }, +}) ``` + -### Mem0 클라이언트를 활용한 고급 외부 메모리 -Mem0 클라이언트를 사용할 때, 'includes', 'excludes', 'custom_categories', 'infer', 'run_id'(이것은 단기 메모리에만 해당)와 같은 파라미터를 사용하여 메모리 구성을 더욱 세밀하게 커스터마이즈할 수 있습니다. -더 자세한 내용은 [Mem0 문서](https://docs.mem0.ai/)에서 확인할 수 있습니다. + +```python +memory = Memory(embedder={ + "provider": "google-generativeai", + "config": { + "model_name": "gemini-embedding-001", + # "api_key": "...", # 또는 GOOGLE_API_KEY 환경 변수 설정 + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "google-vertex", + "config": { + "model_name": "gemini-embedding-001", + "project_id": "your-gcp-project-id", + "location": "us-central1", + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "cohere", + "config": { + "model_name": "embed-english-v3.0", + # "api_key": "...", # 또는 COHERE_API_KEY 환경 변수 설정 + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "voyageai", + "config": { + "model": "voyage-3", + # "api_key": "...", # 또는 VOYAGE_API_KEY 환경 변수 설정 + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "amazon-bedrock", + "config": { + "model_name": "amazon.titan-embed-text-v1", + # 기본 AWS 자격 증명 사용 (boto3 세션) + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "huggingface", + "config": { + "model_name": "sentence-transformers/all-MiniLM-L6-v2", + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "jina", + "config": { + "model_name": "jina-embeddings-v2-base-en", + # "api_key": "...", # 또는 JINA_API_KEY 환경 변수 설정 + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "watsonx", + "config": { + "model_id": "ibm/slate-30m-english-rtrvr", + "api_key": "your-watsonx-api-key", + "project_id": "your-project-id", + "url": "https://us-south.ml.cloud.ibm.com", + }, +}) +``` + + + +```python +# 문자열 목록을 받아 벡터 목록을 반환하는 callable 전달 +def my_embedder(texts: list[str]) -> list[list[float]]: + # 임베딩 로직 + return [[0.1, 0.2, ...] for _ in texts] + +memory = Memory(embedder=my_embedder) +``` + + + +### 제공자 참조 + +| 제공자 | 키 | 일반적인 모델 | 참고 | +| :--- | :--- | :--- | :--- | +| OpenAI | `openai` | `text-embedding-3-small` | 기본값. `OPENAI_API_KEY` 설정. | +| Ollama | `ollama` | `mxbai-embed-large` | 로컬, API 키 불필요. | +| Azure OpenAI | `azure` | `text-embedding-ada-002` | `deployment_id` 필요. | +| Google AI | `google-generativeai` | `gemini-embedding-001` | `GOOGLE_API_KEY` 설정. | +| Google Vertex | `google-vertex` | `gemini-embedding-001` | `project_id` 필요. | +| Cohere | `cohere` | `embed-english-v3.0` | 강력한 다국어 지원. | +| VoyageAI | `voyageai` | `voyage-3` | 검색에 최적화. | +| AWS Bedrock | `amazon-bedrock` | `amazon.titan-embed-text-v1` | boto3 자격 증명 사용. | +| Hugging Face | `huggingface` | `all-MiniLM-L6-v2` | 로컬 sentence-transformers. | +| Jina | `jina` | `jina-embeddings-v2-base-en` | `JINA_API_KEY` 설정. | +| IBM WatsonX | `watsonx` | `ibm/slate-30m-english-rtrvr` | `project_id` 필요. | +| Sentence Transformer | `sentence-transformer` | `all-MiniLM-L6-v2` | 로컬, API 키 불필요. | +| Custom | `custom` | -- | `embedding_callable` 필요. | + + +## LLM 설정 + +메모리는 저장 분석(scope, categories, importance 추론), 통합 결정, 딥 recall 쿼리 분석에 LLM을 사용합니다. 사용할 모델을 설정할 수 있습니다. ```python -import os -from crewai import Agent, Crew, Process, Task -from crewai.memory.external.external_memory import ExternalMemory +from crewai import Memory, LLM -new_categories = [ - {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"}, - {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"}, - {"personal_information": "Basic information about the user including name, preferences, and personality traits"} -] +# 기본값: gpt-4o-mini +memory = Memory() -os.environ["MEM0_API_KEY"] = "your-api-key" +# 다른 OpenAI 모델 사용 +memory = Memory(llm="gpt-4o") -# Create external memory instance with Mem0 Client -external_memory = ExternalMemory( - embedder_config={ - "provider": "mem0", - "config": { - "user_id": "john", - "org_id": "my_org_id", # Optional - "project_id": "my_project_id", # Optional - "api_key": "custom-api-key" # Optional - overrides env var - "run_id": "my_run_id", # Optional - for short-term memory - "includes": "include1", # Optional - "excludes": "exclude1", # Optional - "infer": True # Optional defaults to True - "custom_categories": new_categories # Optional - custom categories for user memory - }, - } -) +# Anthropic 사용 +memory = Memory(llm="anthropic/claude-3-haiku-20240307") -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory, # Separate from basic memory - process=Process.sequential, - verbose=True -) +# 완전한 로컬/비공개 분석을 위해 Ollama 사용 +memory = Memory(llm="ollama/llama3.2") + +# Google Gemini 사용 +memory = Memory(llm="gemini/gemini-2.0-flash") + +# 사용자 정의 설정이 있는 사전 구성된 LLM 인스턴스 전달 +llm = LLM(model="gpt-4o", temperature=0) +memory = Memory(llm=llm) ``` -### 커스텀 스토리지 구현 +LLM은 **지연 초기화**됩니다 -- 처음 필요할 때만 생성됩니다. 즉, API 키가 설정되지 않아도 `Memory()` 생성 시에는 실패하지 않습니다. 오류는 LLM이 실제로 호출될 때만 발생합니다(예: 명시적 scope/categories 없이 저장할 때 또는 딥 recall 중). + +완전한 오프라인/비공개 운영을 위해 LLM과 embedder 모두에 로컬 모델을 사용하세요: + ```python -from crewai.memory.external.external_memory import ExternalMemory -from crewai.memory.storage.interface import Storage - -class CustomStorage(Storage): - def __init__(self): - self.memories = [] - - def save(self, value, metadata=None, agent=None): - self.memories.append({ - "value": value, - "metadata": metadata, - "agent": agent - }) - - def search(self, query, limit=10, score_threshold=0.5): - # Implement your search logic here - return [m for m in self.memories if query.lower() in str(m["value"]).lower()] - - def reset(self): - self.memories = [] - -# Use custom storage -external_memory = ExternalMemory(storage=CustomStorage()) - -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory +memory = Memory( + llm="ollama/llama3.2", + embedder={"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}}, ) ``` -## 🧠 메모리 시스템 비교 -| **카테고리** | **기능** | **기본 메모리** | **외부 메모리** | -|---------------------|--------------------------|-------------------------------|-------------------------------| -| **사용 용이성** | 설정 복잡성 | 간단함 | 보통 | -| | 통합성 | 내장형(컨텍스추얼) | 독립형 | -| **지속성** | 저장소 | 로컬 파일 | 커스텀 / Mem0 | -| | 세션 간 지원 | ✅ | ✅ | -| **개인화** | 사용자별 메모리 | ❌ | ✅ | -| | 커스텀 공급자 | 제한적 | 모든 공급자 | -| **사용 사례 적합성**| 추천 대상 | 대부분의 일반적 사용 사례 | 특화/커스텀 필요 | +## 스토리지 백엔드 -## 지원되는 임베딩 제공업체 +- **기본값**: LanceDB, `./.crewai/memory` 아래에 저장 (또는 환경 변수가 설정된 경우 `$CREWAI_STORAGE_DIR/memory`, 또는 `storage="path/to/dir"`로 전달한 경로). +- **사용자 정의 백엔드**: `StorageBackend` 프로토콜을 구현하고(`crewai.memory.storage.backend` 참조) `Memory(storage=your_backend)`에 인스턴스를 전달합니다. + + +## 탐색(Discovery) + +scope 계층 구조, 카테고리, 레코드를 검사합니다: -### OpenAI (기본값) ```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": {"model": "text-embedding-3-small"} - } -) +memory.tree() # scope 및 레코드 수의 포맷된 트리 +memory.tree("/project", max_depth=2) # 하위 트리 뷰 +memory.info("/project") # ScopeInfo: record_count, categories, oldest/newest +memory.list_scopes("/") # 직계 자식 scope +memory.list_categories() # 카테고리 이름 및 개수 +memory.list_records(scope="/project/alpha", limit=20) # scope의 레코드, 최신순 ``` -### Ollama -```python -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } -) -``` -### Google AI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "google", - "config": { - "api_key": "your-api-key", - "model": "text-embedding-004" - } - } -) -``` +## 오류 시 동작 -### Azure OpenAI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": "your-api-key", - "api_base": "https://your-resource.openai.azure.com/", - "api_version": "2023-05-15", - "model_name": "text-embedding-3-small" - } - } -) -``` +분석 중 LLM이 실패하면(네트워크 오류, 속도 제한, 잘못된 응답) 메모리는 우아하게 저하됩니다: -### Vertex AI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "vertexai", - "config": { - "project_id": "your-project-id", - "region": "your-region", - "api_key": "your-api-key", - "model_name": "textembedding-gecko" - } - } -) -``` +- **저장 분석** -- 경고가 로깅되고 메모리는 기본 scope `/`, 빈 categories, importance `0.5`로 저장됩니다. +- **메모리 추출** -- 전체 콘텐츠가 단일 메모리로 저장되어 누락되지 않습니다. +- **쿼리 분석** -- recall은 단순 scope 선택 및 벡터 검색으로 폴백하여 결과를 계속 반환합니다. -## 보안 모범 사례 +이러한 분석 실패에서는 예외가 발생하지 않으며, 스토리지 또는 embedder 실패만 예외를 발생시킵니다. -### 환경 변수 -```python -import os -from crewai import Crew -# Store sensitive data in environment variables -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": os.getenv("OPENAI_API_KEY"), - "model": "text-embedding-3-small" - } - } -) -``` +## 개인정보 참고 -### 스토리지 보안 -```python -import os -from crewai import Crew -from crewai.memory import LongTermMemory -from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage +메모리 콘텐츠는 분석을 위해 설정된 LLM으로 전송됩니다(저장 시 scope/categories/importance, 쿼리 분석 및 선택적 딥 recall). 민감한 데이터의 경우 로컬 LLM(예: Ollama)을 사용하거나 제공자가 규정 요구 사항을 충족하는지 확인하세요. -# Use secure storage paths -storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage") -os.makedirs(storage_path, mode=0o700, exist_ok=True) # Restricted permissions - -crew = Crew( - memory=True, - long_term_memory=LongTermMemory( - storage=LTMSQLiteStorage( - db_path=f"{storage_path}/memory.db" - ) - ) -) -``` - -## 문제 해결 - -### 일반적인 문제 - -**세션 간에 메모리가 유지되지 않나요?** -- `CREWAI_STORAGE_DIR` 환경 변수를 확인하세요 -- 저장소 디렉터리에 대한 쓰기 권한을 확인하세요 -- `memory=True`로 메모리가 활성화되어 있는지 확인하세요 - -**Mem0 인증 오류가 발생하나요?** -- `MEM0_API_KEY` 환경 변수가 설정되어 있는지 확인하세요 -- Mem0 대시보드에서 API 키 권한을 확인하세요 -- `mem0ai` 패키지가 설치되어 있는지 확인하세요 - -**대용량 데이터셋에서 메모리 사용량이 높은가요?** -- 커스텀 저장소와 함께 외부 메모리 사용을 고려하세요 -- 커스텀 저장소 검색 방법에 페이지네이션을 구현하세요 -- 메모리 사용량을 줄이기 위해 더 작은 임베딩 모델을 사용하세요 - -### 성능 팁 - -- 대부분의 사용 사례에서는 `memory=True`를 사용하세요 (가장 간단하고 빠릅니다) -- 사용자별 지속성이 필요한 경우에만 User Memory를 사용하세요 -- 대규모 또는 특수 요구 사항에는 External Memory를 고려하세요 -- 더 빠른 처리를 위해 더 작은 embedding 모델을 선택하세요 -- 메모리 검색 크기를 제어하기 위해 적절한 검색 한도를 설정하세요 - -## CrewAI의 메모리 시스템 사용의 이점 - -- 🦾 **적응형 학습:** 크루는 시간이 지남에 따라 더욱 효율적으로 변하며, 새로운 정보에 적응하고 작업 접근 방식을 정제합니다. -- 🫡 **향상된 개인화:** 메모리를 통해 에이전트는 사용자 선호도와 과거 상호작용을 기억하여, 맞춤형 경험을 제공합니다. -- 🧠 **향상된 문제 해결:** 풍부한 메모리 저장소에 접근함으로써 에이전트는 과거의 학습과 맥락적 통찰을 활용하여 더 나은 의사 결정을 내릴 수 있습니다. ## 메모리 이벤트 -CrewAI의 이벤트 시스템은 메모리 작업에 대한 강력한 인사이트를 제공합니다. 메모리 이벤트를 활용하면 메모리 시스템의 성능과 동작을 모니터링하고, 디버깅하며, 최적화할 수 있습니다. - -### 사용 가능한 메모리 이벤트 - -CrewAI는 다음과 같은 메모리 관련 이벤트를 발생시킵니다: +모든 메모리 연산은 `source_type="unified_memory"`로 이벤트를 발생시킵니다. 시간, 오류, 콘텐츠를 수신할 수 있습니다. | 이벤트 | 설명 | 주요 속성 | | :---- | :---------- | :------------- | -| **MemoryQueryStartedEvent** | 메모리 쿼리가 시작될 때 발생 | `query`, `limit`, `score_threshold` | -| **MemoryQueryCompletedEvent** | 메모리 쿼리가 성공적으로 완료될 때 발생 | `query`, `results`, `limit`, `score_threshold`, `query_time_ms` | -| **MemoryQueryFailedEvent** | 메모리 쿼리가 실패할 때 발생 | `query`, `limit`, `score_threshold`, `error` | -| **MemorySaveStartedEvent** | 메모리 저장 작업이 시작될 때 발생 | `value`, `metadata`, `agent_role` | -| **MemorySaveCompletedEvent** | 메모리 저장 작업이 성공적으로 완료될 때 발생 | `value`, `metadata`, `agent_role`, `save_time_ms` | -| **MemorySaveFailedEvent** | 메모리 저장 작업이 실패할 때 발생 | `value`, `metadata`, `agent_role`, `error` | -| **MemoryRetrievalStartedEvent** | 태스크 프롬프트에 대한 메모리 검색이 시작될 때 발생 | `task_id` | -| **MemoryRetrievalCompletedEvent** | 메모리 검색이 성공적으로 완료될 때 발생 | `task_id`, `memory_content`, `retrieval_time_ms` | +| **MemoryQueryStartedEvent** | 쿼리 시작 | `query`, `limit` | +| **MemoryQueryCompletedEvent** | 쿼리 성공 | `query`, `results`, `query_time_ms` | +| **MemoryQueryFailedEvent** | 쿼리 실패 | `query`, `error` | +| **MemorySaveStartedEvent** | 저장 시작 | `value`, `metadata` | +| **MemorySaveCompletedEvent** | 저장 성공 | `value`, `save_time_ms` | +| **MemorySaveFailedEvent** | 저장 실패 | `value`, `error` | +| **MemoryRetrievalStartedEvent** | 에이전트 검색 시작 | `task_id` | +| **MemoryRetrievalCompletedEvent** | 에이전트 검색 완료 | `task_id`, `memory_content`, `retrieval_time_ms` | -### 실용적인 응용 사례 - -#### 1. 메모리 성능 모니터링 - -애플리케이션을 최적화하기 위해 메모리 작업 타이밍을 추적하세요: +예: 쿼리 시간 모니터링: ```python -from crewai.events import ( - BaseEventListener, - MemoryQueryCompletedEvent, - MemorySaveCompletedEvent -) -import time - -class MemoryPerformanceMonitor(BaseEventListener): - def __init__(self): - super().__init__() - self.query_times = [] - self.save_times = [] +from crewai.events import BaseEventListener, MemoryQueryCompletedEvent +class MemoryMonitor(BaseEventListener): def setup_listeners(self, crewai_event_bus): @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_memory_query_completed(source, event: MemoryQueryCompletedEvent): - self.query_times.append(event.query_time_ms) - print(f"Memory query completed in {event.query_time_ms:.2f}ms. Query: '{event.query}'") - print(f"Average query time: {sum(self.query_times)/len(self.query_times):.2f}ms") - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_memory_save_completed(source, event: MemorySaveCompletedEvent): - self.save_times.append(event.save_time_ms) - print(f"Memory save completed in {event.save_time_ms:.2f}ms") - print(f"Average save time: {sum(self.save_times)/len(self.save_times):.2f}ms") - -# Create an instance of your listener -memory_monitor = MemoryPerformanceMonitor() + def on_done(source, event): + if getattr(event, "source_type", None) == "unified_memory": + print(f"Query '{event.query}' completed in {event.query_time_ms:.0f}ms") ``` -#### 2. 메모리 내용 로깅 -디버깅 및 인사이트를 위해 메모리 작업을 로깅합니다: +## 문제 해결 +**메모리가 유지되지 않나요?** +- 저장 경로에 쓰기 권한이 있는지 확인하세요(기본값 `./.crewai/memory`). 다른 디렉터리를 사용하려면 `storage="./your_path"`를 전달하거나 `CREWAI_STORAGE_DIR` 환경 변수를 설정하세요. +- crew 사용 시 `memory=True` 또는 `memory=Memory(...)`가 설정되었는지 확인하세요. + +**recall이 느린가요?** +- 일상적인 에이전트 컨텍스트에는 `depth="shallow"`를 사용하세요. 복잡한 쿼리에만 `depth="deep"`을 사용하세요. +- 더 많은 쿼리에서 LLM 분석을 건너뛰려면 `query_analysis_threshold`를 높이세요. + +**로그에 LLM 분석 오류가 있나요?** +- 메모리는 안전한 기본값으로 계속 저장/recall합니다. 전체 LLM 분석을 원하면 API 키, 속도 제한, 모델 가용성을 확인하세요. + +**로그에 백그라운드 저장 오류가 있나요?** +- 메모리 저장은 백그라운드 스레드에서 실행됩니다. 오류는 `MemorySaveFailedEvent`로 발생하지만 에이전트를 중단시키지 않습니다. 근본 원인(보통 LLM 또는 embedder 연결 문제)은 로그를 확인하세요. + +**동시 쓰기 충돌이 있나요?** +- LanceDB 연산은 공유 잠금으로 직렬화되며 충돌 시 자동으로 재시도됩니다. 이는 동일 데이터베이스를 가리키는 여러 `Memory` 인스턴스(예: 에이전트 메모리 + crew 메모리)를 처리합니다. 별도의 조치가 필요하지 않습니다. + +**터미널에서 메모리 탐색:** +```bash +crewai memory # TUI 브라우저 열기 +crewai memory --storage-path ./my_memory # 특정 디렉터리 지정 +``` + +**메모리 초기화(예: 테스트용):** ```python -from crewai.events import ( - BaseEventListener, - MemorySaveStartedEvent, - MemoryQueryStartedEvent, - MemoryRetrievalCompletedEvent -) -import logging - -# Configure logging -logger = logging.getLogger('memory_events') - -class MemoryLogger(BaseEventListener): - def setup_listeners(self, crewai_event_bus): - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_memory_save_started(source, event: MemorySaveStartedEvent): - if event.agent_role: - logger.info(f"Agent '{event.agent_role}' saving memory: {event.value[:50]}...") - else: - logger.info(f"Saving memory: {event.value[:50]}...") - - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_memory_query_started(source, event: MemoryQueryStartedEvent): - logger.info(f"Memory query started: '{event.query}' (limit: {event.limit})") - - @crewai_event_bus.on(MemoryRetrievalCompletedEvent) - def on_memory_retrieval_completed(source, event: MemoryRetrievalCompletedEvent): - if event.task_id: - logger.info(f"Memory retrieved for task {event.task_id} in {event.retrieval_time_ms:.2f}ms") - else: - logger.info(f"Memory retrieved in {event.retrieval_time_ms:.2f}ms") - logger.debug(f"Memory content: {event.memory_content}") - -# Create an instance of your listener -memory_logger = MemoryLogger() +crew.reset_memories(command_type="memory") # 통합 메모리 초기화 +# 또는 Memory 인스턴스에서: +memory.reset() # 모든 scope +memory.reset(scope="/project/old") # 해당 하위 트리만 ``` -#### 3. 오류 추적 및 알림 -메모리 오류를 캡처하고 대응합니다: +## 설정 참조 -```python -from crewai.events import ( - BaseEventListener, - MemorySaveFailedEvent, - MemoryQueryFailedEvent -) -import logging -from typing import Optional +모든 설정은 `Memory(...)`에 키워드 인수로 전달됩니다. 모든 매개변수에는 합리적인 기본값이 있습니다. -# Configure logging -logger = logging.getLogger('memory_errors') - -class MemoryErrorTracker(BaseEventListener): - def __init__(self, notify_email: Optional[str] = None): - super().__init__() - self.notify_email = notify_email - self.error_count = 0 - - def setup_listeners(self, crewai_event_bus): - @crewai_event_bus.on(MemorySaveFailedEvent) - def on_memory_save_failed(source, event: MemorySaveFailedEvent): - self.error_count += 1 - agent_info = f"Agent '{event.agent_role}'" if event.agent_role else "Unknown agent" - error_message = f"Memory save failed: {event.error}. {agent_info}" - logger.error(error_message) - - if self.notify_email and self.error_count % 5 == 0: - self._send_notification(error_message) - - @crewai_event_bus.on(MemoryQueryFailedEvent) - def on_memory_query_failed(source, event: MemoryQueryFailedEvent): - self.error_count += 1 - error_message = f"Memory query failed: {event.error}. Query: '{event.query}'" - logger.error(error_message) - - if self.notify_email and self.error_count % 5 == 0: - self._send_notification(error_message) - - def _send_notification(self, message): - # Implement your notification system (email, Slack, etc.) - print(f"[NOTIFICATION] Would send to {self.notify_email}: {message}") - -# Create an instance of your listener -error_tracker = MemoryErrorTracker(notify_email="admin@example.com") -``` - -### 분석 플랫폼과의 통합 - -메모리 이벤트는 분석 및 모니터링 플랫폼으로 전달되어 성능 지표를 추적하고, 이상 징후를 감지하며, 메모리 사용 패턴을 시각화할 수 있습니다: - -```python -from crewai.events import ( - BaseEventListener, - MemoryQueryCompletedEvent, - MemorySaveCompletedEvent -) - -class MemoryAnalyticsForwarder(BaseEventListener): - def __init__(self, analytics_client): - super().__init__() - self.client = analytics_client - - def setup_listeners(self, crewai_event_bus): - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_memory_query_completed(source, event: MemoryQueryCompletedEvent): - # Forward query metrics to analytics platform - self.client.track_metric({ - "event_type": "memory_query", - "query": event.query, - "duration_ms": event.query_time_ms, - "result_count": len(event.results) if hasattr(event.results, "__len__") else 0, - "timestamp": event.timestamp - }) - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_memory_save_completed(source, event: MemorySaveCompletedEvent): - # Forward save metrics to analytics platform - self.client.track_metric({ - "event_type": "memory_save", - "agent_role": event.agent_role, - "duration_ms": event.save_time_ms, - "timestamp": event.timestamp - }) -``` - -### 메모리 이벤트 리스너를 위한 모범 사례 - -1. **핸들러를 가볍게 유지하세요**: 이벤트 핸들러에서 복잡한 처리를 피하여 성능 저하를 방지하세요. -2. **적절한 로깅 레벨을 사용하세요**: 일반적인 동작에는 INFO, 상세 정보에는 DEBUG, 문제 발생 시에는 ERROR를 사용하세요. -3. **가능하면 메트릭을 배치 처리하세요**: 외부 시스템에 전송하기 전에 메트릭을 누적하세요. -4. **예외를 우아하게 처리하세요**: 예기치 않은 데이터로 인해 이벤트 핸들러가 중단되지 않도록 하세요. -5. **메모리 사용량을 고려하세요**: 대량의 이벤트 데이터를 저장할 때 유의하세요. - -## 결론 - -CrewAI의 memory 시스템을 프로젝트에 통합하는 것은 간단합니다. 제공되는 memory 컴포넌트와 설정을 활용하여, -여러분의 에이전트에 상호작용을 기억하고, reasoning하며, 학습할 수 있는 능력을 신속하게 부여할 수 있습니다. 이를 통해 더욱 향상된 인텔리전스와 역량을 발휘할 수 있습니다. \ No newline at end of file +| 매개변수 | 기본값 | 설명 | +| :--- | :--- | :--- | +| `llm` | `"gpt-4o-mini"` | 분석용 LLM (모델 이름 또는 `BaseLLM` 인스턴스). | +| `storage` | `"lancedb"` | 스토리지 백엔드 (`"lancedb"`, 경로 문자열 또는 `StorageBackend` 인스턴스). | +| `embedder` | `None` (OpenAI 기본값) | Embedder (설정 dict, callable 또는 `None`으로 기본 OpenAI). | +| `recency_weight` | `0.3` | 복합 점수에서 최신성 가중치. | +| `semantic_weight` | `0.5` | 복합 점수에서 의미 유사도 가중치. | +| `importance_weight` | `0.2` | 복합 점수에서 중요도 가중치. | +| `recency_half_life_days` | `30` | 최신성 점수가 절반으로 줄어드는 일수(지수 감쇠). | +| `consolidation_threshold` | `0.85` | 저장 시 통합이 트리거되는 유사도. `1.0`으로 설정하면 비활성화. | +| `consolidation_limit` | `5` | 통합 중 비교할 기존 레코드 최대 수. | +| `default_importance` | `0.5` | 미제공 시 및 LLM 분석이 생략될 때 할당되는 중요도. | +| `batch_dedup_threshold` | `0.98` | `remember_many()` 배치 내 거의 중복 삭제를 위한 코사인 유사도. | +| `confidence_threshold_high` | `0.8` | recall 신뢰도가 이 값 이상이면 결과를 직접 반환. | +| `confidence_threshold_low` | `0.5` | recall 신뢰도가 이 값 미만이면 더 깊은 탐색 트리거. | +| `complex_query_threshold` | `0.7` | 복잡한 쿼리의 경우 이 신뢰도 미만에서 더 깊이 탐색. | +| `exploration_budget` | `1` | 딥 recall 중 LLM 기반 탐색 라운드 수. | +| `query_analysis_threshold` | `200` | 이 길이(문자 수)보다 짧은 쿼리는 딥 recall 중 LLM 분석을 건너뜀. | diff --git a/docs/ko/enterprise/features/flow-hitl-management.mdx b/docs/ko/enterprise/features/flow-hitl-management.mdx index a760a4c44..adb8ee492 100644 --- a/docs/ko/enterprise/features/flow-hitl-management.mdx +++ b/docs/ko/enterprise/features/flow-hitl-management.mdx @@ -38,22 +38,21 @@ CrewAI Enterprise는 AI 워크플로우를 협업적인 인간-AI 프로세스 `@human_feedback` 데코레이터를 사용하여 Flow 내에 인간 검토 체크포인트를 구성합니다. 실행이 검토 포인트에 도달하면 시스템이 일시 중지되고, 담당자에게 이메일로 알리며, 응답을 기다립니다. ```python -from crewai.flow.flow import Flow, start, listen +from crewai.flow.flow import Flow, start, listen, or_ from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult class ContentApprovalFlow(Flow): @start() def generate_content(self): - # AI가 콘텐츠 생성 return "Q1 캠페인용 마케팅 카피 생성..." - @listen(generate_content) @human_feedback( message="브랜드 준수를 위해 이 콘텐츠를 검토해 주세요:", emit=["approved", "rejected", "needs_revision"], ) - def review_content(self, content): - return content + @listen(or_("generate_content", "needs_revision")) + def review_content(self): + return "검토용 마케팅 카피..." @listen("approved") def publish_content(self, result: HumanFeedbackResult): @@ -62,10 +61,6 @@ class ContentApprovalFlow(Flow): @listen("rejected") def archive_content(self, result: HumanFeedbackResult): print(f"콘텐츠 거부됨. 사유: {result.feedback}") - - @listen("needs_revision") - def revise_content(self, result: HumanFeedbackResult): - print(f"수정 요청: {result.feedback}") ``` 완전한 구현 세부 사항은 [Flow에서 인간 피드백](/ko/learn/human-feedback-in-flows) 가이드를 참조하세요. diff --git a/docs/ko/enterprise/guides/deploy-to-amp.mdx b/docs/ko/enterprise/guides/deploy-to-amp.mdx index 5262701ee..66954c840 100644 --- a/docs/ko/enterprise/guides/deploy-to-amp.mdx +++ b/docs/ko/enterprise/guides/deploy-to-amp.mdx @@ -176,6 +176,11 @@ Crew를 GitHub 저장소에 푸시해야 합니다. 아직 Crew를 만들지 않 ![Set Environment Variables](/images/enterprise/set-env-variables.png) + + 프라이빗 Python 패키지를 사용하시나요? 여기에 레지스트리 자격 증명도 추가해야 합니다. + 필요한 변수는 [프라이빗 패키지 레지스트리](/ko/enterprise/guides/private-package-registry)를 참조하세요. + + diff --git a/docs/ko/enterprise/guides/prepare-for-deployment.mdx b/docs/ko/enterprise/guides/prepare-for-deployment.mdx index 9778dde4d..fa4d40109 100644 --- a/docs/ko/enterprise/guides/prepare-for-deployment.mdx +++ b/docs/ko/enterprise/guides/prepare-for-deployment.mdx @@ -256,6 +256,12 @@ Crews와 Flows 모두 `src/project_name/main.py`에 진입점이 있습니다: 1. **LLM API 키** (OpenAI, Anthropic, Google 등) 2. **도구 API 키** - 외부 도구를 사용하는 경우 (Serper 등) + + 프로젝트가 **프라이빗 PyPI 레지스트리**의 패키지에 의존하는 경우, 레지스트리 인증 자격 증명도 + 환경 변수로 구성해야 합니다. 자세한 내용은 + [프라이빗 패키지 레지스트리](/ko/enterprise/guides/private-package-registry) 가이드를 참조하세요. + + 구성 문제를 조기에 발견하기 위해 배포 전에 동일한 환경 변수로 로컬에서 프로젝트를 테스트하세요. diff --git a/docs/ko/enterprise/guides/private-package-registry.mdx b/docs/ko/enterprise/guides/private-package-registry.mdx new file mode 100644 index 000000000..41b07731f --- /dev/null +++ b/docs/ko/enterprise/guides/private-package-registry.mdx @@ -0,0 +1,261 @@ +--- +title: "프라이빗 패키지 레지스트리" +description: "CrewAI AMP에서 인증된 PyPI 레지스트리의 프라이빗 Python 패키지 설치하기" +icon: "lock" +mode: "wide" +--- + + + 이 가이드는 CrewAI AMP에 배포할 때 프라이빗 PyPI 레지스트리(Azure DevOps Artifacts, GitHub Packages, + GitLab, AWS CodeArtifact 등)에서 Python 패키지를 설치하도록 CrewAI 프로젝트를 구성하는 방법을 다룹니다. + + +## 이 가이드가 필요한 경우 + +프로젝트가 공개 PyPI가 아닌 프라이빗 레지스트리에 호스팅된 내부 또는 독점 Python 패키지에 +의존하는 경우, 다음을 수행해야 합니다: + +1. UV에 패키지를 **어디서** 찾을지 알려줍니다 (index URL) +2. UV에 **어떤** 패키지가 해당 index에서 오는지 알려줍니다 (source 매핑) +3. UV가 설치 중에 인증할 수 있도록 **자격 증명**을 제공합니다 + +CrewAI AMP는 의존성 해결 및 설치에 [UV](https://docs.astral.sh/uv/)를 사용합니다. +UV는 `pyproject.toml` 구성과 자격 증명용 환경 변수를 결합하여 인증된 프라이빗 레지스트리를 지원합니다. + +## 1단계: pyproject.toml 구성 + +`pyproject.toml`에서 세 가지 요소가 함께 작동합니다: + +### 1a. 의존성 선언 + +프라이빗 패키지를 다른 의존성과 마찬가지로 `[project.dependencies]`에 추가합니다: + +```toml +[project] +dependencies = [ + "crewai[tools]>=0.100.1,<1.0.0", + "my-private-package>=1.2.0", +] +``` + +### 1b. index 정의 + +프라이빗 레지스트리를 `[[tool.uv.index]]` 아래에 명명된 index로 등록합니다: + +```toml +[[tool.uv.index]] +name = "my-private-registry" +url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/" +explicit = true +``` + + + `name` 필드는 중요합니다 — UV는 이를 사용하여 인증을 위한 환경 변수 이름을 + 구성합니다 (아래 [2단계](#2단계-인증-자격-증명-설정)를 참조하세요). + + `explicit = true`를 설정하면 UV가 모든 패키지에 대해 이 index를 검색하지 않습니다 — + `[tool.uv.sources]`에서 명시적으로 매핑한 패키지만 검색합니다. 이렇게 하면 프라이빗 + 레지스트리에 대한 불필요한 쿼리를 방지하고 의존성 혼동 공격을 차단할 수 있습니다. + + +### 1c. 패키지를 index에 매핑 + +`[tool.uv.sources]`를 사용하여 프라이빗 index에서 해결해야 할 패키지를 UV에 알려줍니다: + +```toml +[tool.uv.sources] +my-private-package = { index = "my-private-registry" } +``` + +### 전체 예시 + +```toml +[project] +name = "my-crew-project" +version = "0.1.0" +requires-python = ">=3.10,<=3.13" +dependencies = [ + "crewai[tools]>=0.100.1,<1.0.0", + "my-private-package>=1.2.0", +] + +[tool.crewai] +type = "crew" + +[[tool.uv.index]] +name = "my-private-registry" +url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/" +explicit = true + +[tool.uv.sources] +my-private-package = { index = "my-private-registry" } +``` + +`pyproject.toml`을 업데이트한 후 lock 파일을 다시 생성합니다: + +```bash +uv lock +``` + + + 업데이트된 `uv.lock`을 항상 `pyproject.toml` 변경 사항과 함께 커밋하세요. + lock 파일은 배포에 필수입니다 — [배포 준비하기](/ko/enterprise/guides/prepare-for-deployment)를 참조하세요. + + +## 2단계: 인증 자격 증명 설정 + +UV는 `pyproject.toml`에서 정의한 index 이름을 기반으로 한 명명 규칙을 따르는 +환경 변수를 사용하여 프라이빗 index에 인증합니다: + +``` +UV_INDEX_{UPPER_NAME}_USERNAME +UV_INDEX_{UPPER_NAME}_PASSWORD +``` + +여기서 `{UPPER_NAME}`은 index 이름을 **대문자**로 변환하고 **하이픈을 언더스코어로 대체**한 것입니다. + +예를 들어, `my-private-registry`라는 이름의 index는 다음을 사용합니다: + +| 변수 | 값 | +|------|-----| +| `UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME` | 레지스트리 사용자 이름 또는 토큰 이름 | +| `UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD` | 레지스트리 비밀번호 또는 토큰/PAT | + + + 이 환경 변수는 CrewAI AMP **환경 변수** 설정을 통해 **반드시** 추가해야 합니다 — + 전역적으로 또는 배포 수준에서. `.env` 파일에 설정하거나 프로젝트에 하드코딩할 수 없습니다. + + 아래 [AMP에서 환경 변수 설정](#amp에서-환경-변수-설정)을 참조하세요. + + +## 레지스트리 제공업체 참조 + +아래 표는 일반적인 레지스트리 제공업체의 index URL 형식과 자격 증명 값을 보여줍니다. +자리 표시자 값을 실제 조직 및 피드 세부 정보로 대체하세요. + +| 제공업체 | Index URL | 사용자 이름 | 비밀번호 | +|---------|-----------|-----------|---------| +| **Azure DevOps Artifacts** | `https://pkgs.dev.azure.com/{org}/_packaging/{feed}/pypi/simple/` | 비어 있지 않은 임의의 문자열 (예: `token`) | Packaging Read 범위의 Personal Access Token (PAT) | +| **GitHub Packages** | `https://pypi.pkg.github.com/{owner}/simple/` | GitHub 사용자 이름 | `read:packages` 범위의 Personal Access Token (classic) | +| **GitLab Package Registry** | `https://gitlab.com/api/v4/projects/{project_id}/packages/pypi/simple/` | `__token__` | `read_api` 범위의 Project 또는 Personal Access Token | +| **AWS CodeArtifact** | `aws codeartifact get-repository-endpoint`의 URL 사용 | `aws` | `aws codeartifact get-authorization-token`의 토큰 | +| **Google Artifact Registry** | `https://{region}-python.pkg.dev/{project}/{repo}/simple/` | `_json_key_base64` | Base64로 인코딩된 서비스 계정 키 | +| **JFrog Artifactory** | `https://{instance}.jfrog.io/artifactory/api/pypi/{repo}/simple/` | 사용자 이름 또는 이메일 | API 키 또는 ID 토큰 | +| **자체 호스팅 (devpi, Nexus 등)** | 레지스트리의 simple API URL | 레지스트리 사용자 이름 | 레지스트리 비밀번호 | + + + **AWS CodeArtifact**의 경우 인증 토큰이 주기적으로 만료됩니다. + 만료되면 `UV_INDEX_*_PASSWORD` 값을 갱신해야 합니다. + CI/CD 파이프라인에서 이를 자동화하는 것을 고려하세요. + + +## AMP에서 환경 변수 설정 + +프라이빗 레지스트리 자격 증명은 CrewAI AMP에서 환경 변수로 구성해야 합니다. +두 가지 옵션이 있습니다: + + + + 1. [CrewAI AMP](https://app.crewai.com)에 로그인합니다 + 2. 자동화로 이동합니다 + 3. **Environment Variables** 탭을 엽니다 + 4. 각 변수 (`UV_INDEX_*_USERNAME` 및 `UV_INDEX_*_PASSWORD`)에 값을 추가합니다 + + 자세한 내용은 [AMP에 배포하기 — 환경 변수 설정하기](/ko/enterprise/guides/deploy-to-amp#환경-변수-설정하기) 단계를 참조하세요. + + + `crewai deploy create`를 실행하기 전에 로컬 `.env` 파일에 변수를 추가합니다. + CLI가 이를 안전하게 플랫폼으로 전송합니다: + + ```bash + # .env + OPENAI_API_KEY=sk-... + UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token + UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat-here + ``` + + ```bash + crewai deploy create + ``` + + + + + 자격 증명을 저장소에 **절대** 커밋하지 마세요. 모든 비밀 정보에는 AMP 환경 변수를 사용하세요. + `.env` 파일은 `.gitignore`에 포함되어야 합니다. + + +기존 배포의 자격 증명을 업데이트하려면 [Crew 업데이트하기 — 환경 변수](/ko/enterprise/guides/update-crew)를 참조하세요. + +## 전체 동작 흐름 + +CrewAI AMP가 자동화를 빌드할 때, 해결 흐름은 다음과 같이 작동합니다: + + + + AMP가 저장소를 가져오고 `pyproject.toml`과 `uv.lock`을 읽습니다. + + + UV가 `[tool.uv.sources]`를 읽어 각 패키지가 어떤 index에서 와야 하는지 결정합니다. + + + 각 프라이빗 index에 대해 UV가 AMP에서 구성한 환경 변수에서 + `UV_INDEX_{NAME}_USERNAME`과 `UV_INDEX_{NAME}_PASSWORD`를 조회합니다. + + + UV가 공개(PyPI) 및 프라이빗(레지스트리) 패키지를 모두 다운로드하고 설치합니다. + + + 모든 의존성이 사용 가능한 상태에서 crew 또는 flow가 시작됩니다. + + + +## 문제 해결 + +### 빌드 중 인증 오류 + +**증상**: 프라이빗 패키지를 해결할 때 `401 Unauthorized` 또는 `403 Forbidden`으로 빌드가 실패합니다. + +**확인사항**: +- `UV_INDEX_*` 환경 변수 이름이 index 이름과 정확히 일치하는지 확인합니다 (대문자, 하이픈 -> 언더스코어) +- 자격 증명이 로컬 `.env`뿐만 아니라 AMP 환경 변수에 설정되어 있는지 확인합니다 +- 토큰/PAT에 패키지 피드에 필요한 읽기 권한이 있는지 확인합니다 +- 토큰이 만료되지 않았는지 확인합니다 (특히 AWS CodeArtifact의 경우) + +### 패키지를 찾을 수 없음 + +**증상**: `No matching distribution found for my-private-package`. + +**확인사항**: +- `pyproject.toml`의 index URL이 `/simple/`로 끝나는지 확인합니다 +- `[tool.uv.sources]` 항목이 올바른 패키지 이름을 올바른 index 이름에 매핑하는지 확인합니다 +- 패키지가 실제로 프라이빗 레지스트리에 게시되어 있는지 확인합니다 +- 동일한 자격 증명으로 로컬에서 `uv lock`을 실행하여 해결이 작동하는지 확인합니다 + +### Lock 파일 충돌 + +**증상**: 프라이빗 index를 추가한 후 `uv lock`이 실패하거나 예상치 못한 결과를 생성합니다. + +**해결책**: 로컬에서 자격 증명을 설정하고 다시 생성합니다: + +```bash +export UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token +export UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat +uv lock +``` + +그런 다음 업데이트된 `uv.lock`을 커밋합니다. + +## 관련 가이드 + + + + 배포 전에 프로젝트 구조와 의존성을 확인합니다. + + + crew 또는 flow를 배포하고 환경 변수를 구성합니다. + + + 환경 변수를 업데이트하고 실행 중인 배포에 변경 사항을 푸시합니다. + + diff --git a/docs/ko/guides/migration/migrating-from-langgraph.mdx b/docs/ko/guides/migration/migrating-from-langgraph.mdx new file mode 100644 index 000000000..fe708602d --- /dev/null +++ b/docs/ko/guides/migration/migrating-from-langgraph.mdx @@ -0,0 +1,518 @@ +--- +title: "LangGraph에서 CrewAI로 옮기기: 엔지니어를 위한 실전 가이드" +description: LangGraph로 이미 구축했다면, 프로젝트를 CrewAI로 빠르게 옮기는 방법을 알아보세요 +icon: switch +mode: "wide" +--- + +LangGraph로 에이전트를 구축해 왔습니다. `StateGraph`와 씨름하고, 조건부 에지를 연결하고, 새벽 2시에 상태 딕셔너리를 디버깅해 본 적도 있죠. 동작은 하지만 — 어느 순간부터 프로덕션으로 가는 더 나은 길이 없을까 고민하게 됩니다. + +있습니다. **CrewAI Flows**는 이벤트 기반 오케스트레이션, 조건부 라우팅, 공유 상태라는 동일한 힘을 훨씬 적은 보일러플레이트와 실제로 다단계 AI 워크플로우를 생각하는 방식에 잘 맞는 정신적 모델로 제공합니다. + +이 글은 핵심 개념을 나란히 비교하고 실제 코드 비교를 보여주며, 다음으로 손이 갈 프레임워크가 왜 CrewAI Flows인지 설명합니다. + +--- + +## 정신적 모델의 전환 + +LangGraph는 **그래프**로 생각하라고 요구합니다: 노드, 에지, 그리고 상태 딕셔너리. 모든 워크플로우는 계산 단계 사이의 전이를 명시적으로 연결하는 방향 그래프입니다. 강력하지만, 특히 워크플로우가 몇 개의 결정 지점이 있는 순차적 흐름일 때 이 추상화는 오버헤드를 가져옵니다. + +CrewAI Flows는 **이벤트**로 생각하라고 요구합니다: 시작하는 메서드, 결과를 듣는 메서드, 실행을 라우팅하는 메서드. 워크플로우의 토폴로지는 명시적 그래프 구성 대신 데코레이터 어노테이션에서 드러납니다. 이것은 단순한 문법 설탕이 아니라 — 파이프라인을 설계하고 읽고 유지하는 방식을 바꿉니다. + +핵심 매핑은 다음과 같습니다: + +| LangGraph 개념 | CrewAI Flows 대응 | +| --- | --- | +| `StateGraph` class | `Flow` class | +| `add_node()` | Methods decorated with `@start`, `@listen` | +| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators | +| `TypedDict` state | Pydantic `BaseModel` state | +| `START` / `END` constants | `@start()` decorator / natural method return | +| `graph.compile()` | `flow.kickoff()` | +| Checkpointer / persistence | Built-in memory (LanceDB-backed) | + +실제로 어떻게 보이는지 살펴보겠습니다. + +--- + +## 데모 1: 간단한 순차 파이프라인 + +주제를 받아 조사하고, 요약을 작성한 뒤, 결과를 포맷팅하는 파이프라인을 만든다고 해봅시다. 각 프레임워크는 이렇게 처리합니다. + +### LangGraph 방식 + +```python +from typing import TypedDict +from langgraph.graph import StateGraph, START, END + +class ResearchState(TypedDict): + topic: str + raw_research: str + summary: str + formatted_output: str + +def research_topic(state: ResearchState) -> dict: + # Call an LLM or search API + result = llm.invoke(f"Research the topic: {state['topic']}") + return {"raw_research": result} + +def write_summary(state: ResearchState) -> dict: + result = llm.invoke( + f"Summarize this research:\n{state['raw_research']}" + ) + return {"summary": result} + +def format_output(state: ResearchState) -> dict: + result = llm.invoke( + f"Format this summary as a polished article section:\n{state['summary']}" + ) + return {"formatted_output": result} + +# Build the graph +graph = StateGraph(ResearchState) +graph.add_node("research", research_topic) +graph.add_node("summarize", write_summary) +graph.add_node("format", format_output) + +graph.add_edge(START, "research") +graph.add_edge("research", "summarize") +graph.add_edge("summarize", "format") +graph.add_edge("format", END) + +# Compile and run +app = graph.compile() +result = app.invoke({"topic": "quantum computing advances in 2026"}) +print(result["formatted_output"]) +``` + +함수를 정의하고 노드로 등록한 다음, 모든 전이를 수동으로 연결합니다. 이렇게 단순한 순서인데도 의례처럼 해야 할 작업이 많습니다. + +### CrewAI Flows 방식 + +```python +from crewai import LLM, Agent, Crew, Process, Task +from crewai.flow.flow import Flow, listen, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class ResearchState(BaseModel): + topic: str = "" + raw_research: str = "" + summary: str = "" + formatted_output: str = "" + +class ResearchFlow(Flow[ResearchState]): + @start() + def research_topic(self): + # Option 1: Direct LLM call + result = llm.call(f"Research the topic: {self.state.topic}") + self.state.raw_research = result + return result + + @listen(research_topic) + def write_summary(self, research_output): + # Option 2: A single agent + summarizer = Agent( + role="Research Summarizer", + goal="Produce concise, accurate summaries of research content", + backstory="You are an expert at distilling complex research into clear, " + "digestible summaries.", + llm=llm, + verbose=True, + ) + result = summarizer.kickoff( + f"Summarize this research:\n{self.state.raw_research}" + ) + self.state.summary = str(result) + return self.state.summary + + @listen(write_summary) + def format_output(self, summary_output): + # Option 3: a complete crew (with one or more agents) + formatter = Agent( + role="Content Formatter", + goal="Transform research summaries into polished, publication-ready article sections", + backstory="You are a skilled editor with expertise in structuring and " + "presenting technical content for a general audience.", + llm=llm, + verbose=True, + ) + format_task = Task( + description=f"Format this summary as a polished article section:\n{self.state.summary}", + expected_output="A well-structured, polished article section ready for publication.", + agent=formatter, + ) + crew = Crew( + agents=[formatter], + tasks=[format_task], + process=Process.sequential, + verbose=True, + ) + result = crew.kickoff() + self.state.formatted_output = str(result) + return self.state.formatted_output + +# Run the flow +flow = ResearchFlow() +flow.state.topic = "quantum computing advances in 2026" +result = flow.kickoff() +print(flow.state.formatted_output) + +``` + +눈에 띄는 차이점이 있습니다: 그래프 구성 없음, 에지 연결 없음, 컴파일 단계 없음. 실행 순서는 로직이 있는 곳에서 바로 선언됩니다. `@start()`는 진입점을 표시하고, `@listen(method_name)`은 단계들을 연결합니다. 상태는 타입 안전성, 검증, IDE 자동 완성까지 제공하는 제대로 된 Pydantic 모델입니다. + +--- + +## 데모 2: 조건부 라우팅 + +여기서 흥미로워집니다. 콘텐츠 유형에 따라 서로 다른 처리 경로로 라우팅하는 파이프라인을 만든다고 해봅시다. + +### LangGraph 방식 + +```python +from typing import TypedDict, Literal +from langgraph.graph import StateGraph, START, END + +class ContentState(TypedDict): + input_text: str + content_type: str + result: str + +def classify_content(state: ContentState) -> dict: + content_type = llm.invoke( + f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}" + ) + return {"content_type": content_type.strip().lower()} + +def process_technical(state: ContentState) -> dict: + result = llm.invoke(f"Process as technical doc:\n{state['input_text']}") + return {"result": result} + +def process_creative(state: ContentState) -> dict: + result = llm.invoke(f"Process as creative writing:\n{state['input_text']}") + return {"result": result} + +def process_business(state: ContentState) -> dict: + result = llm.invoke(f"Process as business content:\n{state['input_text']}") + return {"result": result} + +# Routing function +def route_content(state: ContentState) -> Literal["technical", "creative", "business"]: + return state["content_type"] + +# Build the graph +graph = StateGraph(ContentState) +graph.add_node("classify", classify_content) +graph.add_node("technical", process_technical) +graph.add_node("creative", process_creative) +graph.add_node("business", process_business) + +graph.add_edge(START, "classify") +graph.add_conditional_edges( + "classify", + route_content, + { + "technical": "technical", + "creative": "creative", + "business": "business", + } +) +graph.add_edge("technical", END) +graph.add_edge("creative", END) +graph.add_edge("business", END) + +app = graph.compile() +result = app.invoke({"input_text": "Explain how TCP handshakes work"}) +``` + +별도의 라우팅 함수, 명시적 조건부 에지 매핑, 그리고 모든 분기에 대한 종료 에지가 필요합니다. 라우팅 결정 로직이 그 결정을 만들어 내는 노드와 분리됩니다. + +### CrewAI Flows 방식 + +```python +from crewai import LLM, Agent +from crewai.flow.flow import Flow, listen, router, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class ContentState(BaseModel): + input_text: str = "" + content_type: str = "" + result: str = "" + +class ContentFlow(Flow[ContentState]): + @start() + def classify_content(self): + self.state.content_type = ( + llm.call( + f"Classify this content as 'technical', 'creative', or 'business':\n" + f"{self.state.input_text}" + ) + .strip() + .lower() + ) + return self.state.content_type + + @router(classify_content) + def route_content(self, classification): + if classification == "technical": + return "process_technical" + elif classification == "creative": + return "process_creative" + else: + return "process_business" + + @listen("process_technical") + def handle_technical(self): + agent = Agent( + role="Technical Writer", + goal="Produce clear, accurate technical documentation", + backstory="You are an expert technical writer who specializes in " + "explaining complex technical concepts precisely.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as technical doc:\n{self.state.input_text}") + ) + + @listen("process_creative") + def handle_creative(self): + agent = Agent( + role="Creative Writer", + goal="Craft engaging and imaginative creative content", + backstory="You are a talented creative writer with a flair for " + "compelling storytelling and vivid expression.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as creative writing:\n{self.state.input_text}") + ) + + @listen("process_business") + def handle_business(self): + agent = Agent( + role="Business Writer", + goal="Produce professional, results-oriented business content", + backstory="You are an experienced business writer who communicates " + "strategy and value clearly to professional audiences.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as business content:\n{self.state.input_text}") + ) + +flow = ContentFlow() +flow.state.input_text = "Explain how TCP handshakes work" +flow.kickoff() +print(flow.state.result) + +``` + +`@router()` 데코레이터는 메서드를 결정 지점으로 만듭니다. 리스너와 매칭되는 문자열을 반환하므로, 매핑 딕셔너리도, 별도의 라우팅 함수도 필요 없습니다. 분기 로직이 Python `if` 문처럼 읽히는 이유는, 실제로 `if` 문이기 때문입니다. + +--- + +## 데모 3: AI 에이전트 Crew를 Flow에 통합하기 + +여기서 CrewAI의 진짜 힘이 드러납니다. Flows는 LLM 호출을 연결하는 것에 그치지 않고 자율적인 에이전트 **Crew** 전체를 오케스트레이션합니다. 이는 LangGraph에 기본으로 대응되는 개념이 없습니다. + +```python +from crewai import Agent, Task, Crew +from crewai.flow.flow import Flow, listen, start +from pydantic import BaseModel + +class ArticleState(BaseModel): + topic: str = "" + research: str = "" + draft: str = "" + final_article: str = "" + +class ArticleFlow(Flow[ArticleState]): + + @start() + def run_research_crew(self): + """A full Crew of agents handles research.""" + researcher = Agent( + role="Senior Research Analyst", + goal=f"Produce comprehensive research on: {self.state.topic}", + backstory="You're a veteran analyst known for thorough, " + "well-sourced research reports.", + llm="gpt-4o" + ) + + research_task = Task( + description=f"Research '{self.state.topic}' thoroughly. " + "Cover key trends, data points, and expert opinions.", + expected_output="A detailed research brief with sources.", + agent=researcher + ) + + crew = Crew(agents=[researcher], tasks=[research_task]) + result = crew.kickoff() + self.state.research = result.raw + return result.raw + + @listen(run_research_crew) + def run_writing_crew(self, research_output): + """A different Crew handles writing.""" + writer = Agent( + role="Technical Writer", + goal="Write a compelling article based on provided research.", + backstory="You turn complex research into engaging, clear prose.", + llm="gpt-4o" + ) + + editor = Agent( + role="Senior Editor", + goal="Review and polish articles for publication quality.", + backstory="20 years of editorial experience at top tech publications.", + llm="gpt-4o" + ) + + write_task = Task( + description=f"Write an article based on this research:\n{self.state.research}", + expected_output="A well-structured draft article.", + agent=writer + ) + + edit_task = Task( + description="Review, fact-check, and polish the draft article.", + expected_output="A publication-ready article.", + agent=editor + ) + + crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task]) + result = crew.kickoff() + self.state.final_article = result.raw + return result.raw + +# Run the full pipeline +flow = ArticleFlow() +flow.state.topic = "The Future of Edge AI" +flow.kickoff() +print(flow.state.final_article) +``` + +핵심 인사이트는 다음과 같습니다: **Flows는 오케스트레이션 레이어를, Crews는 지능 레이어를 제공합니다.** Flow의 각 단계는 각자의 역할, 목표, 도구를 가진 협업 에이전트 팀을 띄울 수 있습니다. 구조화되고 예측 가능한 제어 흐름 *그리고* 자율적 에이전트 협업 — 두 세계의 장점을 모두 얻습니다. + +LangGraph에서 비슷한 것을 하려면 노드 함수 안에 에이전트 통신 프로토콜, 도구 호출 루프, 위임 로직을 직접 구현해야 합니다. 가능하긴 하지만, 매번 처음부터 배관을 만드는 셈입니다. + +--- + +## 데모 4: 병렬 실행과 동기화 + +실제 파이프라인은 종종 작업을 병렬로 분기하고 결과를 합쳐야 합니다. CrewAI Flows는 `and_`와 `or_` 연산자로 이를 우아하게 처리합니다. + +```python +from crewai import LLM +from crewai.flow.flow import Flow, and_, listen, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class AnalysisState(BaseModel): + topic: str = "" + market_data: str = "" + tech_analysis: str = "" + competitor_intel: str = "" + final_report: str = "" + +class ParallelAnalysisFlow(Flow[AnalysisState]): + @start() + def start_method(self): + pass + + @listen(start_method) + def gather_market_data(self): + # Your agentic or deterministic code + pass + + @listen(start_method) + def run_tech_analysis(self): + # Your agentic or deterministic code + pass + + @listen(start_method) + def gather_competitor_intel(self): + # Your agentic or deterministic code + pass + + @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel)) + def synthesize_report(self): + # Your agentic or deterministic code + pass + +flow = ParallelAnalysisFlow() +flow.state.topic = "AI-powered developer tools" +flow.kickoff() + +``` + +여러 `@start()` 데코레이터는 병렬로 실행됩니다. `@listen` 데코레이터의 `and_()` 결합자는 `synthesize_report`가 *세 가지* 상위 메서드가 모두 완료된 뒤에만 실행되도록 보장합니다. *어떤* 상위 작업이든 끝나는 즉시 진행하고 싶다면 `or_()`도 사용할 수 있습니다. + +LangGraph에서는 병렬 분기, 동기화 노드, 신중한 상태 병합이 포함된 fan-out/fan-in 패턴을 만들어야 하며 — 모든 것을 에지로 명시적으로 연결해야 합니다. + +--- + +## 프로덕션에서 CrewAI Flows를 쓰는 이유 + +깔끔한 문법을 넘어, Flows는 여러 프로덕션 핵심 이점을 제공합니다: + +**내장 상태 지속성.** Flow 상태는 LanceDB에 의해 백업되므로 워크플로우가 크래시에서 살아남고, 재개될 수 있으며, 실행 간에 지식을 축적할 수 있습니다. LangGraph는 별도의 체크포인터를 구성해야 합니다. + +**타입 안전한 상태 관리.** Pydantic 모델은 즉시 검증, 직렬화, IDE 지원을 제공합니다. LangGraph의 `TypedDict` 상태는 런타임 검증을 하지 않습니다. + +**일급 에이전트 오케스트레이션.** Crews는 기본 프리미티브입니다. 역할, 목표, 배경, 도구를 가진 에이전트를 정의하고, Flow의 구조적 틀 안에서 자율적으로 협업하게 합니다. 다중 에이전트 조율을 다시 만들 필요가 없습니다. + +**더 단순한 정신적 모델.** 데코레이터는 의도를 선언합니다. `@start`는 "여기서 시작", `@listen(x)`는 "x 이후 실행", `@router(x)`는 "x 이후 어디로 갈지 결정"을 의미합니다. 코드는 자신이 설명하는 워크플로우처럼 읽힙니다. + +**CLI 통합.** `crewai run`으로 Flows를 실행합니다. 별도의 컴파일 단계나 그래프 직렬화가 없습니다. Flow는 Python 클래스이며, 그대로 실행됩니다. + +--- + +## 마이그레이션 치트 시트 + +LangGraph 코드베이스를 CrewAI Flows로 옮기고 싶다면, 다음의 실전 변환 가이드를 참고하세요: + +1. **상태를 매핑하세요.** `TypedDict`를 Pydantic `BaseModel`로 변환하고 모든 필드에 기본값을 추가하세요. +2. **노드를 메서드로 변환하세요.** 각 `add_node` 함수는 `Flow` 서브클래스의 메서드가 됩니다. `state["field"]` 읽기는 `self.state.field`로 바꾸세요. +3. **에지를 데코레이터로 교체하세요.** `add_edge(START, "first_node")`는 첫 메서드의 `@start()`가 됩니다. 순차적인 `add_edge("a", "b")`는 `b` 메서드의 `@listen(a)`가 됩니다. +4. **조건부 에지는 `@router`로 교체하세요.** 라우팅 함수와 `add_conditional_edges()` 매핑은 하나의 `@router()` 메서드로 통합하고, 라우트 문자열을 반환하세요. +5. **compile + invoke를 kickoff으로 교체하세요.** `graph.compile()`를 제거하고 `flow.kickoff()`를 호출하세요. +6. **Crew가 들어갈 지점을 고려하세요.** 복잡한 다단계 에이전트 로직이 있는 노드는 Crew로 분리할 후보입니다. 이 부분에서 가장 큰 품질 향상을 체감할 수 있습니다. + +--- + +## 시작하기 + +CrewAI를 설치하고 새 Flow 프로젝트를 스캐폴딩하세요: + +```bash +pip install crewai +crewai create flow my_first_flow +cd my_first_flow +``` + +이렇게 하면 바로 편집 가능한 Flow 클래스, 설정 파일, 그리고 `type = "flow"`가 이미 설정된 `pyproject.toml`이 포함된 프로젝트 구조가 생성됩니다. 다음으로 실행하세요: + +```bash +crewai run +``` + +그 다음부터는 에이전트를 추가하고 리스너를 연결한 뒤, 배포하면 됩니다. + +--- + +## 마무리 + +LangGraph는 AI 워크플로우에 구조가 필요하다는 사실을 생태계에 일깨워 주었습니다. 중요한 교훈이었습니다. 하지만 CrewAI Flows는 그 교훈을 더 빠르게 쓰고, 더 쉽게 읽으며, 프로덕션에서 더 강력한 형태로 제공합니다 — 특히 워크플로우에 여러 에이전트의 협업이 포함될 때 그렇습니다. + +단일 에이전트 체인을 넘는 무엇인가를 만들고 있다면, Flows를 진지하게 검토해 보세요. 데코레이터 기반 모델, Crews의 네이티브 통합, 내장 상태 관리를 통해 배관 작업에 쓰는 시간을 줄이고, 중요한 문제에 더 많은 시간을 쓸 수 있습니다. + +`crewai create flow`로 시작하세요. 후회하지 않을 겁니다. diff --git a/docs/ko/learn/human-feedback-in-flows.mdx b/docs/ko/learn/human-feedback-in-flows.mdx index 6ba92c37e..a6305ca8a 100644 --- a/docs/ko/learn/human-feedback-in-flows.mdx +++ b/docs/ko/learn/human-feedback-in-flows.mdx @@ -73,6 +73,8 @@ flow.kickoff() | `default_outcome` | `str` | 아니오 | 피드백이 제공되지 않을 때 사용할 outcome. `emit`에 있어야 합니다 | | `metadata` | `dict` | 아니오 | 엔터프라이즈 통합을 위한 추가 데이터 | | `provider` | `HumanFeedbackProvider` | 아니오 | 비동기/논블로킹 피드백을 위한 커스텀 프로바이더. [비동기 인간 피드백](#비동기-인간-피드백-논블로킹) 참조 | +| `learn` | `bool` | 아니오 | HITL 학습 활성화: 피드백에서 교훈을 추출하고 향후 출력을 사전 검토합니다. 기본값 `False`. [피드백에서 학습하기](#피드백에서-학습하기) 참조 | +| `learn_limit` | `int` | 아니오 | 사전 검토를 위해 불러올 최대 과거 교훈 수. 기본값 `5` | ### 기본 사용법 (라우팅 없음) @@ -96,33 +98,43 @@ def handle_feedback(self, result): `emit`을 지정하면, 데코레이터는 라우터가 됩니다. 인간의 자유 형식 피드백이 LLM에 의해 해석되어 지정된 outcome 중 하나로 매핑됩니다: ```python Code -@start() -@human_feedback( - message="이 콘텐츠의 출판을 승인하시겠습니까?", - emit=["approved", "rejected", "needs_revision"], - llm="gpt-4o-mini", - default_outcome="needs_revision", -) -def review_content(self): - return "블로그 게시물 초안 내용..." +from crewai.flow.flow import Flow, start, listen, or_ +from crewai.flow.human_feedback import human_feedback -@listen("approved") -def publish(self, result): - print(f"출판 중! 사용자 의견: {result.feedback}") +class ReviewFlow(Flow): + @start() + def generate_content(self): + return "블로그 게시물 초안 내용..." -@listen("rejected") -def discard(self, result): - print(f"폐기됨. 이유: {result.feedback}") + @human_feedback( + message="이 콘텐츠의 출판을 승인하시겠습니까?", + emit=["approved", "rejected", "needs_revision"], + llm="gpt-4o-mini", + default_outcome="needs_revision", + ) + @listen(or_("generate_content", "needs_revision")) + def review_content(self): + return "블로그 게시물 초안 내용..." -@listen("needs_revision") -def revise(self, result): - print(f"다음을 기반으로 수정 중: {result.feedback}") + @listen("approved") + def publish(self, result): + print(f"출판 중! 사용자 의견: {result.feedback}") + + @listen("rejected") + def discard(self, result): + print(f"폐기됨. 이유: {result.feedback}") ``` +사용자가 "더 자세한 내용이 필요합니다"와 같이 말하면, LLM이 이를 `"needs_revision"`으로 매핑하고, `or_()`를 통해 `review_content`가 다시 트리거됩니다 — 수정 루프가 생성됩니다. outcome이 `"approved"` 또는 `"rejected"`가 될 때까지 루프가 계속됩니다. + LLM은 가능한 경우 구조화된 출력(function calling)을 사용하여 응답이 지정된 outcome 중 하나임을 보장합니다. 이로 인해 라우팅이 신뢰할 수 있고 예측 가능해집니다. + +`@start()` 메서드는 flow 시작 시 한 번만 실행됩니다. 수정 루프가 필요한 경우, start 메서드를 review 메서드와 분리하고 review 메서드에 `@listen(or_("trigger", "revision_outcome"))`를 사용하여 self-loop을 활성화하세요. + + ## HumanFeedbackResult `HumanFeedbackResult` 데이터클래스는 인간 피드백 상호작용에 대한 모든 정보를 포함합니다: @@ -191,116 +203,162 @@ def summarize(self): ```python Code -from crewai.flow.flow import Flow, start, listen +from crewai.flow.flow import Flow, start, listen, or_ from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult from pydantic import BaseModel class ContentState(BaseModel): - topic: str = "" draft: str = "" - final_content: str = "" revision_count: int = 0 + status: str = "pending" class ContentApprovalFlow(Flow[ContentState]): - """콘텐츠를 생성하고 인간의 승인을 받는 Flow입니다.""" + """콘텐츠를 생성하고 승인될 때까지 반복하는 Flow.""" @start() - def get_topic(self): - self.state.topic = input("어떤 주제에 대해 글을 쓸까요? ") - return self.state.topic - - @listen(get_topic) - def generate_draft(self, topic): - # 실제 사용에서는 LLM을 호출합니다 - self.state.draft = f"# {topic}\n\n{topic}에 대한 초안입니다..." + def generate_draft(self): + self.state.draft = "# AI 안전\n\nAI 안전에 대한 초안..." return self.state.draft - @listen(generate_draft) @human_feedback( - message="이 초안을 검토해 주세요. 'approved', 'rejected'로 답하거나 수정 피드백을 제공해 주세요:", + message="이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요:", emit=["approved", "rejected", "needs_revision"], llm="gpt-4o-mini", default_outcome="needs_revision", ) - def review_draft(self, draft): - return draft + @listen(or_("generate_draft", "needs_revision")) + def review_draft(self): + self.state.revision_count += 1 + return f"{self.state.draft} (v{self.state.revision_count})" @listen("approved") def publish_content(self, result: HumanFeedbackResult): - self.state.final_content = result.output - print("\n✅ 콘텐츠가 승인되어 출판되었습니다!") - print(f"검토자 코멘트: {result.feedback}") + self.state.status = "published" + print(f"콘텐츠 승인 및 게시! 리뷰어 의견: {result.feedback}") return "published" @listen("rejected") def handle_rejection(self, result: HumanFeedbackResult): - print("\n❌ 콘텐츠가 거부되었습니다") - print(f"이유: {result.feedback}") + self.state.status = "rejected" + print(f"콘텐츠 거부됨. 이유: {result.feedback}") return "rejected" - @listen("needs_revision") - def revise_content(self, result: HumanFeedbackResult): - self.state.revision_count += 1 - print(f"\n📝 수정 #{self.state.revision_count} 요청됨") - print(f"피드백: {result.feedback}") - # 실제 Flow에서는 generate_draft로 돌아갈 수 있습니다 - # 이 예제에서는 단순히 확인합니다 - return "revision_requested" - - -# Flow 실행 flow = ContentApprovalFlow() result = flow.kickoff() -print(f"\nFlow 완료. 요청된 수정: {flow.state.revision_count}") +print(f"\nFlow 완료. 상태: {flow.state.status}, 검토 횟수: {flow.state.revision_count}") ``` ```text Output -어떤 주제에 대해 글을 쓸까요? AI 안전 +================================================== +OUTPUT FOR REVIEW: +================================================== +# AI 안전 + +AI 안전에 대한 초안... (v1) +================================================== + +이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요: +(Press Enter to skip, or type your feedback) + +Your feedback: 더 자세한 내용이 필요합니다 ================================================== OUTPUT FOR REVIEW: ================================================== # AI 안전 -AI 안전에 대한 초안입니다... +AI 안전에 대한 초안... (v2) ================================================== -이 초안을 검토해 주세요. 'approved', 'rejected'로 답하거나 수정 피드백을 제공해 주세요: +이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요: (Press Enter to skip, or type your feedback) Your feedback: 좋아 보입니다, 승인! -✅ 콘텐츠가 승인되어 출판되었습니다! -검토자 코멘트: 좋아 보입니다, 승인! +콘텐츠 승인 및 게시! 리뷰어 의견: 좋아 보입니다, 승인! -Flow 완료. 요청된 수정: 0 +Flow 완료. 상태: published, 검토 횟수: 2 ``` ## 다른 데코레이터와 결합하기 -`@human_feedback` 데코레이터는 다른 Flow 데코레이터와 함께 작동합니다. 가장 안쪽 데코레이터(함수에 가장 가까운)로 배치하세요: +`@human_feedback` 데코레이터는 `@start()`, `@listen()`, `or_()`와 함께 작동합니다. 데코레이터 순서는 두 가지 모두 동작합니다—프레임워크가 양방향으로 속성을 전파합니다—하지만 권장 패턴은 다음과 같습니다: ```python Code -# 올바름: @human_feedback이 가장 안쪽(함수에 가장 가까움) +# Flow 시작 시 일회성 검토 (self-loop 없음) @start() -@human_feedback(message="이것을 검토해 주세요:") +@human_feedback(message="이것을 검토해 주세요:", emit=["approved", "rejected"], llm="gpt-4o-mini") def my_start_method(self): return "content" +# 리스너에서 선형 검토 (self-loop 없음) @listen(other_method) -@human_feedback(message="이것도 검토해 주세요:") +@human_feedback(message="이것도 검토해 주세요:", emit=["good", "bad"], llm="gpt-4o-mini") def my_listener(self, data): return f"processed: {data}" + +# Self-loop: 수정을 위해 반복할 수 있는 검토 +@human_feedback(message="승인 또는 수정 요청?", emit=["approved", "revise"], llm="gpt-4o-mini") +@listen(or_("upstream_method", "revise")) +def review_with_loop(self): + return "content for review" ``` - -`@human_feedback`를 가장 안쪽 데코레이터(마지막/함수에 가장 가까움)로 배치하여 메서드를 직접 래핑하고 Flow 시스템에 전달하기 전에 반환 값을 캡처할 수 있도록 하세요. - +### Self-loop 패턴 + +수정 루프를 만들려면 `or_()`를 사용하여 검토 메서드가 **상위 트리거**와 **자체 수정 outcome**을 모두 리스닝해야 합니다: + +```python Code +@start() +def generate(self): + return "initial draft" + +@human_feedback( + message="승인하시겠습니까, 아니면 변경을 요청하시겠습니까?", + emit=["revise", "approved"], + llm="gpt-4o-mini", + default_outcome="approved", +) +@listen(or_("generate", "revise")) +def review(self): + return "content" + +@listen("approved") +def publish(self): + return "published" +``` + +outcome이 `"revise"`이면 flow가 `review`로 다시 라우팅됩니다 (`or_()`를 통해 `"revise"`를 리스닝하기 때문). outcome이 `"approved"`이면 flow가 `publish`로 계속됩니다. flow 엔진이 라우터를 "한 번만 실행" 규칙에서 제외하여 각 루프 반복마다 재실행할 수 있기 때문에 이 패턴이 동작합니다. + +### 체인된 라우터 + +한 라우터의 outcome으로 트리거된 리스너가 그 자체로 라우터가 될 수 있습니다: + +```python Code +@start() +@human_feedback(message="첫 번째 검토:", emit=["approved", "rejected"], llm="gpt-4o-mini") +def draft(self): + return "draft content" + +@listen("approved") +@human_feedback(message="최종 검토:", emit=["publish", "revise"], llm="gpt-4o-mini") +def final_review(self, prev): + return "final content" + +@listen("publish") +def on_publish(self, prev): + return "published" +``` + +### 제한 사항 + +- **`@start()` 메서드는 한 번만 실행**: `@start()` 메서드는 self-loop할 수 없습니다. 수정 주기가 필요하면 별도의 `@start()` 메서드를 진입점으로 사용하고 `@listen()` 메서드에 `@human_feedback`를 배치하세요. +- **동일 메서드에 `@start()` + `@listen()` 불가**: 이는 Flow 프레임워크 제약입니다. 메서드는 시작점이거나 리스너여야 하며, 둘 다일 수 없습니다. ## 모범 사례 @@ -514,9 +572,9 @@ class ContentPipeline(Flow): @start() @human_feedback( message="이 콘텐츠의 출판을 승인하시겠습니까?", - emit=["approved", "rejected", "needs_revision"], + emit=["approved", "rejected"], llm="gpt-4o-mini", - default_outcome="needs_revision", + default_outcome="rejected", provider=SlackNotificationProvider("#content-reviews"), ) def generate_content(self): @@ -532,11 +590,6 @@ class ContentPipeline(Flow): print(f"보관됨. 이유: {result.feedback}") return {"status": "archived"} - @listen("needs_revision") - def queue_revision(self, result): - print(f"수정 대기열에 추가됨: {result.feedback}") - return {"status": "revision_needed"} - # Flow 시작 (Slack 응답을 기다리며 일시 중지) def start_content_pipeline(): @@ -576,6 +629,64 @@ async def on_slack_feedback_async(flow_id: str, slack_message: str): 5. **자동 영속성**: `HumanFeedbackPending`이 발생하면 상태가 자동으로 저장되며 기본적으로 `SQLiteFlowPersistence` 사용 6. **커스텀 영속성**: 필요한 경우 `from_pending()`에 커스텀 영속성 인스턴스 전달 +## 피드백에서 학습하기 + +`learn=True` 매개변수는 인간 검토자와 메모리 시스템 간의 피드백 루프를 활성화합니다. 활성화되면 시스템은 과거 인간의 수정 사항에서 학습하여 출력을 점진적으로 개선합니다. + +### 작동 방식 + +1. **피드백 후**: LLM이 출력 + 피드백에서 일반화 가능한 교훈을 추출하고 `source="hitl"`로 메모리에 저장합니다. 피드백이 단순한 승인(예: "좋아 보입니다")인 경우 아무것도 저장하지 않습니다. +2. **다음 검토 전**: 과거 HITL 교훈을 메모리에서 불러와 LLM이 인간이 보기 전에 출력을 개선하는 데 적용합니다. + +시간이 지남에 따라 각 수정 사항이 향후 검토에 반영되므로 인간은 점진적으로 더 나은 사전 검토된 출력을 보게 됩니다. + +### 예제 + +```python Code +class ArticleReviewFlow(Flow): + @start() + def generate_article(self): + return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw + + @human_feedback( + message="이 글 초안을 검토해 주세요:", + emit=["approved", "needs_revision"], + llm="gpt-4o-mini", + learn=True, + ) + @listen(or_("generate_article", "needs_revision")) + def review_article(self): + return self.last_human_feedback.output if self.last_human_feedback else "article draft" + + @listen("approved") + def publish(self): + print(f"Publishing: {self.last_human_feedback.output}") +``` + +**첫 번째 실행**: 인간이 원시 출력을 보고 "사실에 대한 주장에는 항상 인용을 포함하세요."라고 말합니다. 교훈이 추출되어 메모리에 저장됩니다. + +**두 번째 실행**: 시스템이 인용 교훈을 불러와 출력을 사전 검토하여 인용을 추가한 후 개선된 버전을 표시합니다. 인간의 역할이 "모든 것을 수정"에서 "시스템이 놓친 것을 찾기"로 전환됩니다. + +### 구성 + +| 매개변수 | 기본값 | 설명 | +|-----------|--------|------| +| `learn` | `False` | HITL 학습 활성화 | +| `learn_limit` | `5` | 사전 검토를 위해 불러올 최대 과거 교훈 수 | + +### 주요 설계 결정 + +- **모든 것에 동일한 LLM 사용**: 데코레이터의 `llm` 매개변수는 outcome 매핑, 교훈 추출, 사전 검토에 공유됩니다. 여러 모델을 구성할 필요가 없습니다. +- **구조화된 출력**: 추출과 사전 검토 모두 LLM이 지원하는 경우 Pydantic 모델과 함께 function calling을 사용하고, 그렇지 않으면 텍스트 파싱으로 폴백합니다. +- **논블로킹 저장**: 교훈은 백그라운드 스레드에서 실행되는 `remember_many()`를 통해 저장됩니다 -- Flow는 즉시 계속됩니다. +- **우아한 저하**: 추출 중 LLM이 실패하면 아무것도 저장하지 않습니다. 사전 검토 중 실패하면 원시 출력이 표시됩니다. 어느 쪽의 실패도 Flow를 차단하지 않습니다. +- **범위/카테고리 불필요**: 교훈을 저장할 때 `source`만 전달됩니다. 인코딩 파이프라인이 범위, 카테고리, 중요도를 자동으로 추론합니다. + + +`learn=True`는 Flow에 메모리가 사용 가능해야 합니다. Flow는 기본적으로 자동으로 메모리를 얻지만, `_skip_auto_memory`로 비활성화한 경우 HITL 학습은 조용히 건너뜁니다. + + + ## 관련 문서 - [Flow 개요](/ko/concepts/flows) - CrewAI Flow에 대해 알아보기 @@ -583,3 +694,4 @@ async def on_slack_feedback_async(flow_id: str, slack_message: str): - [Flow 영속성](/ko/concepts/flows#persistence) - Flow 상태 영속화 - [@router를 사용한 라우팅](/ko/concepts/flows#router) - 조건부 라우팅에 대해 더 알아보기 - [실행 시 인간 입력](/ko/learn/human-input-on-execution) - 태스크 수준 인간 입력 +- [메모리](/ko/concepts/memory) - HITL 학습에서 사용되는 통합 메모리 시스템 diff --git a/docs/ko/learn/llm-connections.mdx b/docs/ko/learn/llm-connections.mdx index f373d8a89..6976ab8e0 100644 --- a/docs/ko/learn/llm-connections.mdx +++ b/docs/ko/learn/llm-connections.mdx @@ -7,7 +7,7 @@ mode: "wide" ## CrewAI를 LLM에 연결하기 -CrewAI는 LiteLLM을 사용하여 다양한 언어 모델(LLM)에 연결합니다. 이 통합은 높은 다양성을 제공하여, 여러 공급자의 모델을 간단하고 통합된 인터페이스로 사용할 수 있게 해줍니다. +CrewAI는 가장 인기 있는 제공자(OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock)에 대해 네이티브 SDK 통합을 통해 LLM에 연결하며, 그 외 모든 제공자에 대해서는 LiteLLM을 유연한 폴백으로 사용합니다. 기본적으로 CrewAI는 `gpt-4o-mini` 모델을 사용합니다. 이는 `OPENAI_MODEL_NAME` 환경 변수에 의해 결정되며, 설정되지 않은 경우 기본값은 "gpt-4o-mini"입니다. @@ -41,6 +41,14 @@ LiteLLM은 다음을 포함하되 이에 국한되지 않는 다양한 프로바 지원되는 프로바이더의 전체 및 최신 목록은 [LiteLLM 프로바이더 문서](https://docs.litellm.ai/docs/providers)를 참조하세요. + + 네이티브 통합에서 지원하지 않는 제공자를 사용하려면 LiteLLM을 프로젝트에 의존성으로 추가하세요: + ```bash + uv add 'crewai[litellm]' + ``` + 네이티브 제공자(OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock)는 자체 SDK extras를 사용합니다 — [공급자 구성 예시](/ko/concepts/llms#공급자-구성-예시)를 참조하세요. + + ## LLM 변경하기 CrewAI agent에서 다른 LLM을 사용하려면 여러 가지 방법이 있습니다: diff --git a/docs/ko/observability/tracing.mdx b/docs/ko/observability/tracing.mdx index 1691f01ae..eae6188f6 100644 --- a/docs/ko/observability/tracing.mdx +++ b/docs/ko/observability/tracing.mdx @@ -35,7 +35,7 @@ crewai login 아직 설치하지 않았다면 CLI 도구와 함께 CrewAI를 설치하세요: ```bash -uv add crewai[tools] +uv add 'crewai[tools]' ``` 그런 다음 CrewAI AMP 계정으로 CLI를 인증하세요: diff --git a/docs/ko/tools/automation/composiotool.mdx b/docs/ko/tools/automation/composiotool.mdx index 15c477e34..890360425 100644 --- a/docs/ko/tools/automation/composiotool.mdx +++ b/docs/ko/tools/automation/composiotool.mdx @@ -18,77 +18,46 @@ Composio는 AI 에이전트를 250개 이상의 도구와 연결할 수 있는 Composio 도구를 프로젝트에 통합하려면 아래 지침을 따르세요: ```shell -pip install composio-crewai +pip install composio composio-crewai pip install crewai ``` -설치가 완료된 후, `composio login`을 실행하거나 Composio API 키를 `COMPOSIO_API_KEY`로 export하세요. Composio API 키는 [여기](https://app.composio.dev)에서 받을 수 있습니다. +설치가 완료되면 Composio API 키를 `COMPOSIO_API_KEY`로 설정하세요. Composio API 키는 [여기](https://platform.composio.dev)에서 받을 수 있습니다. ## 예시 -다음 예시는 도구를 초기화하고 github action을 실행하는 방법을 보여줍니다: +다음 예시는 도구를 초기화하고 GitHub 액션을 실행하는 방법을 보여줍니다: -1. Composio 도구 세트 초기화 +1. CrewAI Provider와 함께 Composio 초기화 ```python Code -from composio_crewai import ComposioToolSet, App, Action +from composio_crewai import ComposioProvider +from composio import Composio from crewai import Agent, Task, Crew -toolset = ComposioToolSet() +composio = Composio(provider=ComposioProvider()) ``` -2. GitHub 계정 연결 +2. 새 Composio 세션을 만들고 도구 가져오기 -```shell CLI -composio add github -``` -```python Code -request = toolset.initiate_connection(app=App.GITHUB) -print(f"Open this URL to authenticate: {request.redirectUrl}") +```python +session = composio.create( + user_id="your-user-id", + toolkits=["gmail", "github"] # optional, default is all toolkits +) +tools = session.tools() ``` +세션 및 사용자 관리에 대한 자세한 내용은 [여기](https://docs.composio.dev/docs/configuring-sessions)를 참고하세요. -3. 도구 가져오기 +3. 사용자 수동 인증하기 -- 앱에서 모든 도구를 가져오기 (프로덕션 환경에서는 권장하지 않음): +Composio는 에이전트 채팅 세션 중에 사용자를 자동으로 인증합니다. 하지만 `authorize` 메서드를 호출해 사용자를 수동으로 인증할 수도 있습니다. ```python Code -tools = toolset.get_tools(apps=[App.GITHUB]) +connection_request = session.authorize("github") +print(f"Open this URL to authenticate: {connection_request.redirect_url}") ``` -- 태그를 기반으로 도구 필터링: -```python Code -tag = "users" - -filtered_action_enums = toolset.find_actions_by_tags( - App.GITHUB, - tags=[tag], -) - -tools = toolset.get_tools(actions=filtered_action_enums) -``` - -- 사용 사례를 기반으로 도구 필터링: -```python Code -use_case = "Star a repository on GitHub" - -filtered_action_enums = toolset.find_actions_by_use_case( - App.GITHUB, use_case=use_case, advanced=False -) - -tools = toolset.get_tools(actions=filtered_action_enums) -``` -`advanced`를 True로 설정하면 복잡한 사용 사례를 위한 액션을 가져올 수 있습니다 - -- 특정 도구 사용하기: - -이 데모에서는 GitHub 앱의 `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` 액션을 사용합니다. -```python Code -tools = toolset.get_tools( - actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER] -) -``` -액션 필터링에 대해 더 자세한 내용을 보려면 [여기](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions)를 참고하세요. - 4. 에이전트 정의 ```python Code @@ -116,4 +85,4 @@ crew = Crew(agents=[crewai_agent], tasks=[task]) crew.kickoff() ``` -* 더욱 자세한 도구 리스트는 [여기](https://app.composio.dev)에서 확인하실 수 있습니다. \ No newline at end of file +* 더욱 자세한 도구 목록은 [여기](https://docs.composio.dev/toolkits)에서 확인할 수 있습니다. \ No newline at end of file diff --git a/docs/pt-BR/changelog.mdx b/docs/pt-BR/changelog.mdx index 8db611923..6c789abc2 100644 --- a/docs/pt-BR/changelog.mdx +++ b/docs/pt-BR/changelog.mdx @@ -4,6 +4,138 @@ description: "Atualizações de produto, melhorias e correções do CrewAI" icon: "clock" mode: "wide" --- + + ## v1.10.1 + + [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1) + + ## O que mudou + + ### Recursos + - Atualizar Gemini GenAI + + ### Correções de Bugs + - Ajustar o valor do listener do executor para evitar recursão + - Agrupar partes da resposta da função paralela em um único objeto Content no Gemini + - Exibir a saída de pensamento dos modelos de pensamento no Gemini + - Carregar ferramentas MCP e da plataforma quando as ferramentas do agente forem None + - Suportar ambientes Jupyter com loops de eventos em A2A + - Usar ID anônimo para rastreamentos efêmeros + - Passar condicionalmente o cabeçalho plus + - Ignorar o registro do manipulador de sinal em threads não principais para telemetria + - Injetar erros de ferramentas como observações e resolver colisões de nomes + - Atualizar pypdf de 4.x para 6.7.4 para resolver alertas do Dependabot + - Resolver alertas de segurança críticos e altos do Dependabot + + ### Documentação + - Sincronizar a documentação da ferramenta Composio entre locais + + ## Contribuidores + + @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96 + + + + + ## v1.10.1a1 + + [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1) + + ## O que Mudou + + ### Funcionalidades + - Implementar suporte a invocação assíncrona em métodos de callback de etapas + - Implementar carregamento sob demanda para dependências pesadas no módulo de Memória + + ### Documentação + - Atualizar changelog e versão para v1.10.0 + + ### Refatoração + - Refatorar métodos de callback de etapas para suportar invocação assíncrona + - Refatorar para implementar carregamento sob demanda para dependências pesadas no módulo de Memória + + ### Correções de Bugs + - Corrigir branch para notas de lançamento + + ## Contribuidores + + @greysonlalonde, @joaomdmoura + + + + + ## v1.10.1a1 + + [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1) + + ## O que Mudou + + ### Refatoração + - Refatorar métodos de callback de etapas para suportar invocação assíncrona + - Implementar carregamento sob demanda para dependências pesadas no módulo de Memória + + ### Documentação + - Atualizar changelog e versão para v1.10.0 + + ### Correções de Bugs + - Criar branch para notas de lançamento + + ## Contribuidores + + @greysonlalonde, @joaomdmoura + + + + + ## v1.10.0 + + [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.0) + + ## O que Mudou + + ### Recursos + - Aprimorar a resolução da ferramenta MCP e eventos relacionados + - Atualizar a versão do lancedb e adicionar pacotes lance-namespace + - Aprimorar a análise e validação de argumentos JSON no CrewAgentExecutor e BaseTool + - Migrar o cliente HTTP da CLI de requests para httpx + - Adicionar documentação versionada + - Adicionar detecção de versões removidas para notas de versão + - Implementar tratamento de entrada do usuário em Flows + - Aprimorar a funcionalidade de auto-loop HITL nos testes de integração de feedback humano + - Adicionar started_event_id e definir no eventbus + - Atualizar automaticamente tools.specs + + ### Correções de Bugs + - Validar kwargs da ferramenta mesmo quando vazios para evitar TypeError crípticos + - Preservar tipos nulos nos esquemas de parâmetros da ferramenta para LLM + - Mapear output_pydantic/output_json para saída estruturada nativa + - Garantir que callbacks sejam executados/aguardados se forem promessas + - Capturar o nome do método no contexto da exceção + - Preservar tipo enum no resultado do roteador; melhorar tipos + - Corrigir fluxos cíclicos que quebram silenciosamente quando o ID de persistência é passado nas entradas + - Corrigir o formato da flag da CLI de --skip-provider para --skip_provider + - Garantir que o fluxo de chamada da ferramenta OpenAI seja finalizado + - Resolver ponteiros $ref de esquema complexos nas ferramentas MCP + - Impor additionalProperties=false nos esquemas + - Rejeitar nomes de scripts reservados para pastas de equipe + - Resolver condição de corrida no teste de emissão de eventos de guardrail + + ### Documentação + - Adicionar nota de dependência litellm para provedores de LLM não nativos + - Esclarecer o modelo de segurança NL2SQL e orientações de fortalecimento + - Adicionar 96 ações ausentes em 9 integrações + + ### Refatoração + - Refatorar crew para provider + - Extrair HITL para padrão de provider + - Melhorar tipagem e registro de hooks + + ## Contribuidores + + @dependabot[bot], @github-actions[bot], @github-code-quality[bot], @greysonlalonde, @heitorado, @hobostay, @joaomdmoura, @johnvan7, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha, @mplachta, @nicoferdi96, @theCyberTech, @thiagomoretto, @vinibrsl + + + ## v1.9.0 diff --git a/docs/pt-BR/concepts/llms.mdx b/docs/pt-BR/concepts/llms.mdx index 3343660ab..22f267c93 100644 --- a/docs/pt-BR/concepts/llms.mdx +++ b/docs/pt-BR/concepts/llms.mdx @@ -105,6 +105,15 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod + + O CrewAI oferece integrações nativas via SDK para OpenAI, Anthropic, Google (Gemini API), Azure e AWS Bedrock — sem necessidade de instalação extra além dos extras específicos do provedor (ex.: `uv add "crewai[openai]"`). + + Todos os outros provedores são alimentados pelo **LiteLLM**. Se você planeja usar algum deles, adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` + + ## Exemplos de Configuração de Provedores O CrewAI suporta uma grande variedade de provedores de LLM, cada um com recursos, métodos de autenticação e capacidades de modelo únicos. @@ -214,6 +223,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Texto, Imagem | Texto | | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Texto | Texto | | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Texto | Texto | + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -354,6 +368,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co | gemini-1.5-flash | 1M tokens | Modelo multimodal equilibrado, bom para maioria das tarefas | | gemini-1.5-flash-8B | 1M tokens | Mais rápido, mais eficiente em custo, adequado para tarefas de alta frequência | | gemini-1.5-pro | 2M tokens | Melhor desempenho para uma ampla variedade de tarefas de raciocínio, incluindo lógica, codificação e colaboração criativa | + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -438,6 +457,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co model="sagemaker/" ) ``` + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -453,6 +477,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co temperature=0.7 ) ``` + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -539,6 +568,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co | rakuten/rakutenai-7b-instruct | 1.024 tokens | LLM topo de linha, compreensão, raciocínio e geração textual.| | rakuten/rakutenai-7b-chat | 1.024 tokens | LLM topo de linha, compreensão, raciocínio e geração textual.| | baichuan-inc/baichuan2-13b-chat | 4.096 tokens | Suporte a chat em chinês/inglês, programação, matemática, seguir instruções, resolver quizzes.| + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -579,6 +613,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co # ... ``` + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -600,6 +639,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co | Llama 3.1 70B/8B | 131.072 tokens | Alta performance e tarefas de contexto grande| | Llama 3.2 Série | 8.192 tokens | Tarefas gerais | | Mixtral 8x7B | 32.768 tokens | Equilíbrio entre performance e contexto | + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -622,6 +666,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co base_url="https://api.watsonx.ai/v1" ) ``` + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -635,6 +684,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co base_url="http://localhost:11434" ) ``` + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -650,6 +704,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co temperature=0.7 ) ``` + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -665,6 +724,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co base_url="https://api.perplexity.ai/" ) ``` + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -679,6 +743,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct" ) ``` + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -702,6 +771,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co | Llama 3.2 Série | 8.192 tokens | Tarefas gerais e multimodais | | Llama 3.3 70B | Até 131.072 tokens | Desempenho e qualidade de saída elevada | | Família Qwen2 | 8.192 tokens | Desempenho e qualidade de saída elevada | + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -727,6 +801,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co - Equilíbrio entre velocidade e qualidade - Suporte a longas janelas de contexto + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` @@ -749,6 +828,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co - openrouter/deepseek/deepseek-r1 - openrouter/deepseek/deepseek-chat + + **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` diff --git a/docs/pt-BR/concepts/memory.mdx b/docs/pt-BR/concepts/memory.mdx index f7daa1560..3931ed6ab 100644 --- a/docs/pt-BR/concepts/memory.mdx +++ b/docs/pt-BR/concepts/memory.mdx @@ -1,967 +1,878 @@ --- title: Memória -description: Aproveitando sistemas de memória no framework CrewAI para aprimorar as capacidades dos agentes. +description: Aproveitando o sistema de memória unificado no CrewAI para aprimorar as capacidades dos agentes. icon: database mode: "wide" --- ## Visão Geral -O framework CrewAI oferece um sistema de memória sofisticado projetado para aprimorar significativamente as capacidades dos agentes de IA. O CrewAI disponibiliza **três abordagens distintas de memória** que atendem a diferentes casos de uso: +O CrewAI oferece um **sistema de memória unificado** -- uma única classe `Memory` que substitui memórias de curto prazo, longo prazo, entidades e externa por uma API inteligente. A memória usa um LLM para analisar o conteúdo ao salvar (inferindo escopo, categorias e importância) e suporta recall com profundidade adaptativa e pontuação composta que combina similaridade semântica, recência e importância. -1. **Sistema Básico de Memória** - Memória de curto prazo, longo prazo e de entidades integradas -2. **Memória Externa** - Provedores de memória externos autônomos +Você pode usar a memória de quatro formas: **standalone** (scripts, notebooks), **com Crews**, **com Agentes** ou **dentro de Flows**. -## Componentes do Sistema de Memória +## Início Rápido -| Componente | Descrição | -| :--------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Memória de Curto Prazo** | Armazena temporariamente interações e resultados recentes usando `RAG`, permitindo que os agentes recordem e utilizem informações relevantes ao contexto atual durante as execuções. | -| **Memória de Longo Prazo** | Preserva informações valiosas e aprendizados de execuções passadas, permitindo que os agentes construam e refinem seu conhecimento ao longo do tempo. | -| **Memória de Entidades** | Captura e organiza informações sobre entidades (pessoas, lugares, conceitos) encontradas durante tarefas, facilitando um entendimento mais profundo e o mapeamento de relacionamentos. Utiliza `RAG` para armazenar informações de entidades. | -| **Memória Contextual** | Mantém o contexto das interações combinando `ShortTermMemory`, `LongTermMemory` , `ExternalMemory` e `EntityMemory`, auxiliando na coerência e relevância das respostas dos agentes ao longo de uma sequência de tarefas ou conversas. | - -## 1. Sistema Básico de Memória (Recomendado) - -A abordagem mais simples e comum de uso. Ative a memória para sua crew com um único parâmetro: - -### Início Rápido ```python -from crewai import Crew, Agent, Task, Process +from crewai import Memory -# Habilitar o sistema básico de memória +memory = Memory() + +# Armazenar -- o LLM infere escopo, categorias e importância +memory.remember("Decidimos usar PostgreSQL para o banco de dados de usuários.") + +# Recuperar -- resultados ranqueados por pontuação composta (semântica + recência + importância) +matches = memory.recall("Qual banco de dados escolhemos?") +for m in matches: + print(f"[{m.score:.2f}] {m.record.content}") + +# Ajustar pontuação para um projeto dinâmico +memory = Memory(recency_weight=0.5, recency_half_life_days=7) + +# Esquecer +memory.forget(scope="/project/old") + +# Explorar a árvore de escopos auto-organizada +print(memory.tree()) +print(memory.info("/")) +``` + +## Quatro Formas de Usar Memória + +### Standalone + +Use memória em scripts, notebooks, ferramentas CLI ou como base de conhecimento independente -- sem agentes ou crews necessários. + +```python +from crewai import Memory + +memory = Memory() + +# Construir conhecimento +memory.remember("O limite da API é 1000 requisições por minuto.") +memory.remember("Nosso ambiente de staging usa a porta 8080.") +memory.remember("A equipe concordou em usar feature flags para todos os novos lançamentos.") + +# Depois, recupere o que precisar +matches = memory.recall("Quais são nossos limites de API?", limit=5) +for m in matches: + print(f"[{m.score:.2f}] {m.record.content}") + +# Extrair fatos atômicos de um texto mais longo +raw = """Notas da reunião: Decidimos migrar do MySQL para PostgreSQL +no próximo trimestre. O orçamento é de $50k. Sarah liderará a migração.""" + +facts = memory.extract_memories(raw) +# ["Migração de MySQL para PostgreSQL planejada para o próximo trimestre", +# "Orçamento da migração de banco de dados é $50k", +# "Sarah liderará a migração do banco de dados"] + +for fact in facts: + memory.remember(fact) +``` + +### Com Crews + +Passe `memory=True` para configurações padrão, ou passe uma instância `Memory` configurada para comportamento customizado. + +```python +from crewai import Crew, Agent, Task, Process, Memory + +# Opção 1: Memória padrão crew = Crew( - agents=[...], - tasks=[...], + agents=[researcher, writer], + tasks=[research_task, writing_task], process=Process.sequential, - memory=True, # Ativa memória de curto prazo, longo prazo e de entidades - verbose=True -) -``` - -### Como Funciona -- **Memória de Curto Prazo**: Usa ChromaDB com RAG para o contexto atual -- **Memória de Longo Prazo**: Usa SQLite3 para armazenar resultados de tarefas entre sessões -- **Memória de Entidades**: Usa RAG para rastrear entidades (pessoas, lugares, conceitos) -- **Local de Armazenamento**: Localidade específica da plataforma via pacote `appdirs` -- **Diretório de Armazenamento Personalizado**: Defina a variável de ambiente `CREWAI_STORAGE_DIR` - -## Transparência no Local de Armazenamento - - -**Compreendendo os Locais de Armazenamento**: CrewAI utiliza diretórios específicos da plataforma para guardar arquivos de memória e conhecimento seguindo as convenções do sistema operacional. Conhecer esses locais ajuda na implantação em produção, backups e depuração. - - -### Onde o CrewAI Armazena os Arquivos - -Por padrão, o CrewAI usa a biblioteca `appdirs` para determinar os locais de armazenamento conforme a convenção da plataforma. Veja exatamente onde seus arquivos são armazenados: - -#### Locais de Armazenamento Padrão por Plataforma - -**macOS:** -``` -~/Library/Application Support/CrewAI/{project_name}/ -├── knowledge/ # Arquivos base de conhecimento ChromaDB -├── short_term_memory/ # Arquivos de memória de curto prazo ChromaDB -├── long_term_memory/ # Arquivos de memória de longo prazo ChromaDB -├── entities/ # Arquivos de memória de entidades ChromaDB -└── long_term_memory_storage.db # Banco de dados SQLite -``` - -**Linux:** -``` -~/.local/share/CrewAI/{project_name}/ -├── knowledge/ -├── short_term_memory/ -├── long_term_memory/ -├── entities/ -└── long_term_memory_storage.db -``` - -**Windows:** -``` -C:\Users\{username}\AppData\Local\CrewAI\{project_name}\ -├── knowledge\ -├── short_term_memory\ -├── long_term_memory\ -├── entities\ -└── long_term_memory_storage.db -``` - -### Encontrando Seu Local de Armazenamento - -Para ver exatamente onde o CrewAI está armazenando arquivos em seu sistema: - -```python -from crewai.utilities.paths import db_storage_path -import os - -# Obter o caminho base de armazenamento -storage_path = db_storage_path() -print(f"CrewAI storage location: {storage_path}") - -# Listar todos os diretórios e arquivos do CrewAI -if os.path.exists(storage_path): - print("\nStored files and directories:") - for item in os.listdir(storage_path): - item_path = os.path.join(storage_path, item) - if os.path.isdir(item_path): - print(f"📁 {item}/") - # Exibir coleções ChromaDB - if os.path.exists(item_path): - for subitem in os.listdir(item_path): - print(f" └── {subitem}") - else: - print(f"📄 {item}") -else: - print("No CrewAI storage directory found yet.") -``` - -### Controlando Locais de Armazenamento - -#### Opção 1: Variável de Ambiente (Recomendado) -```python -import os -from crewai import Crew - -# Definir local de armazenamento personalizado -os.environ["CREWAI_STORAGE_DIR"] = "./my_project_storage" - -# Toda a memória e conhecimento serão salvos em ./my_project_storage/ -crew = Crew( - agents=[...], - tasks=[...], - memory=True -) -``` - -#### Opção 2: Caminho de Armazenamento Personalizado -```python -import os -from crewai import Crew -from crewai.memory import LongTermMemory -from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage - -# Configurar local de armazenamento personalizado -custom_storage_path = "./storage" -os.makedirs(custom_storage_path, exist_ok=True) - -crew = Crew( memory=True, - long_term_memory=LongTermMemory( - storage=LTMSQLiteStorage( - db_path=f"{custom_storage_path}/memory.db" - ) - ) -) -``` - -#### Opção 3: Armazenamento Específico de Projeto -```python -import os -from pathlib import Path - -# Armazenar no diretório do projeto -project_root = Path(__file__).parent -storage_dir = project_root / "crewai_storage" - -os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir) - -# Todo o armazenamento ficará agora na pasta do projeto -``` - -### Padrão do Provedor de Embedding - - -**Provedor de Embedding Padrão**: O CrewAI utiliza embeddings do OpenAI por padrão para garantir consistência e confiabilidade. Você pode facilmente customizar para combinar com seu provedor LLM ou utilizar embeddings locais. - - -#### Compreendendo o Comportamento Padrão -```python -# Ao utilizar Claude como seu LLM... -from crewai import Agent, LLM - -agent = Agent( - role="Analyst", - goal="Analyze data", - backstory="Expert analyst", - llm=LLM(provider="anthropic", model="claude-3-sonnet") # Usando Claude + verbose=True, ) -# O CrewAI usará embeddings OpenAI por padrão para garantir consistência -# Você pode customizar facilmente para combinar com seu provedor preferido -``` - -#### Personalizando Provedores de Embedding -```python -from crewai import Crew - -# Opção 1: Combinar com seu provedor de LLM +# Opção 2: Memória customizada com pontuação ajustada +memory = Memory( + recency_weight=0.4, + semantic_weight=0.4, + importance_weight=0.2, + recency_half_life_days=14, +) crew = Crew( - agents=[agent], - tasks=[task], - memory=True, - embedder={ - "provider": "anthropic", # Combine com seu provedor de LLM - "config": { - "api_key": "your-anthropic-key", - "model": "text-embedding-3-small" - } - } -) - -# Opção 2: Use embeddings locais (sem chamadas para API externa) -crew = Crew( - agents=[agent], - tasks=[task], - memory=True, - embedder={ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } + agents=[researcher, writer], + tasks=[research_task, writing_task], + memory=memory, ) ``` -### Depuração de Problemas de Armazenamento +Quando `memory=True`, a crew cria um `Memory()` padrão e repassa a configuração de `embedder` da crew automaticamente. Todos os agentes compartilham a memória da crew, a menos que um agente tenha sua própria. + +Após cada tarefa, a crew extrai automaticamente fatos discretos da saída da tarefa e os armazena. Antes de cada tarefa, o agente recupera contexto relevante da memória e o injeta no prompt da tarefa. + +### Com Agentes + +Agentes podem usar a memória compartilhada da crew (padrão) ou receber uma visão com escopo para contexto privado. -#### Verifique Permissões do Armazenamento ```python -import os -from crewai.utilities.paths import db_storage_path +from crewai import Agent, Memory -storage_path = db_storage_path() -print(f"Storage path: {storage_path}") -print(f"Path exists: {os.path.exists(storage_path)}") -print(f"Is writable: {os.access(storage_path, os.W_OK) if os.path.exists(storage_path) else 'Path does not exist'}") +memory = Memory() -# Crie com permissões apropriadas -if not os.path.exists(storage_path): - os.makedirs(storage_path, mode=0o755, exist_ok=True) - print(f"Created storage directory: {storage_path}") +# Pesquisador recebe um escopo privado -- só vê /agent/researcher +researcher = Agent( + role="Researcher", + goal="Encontrar e analisar informações", + backstory="Pesquisador experiente com atenção aos detalhes", + memory=memory.scope("/agent/researcher"), +) + +# Escritor usa memória compartilhada da crew (sem memória própria) +writer = Agent( + role="Writer", + goal="Produzir conteúdo claro e bem estruturado", + backstory="Escritor técnico experiente", + # memory não definido -- usa crew._memory quando a crew tem memória habilitada +) ``` -#### Inspecione Coleções do ChromaDB +Esse padrão dá ao pesquisador descobertas privadas enquanto o escritor lê da memória compartilhada da crew. + +### Com Flows + +Todo Flow possui memória integrada. Use `self.remember()`, `self.recall()` e `self.extract_memories()` dentro de qualquer método do flow. + ```python -import chromadb -from crewai.utilities.paths import db_storage_path +from crewai.flow.flow import Flow, listen, start -# Conecte-se ao ChromaDB do CrewAI -storage_path = db_storage_path() -chroma_path = os.path.join(storage_path, "knowledge") +class ResearchFlow(Flow): + @start() + def gather_data(self): + findings = "PostgreSQL suporta 10k conexões simultâneas. MySQL limita a 5k." + self.remember(findings, scope="/research/databases") + return findings -if os.path.exists(chroma_path): - client = chromadb.PersistentClient(path=chroma_path) - collections = client.list_collections() - - print("ChromaDB Collections:") - for collection in collections: - print(f" - {collection.name}: {collection.count()} documentos") -else: - print("No ChromaDB storage found") + @listen(gather_data) + def write_report(self, findings): + # Recuperar pesquisas anteriores para fornecer contexto + past = self.recall("benchmarks de performance de banco de dados") + context = "\n".join(f"- {m.record.content}" for m in past) + return f"Relatório:\nNovas descobertas: {findings}\nContexto anterior:\n{context}" ``` -#### Resetar Armazenamento (Depuração) +Veja a [documentação de Flows](/concepts/flows) para mais informações sobre memória em Flows. + + +## Escopos Hierárquicos + +### O Que São Escopos + +As memórias são organizadas em uma árvore hierárquica de escopos, similar a um sistema de arquivos. Cada escopo é um caminho como `/`, `/project/alpha` ou `/agent/researcher/findings`. + +``` +/ + /company + /company/engineering + /company/product + /project + /project/alpha + /project/beta + /agent + /agent/researcher + /agent/writer +``` + +Escopos fornecem **memória dependente de contexto** -- quando você faz recall dentro de um escopo, busca apenas naquela ramificação da árvore, melhorando tanto a precisão quanto o desempenho. + +### Como a Inferência de Escopo Funciona + +Quando você chama `remember()` sem especificar um escopo, o LLM analisa o conteúdo e a árvore de escopos existente, e sugere o melhor posicionamento. Se nenhum escopo existente é adequado, ele cria um novo. Com o tempo, a árvore de escopos cresce organicamente a partir do conteúdo -- você não precisa projetar um esquema antecipadamente. + ```python -from crewai import Crew +memory = Memory() -# Limpar todo o armazenamento de memória -crew = Crew(agents=[...], tasks=[...], memory=True) +# LLM infere escopo a partir do conteúdo +memory.remember("Escolhemos PostgreSQL para o banco de dados de usuários.") +# -> pode ser colocado em /project/decisions ou /engineering/database -# Limpar tipos específicos de memória -crew.reset_memories(command_type='short') # Memória de curto prazo -crew.reset_memories(command_type='long') # Memória de longo prazo -crew.reset_memories(command_type='entity') # Memória de entidades -crew.reset_memories(command_type='knowledge') # Armazenamento de conhecimento +# Você também pode especificar o escopo explicitamente +memory.remember("Velocidade do sprint é 42 pontos", scope="/team/metrics") ``` -### Melhores Práticas para Produção +### Visualizando a Árvore de Escopos -1. **Defina o `CREWAI_STORAGE_DIR`** para um local conhecido em produção para maior controle -2. **Escolha explicitamente provedores de embeddings** para coincidir com seu setup de LLM -3. **Monitore o tamanho do diretório de armazenamento** em casos de grande escala -4. **Inclua diretórios de armazenamento** em sua política de backup -5. **Defina permissões apropriadas de arquivo** (0o755 para diretórios, 0o644 para arquivos) -6. **Use caminhos relativos ao projeto** para implantações containerizadas - -### Problemas Comuns de Armazenamento - -**Erros "ChromaDB permission denied":** -```bash -# Corrija permissões -chmod -R 755 ~/.local/share/CrewAI/ -``` - -**Erros "Database is locked":** ```python -# Certifique-se que apenas uma instância CrewAI acesse o armazenamento -import fcntl -import os +print(memory.tree()) +# / (15 records) +# /project (8 records) +# /project/alpha (5 records) +# /project/beta (3 records) +# /agent (7 records) +# /agent/researcher (4 records) +# /agent/writer (3 records) -storage_path = db_storage_path() -lock_file = os.path.join(storage_path, ".crewai.lock") - -with open(lock_file, 'w') as f: - fcntl.flock(f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) - # Seu código CrewAI aqui +print(memory.info("/project/alpha")) +# ScopeInfo(path='/project/alpha', record_count=5, +# categories=['architecture', 'database'], +# oldest_record=datetime(...), newest_record=datetime(...), +# child_scopes=[]) ``` -**Armazenamento não persiste entre execuções:** +### MemoryScope: Visões de Subárvore + +Um `MemoryScope` restringe todas as operações a uma ramificação da árvore. O agente ou código que o utiliza só pode ver e escrever dentro daquela subárvore. + ```python -# Verifique se o local do armazenamento é consistente -import os -print("CREWAI_STORAGE_DIR:", os.getenv("CREWAI_STORAGE_DIR")) -print("Current working directory:", os.getcwd()) -print("Computed storage path:", db_storage_path()) +memory = Memory() + +# Criar um escopo para um agente específico +agent_memory = memory.scope("/agent/researcher") + +# Tudo é relativo a /agent/researcher +agent_memory.remember("Encontrados três papers relevantes sobre memória de LLM.") +# -> armazenado em /agent/researcher + +agent_memory.recall("papers relevantes") +# -> busca apenas em /agent/researcher + +# Restringir ainda mais com subscope +project_memory = agent_memory.subscope("project-alpha") +# -> /agent/researcher/project-alpha ``` -## Configuração Personalizada de Embedders +### Boas Práticas para Design de Escopos -O CrewAI suporta múltiplos provedores de embeddings para oferecer flexibilidade na escolha da melhor opção para seu caso de uso. Aqui está um guia completo para configuração de diferentes provedores de embeddings para seu sistema de memória. +- **Comece plano, deixe o LLM organizar.** Não projete demais sua hierarquia de escopos antecipadamente. Comece com `memory.remember(content)` e deixe a inferência de escopo do LLM criar estrutura conforme o conteúdo se acumula. -### Por que Escolher Diferentes Provedores de Embeddings? +- **Use padrões `/{tipo_entidade}/{identificador}`.** Hierarquias naturais emergem de padrões como `/project/alpha`, `/agent/researcher`, `/company/engineering`, `/customer/acme-corp`. -- **Otimização de Custos**: Embeddings locais (Ollama) são gratuitos após configuração inicial -- **Privacidade**: Mantenha seus dados locais com Ollama ou use seu provedor preferido na nuvem -- **Desempenho**: Alguns modelos têm melhor desempenho para domínios ou idiomas específicos -- **Consistência**: Combine seu provedor de embedding com o de LLM -- **Conformidade**: Atenda a requisitos regulatórios ou organizacionais +- **Escopo por preocupação, não por tipo de dado.** Use `/project/alpha/decisions` em vez de `/decisions/project/alpha`. Isso mantém conteúdo relacionado junto. -### OpenAI Embeddings (Padrão) +- **Mantenha profundidade rasa (2-3 níveis).** Escopos profundamente aninhados ficam muito esparsos. `/project/alpha/architecture` é bom; `/project/alpha/architecture/decisions/databases/postgresql` é demais. -A OpenAI oferece embeddings confiáveis e de alta qualidade para a maioria dos cenários. +- **Use escopos explícitos quando souber, deixe o LLM inferir quando não souber.** Se está armazenando uma decisão de projeto conhecida, passe `scope="/project/alpha/decisions"`. Se está armazenando saída livre de um agente, omita o escopo e deixe o LLM decidir. + +### Exemplos de Casos de Uso + +**Equipe multi-projeto:** +```python +memory = Memory() +# Cada projeto recebe sua própria ramificação +memory.remember("Usando arquitetura de microsserviços", scope="/project/alpha/architecture") +memory.remember("API GraphQL para apps cliente", scope="/project/beta/api") + +# Recall em todos os projetos +memory.recall("decisões de design de API") + +# Ou dentro de um projeto específico +memory.recall("design de API", scope="/project/beta") +``` + +**Contexto privado por agente com conhecimento compartilhado:** +```python +memory = Memory() + +# Pesquisador tem descobertas privadas +researcher_memory = memory.scope("/agent/researcher") + +# Escritor pode ler de seu próprio escopo e do conhecimento compartilhado da empresa +writer_view = memory.slice( + scopes=["/agent/writer", "/company/knowledge"], + read_only=True, +) +``` + +**Suporte ao cliente (contexto por cliente):** +```python +memory = Memory() + +# Cada cliente recebe contexto isolado +memory.remember("Prefere comunicação por email", scope="/customer/acme-corp") +memory.remember("Plano enterprise, 50 licenças", scope="/customer/acme-corp") + +# Docs de produto compartilhados são acessíveis a todos os agentes +memory.remember("Limite de taxa é 1000 req/min no plano enterprise", scope="/product/docs") +``` + + +## Fatias de Memória (Memory Slices) + +### O Que São Fatias + +Um `MemorySlice` é uma visão sobre múltiplos escopos, possivelmente disjuntos. Diferente de um escopo (que restringe a uma subárvore), uma fatia permite recall de várias ramificações simultaneamente. + +### Quando Usar Fatias vs Escopos + +- **Escopo**: Use quando um agente ou bloco de código deve ser restrito a uma única subárvore. Exemplo: um agente que só vê `/agent/researcher`. +- **Fatia**: Use quando precisar combinar contexto de múltiplas ramificações. Exemplo: um agente que lê de seu próprio escopo mais conhecimento compartilhado da empresa. + +### Fatias Somente Leitura + +O padrão mais comum: dar a um agente acesso de leitura a múltiplas ramificações sem permitir que ele escreva em áreas compartilhadas. + +```python +memory = Memory() + +# Agente pode fazer recall de seu próprio escopo E do conhecimento da empresa, +# mas não pode escrever no conhecimento da empresa +agent_view = memory.slice( + scopes=["/agent/researcher", "/company/knowledge"], + read_only=True, +) + +matches = agent_view.recall("políticas de segurança da empresa", limit=5) +# Busca em /agent/researcher e /company/knowledge, mescla e ranqueia resultados + +agent_view.remember("nova descoberta") # Levanta PermissionError (somente leitura) +``` + +### Fatias de Leitura e Escrita + +Quando somente leitura está desabilitado, você pode escrever em qualquer um dos escopos incluídos, mas deve especificar qual escopo explicitamente. + +```python +view = memory.slice(scopes=["/team/alpha", "/team/beta"], read_only=False) + +# Deve especificar escopo ao escrever +view.remember("Decisão entre equipes", scope="/team/alpha", categories=["decisions"]) +``` + + +## Pontuação Composta + +Os resultados do recall são ranqueados por uma combinação ponderada de três sinais: + +``` +composite = semantic_weight * similarity + recency_weight * decay + importance_weight * importance +``` + +Onde: +- **similarity** = `1 / (1 + distance)` do índice vetorial (0 a 1) +- **decay** = `0.5^(age_days / half_life_days)` -- decaimento exponencial (1.0 para hoje, 0.5 na meia-vida) +- **importance** = pontuação de importância do registro (0 a 1), definida no momento da codificação + +Configure diretamente no construtor do `Memory`: + +```python +# Retrospectiva de sprint: favorecer memórias recentes, meia-vida curta +memory = Memory( + recency_weight=0.5, + semantic_weight=0.3, + importance_weight=0.2, + recency_half_life_days=7, +) + +# Base de conhecimento de arquitetura: favorecer memórias importantes, meia-vida longa +memory = Memory( + recency_weight=0.1, + semantic_weight=0.5, + importance_weight=0.4, + recency_half_life_days=180, +) +``` + +Cada `MemoryMatch` inclui uma lista `match_reasons` para que você possa ver por que um resultado ficou na posição que ficou (ex.: `["semantic", "recency", "importance"]`). + + +## Camada de Análise LLM + +A memória usa o LLM de três formas: + +1. **Ao salvar** -- Quando você omite escopo, categorias ou importância, o LLM analisa o conteúdo e sugere escopo, categorias, importância e metadados (entidades, datas, tópicos). +2. **Ao fazer recall** -- Para recall profundo/automático, o LLM analisa a consulta (palavras-chave, dicas temporais, escopos sugeridos, complexidade) para guiar a recuperação. +3. **Extrair memórias** -- `extract_memories(content)` quebra texto bruto (ex.: saída de tarefa) em afirmações de memória discretas. Os agentes usam isso antes de chamar `remember()` em cada afirmação para que fatos atômicos sejam armazenados em vez de um bloco grande. + +Toda análise degrada graciosamente em caso de falha do LLM -- veja [Comportamento em Caso de Falha](#comportamento-em-caso-de-falha). + + +## Consolidação de Memória + +Ao salvar novo conteúdo, o pipeline de codificação verifica automaticamente registros similares existentes no armazenamento. Se a similaridade estiver acima de `consolidation_threshold` (padrão 0.85), o LLM decide o que fazer: + +- **keep** -- O registro existente ainda é preciso e não é redundante. +- **update** -- O registro existente deve ser atualizado com novas informações (o LLM fornece o conteúdo mesclado). +- **delete** -- O registro existente está desatualizado, substituído ou contradito. +- **insert_new** -- Se o novo conteúdo também deve ser inserido como um registro separado. + +Isso evita o acúmulo de duplicatas. Por exemplo, se você salvar "CrewAI garante operação confiável" três vezes, a consolidação reconhece as duplicatas e mantém apenas um registro. + +### Dedup Intra-batch + +Ao usar `remember_many()`, os itens dentro do mesmo batch são comparados entre si antes de atingir o armazenamento. Se dois itens tiverem similaridade de cosseno >= `batch_dedup_threshold` (padrão 0.98), o posterior é silenciosamente descartado. Isso captura duplicatas exatas ou quase exatas dentro de um único batch sem chamadas ao LLM (pura matemática vetorial). + +```python +# Apenas 2 registros são armazenados (o terceiro é quase duplicata do primeiro) +memory.remember_many([ + "CrewAI supports complex workflows.", + "Python is a great language.", + "CrewAI supports complex workflows.", # descartado pelo dedup intra-batch +]) +``` + + +## Saves Não-Bloqueantes + +`remember_many()` é **não-bloqueante** -- ele envia o pipeline de codificação para uma thread em background e retorna imediatamente. Isso significa que o agente pode continuar para a próxima tarefa enquanto as memórias estão sendo salvas. + +```python +# Retorna imediatamente -- save acontece em background +memory.remember_many(["Fato A.", "Fato B.", "Fato C."]) + +# recall() espera automaticamente saves pendentes antes de buscar +matches = memory.recall("fatos") # vê todos os 3 registros +``` + +### Barreira de Leitura + +Cada chamada `recall()` executa automaticamente `drain_writes()` antes de buscar, garantindo que a consulta sempre veja os registros mais recentes persistidos. Isso é transparente -- você nunca precisa pensar nisso. + +### Encerramento da Crew + +Quando uma crew termina, `kickoff()` drena todos os saves de memória pendentes em seu bloco `finally`, então nenhum save é perdido mesmo que a crew complete enquanto saves em background estão em andamento. + +### Uso Standalone + +Para scripts ou notebooks onde não há ciclo de vida de crew, chame `drain_writes()` ou `close()` explicitamente: + +```python +memory = Memory() +memory.remember_many(["Fato A.", "Fato B."]) + +# Opção 1: Esperar saves pendentes +memory.drain_writes() + +# Opção 2: Drenar e encerrar o pool de background +memory.close() +``` + + +## Origem e Privacidade + +Cada registro de memória pode carregar uma tag `source` para rastreamento de procedência e uma flag `private` para controle de acesso. + +### Rastreamento de Origem + +O parâmetro `source` identifica de onde uma memória veio: + +```python +# Marcar memórias com sua origem +memory.remember("Usuário prefere modo escuro", source="user:alice") +memory.remember("Configuração do sistema atualizada", source="admin") +memory.remember("Agente encontrou um bug", source="agent:debugger") + +# Recuperar apenas memórias de uma origem específica +matches = memory.recall("preferências do usuário", source="user:alice") +``` + +### Memórias Privadas + +Memórias privadas só são visíveis no recall quando o `source` corresponde: + +```python +# Armazenar uma memória privada +memory.remember("A chave de API da Alice é sk-...", source="user:alice", private=True) + +# Este recall vê a memória privada (source corresponde) +matches = memory.recall("chave de API", source="user:alice") + +# Este recall NÃO a vê (source diferente) +matches = memory.recall("chave de API", source="user:bob") + +# Acesso admin: ver todos os registros privados independente do source +matches = memory.recall("chave de API", include_private=True) +``` + +Isso é particularmente útil em implantações multi-usuário ou corporativas onde memórias de diferentes usuários devem ser isoladas. + + +## RecallFlow (Recall Profundo) + +`recall()` suporta duas profundidades: + +- **`depth="shallow"`** -- Busca vetorial direta com pontuação composta. Rápido (~200ms), sem chamadas ao LLM. +- **`depth="deep"` (padrão)** -- Executa um RecallFlow em múltiplas etapas: análise da consulta, seleção de escopo, busca vetorial paralela, roteamento baseado em confiança e exploração recursiva opcional quando a confiança é baixa. + +**Pulo inteligente do LLM**: Consultas com menos de `query_analysis_threshold` (padrão 200 caracteres) pulam a análise de consulta do LLM inteiramente, mesmo no modo deep. Consultas curtas como "Qual banco de dados usamos?" já são boas frases de busca -- a análise do LLM agrega pouco valor. Isso economiza ~1-3s por recall para consultas curtas típicas. Apenas consultas mais longas (ex.: descrições completas de tarefas) passam pela destilação do LLM em sub-consultas direcionadas. + +```python +# Shallow: busca vetorial pura, sem LLM +matches = memory.recall("O que decidimos?", limit=10, depth="shallow") + +# Deep (padrão): recuperação inteligente com análise LLM para consultas longas +matches = memory.recall( + "Resuma todas as decisões de arquitetura deste trimestre", + limit=10, + depth="deep", +) +``` + +Os limiares de confiança que controlam o roteador do RecallFlow são configuráveis: + +```python +memory = Memory( + confidence_threshold_high=0.9, # Só sintetizar quando muito confiante + confidence_threshold_low=0.4, # Explorar mais profundamente de forma mais agressiva + exploration_budget=2, # Permitir até 2 rodadas de exploração + query_analysis_threshold=200, # Pular LLM para consultas menores que isso +) +``` + + +## Configuração de Embedder + +A memória precisa de um modelo de embedding para converter texto em vetores para busca semântica. Você pode configurar de três formas. + +### Passando Diretamente para o Memory + +```python +from crewai import Memory + +# Como um dict de configuração +memory = Memory(embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}}) + +# Como um callable pré-construído +from crewai.rag.embeddings.factory import build_embedder +embedder = build_embedder({"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}}) +memory = Memory(embedder=embedder) +``` + +### Via Configuração de Embedder da Crew + +Quando usar `memory=True`, a configuração de `embedder` da crew é repassada: ```python from crewai import Crew -# Configuração básica OpenAI (usa a variável de ambiente OPENAI_API_KEY) crew = Crew( agents=[...], tasks=[...], memory=True, - embedder={ - "provider": "openai", - "config": { - "model": "text-embedding-3-small" # ou "text-embedding-3-large" - } - } -) - -# Configuração avançada OpenAI -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": "your-openai-api-key", # Opcional: sobrescreve variável de ambiente - "model": "text-embedding-3-large", - "dimensions": 1536, # Opcional: reduz as dimensões para armazenamento menor - "organization_id": "your-org-id" # Opcional: para contas organizacionais - } - } + embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}}, ) ``` -### Azure OpenAI Embeddings - -Para empresas que utilizam deploys Azure OpenAI. +### Exemplos por Provedor + + ```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", # Use openai como provider para Azure - "config": { - "api_key": "your-azure-api-key", - "api_base": "https://your-resource.openai.azure.com/", - "api_type": "azure", - "api_version": "2023-05-15", - "model": "text-embedding-3-small", - "deployment_id": "your-deployment-name" # Nome do deploy Azure - } - } -) -``` - -### Google AI Embeddings - -Use modelos de embeddings de texto do Google para integração com serviços do Google Cloud. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "google", - "config": { - "api_key": "your-google-api-key", - "model": "text-embedding-004" # ou "text-embedding-preview-0409" - } - } -) -``` - -### Vertex AI Embeddings - -Para usuários do Google Cloud com acesso ao Vertex AI. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "vertexai", - "config": { - "project_id": "your-gcp-project-id", - "region": "us-central1", # ou sua região preferencial - "api_key": "your-service-account-key", - "model_name": "textembedding-gecko" - } - } -) -``` - -### Ollama Embeddings (Local) - -Execute embeddings localmente para privacidade e economia. - -```python -# Primeiro, instale e rode Ollama localmente, depois baixe um modelo de embedding: -# ollama pull mxbai-embed-large - -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": { - "model": "mxbai-embed-large", # ou "nomic-embed-text" - "url": "http://localhost:11434/api/embeddings" # URL padrão do Ollama - } - } -) - -# Para instalações personalizadas do Ollama -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": { - "model": "mxbai-embed-large", - "url": "http://your-ollama-server:11434/api/embeddings" - } - } -) -``` - -### Cohere Embeddings - -Utilize os modelos de embedding da Cohere para suporte multilíngue. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "cohere", - "config": { - "api_key": "your-cohere-api-key", - "model": "embed-english-v3.0" # ou "embed-multilingual-v3.0" - } - } -) -``` - -### VoyageAI Embeddings - -Embeddings de alto desempenho otimizados para tarefas de recuperação. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "voyageai", - "config": { - "api_key": "your-voyage-api-key", - "model": "voyage-large-2", # ou "voyage-code-2" para código - "input_type": "document" # ou "query" - } - } -) -``` - -### AWS Bedrock Embeddings - -Para usuários AWS com acesso ao Bedrock. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "bedrock", - "config": { - "aws_access_key_id": "your-access-key", - "aws_secret_access_key": "your-secret-key", - "region_name": "us-east-1", - "model": "amazon.titan-embed-text-v1" - } - } -) -``` - -### Hugging Face Embeddings - -Utilize modelos open-source do Hugging Face. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "huggingface", - "config": { - "api_key": "your-hf-token", # Opcional para modelos públicos - "model": "sentence-transformers/all-MiniLM-L6-v2" - } - } -) -``` - -### IBM Watson Embeddings - -Para usuários do IBM Cloud. - -```python -crew = Crew( - memory=True, - embedder={ - "provider": "watson", - "config": { - "api_key": "your-watson-api-key", - "url": "your-watson-instance-url", - "model": "ibm/slate-125m-english-rtrvr" - } - } -) -``` - -### Como Escolher o Provedor de Embedding Certo - -| Provedor | Melhor Para | Prós | Contras | -|:---------|:----------|:------|:------| -| **OpenAI** | Uso geral, confiabilidade | Alta qualidade, bem testado | Custo, requer chave de API | -| **Ollama** | Privacidade, economia | Gratuito, local, privado | Requer configuração local | -| **Google AI** | Ecossistema Google | Bom desempenho | Requer conta Google | -| **Azure OpenAI** | Empresas, conformidade | Recursos corporativos | Configuração mais complexa | -| **Cohere** | Conteúdo multilíngue | Excelente suporte a idiomas | Uso especializado | -| **VoyageAI** | Tarefas de busca e recuperação | Otimizado para pesquisa | Provedor mais novo | - -### Configuração via Variável de Ambiente - -Para segurança, armazene chaves de API em variáveis de ambiente: - -```python -import os - -# Configurar variáveis de ambiente -os.environ["OPENAI_API_KEY"] = "your-openai-key" -os.environ["GOOGLE_API_KEY"] = "your-google-key" -os.environ["COHERE_API_KEY"] = "your-cohere-key" - -# Use sem expor as chaves no código -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "model": "text-embedding-3-small" - # A chave de API será carregada automaticamente da variável de ambiente - } - } -) -``` - -### Testando Diferentes Provedores de Embedding - -Compare provedores de embedding para o seu caso de uso específico: - -```python -from crewai import Crew -from crewai.utilities.paths import db_storage_path - -# Testar diferentes provedores com os mesmos dados -providers_to_test = [ - { - "name": "OpenAI", - "config": { - "provider": "openai", - "config": {"model": "text-embedding-3-small"} - } - }, - { - "name": "Ollama", - "config": { - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } - } -] - -for provider in providers_to_test: - print(f"\nTesting {provider['name']} embeddings...") - - # Criar crew com embedder específico - crew = Crew( - agents=[...], - tasks=[...], - memory=True, - embedder=provider['config'] - ) - - # Execute o teste e meça o desempenho - result = crew.kickoff() - print(f"{provider['name']} completed successfully") -``` - -### Solução de Problemas de Embeddings - -**Erros de modelo não encontrado:** -```python -# Verifique disponibilidade do modelo -from crewai.rag.embeddings.configurator import EmbeddingConfigurator - -configurator = EmbeddingConfigurator() -try: - embedder = configurator.configure_embedder({ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - }) - print("Embedder configured successfully") -except Exception as e: - print(f"Configuration error: {e}") -``` - -**Problemas com chave de API:** -```python -import os - -# Verifique se as chaves de API estão configuradas -required_keys = ["OPENAI_API_KEY", "GOOGLE_API_KEY", "COHERE_API_KEY"] -for key in required_keys: - if os.getenv(key): - print(f"✅ {key} is set") - else: - print(f"❌ {key} is not set") -``` - -**Comparação de desempenho:** -```python -import time - -def test_embedding_performance(embedder_config, test_text="This is a test document"): - start_time = time.time() - - crew = Crew( - agents=[...], - tasks=[...], - memory=True, - embedder=embedder_config - ) - - # Simula operação de memória - crew.kickoff() - - end_time = time.time() - return end_time - start_time - -# Comparar desempenho -openai_time = test_embedding_performance({ +memory = Memory(embedder={ "provider": "openai", - "config": {"model": "text-embedding-3-small"} + "config": { + "model_name": "text-embedding-3-small", + # "api_key": "sk-...", # ou defina OPENAI_API_KEY + }, }) +``` + -ollama_time = test_embedding_performance({ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} + +```python +memory = Memory(embedder={ + "provider": "ollama", + "config": { + "model_name": "mxbai-embed-large", + "url": "http://localhost:11434/api/embeddings", + }, }) - -print(f"OpenAI: {openai_time:.2f}s") -print(f"Ollama: {ollama_time:.2f}s") ``` + -## 2. Memória Externa - -A Memória Externa fornece um sistema de memória autônomo que opera independentemente da memória interna da crew. Isso é ideal para provedores de memória especializados ou compartilhamento de memória entre aplicações. - -### Memória Externa Básica com Mem0 + ```python -import os -from crewai import Agent, Crew, Process, Task -from crewai.memory.external.external_memory import ExternalMemory - -# Create external memory instance with local Mem0 Configuration -external_memory = ExternalMemory( - embedder_config={ - "provider": "mem0", - "config": { - "user_id": "john", - "local_mem0_config": { - "vector_store": { - "provider": "qdrant", - "config": {"host": "localhost", "port": 6333} - }, - "llm": { - "provider": "openai", - "config": {"api_key": "your-api-key", "model": "gpt-4"} - }, - "embedder": { - "provider": "openai", - "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"} - } - }, - "infer": True # Optional defaults to True - }, - } -) - -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory, # Separate from basic memory - process=Process.sequential, - verbose=True -) +memory = Memory(embedder={ + "provider": "azure", + "config": { + "deployment_id": "your-embedding-deployment", + "api_key": "your-azure-api-key", + "api_base": "https://your-resource.openai.azure.com", + "api_version": "2024-02-01", + }, +}) ``` + -### Memória Externa Avançada com o Cliente Mem0 -Ao usar o Cliente Mem0, você pode personalizar ainda mais a configuração de memória usando parâmetros como "includes", "excludes", "custom_categories", "infer" e "run_id" (apenas para memória de curto prazo). -Você pode encontrar mais detalhes na [documentação do Mem0](https://docs.mem0.ai/). + +```python +memory = Memory(embedder={ + "provider": "google-generativeai", + "config": { + "model_name": "gemini-embedding-001", + # "api_key": "...", # ou defina GOOGLE_API_KEY + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "google-vertex", + "config": { + "model_name": "gemini-embedding-001", + "project_id": "your-gcp-project-id", + "location": "us-central1", + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "cohere", + "config": { + "model_name": "embed-english-v3.0", + # "api_key": "...", # ou defina COHERE_API_KEY + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "voyageai", + "config": { + "model": "voyage-3", + # "api_key": "...", # ou defina VOYAGE_API_KEY + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "amazon-bedrock", + "config": { + "model_name": "amazon.titan-embed-text-v1", + # Usa credenciais AWS padrão (sessão boto3) + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "huggingface", + "config": { + "model_name": "sentence-transformers/all-MiniLM-L6-v2", + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "jina", + "config": { + "model_name": "jina-embeddings-v2-base-en", + # "api_key": "...", # ou defina JINA_API_KEY + }, +}) +``` + + + +```python +memory = Memory(embedder={ + "provider": "watsonx", + "config": { + "model_id": "ibm/slate-30m-english-rtrvr", + "api_key": "your-watsonx-api-key", + "project_id": "your-project-id", + "url": "https://us-south.ml.cloud.ibm.com", + }, +}) +``` + + + +```python +# Passe qualquer callable que receba uma lista de strings e retorne uma lista de vetores +def my_embedder(texts: list[str]) -> list[list[float]]: + # Sua lógica de embedding aqui + return [[0.1, 0.2, ...] for _ in texts] + +memory = Memory(embedder=my_embedder) +``` + + + +### Referência de Provedores + +| Provedor | Chave | Modelo Típico | Notas | +| :--- | :--- | :--- | :--- | +| OpenAI | `openai` | `text-embedding-3-small` | Padrão. Defina `OPENAI_API_KEY`. | +| Ollama | `ollama` | `mxbai-embed-large` | Local, sem API key. | +| Azure OpenAI | `azure` | `text-embedding-ada-002` | Requer `deployment_id`. | +| Google AI | `google-generativeai` | `gemini-embedding-001` | Defina `GOOGLE_API_KEY`. | +| Google Vertex | `google-vertex` | `gemini-embedding-001` | Requer `project_id`. | +| Cohere | `cohere` | `embed-english-v3.0` | Forte suporte multilíngue. | +| VoyageAI | `voyageai` | `voyage-3` | Otimizado para retrieval. | +| AWS Bedrock | `amazon-bedrock` | `amazon.titan-embed-text-v1` | Usa credenciais boto3. | +| Hugging Face | `huggingface` | `all-MiniLM-L6-v2` | Sentence-transformers local. | +| Jina | `jina` | `jina-embeddings-v2-base-en` | Defina `JINA_API_KEY`. | +| IBM WatsonX | `watsonx` | `ibm/slate-30m-english-rtrvr` | Requer `project_id`. | +| Sentence Transformer | `sentence-transformer` | `all-MiniLM-L6-v2` | Local, sem API key. | +| Custom | `custom` | -- | Requer `embedding_callable`. | + + +## Configuração de LLM + +A memória usa um LLM para análise de save (inferência de escopo, categorias e importância), decisões de consolidação e análise de consulta no recall profundo. Você pode configurar qual modelo usar. ```python -import os -from crewai import Agent, Crew, Process, Task -from crewai.memory.external.external_memory import ExternalMemory +from crewai import Memory, LLM -new_categories = [ - {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"}, - {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"}, - {"personal_information": "Basic information about the user including name, preferences, and personality traits"} -] +# Padrão: gpt-4o-mini +memory = Memory() -os.environ["MEM0_API_KEY"] = "your-api-key" +# Usar um modelo OpenAI diferente +memory = Memory(llm="gpt-4o") -# Create external memory instance with Mem0 Client -external_memory = ExternalMemory( - embedder_config={ - "provider": "mem0", - "config": { - "user_id": "john", - "org_id": "my_org_id", # Optional - "project_id": "my_project_id", # Optional - "api_key": "custom-api-key" # Optional - overrides env var - "run_id": "my_run_id", # Optional - for short-term memory - "includes": "include1", # Optional - "excludes": "exclude1", # Optional - "infer": True # Optional defaults to True - "custom_categories": new_categories # Optional - custom categories for user memory - }, - } -) +# Usar Anthropic +memory = Memory(llm="anthropic/claude-3-haiku-20240307") -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory, # Separate from basic memory - process=Process.sequential, - verbose=True -) +# Usar Ollama para análise totalmente local/privada +memory = Memory(llm="ollama/llama3.2") + +# Usar Google Gemini +memory = Memory(llm="gemini/gemini-2.0-flash") + +# Passar uma instância LLM pré-configurada com configurações customizadas +llm = LLM(model="gpt-4o", temperature=0) +memory = Memory(llm=llm) ``` -### Implementação Personalizada de Armazenamento +O LLM é inicializado **lazily** -- ele só é criado quando necessário pela primeira vez. Isso significa que `Memory()` nunca falha no momento da construção, mesmo que chaves de API não estejam definidas. Erros só aparecem quando o LLM é realmente chamado (ex.: ao salvar sem escopo/categorias explícitos, ou durante recall profundo). + +Para operação totalmente offline/privada, use um modelo local tanto para o LLM quanto para o embedder: + ```python -from crewai.memory.external.external_memory import ExternalMemory -from crewai.memory.storage.interface import Storage - -class CustomStorage(Storage): - def __init__(self): - self.memories = [] - - def save(self, value, metadata=None, agent=None): - self.memories.append({ - "value": value, - "metadata": metadata, - "agent": agent - }) - - def search(self, query, limit=10, score_threshold=0.5): - # Implemente sua lógica de busca aqui - return [m for m in self.memories if query.lower() in str(m["value"]).lower()] - - def reset(self): - self.memories = [] - -# Usando armazenamento customizado -external_memory = ExternalMemory(storage=CustomStorage()) - -crew = Crew( - agents=[...], - tasks=[...], - external_memory=external_memory +memory = Memory( + llm="ollama/llama3.2", + embedder={"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}}, ) ``` -## 🧠 Comparação dos Sistemas de Memória -| **Categoria** | **Recurso** | **Memória Básica** | **Memória Externa** | -|------------------------|-------------------------------|-------------------------------|----------------------------------| -| **Facilidade de Uso** | Complexidade de Setup | Simples | Média | -| | Integração | Contextual integrada | Autônoma | -| **Persistência** | Armazenamento | Arquivos locais | Customizada / Mem0 | -| | Multi-sessão | ✅ | ✅ | -| **Personalização** | Especificidade do Usuário | ❌ | ✅ | -| | Provedores Customizados | Limitado | Qualquer provedor | -| **Aplicação Recomendada** | Recomendado para | Maioria dos casos | Necessidades especializadas | +## Backend de Armazenamento + +- **Padrão**: LanceDB, armazenado em `./.crewai/memory` (ou `$CREWAI_STORAGE_DIR/memory` se a variável de ambiente estiver definida, ou o caminho que você passar como `storage="path/to/dir"`). +- **Backend customizado**: Implemente o protocolo `StorageBackend` (veja `crewai.memory.storage.backend`) e passe uma instância para `Memory(storage=your_backend)`. -## Provedores de Embedding Suportados +## Descoberta + +Inspecione a hierarquia de escopos, categorias e registros: -### OpenAI (Padrão) ```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": {"model": "text-embedding-3-small"} - } -) +memory.tree() # Árvore formatada de escopos e contagem de registros +memory.tree("/project", max_depth=2) # Visão de subárvore +memory.info("/project") # ScopeInfo: record_count, categories, oldest/newest +memory.list_scopes("/") # Escopos filhos imediatos +memory.list_categories() # Nomes e contagens de categorias +memory.list_records(scope="/project/alpha", limit=20) # Registros em um escopo, mais recentes primeiro ``` -### Ollama + +## Comportamento em Caso de Falha + +Se o LLM falhar durante a análise (erro de rede, limite de taxa, resposta inválida), a memória degrada graciosamente: + +- **Análise de save** -- Um aviso é registrado e a memória ainda é armazenada com escopo padrão `/`, categorias vazias e importância `0.5`. +- **Extrair memórias** -- O conteúdo completo é armazenado como uma única memória para que nada seja descartado. +- **Análise de consulta** -- O recall usa fallback para seleção simples de escopo e busca vetorial, então você ainda obtém resultados. + +Nenhuma exceção é levantada para essas falhas de análise; apenas falhas de armazenamento ou do embedder irão levantar. + + +## Nota sobre Privacidade + +O conteúdo da memória é enviado ao LLM configurado para análise (escopo/categorias/importância no save, análise de consulta e recall profundo opcional). Para dados sensíveis, use um LLM local (ex.: Ollama) ou garanta que seu provedor atenda aos requisitos de conformidade. + + +## Eventos de Memória + +Todas as operações de memória emitem eventos com `source_type="unified_memory"`. Você pode escutar para timing, erros e conteúdo. + +| Evento | Descrição | Propriedades Principais | +| :---- | :---------- | :------------- | +| **MemoryQueryStartedEvent** | Consulta inicia | `query`, `limit` | +| **MemoryQueryCompletedEvent** | Consulta bem-sucedida | `query`, `results`, `query_time_ms` | +| **MemoryQueryFailedEvent** | Consulta falha | `query`, `error` | +| **MemorySaveStartedEvent** | Save inicia | `value`, `metadata` | +| **MemorySaveCompletedEvent** | Save bem-sucedido | `value`, `save_time_ms` | +| **MemorySaveFailedEvent** | Save falha | `value`, `error` | +| **MemoryRetrievalStartedEvent** | Retrieval do agente inicia | `task_id` | +| **MemoryRetrievalCompletedEvent** | Retrieval do agente completo | `task_id`, `memory_content`, `retrieval_time_ms` | + +Exemplo: monitorar tempo de consulta: + ```python -crew = Crew( - memory=True, - embedder={ - "provider": "ollama", - "config": {"model": "mxbai-embed-large"} - } -) +from crewai.events import BaseEventListener, MemoryQueryCompletedEvent + +class MemoryMonitor(BaseEventListener): + def setup_listeners(self, crewai_event_bus): + @crewai_event_bus.on(MemoryQueryCompletedEvent) + def on_done(source, event): + if getattr(event, "source_type", None) == "unified_memory": + print(f"Query '{event.query}' completou em {event.query_time_ms:.0f}ms") ``` -### Google AI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "google", - "config": { - "api_key": "your-api-key", - "model": "text-embedding-004" - } - } -) -``` - -### Azure OpenAI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": "your-api-key", - "api_base": "https://your-resource.openai.azure.com/", - "api_version": "2023-05-15", - "model_name": "text-embedding-3-small" - } - } -) -``` - -### Vertex AI -```python -crew = Crew( - memory=True, - embedder={ - "provider": "vertexai", - "config": { - "project_id": "your-project-id", - "region": "your-region", - "api_key": "your-api-key", - "model_name": "textembedding-gecko" - } - } -) -``` - -## Melhores Práticas de Segurança - -### Variáveis de Ambiente -```python -import os -from crewai import Crew - -# Armazene dados sensíveis em variáveis de ambiente -crew = Crew( - memory=True, - embedder={ - "provider": "openai", - "config": { - "api_key": os.getenv("OPENAI_API_KEY"), - "model": "text-embedding-3-small" - } - } -) -``` - -### Segurança no Armazenamento -```python -import os -from crewai import Crew -from crewai.memory import LongTermMemory -from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage - -# Use caminhos seguros para armazenamento -storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage") -os.makedirs(storage_path, mode=0o700, exist_ok=True) # Permissões restritas - -crew = Crew( - memory=True, - long_term_memory=LongTermMemory( - storage=LTMSQLiteStorage( - db_path=f"{storage_path}/memory.db" - ) - ) -) -``` ## Solução de Problemas -### Problemas Comuns +**Memória não persiste?** +- Garanta que o caminho de armazenamento seja gravável (padrão `./.crewai/memory`). Passe `storage="./your_path"` para usar outro diretório, ou defina a variável de ambiente `CREWAI_STORAGE_DIR`. +- Ao usar uma crew, confirme que `memory=True` ou `memory=Memory(...)` está definido. -**A memória não está persistindo entre sessões?** -- Verifique a variável de ambiente `CREWAI_STORAGE_DIR` -- Garanta permissões de escrita no diretório de armazenamento -- Certifique-se que a memória está ativada com `memory=True` +**Recall lento?** +- Use `depth="shallow"` para contexto rotineiro do agente. Reserve `depth="deep"` para consultas complexas. +- Aumente `query_analysis_threshold` para pular a análise do LLM em mais consultas. -**Erros de autenticação no Mem0?** -- Verifique se a variável de ambiente `MEM0_API_KEY` está definida -- Confira permissões da chave de API no painel do Mem0 -- Certifique-se de que o pacote `mem0ai` está instalado +**Erros de análise LLM nos logs?** +- A memória ainda salva/recupera com padrões seguros. Verifique chaves de API, limites de taxa e disponibilidade do modelo se quiser análise LLM completa. -**Alto uso de memória com grandes volumes de dados?** -- Considere usar Memória Externa com armazenamento personalizado -- Implemente paginação nos métodos de busca do armazenamento customizado -- Utilize modelos de embedding menores para menor consumo de memória +**Erros de save em background nos logs?** +- Os saves de memória rodam em uma thread em background. Erros são emitidos como `MemorySaveFailedEvent` mas não derrubam o agente. Verifique os logs para a causa raiz (geralmente problemas de conexão com LLM ou embedder). -### Dicas de Desempenho +**Conflitos de escrita concorrente?** +- As operações do LanceDB são serializadas com um lock compartilhado e reexecutadas automaticamente em caso de conflito. Isso lida com múltiplas instâncias `Memory` apontando para o mesmo banco de dados (ex.: memória do agente + memória da crew). Nenhuma ação necessária. -- Use `memory=True` para a maioria dos casos (mais simples e rápido) -- Só utilize Memória de Usuário se precisar de persistência específica por usuário -- Considere Memória Externa para necessidades de grande escala ou especializadas -- Prefira modelos de embedding menores para maior rapidez -- Defina limites apropriados de busca para controlar o tamanho da recuperação +**Navegar na memória pelo terminal:** +```bash +crewai memory # Abre o navegador TUI +crewai memory --storage-path ./my_memory # Apontar para um diretório específico +``` -## Benefícios do Sistema de Memória do CrewAI +**Resetar memória (ex.: para testes):** +```python +crew.reset_memories(command_type="memory") # Reseta memória unificada +# Ou em uma instância Memory: +memory.reset() # Todos os escopos +memory.reset(scope="/project/old") # Apenas essa subárvore +``` -- 🦾 **Aprendizado Adaptativo:** As crews tornam-se mais eficientes ao longo do tempo, adaptando-se a novas informações e refinando sua abordagem para tarefas. -- 🫡 **Personalização Avançada:** A memória permite que agentes lembrem preferências do usuário e interações passadas, proporcionando experiências personalizadas. -- 🧠 **Melhoria na Resolução de Problemas:** O acesso a um rico acervo de memória auxilia os agentes a tomar decisões mais informadas, recorrendo a aprendizados prévios e contextuais. -## Conclusão +## Referência de Configuração -Integrar o sistema de memória do CrewAI em seus projetos é simples. Ao aproveitar os componentes e configurações oferecidos, -você rapidamente capacita seus agentes a lembrar, raciocinar e aprender com suas interações, desbloqueando novos níveis de inteligência e capacidade. +Toda a configuração é passada como argumentos nomeados para `Memory(...)`. Cada parâmetro tem um padrão sensato. + +| Parâmetro | Padrão | Descrição | +| :--- | :--- | :--- | +| `llm` | `"gpt-4o-mini"` | LLM para análise (nome do modelo ou instância `BaseLLM`). | +| `storage` | `"lancedb"` | Backend de armazenamento (`"lancedb"`, string de caminho ou instância `StorageBackend`). | +| `embedder` | `None` (OpenAI padrão) | Embedder (dict de config, callable ou `None` para OpenAI padrão). | +| `recency_weight` | `0.3` | Peso da recência na pontuação composta. | +| `semantic_weight` | `0.5` | Peso da similaridade semântica na pontuação composta. | +| `importance_weight` | `0.2` | Peso da importância na pontuação composta. | +| `recency_half_life_days` | `30` | Dias para a pontuação de recência cair pela metade (decaimento exponencial). | +| `consolidation_threshold` | `0.85` | Similaridade acima da qual a consolidação é ativada no save. Defina `1.0` para desativar. | +| `consolidation_limit` | `5` | Máx. de registros existentes para comparar durante consolidação. | +| `default_importance` | `0.5` | Importância atribuída quando não fornecida e a análise LLM é pulada. | +| `batch_dedup_threshold` | `0.98` | Similaridade de cosseno para descartar quase-duplicatas dentro de um batch `remember_many()`. | +| `confidence_threshold_high` | `0.8` | Confiança de recall acima da qual resultados são retornados diretamente. | +| `confidence_threshold_low` | `0.5` | Confiança de recall abaixo da qual exploração mais profunda é ativada. | +| `complex_query_threshold` | `0.7` | Para consultas complexas, explorar mais profundamente abaixo desta confiança. | +| `exploration_budget` | `1` | Número de rodadas de exploração por LLM durante recall profundo. | +| `query_analysis_threshold` | `200` | Consultas menores que isso (em caracteres) pulam análise LLM durante recall profundo. | diff --git a/docs/pt-BR/enterprise/features/flow-hitl-management.mdx b/docs/pt-BR/enterprise/features/flow-hitl-management.mdx index 1a6651203..d1f05e55f 100644 --- a/docs/pt-BR/enterprise/features/flow-hitl-management.mdx +++ b/docs/pt-BR/enterprise/features/flow-hitl-management.mdx @@ -38,22 +38,21 @@ O CrewAI Enterprise oferece um sistema abrangente de gerenciamento Human-in-the- Configure checkpoints de revisão humana em seus Flows usando o decorador `@human_feedback`. Quando a execução atinge um ponto de revisão, o sistema pausa, notifica o responsável via email e aguarda uma resposta. ```python -from crewai.flow.flow import Flow, start, listen +from crewai.flow.flow import Flow, start, listen, or_ from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult class ContentApprovalFlow(Flow): @start() def generate_content(self): - # IA gera conteúdo return "Texto de marketing gerado para campanha Q1..." - @listen(generate_content) @human_feedback( message="Por favor, revise este conteúdo para conformidade com a marca:", emit=["approved", "rejected", "needs_revision"], ) - def review_content(self, content): - return content + @listen(or_("generate_content", "needs_revision")) + def review_content(self): + return "Texto de marketing para revisão..." @listen("approved") def publish_content(self, result: HumanFeedbackResult): @@ -62,10 +61,6 @@ class ContentApprovalFlow(Flow): @listen("rejected") def archive_content(self, result: HumanFeedbackResult): print(f"Conteúdo rejeitado. Motivo: {result.feedback}") - - @listen("needs_revision") - def revise_content(self, result: HumanFeedbackResult): - print(f"Revisão solicitada: {result.feedback}") ``` Para detalhes completos de implementação, consulte o guia [Feedback Humano em Flows](/pt-BR/learn/human-feedback-in-flows). diff --git a/docs/pt-BR/enterprise/guides/deploy-to-amp.mdx b/docs/pt-BR/enterprise/guides/deploy-to-amp.mdx index c6dc35018..7d469b993 100644 --- a/docs/pt-BR/enterprise/guides/deploy-to-amp.mdx +++ b/docs/pt-BR/enterprise/guides/deploy-to-amp.mdx @@ -176,6 +176,11 @@ Você precisa enviar seu crew para um repositório do GitHub. Caso ainda não te ![Definir Variáveis de Ambiente](/images/enterprise/set-env-variables.png) + + Usando pacotes Python privados? Você também precisará adicionar suas credenciais de registro aqui. + Consulte [Registros de Pacotes Privados](/pt-BR/enterprise/guides/private-package-registry) para as variáveis necessárias. + + diff --git a/docs/pt-BR/enterprise/guides/prepare-for-deployment.mdx b/docs/pt-BR/enterprise/guides/prepare-for-deployment.mdx index bf81b8f7a..f22679759 100644 --- a/docs/pt-BR/enterprise/guides/prepare-for-deployment.mdx +++ b/docs/pt-BR/enterprise/guides/prepare-for-deployment.mdx @@ -256,6 +256,12 @@ Antes da implantação, certifique-se de ter: 1. **Chaves de API de LLM** prontas (OpenAI, Anthropic, Google, etc.) 2. **Chaves de API de ferramentas** se estiver usando ferramentas externas (Serper, etc.) + + Se seu projeto depende de pacotes de um **registro PyPI privado**, você também precisará configurar + credenciais de autenticação do registro como variáveis de ambiente. Consulte o guia + [Registros de Pacotes Privados](/pt-BR/enterprise/guides/private-package-registry) para mais detalhes. + + Teste seu projeto localmente com as mesmas variáveis de ambiente antes de implantar para detectar problemas de configuração antecipadamente. diff --git a/docs/pt-BR/enterprise/guides/private-package-registry.mdx b/docs/pt-BR/enterprise/guides/private-package-registry.mdx new file mode 100644 index 000000000..3950ead8d --- /dev/null +++ b/docs/pt-BR/enterprise/guides/private-package-registry.mdx @@ -0,0 +1,263 @@ +--- +title: "Registros de Pacotes Privados" +description: "Instale pacotes Python privados de registros PyPI autenticados no CrewAI AMP" +icon: "lock" +mode: "wide" +--- + + + Este guia aborda como configurar seu projeto CrewAI para instalar pacotes Python + de registros PyPI privados (Azure DevOps Artifacts, GitHub Packages, GitLab, AWS CodeArtifact, etc.) + ao implantar no CrewAI AMP. + + +## Quando Você Precisa Disso + +Se seu projeto depende de pacotes Python internos ou proprietários hospedados em um registro privado +em vez do PyPI público, você precisará: + +1. Informar ao UV **onde** encontrar o pacote (uma URL de index) +2. Informar ao UV **quais** pacotes vêm desse index (um mapeamento de source) +3. Fornecer **credenciais** para que o UV possa autenticar durante a instalação + +O CrewAI AMP usa [UV](https://docs.astral.sh/uv/) para resolução e instalação de dependências. +O UV suporta registros privados autenticados por meio da configuração do `pyproject.toml` combinada +com variáveis de ambiente para credenciais. + +## Passo 1: Configurar o pyproject.toml + +Três elementos trabalham juntos no seu `pyproject.toml`: + +### 1a. Declarar a dependência + +Adicione o pacote privado ao seu `[project.dependencies]` como qualquer outra dependência: + +```toml +[project] +dependencies = [ + "crewai[tools]>=0.100.1,<1.0.0", + "my-private-package>=1.2.0", +] +``` + +### 1b. Definir o index + +Registre seu registro privado como um index nomeado em `[[tool.uv.index]]`: + +```toml +[[tool.uv.index]] +name = "my-private-registry" +url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/" +explicit = true +``` + + + O campo `name` é importante — o UV o utiliza para construir os nomes das variáveis de ambiente + para autenticação (veja o [Passo 2](#passo-2-configurar-credenciais-de-autenticação) abaixo). + + Definir `explicit = true` significa que o UV não consultará esse index para todos os pacotes — apenas + os que você mapear explicitamente em `[tool.uv.sources]`. Isso evita consultas desnecessárias + ao seu registro privado e protege contra ataques de confusão de dependências. + + +### 1c. Mapear o pacote para o index + +Informe ao UV quais pacotes devem ser resolvidos a partir do seu index privado usando `[tool.uv.sources]`: + +```toml +[tool.uv.sources] +my-private-package = { index = "my-private-registry" } +``` + +### Exemplo completo + +```toml +[project] +name = "my-crew-project" +version = "0.1.0" +requires-python = ">=3.10,<=3.13" +dependencies = [ + "crewai[tools]>=0.100.1,<1.0.0", + "my-private-package>=1.2.0", +] + +[tool.crewai] +type = "crew" + +[[tool.uv.index]] +name = "my-private-registry" +url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/" +explicit = true + +[tool.uv.sources] +my-private-package = { index = "my-private-registry" } +``` + +Após atualizar o `pyproject.toml`, regenere seu arquivo lock: + +```bash +uv lock +``` + + + Sempre faça commit do `uv.lock` atualizado junto com as alterações no `pyproject.toml`. + O arquivo lock é obrigatório para implantação — veja [Preparar para Implantação](/pt-BR/enterprise/guides/prepare-for-deployment). + + +## Passo 2: Configurar Credenciais de Autenticação + +O UV autentica em indexes privados usando variáveis de ambiente que seguem uma convenção de nomenclatura +baseada no nome do index que você definiu no `pyproject.toml`: + +``` +UV_INDEX_{UPPER_NAME}_USERNAME +UV_INDEX_{UPPER_NAME}_PASSWORD +``` + +Onde `{UPPER_NAME}` é o nome do seu index convertido para **maiúsculas** com **hifens substituídos por underscores**. + +Por exemplo, um index chamado `my-private-registry` usa: + +| Variável | Valor | +|----------|-------| +| `UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME` | Seu nome de usuário ou nome do token do registro | +| `UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD` | Sua senha ou token/PAT do registro | + + + Essas variáveis de ambiente **devem** ser adicionadas pelas configurações de **Variáveis de Ambiente** do CrewAI AMP — + globalmente ou no nível da implantação. Elas não podem ser definidas em arquivos `.env` ou codificadas no seu projeto. + + Veja [Configurar Variáveis de Ambiente no AMP](#configurar-variáveis-de-ambiente-no-amp) abaixo. + + +## Referência de Provedores de Registro + +A tabela abaixo mostra o formato da URL de index e os valores de credenciais para provedores de registro comuns. +Substitua os valores de exemplo pelos detalhes reais da sua organização e feed. + +| Provedor | URL do Index | Usuário | Senha | +|----------|-------------|---------|-------| +| **Azure DevOps Artifacts** | `https://pkgs.dev.azure.com/{org}/_packaging/{feed}/pypi/simple/` | Qualquer string não vazia (ex: `token`) | Personal Access Token (PAT) com escopo Packaging Read | +| **GitHub Packages** | `https://pypi.pkg.github.com/{owner}/simple/` | Nome de usuário do GitHub | Personal Access Token (classic) com escopo `read:packages` | +| **GitLab Package Registry** | `https://gitlab.com/api/v4/projects/{project_id}/packages/pypi/simple/` | `__token__` | Project ou Personal Access Token com escopo `read_api` | +| **AWS CodeArtifact** | Use a URL de `aws codeartifact get-repository-endpoint` | `aws` | Token de `aws codeartifact get-authorization-token` | +| **Google Artifact Registry** | `https://{region}-python.pkg.dev/{project}/{repo}/simple/` | `_json_key_base64` | Chave de conta de serviço codificada em Base64 | +| **JFrog Artifactory** | `https://{instance}.jfrog.io/artifactory/api/pypi/{repo}/simple/` | Nome de usuário ou email | Chave API ou token de identidade | +| **Auto-hospedado (devpi, Nexus, etc.)** | URL da API simple do seu registro | Nome de usuário do registro | Senha do registro | + + + Para **AWS CodeArtifact**, o token de autorização expira periodicamente. + Você precisará atualizar o valor de `UV_INDEX_*_PASSWORD` quando ele expirar. + Considere automatizar isso no seu pipeline de CI/CD. + + +## Configurar Variáveis de Ambiente no AMP + +As credenciais do registro privado devem ser configuradas como variáveis de ambiente no CrewAI AMP. +Você tem duas opções: + + + + 1. Faça login no [CrewAI AMP](https://app.crewai.com) + 2. Navegue até sua automação + 3. Abra a aba **Environment Variables** + 4. Adicione cada variável (`UV_INDEX_*_USERNAME` e `UV_INDEX_*_PASSWORD`) com seu valor + + Veja o passo [Deploy para AMP — Definir Variáveis de Ambiente](/pt-BR/enterprise/guides/deploy-to-amp#definir-as-variáveis-de-ambiente) para detalhes. + + + Adicione as variáveis ao seu arquivo `.env` local antes de executar `crewai deploy create`. + A CLI as transferirá com segurança para a plataforma: + + ```bash + # .env + OPENAI_API_KEY=sk-... + UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token + UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat-here + ``` + + ```bash + crewai deploy create + ``` + + + + + **Nunca** faça commit de credenciais no seu repositório. Use variáveis de ambiente do AMP para todos os segredos. + O arquivo `.env` deve estar listado no `.gitignore`. + + +Para atualizar credenciais em uma implantação existente, veja [Atualizar Seu Crew — Variáveis de Ambiente](/pt-BR/enterprise/guides/update-crew). + +## Como Tudo se Conecta + +Quando o CrewAI AMP faz o build da sua automação, o fluxo de resolução funciona assim: + + + + O AMP busca seu repositório e lê o `pyproject.toml` e o `uv.lock`. + + + O UV lê `[tool.uv.sources]` para determinar de qual index cada pacote deve vir. + + + Para cada index privado, o UV busca `UV_INDEX_{NAME}_USERNAME` e `UV_INDEX_{NAME}_PASSWORD` + nas variáveis de ambiente que você configurou no AMP. + + + O UV baixa e instala todos os pacotes — tanto públicos (do PyPI) quanto privados (do seu registro). + + + Seu crew ou flow inicia com todas as dependências disponíveis. + + + +## Solução de Problemas + +### Erros de Autenticação Durante o Build + +**Sintoma**: Build falha com `401 Unauthorized` ou `403 Forbidden` ao resolver um pacote privado. + +**Verifique**: +- Os nomes das variáveis de ambiente `UV_INDEX_*` correspondem exatamente ao nome do seu index (maiúsculas, hifens -> underscores) +- As credenciais estão definidas nas variáveis de ambiente do AMP, não apenas em um `.env` local +- Seu token/PAT tem as permissões de leitura necessárias para o feed de pacotes +- O token não expirou (especialmente relevante para AWS CodeArtifact) + +### Pacote Não Encontrado + +**Sintoma**: `No matching distribution found for my-private-package`. + +**Verifique**: +- A URL do index no `pyproject.toml` termina com `/simple/` +- A entrada `[tool.uv.sources]` mapeia o nome correto do pacote para o nome correto do index +- O pacote está realmente publicado no seu registro privado +- Execute `uv lock` localmente com as mesmas credenciais para verificar se a resolução funciona + +### Conflitos no Arquivo Lock + +**Sintoma**: `uv lock` falha ou produz resultados inesperados após adicionar um index privado. + +**Solução**: Defina as credenciais localmente e regenere: + +```bash +export UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token +export UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat +uv lock +``` + +Em seguida, faça commit do `uv.lock` atualizado. + +## Guias Relacionados + + + + Verifique a estrutura do projeto e as dependências antes de implantar. + + + Implante seu crew ou flow e configure variáveis de ambiente. + + + Atualize variáveis de ambiente e envie alterações para uma implantação em execução. + + diff --git a/docs/pt-BR/guides/migration/migrating-from-langgraph.mdx b/docs/pt-BR/guides/migration/migrating-from-langgraph.mdx new file mode 100644 index 000000000..4889c91f6 --- /dev/null +++ b/docs/pt-BR/guides/migration/migrating-from-langgraph.mdx @@ -0,0 +1,518 @@ +--- +title: "Migrando do LangGraph para o CrewAI: um guia prático para engenheiros" +description: Se você já construiu com LangGraph, saiba como portar rapidamente seus projetos para o CrewAI +icon: switch +mode: "wide" +--- + +Você construiu agentes com LangGraph. Já lutou com o `StateGraph`, ligou arestas condicionais e depurou dicionários de estado às 2 da manhã. Funciona — mas, em algum momento, você começou a se perguntar se existe um caminho melhor para produção. + +Existe. **CrewAI Flows** entrega o mesmo poder — orquestração orientada a eventos, roteamento condicional, estado compartilhado — com muito menos boilerplate e um modelo mental que se alinha a como você realmente pensa sobre fluxos de trabalho de IA em múltiplas etapas. + +Este artigo apresenta os conceitos principais lado a lado, mostra comparações reais de código e demonstra por que o CrewAI Flows é o framework que você vai querer usar a seguir. + +--- + +## A Mudança de Modelo Mental + +LangGraph pede que você pense em **grafos**: nós, arestas e dicionários de estado. Todo workflow é um grafo direcionado em que você conecta explicitamente as transições entre as etapas de computação. É poderoso, mas a abstração traz overhead — especialmente quando o seu fluxo é fundamentalmente sequencial com alguns pontos de decisão. + +CrewAI Flows pede que você pense em **eventos**: métodos que iniciam, métodos que escutam resultados e métodos que roteiam a execução. A topologia do workflow emerge de anotações com decorators, em vez de construção explícita do grafo. Isso não é apenas açúcar sintático — muda como você projeta, lê e mantém seus pipelines. + +Veja o mapeamento principal: + +| Conceito no LangGraph | Equivalente no CrewAI Flows | +| --- | --- | +| `StateGraph` class | `Flow` class | +| `add_node()` | Methods decorated with `@start`, `@listen` | +| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators | +| `TypedDict` state | Pydantic `BaseModel` state | +| `START` / `END` constants | `@start()` decorator / natural method return | +| `graph.compile()` | `flow.kickoff()` | +| Checkpointer / persistence | Built-in memory (LanceDB-backed) | + +Vamos ver como isso fica na prática. + +--- + +## Demo 1: Um Pipeline Sequencial Simples + +Imagine que você está construindo um pipeline que recebe um tema, pesquisa, escreve um resumo e formata a saída. Veja como cada framework lida com isso. + +### Abordagem com LangGraph + +```python +from typing import TypedDict +from langgraph.graph import StateGraph, START, END + +class ResearchState(TypedDict): + topic: str + raw_research: str + summary: str + formatted_output: str + +def research_topic(state: ResearchState) -> dict: + # Call an LLM or search API + result = llm.invoke(f"Research the topic: {state['topic']}") + return {"raw_research": result} + +def write_summary(state: ResearchState) -> dict: + result = llm.invoke( + f"Summarize this research:\n{state['raw_research']}" + ) + return {"summary": result} + +def format_output(state: ResearchState) -> dict: + result = llm.invoke( + f"Format this summary as a polished article section:\n{state['summary']}" + ) + return {"formatted_output": result} + +# Build the graph +graph = StateGraph(ResearchState) +graph.add_node("research", research_topic) +graph.add_node("summarize", write_summary) +graph.add_node("format", format_output) + +graph.add_edge(START, "research") +graph.add_edge("research", "summarize") +graph.add_edge("summarize", "format") +graph.add_edge("format", END) + +# Compile and run +app = graph.compile() +result = app.invoke({"topic": "quantum computing advances in 2026"}) +print(result["formatted_output"]) +``` + +Você define funções, registra-as como nós e conecta manualmente cada transição. Para uma sequência simples como essa, há muita cerimônia. + +### Abordagem com CrewAI Flows + +```python +from crewai import LLM, Agent, Crew, Process, Task +from crewai.flow.flow import Flow, listen, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class ResearchState(BaseModel): + topic: str = "" + raw_research: str = "" + summary: str = "" + formatted_output: str = "" + +class ResearchFlow(Flow[ResearchState]): + @start() + def research_topic(self): + # Option 1: Direct LLM call + result = llm.call(f"Research the topic: {self.state.topic}") + self.state.raw_research = result + return result + + @listen(research_topic) + def write_summary(self, research_output): + # Option 2: A single agent + summarizer = Agent( + role="Research Summarizer", + goal="Produce concise, accurate summaries of research content", + backstory="You are an expert at distilling complex research into clear, " + "digestible summaries.", + llm=llm, + verbose=True, + ) + result = summarizer.kickoff( + f"Summarize this research:\n{self.state.raw_research}" + ) + self.state.summary = str(result) + return self.state.summary + + @listen(write_summary) + def format_output(self, summary_output): + # Option 3: a complete crew (with one or more agents) + formatter = Agent( + role="Content Formatter", + goal="Transform research summaries into polished, publication-ready article sections", + backstory="You are a skilled editor with expertise in structuring and " + "presenting technical content for a general audience.", + llm=llm, + verbose=True, + ) + format_task = Task( + description=f"Format this summary as a polished article section:\n{self.state.summary}", + expected_output="A well-structured, polished article section ready for publication.", + agent=formatter, + ) + crew = Crew( + agents=[formatter], + tasks=[format_task], + process=Process.sequential, + verbose=True, + ) + result = crew.kickoff() + self.state.formatted_output = str(result) + return self.state.formatted_output + +# Run the flow +flow = ResearchFlow() +flow.state.topic = "quantum computing advances in 2026" +result = flow.kickoff() +print(flow.state.formatted_output) + +``` + +Repare a diferença: nada de construção de grafo, de ligação de arestas, nem de etapa de compilação. A ordem de execução é declarada exatamente onde a lógica vive. `@start()` marca o ponto de entrada, e `@listen(method_name)` encadeia as etapas. O estado é um modelo Pydantic de verdade, com segurança de tipos, validação e auto-complete na IDE. + +--- + +## Demo 2: Roteamento Condicional + +Aqui é que fica interessante. Digamos que você está construindo um pipeline de conteúdo que roteia para diferentes caminhos de processamento com base no tipo de conteúdo detectado. + +### Abordagem com LangGraph + +```python +from typing import TypedDict, Literal +from langgraph.graph import StateGraph, START, END + +class ContentState(TypedDict): + input_text: str + content_type: str + result: str + +def classify_content(state: ContentState) -> dict: + content_type = llm.invoke( + f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}" + ) + return {"content_type": content_type.strip().lower()} + +def process_technical(state: ContentState) -> dict: + result = llm.invoke(f"Process as technical doc:\n{state['input_text']}") + return {"result": result} + +def process_creative(state: ContentState) -> dict: + result = llm.invoke(f"Process as creative writing:\n{state['input_text']}") + return {"result": result} + +def process_business(state: ContentState) -> dict: + result = llm.invoke(f"Process as business content:\n{state['input_text']}") + return {"result": result} + +# Routing function +def route_content(state: ContentState) -> Literal["technical", "creative", "business"]: + return state["content_type"] + +# Build the graph +graph = StateGraph(ContentState) +graph.add_node("classify", classify_content) +graph.add_node("technical", process_technical) +graph.add_node("creative", process_creative) +graph.add_node("business", process_business) + +graph.add_edge(START, "classify") +graph.add_conditional_edges( + "classify", + route_content, + { + "technical": "technical", + "creative": "creative", + "business": "business", + } +) +graph.add_edge("technical", END) +graph.add_edge("creative", END) +graph.add_edge("business", END) + +app = graph.compile() +result = app.invoke({"input_text": "Explain how TCP handshakes work"}) +``` + +Você precisa de uma função de roteamento separada, de um mapeamento explícito de arestas condicionais e de arestas de término para cada ramificação. A lógica de roteamento fica desacoplada do nó que produz a decisão. + +### Abordagem com CrewAI Flows + +```python +from crewai import LLM, Agent +from crewai.flow.flow import Flow, listen, router, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class ContentState(BaseModel): + input_text: str = "" + content_type: str = "" + result: str = "" + +class ContentFlow(Flow[ContentState]): + @start() + def classify_content(self): + self.state.content_type = ( + llm.call( + f"Classify this content as 'technical', 'creative', or 'business':\n" + f"{self.state.input_text}" + ) + .strip() + .lower() + ) + return self.state.content_type + + @router(classify_content) + def route_content(self, classification): + if classification == "technical": + return "process_technical" + elif classification == "creative": + return "process_creative" + else: + return "process_business" + + @listen("process_technical") + def handle_technical(self): + agent = Agent( + role="Technical Writer", + goal="Produce clear, accurate technical documentation", + backstory="You are an expert technical writer who specializes in " + "explaining complex technical concepts precisely.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as technical doc:\n{self.state.input_text}") + ) + + @listen("process_creative") + def handle_creative(self): + agent = Agent( + role="Creative Writer", + goal="Craft engaging and imaginative creative content", + backstory="You are a talented creative writer with a flair for " + "compelling storytelling and vivid expression.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as creative writing:\n{self.state.input_text}") + ) + + @listen("process_business") + def handle_business(self): + agent = Agent( + role="Business Writer", + goal="Produce professional, results-oriented business content", + backstory="You are an experienced business writer who communicates " + "strategy and value clearly to professional audiences.", + llm=llm, + verbose=True, + ) + self.state.result = str( + agent.kickoff(f"Process as business content:\n{self.state.input_text}") + ) + +flow = ContentFlow() +flow.state.input_text = "Explain how TCP handshakes work" +flow.kickoff() +print(flow.state.result) + +``` + +O decorator `@router()` transforma um método em um ponto de decisão. Ele retorna uma string que corresponde a um listener — sem dicionários de mapeamento, sem funções de roteamento separadas. A lógica de ramificação parece um `if` em Python porque *é* um. + +--- + +## Demo 3: Integrando Crews de Agentes de IA em Flows + +É aqui que o verdadeiro poder do CrewAI aparece. Flows não servem apenas para encadear chamadas de LLM — elas orquestram **Crews** completas de agentes autônomos. Isso é algo para o qual o LangGraph simplesmente não tem um equivalente nativo. + +```python +from crewai import Agent, Task, Crew +from crewai.flow.flow import Flow, listen, start +from pydantic import BaseModel + +class ArticleState(BaseModel): + topic: str = "" + research: str = "" + draft: str = "" + final_article: str = "" + +class ArticleFlow(Flow[ArticleState]): + + @start() + def run_research_crew(self): + """A full Crew of agents handles research.""" + researcher = Agent( + role="Senior Research Analyst", + goal=f"Produce comprehensive research on: {self.state.topic}", + backstory="You're a veteran analyst known for thorough, " + "well-sourced research reports.", + llm="gpt-4o" + ) + + research_task = Task( + description=f"Research '{self.state.topic}' thoroughly. " + "Cover key trends, data points, and expert opinions.", + expected_output="A detailed research brief with sources.", + agent=researcher + ) + + crew = Crew(agents=[researcher], tasks=[research_task]) + result = crew.kickoff() + self.state.research = result.raw + return result.raw + + @listen(run_research_crew) + def run_writing_crew(self, research_output): + """A different Crew handles writing.""" + writer = Agent( + role="Technical Writer", + goal="Write a compelling article based on provided research.", + backstory="You turn complex research into engaging, clear prose.", + llm="gpt-4o" + ) + + editor = Agent( + role="Senior Editor", + goal="Review and polish articles for publication quality.", + backstory="20 years of editorial experience at top tech publications.", + llm="gpt-4o" + ) + + write_task = Task( + description=f"Write an article based on this research:\n{self.state.research}", + expected_output="A well-structured draft article.", + agent=writer + ) + + edit_task = Task( + description="Review, fact-check, and polish the draft article.", + expected_output="A publication-ready article.", + agent=editor + ) + + crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task]) + result = crew.kickoff() + self.state.final_article = result.raw + return result.raw + +# Run the full pipeline +flow = ArticleFlow() +flow.state.topic = "The Future of Edge AI" +flow.kickoff() +print(flow.state.final_article) +``` + +Este é o insight-chave: **Flows fornecem a camada de orquestração, e Crews fornecem a camada de inteligência.** Cada etapa em um Flow pode subir uma equipe completa de agentes colaborativos, cada um com seus próprios papéis, objetivos e ferramentas. Você obtém fluxo de controle estruturado e previsível *e* colaboração autônoma de agentes — o melhor dos dois mundos. + +No LangGraph, alcançar algo similar significa implementar manualmente protocolos de comunicação entre agentes, loops de chamada de ferramentas e lógica de delegação dentro das funções dos nós. É possível, mas é encanamento que você constrói do zero todas as vezes. + +--- + +## Demo 4: Execução Paralela e Sincronização + +Pipelines do mundo real frequentemente precisam dividir o trabalho e juntar os resultados. O CrewAI Flows lida com isso de forma elegante com os operadores `and_` e `or_`. + +```python +from crewai import LLM +from crewai.flow.flow import Flow, and_, listen, start +from pydantic import BaseModel + +llm = LLM(model="openai/gpt-5.2") + +class AnalysisState(BaseModel): + topic: str = "" + market_data: str = "" + tech_analysis: str = "" + competitor_intel: str = "" + final_report: str = "" + +class ParallelAnalysisFlow(Flow[AnalysisState]): + @start() + def start_method(self): + pass + + @listen(start_method) + def gather_market_data(self): + # Your agentic or deterministic code + pass + + @listen(start_method) + def run_tech_analysis(self): + # Your agentic or deterministic code + pass + + @listen(start_method) + def gather_competitor_intel(self): + # Your agentic or deterministic code + pass + + @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel)) + def synthesize_report(self): + # Your agentic or deterministic code + pass + +flow = ParallelAnalysisFlow() +flow.state.topic = "AI-powered developer tools" +flow.kickoff() + +``` + +Vários decorators `@start()` disparam em paralelo. O combinador `and_()` no decorator `@listen` garante que `synthesize_report` só execute depois que *todos os três* métodos upstream forem concluídos. Também existe `or_()` para quando você quer prosseguir assim que *qualquer* tarefa upstream terminar. + +No LangGraph, você precisaria construir um padrão fan-out/fan-in com ramificações paralelas, um nó de sincronização e uma mesclagem de estado cuidadosa — tudo conectado explicitamente por arestas. + +--- + +## Por que CrewAI Flows em Produção + +Além de uma sintaxe mais limpa, Flows entrega várias vantagens críticas para produção: + +**Persistência de estado integrada.** O estado do Flow é respaldado pelo LanceDB, o que significa que seus workflows podem sobreviver a falhas, ser retomados e acumular conhecimento entre execuções. No LangGraph, você precisa configurar um checkpointer separado. + +**Gerenciamento de estado com segurança de tipos.** Modelos Pydantic oferecem validação, serialização e suporte de IDE prontos para uso. Estados `TypedDict` do LangGraph não validam em runtime. + +**Orquestração de agentes de primeira classe.** Crews são um primitivo nativo. Você define agentes com papéis, objetivos, histórias e ferramentas — e eles colaboram de forma autônoma dentro do envelope estruturado de um Flow. Não é preciso reinventar a coordenação multiagente. + +**Modelo mental mais simples.** Decorators declaram intenção. `@start` significa "comece aqui". `@listen(x)` significa "execute depois de x". `@router(x)` significa "decida para onde ir depois de x". O código lê como o workflow que ele descreve. + +**Integração com CLI.** Execute flows com `crewai run`. Sem etapa de compilação separada, sem serialização de grafo. Seu Flow é uma classe Python, e ele roda como tal. + +--- + +## Cheat Sheet de Migração + +Se você está com uma base de código LangGraph e quer migrar para o CrewAI Flows, aqui vai um guia prático de conversão: + +1. **Mapeie seu estado.** Converta seu `TypedDict` para um `BaseModel` do Pydantic. Adicione valores padrão para todos os campos. +2. **Converta nós em métodos.** Cada função de `add_node` vira um método na sua subclasse de `Flow`. Substitua leituras `state["field"]` por `self.state.field`. +3. **Substitua arestas por decorators.** `add_edge(START, "first_node")` vira `@start()` no primeiro método. A sequência `add_edge("a", "b")` vira `@listen(a)` no método `b`. +4. **Substitua arestas condicionais por `@router`.** A função de roteamento e o mapeamento do `add_conditional_edges()` viram um único método `@router()` que retorna a string de rota. +5. **Troque compile + invoke por kickoff.** Remova `graph.compile()`. Chame `flow.kickoff()`. +6. **Considere onde as Crews se encaixam.** Qualquer nó com lógica complexa de agentes em múltiplas etapas é um candidato a extração para uma Crew. É aqui que você verá a maior melhoria de qualidade. + +--- + +## Primeiros Passos + +Instale o CrewAI e crie o scaffold de um novo projeto Flow: + +```bash +pip install crewai +crewai create flow my_first_flow +cd my_first_flow +``` + +Isso gera uma estrutura de projeto com uma classe Flow pronta para edição, arquivos de configuração e um `pyproject.toml` com `type = "flow"` já definido. Execute com: + +```bash +crewai run +``` + +A partir daí, adicione seus agentes, conecte seus listeners e publique. + +--- + +## Considerações Finais + +O LangGraph ensinou ao ecossistema que workflows de IA precisam de estrutura. Essa foi uma lição importante. Mas o CrewAI Flows pega essa lição e a entrega de um jeito mais rápido de escrever, mais fácil de ler e mais poderoso em produção — especialmente quando seus workflows envolvem múltiplos agentes colaborando. + +Se você está construindo algo além de uma cadeia de agente único, dê uma olhada séria no Flows. O modelo baseado em decorators, a integração nativa com Crews e o gerenciamento de estado embutido significam menos tempo com encanamento e mais tempo nos problemas que importam. + +Comece com `crewai create flow`. Você não vai olhar para trás. diff --git a/docs/pt-BR/learn/human-feedback-in-flows.mdx b/docs/pt-BR/learn/human-feedback-in-flows.mdx index c847bf31a..ad4d068cd 100644 --- a/docs/pt-BR/learn/human-feedback-in-flows.mdx +++ b/docs/pt-BR/learn/human-feedback-in-flows.mdx @@ -73,6 +73,8 @@ Quando este flow é executado, ele irá: | `default_outcome` | `str` | Não | Outcome a usar se nenhum feedback for fornecido. Deve estar em `emit` | | `metadata` | `dict` | Não | Dados adicionais para integrações enterprise | | `provider` | `HumanFeedbackProvider` | Não | Provider customizado para feedback assíncrono/não-bloqueante. Veja [Feedback Humano Assíncrono](#feedback-humano-assíncrono-não-bloqueante) | +| `learn` | `bool` | Não | Habilitar aprendizado HITL: destila lições do feedback e pré-revisa saídas futuras. Padrão `False`. Veja [Aprendendo com Feedback](#aprendendo-com-feedback) | +| `learn_limit` | `int` | Não | Máximo de lições passadas para recuperar na pré-revisão. Padrão `5` | ### Uso Básico (Sem Roteamento) @@ -96,33 +98,43 @@ def handle_feedback(self, result): Quando você especifica `emit`, o decorador se torna um roteador. O feedback livre do humano é interpretado por um LLM e mapeado para um dos outcomes especificados: ```python Code -@start() -@human_feedback( - message="Você aprova este conteúdo para publicação?", - emit=["approved", "rejected", "needs_revision"], - llm="gpt-4o-mini", - default_outcome="needs_revision", -) -def review_content(self): - return "Rascunho do post do blog aqui..." +from crewai.flow.flow import Flow, start, listen, or_ +from crewai.flow.human_feedback import human_feedback -@listen("approved") -def publish(self, result): - print(f"Publicando! Usuário disse: {result.feedback}") +class ReviewFlow(Flow): + @start() + def generate_content(self): + return "Rascunho do post do blog aqui..." -@listen("rejected") -def discard(self, result): - print(f"Descartando. Motivo: {result.feedback}") + @human_feedback( + message="Você aprova este conteúdo para publicação?", + emit=["approved", "rejected", "needs_revision"], + llm="gpt-4o-mini", + default_outcome="needs_revision", + ) + @listen(or_("generate_content", "needs_revision")) + def review_content(self): + return "Rascunho do post do blog aqui..." -@listen("needs_revision") -def revise(self, result): - print(f"Revisando baseado em: {result.feedback}") + @listen("approved") + def publish(self, result): + print(f"Publicando! Usuário disse: {result.feedback}") + + @listen("rejected") + def discard(self, result): + print(f"Descartando. Motivo: {result.feedback}") ``` +Quando o humano diz algo como "precisa de mais detalhes", o LLM mapeia para `"needs_revision"`, que dispara `review_content` novamente via `or_()` — criando um loop de revisão. O loop continua até que o outcome seja `"approved"` ou `"rejected"`. + O LLM usa saídas estruturadas (function calling) quando disponível para garantir que a resposta seja um dos seus outcomes especificados. Isso torna o roteamento confiável e previsível. + +Um método `@start()` só executa uma vez no início do flow. Se você precisa de um loop de revisão, separe o método start do método de revisão e use `@listen(or_("trigger", "revision_outcome"))` no método de revisão para habilitar o self-loop. + + ## HumanFeedbackResult O dataclass `HumanFeedbackResult` contém todas as informações sobre uma interação de feedback humano: @@ -191,116 +203,162 @@ Aqui está um exemplo completo implementando um fluxo de revisão e aprovação ```python Code -from crewai.flow.flow import Flow, start, listen +from crewai.flow.flow import Flow, start, listen, or_ from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult from pydantic import BaseModel class ContentState(BaseModel): - topic: str = "" draft: str = "" - final_content: str = "" revision_count: int = 0 + status: str = "pending" class ContentApprovalFlow(Flow[ContentState]): - """Um flow que gera conteúdo e obtém aprovação humana.""" + """Um flow que gera conteúdo e faz loop até o humano aprovar.""" @start() - def get_topic(self): - self.state.topic = input("Sobre qual tópico devo escrever? ") - return self.state.topic - - @listen(get_topic) - def generate_draft(self, topic): - # Em uso real, isso chamaria um LLM - self.state.draft = f"# {topic}\n\nEste é um rascunho sobre {topic}..." + def generate_draft(self): + self.state.draft = "# IA Segura\n\nEste é um rascunho sobre IA Segura..." return self.state.draft - @listen(generate_draft) @human_feedback( - message="Por favor, revise este rascunho. Responda 'approved', 'rejected', ou forneça feedback de revisão:", + message="Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar:", emit=["approved", "rejected", "needs_revision"], llm="gpt-4o-mini", default_outcome="needs_revision", ) - def review_draft(self, draft): - return draft + @listen(or_("generate_draft", "needs_revision")) + def review_draft(self): + self.state.revision_count += 1 + return f"{self.state.draft} (v{self.state.revision_count})" @listen("approved") def publish_content(self, result: HumanFeedbackResult): - self.state.final_content = result.output - print("\n✅ Conteúdo aprovado e publicado!") - print(f"Comentário do revisor: {result.feedback}") + self.state.status = "published" + print(f"Conteúdo aprovado e publicado! Revisor disse: {result.feedback}") return "published" @listen("rejected") def handle_rejection(self, result: HumanFeedbackResult): - print("\n❌ Conteúdo rejeitado") - print(f"Motivo: {result.feedback}") + self.state.status = "rejected" + print(f"Conteúdo rejeitado. Motivo: {result.feedback}") return "rejected" - @listen("needs_revision") - def revise_content(self, result: HumanFeedbackResult): - self.state.revision_count += 1 - print(f"\n📝 Revisão #{self.state.revision_count} solicitada") - print(f"Feedback: {result.feedback}") - # Em um flow real, você pode voltar para generate_draft - # Para este exemplo, apenas reconhecemos - return "revision_requested" - - -# Executar o flow flow = ContentApprovalFlow() result = flow.kickoff() -print(f"\nFlow concluído. Revisões solicitadas: {flow.state.revision_count}") +print(f"\nFlow finalizado. Status: {flow.state.status}, Revisões: {flow.state.revision_count}") ``` ```text Output -Sobre qual tópico devo escrever? Segurança em IA +================================================== +OUTPUT FOR REVIEW: +================================================== +# IA Segura + +Este é um rascunho sobre IA Segura... (v1) +================================================== + +Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar: +(Press Enter to skip, or type your feedback) + +Your feedback: Preciso de mais detalhes sobre segurança em IA. ================================================== OUTPUT FOR REVIEW: ================================================== -# Segurança em IA +# IA Segura -Este é um rascunho sobre Segurança em IA... +Este é um rascunho sobre IA Segura... (v2) ================================================== -Por favor, revise este rascunho. Responda 'approved', 'rejected', ou forneça feedback de revisão: +Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar: (Press Enter to skip, or type your feedback) Your feedback: Parece bom, aprovado! -✅ Conteúdo aprovado e publicado! -Comentário do revisor: Parece bom, aprovado! +Conteúdo aprovado e publicado! Revisor disse: Parece bom, aprovado! -Flow concluído. Revisões solicitadas: 0 +Flow finalizado. Status: published, Revisões: 2 ``` ## Combinando com Outros Decoradores -O decorador `@human_feedback` funciona com outros decoradores de flow. Coloque-o como o decorador mais interno (mais próximo da função): +O decorador `@human_feedback` funciona com `@start()`, `@listen()` e `or_()`. Ambas as ordens de decoradores funcionam — o framework propaga atributos em ambas as direções — mas os padrões recomendados são: ```python Code -# Correto: @human_feedback é o mais interno (mais próximo da função) +# Revisão única no início do flow (sem self-loop) @start() -@human_feedback(message="Revise isto:") +@human_feedback(message="Revise isto:", emit=["approved", "rejected"], llm="gpt-4o-mini") def my_start_method(self): return "content" +# Revisão linear em um listener (sem self-loop) @listen(other_method) -@human_feedback(message="Revise isto também:") +@human_feedback(message="Revise isto também:", emit=["good", "bad"], llm="gpt-4o-mini") def my_listener(self, data): return f"processed: {data}" + +# Self-loop: revisão que pode voltar para revisões +@human_feedback(message="Aprovar ou revisar?", emit=["approved", "revise"], llm="gpt-4o-mini") +@listen(or_("upstream_method", "revise")) +def review_with_loop(self): + return "content for review" ``` - -Coloque `@human_feedback` como o decorador mais interno (último/mais próximo da função) para que ele envolva o método diretamente e possa capturar o valor de retorno antes de passar para o sistema de flow. - +### Padrão de self-loop + +Para criar um loop de revisão, o método de revisão deve escutar **ambos** um gatilho upstream e seu próprio outcome de revisão usando `or_()`: + +```python Code +@start() +def generate(self): + return "initial draft" + +@human_feedback( + message="Aprovar ou solicitar alterações?", + emit=["revise", "approved"], + llm="gpt-4o-mini", + default_outcome="approved", +) +@listen(or_("generate", "revise")) +def review(self): + return "content" + +@listen("approved") +def publish(self): + return "published" +``` + +Quando o outcome é `"revise"`, o flow roteia de volta para `review` (porque ele escuta `"revise"` via `or_()`). Quando o outcome é `"approved"`, o flow continua para `publish`. Isso funciona porque o engine de flow isenta roteadores da regra "fire once", permitindo que eles re-executem em cada iteração do loop. + +### Roteadores encadeados + +Um listener disparado pelo outcome de um roteador pode ser ele mesmo um roteador: + +```python Code +@start() +@human_feedback(message="Primeira revisão:", emit=["approved", "rejected"], llm="gpt-4o-mini") +def draft(self): + return "draft content" + +@listen("approved") +@human_feedback(message="Revisão final:", emit=["publish", "revise"], llm="gpt-4o-mini") +def final_review(self, prev): + return "final content" + +@listen("publish") +def on_publish(self, prev): + return "published" +``` + +### Limitações + +- **Métodos `@start()` executam uma vez**: Um método `@start()` não pode fazer self-loop. Se você precisa de um ciclo de revisão, use um método `@start()` separado como ponto de entrada e coloque o `@human_feedback` em um método `@listen()`. +- **Sem `@start()` + `@listen()` no mesmo método**: Esta é uma restrição do framework de Flow. Um método é ou um ponto de início ou um listener, não ambos. ## Melhores Práticas @@ -514,9 +572,9 @@ class ContentPipeline(Flow): @start() @human_feedback( message="Aprova este conteúdo para publicação?", - emit=["approved", "rejected", "needs_revision"], + emit=["approved", "rejected"], llm="gpt-4o-mini", - default_outcome="needs_revision", + default_outcome="rejected", provider=SlackNotificationProvider("#content-reviews"), ) def generate_content(self): @@ -532,11 +590,6 @@ class ContentPipeline(Flow): print(f"Arquivado. Motivo: {result.feedback}") return {"status": "archived"} - @listen("needs_revision") - def queue_revision(self, result): - print(f"Na fila para revisão: {result.feedback}") - return {"status": "revision_needed"} - # Iniciando o flow (vai pausar e aguardar resposta do Slack) def start_content_pipeline(): @@ -576,6 +629,64 @@ Se você está usando um framework web assíncrono (FastAPI, aiohttp, Slack Bolt 5. **Persistência automática**: O estado é automaticamente salvo quando `HumanFeedbackPending` é lançado e usa `SQLiteFlowPersistence` por padrão 6. **Persistência customizada**: Passe uma instância de persistência customizada para `from_pending()` se necessário +## Aprendendo com Feedback + +O parâmetro `learn=True` habilita um ciclo de feedback entre revisores humanos e o sistema de memória. Quando habilitado, o sistema melhora progressivamente suas saídas aprendendo com correções humanas anteriores. + +### Como Funciona + +1. **Após o feedback**: O LLM extrai lições generalizáveis da saída + feedback e as armazena na memória com `source="hitl"`. Se o feedback for apenas aprovação (ex: "parece bom"), nada é armazenado. +2. **Antes da próxima revisão**: Lições HITL passadas são recuperadas da memória e aplicadas pelo LLM para melhorar a saída antes que o humano a veja. + +Com o tempo, o humano vê saídas pré-revisadas progressivamente melhores porque cada correção informa revisões futuras. + +### Exemplo + +```python Code +class ArticleReviewFlow(Flow): + @start() + def generate_article(self): + return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw + + @human_feedback( + message="Revise este rascunho do artigo:", + emit=["approved", "needs_revision"], + llm="gpt-4o-mini", + learn=True, # enable HITL learning + ) + @listen(or_("generate_article", "needs_revision")) + def review_article(self): + return self.last_human_feedback.output if self.last_human_feedback else "article draft" + + @listen("approved") + def publish(self): + print(f"Publishing: {self.last_human_feedback.output}") +``` + +**Primeira execução**: O humano vê a saída bruta e diz "Sempre inclua citações para afirmações factuais." A lição é destilada e armazenada na memória. + +**Segunda execução**: O sistema recupera a lição sobre citações, pré-revisa a saída para adicionar citações e então mostra a versão melhorada. O trabalho do humano muda de "corrigir tudo" para "identificar o que o sistema deixou passar." + +### Configuração + +| Parâmetro | Padrão | Descrição | +|-----------|--------|-----------| +| `learn` | `False` | Habilitar aprendizado HITL | +| `learn_limit` | `5` | Máximo de lições passadas para recuperar na pré-revisão | + +### Decisões de Design Principais + +- **Mesmo LLM para tudo**: O parâmetro `llm` no decorador é compartilhado pelo mapeamento de outcome, destilação de lições e pré-revisão. Não é necessário configurar múltiplos modelos. +- **Saída estruturada**: Tanto a destilação quanto a pré-revisão usam function calling com modelos Pydantic quando o LLM suporta, com fallback para parsing de texto caso contrário. +- **Armazenamento não-bloqueante**: Lições são armazenadas via `remember_many()` que executa em uma thread em segundo plano -- o flow continua imediatamente. +- **Degradação graciosa**: Se o LLM falhar durante a destilação, nada é armazenado. Se falhar durante a pré-revisão, a saída bruta é mostrada. Nenhuma falha bloqueia o flow. +- **Sem escopo/categorias necessários**: Ao armazenar lições, apenas `source` é passado. O pipeline de codificação infere escopo, categorias e importância automaticamente. + + +`learn=True` requer que o Flow tenha memória disponível. Flows obtêm memória automaticamente por padrão, mas se você a desabilitou com `_skip_auto_memory`, o aprendizado HITL será silenciosamente ignorado. + + + ## Documentação Relacionada - [Visão Geral de Flows](/pt-BR/concepts/flows) - Aprenda sobre CrewAI Flows @@ -583,3 +694,4 @@ Se você está usando um framework web assíncrono (FastAPI, aiohttp, Slack Bolt - [Persistência de Flows](/pt-BR/concepts/flows#persistence) - Persistindo estado de flows - [Roteamento com @router](/pt-BR/concepts/flows#router) - Mais sobre roteamento condicional - [Input Humano na Execução](/pt-BR/learn/human-input-on-execution) - Input humano no nível de task +- [Memória](/pt-BR/concepts/memory) - O sistema unificado de memória usado pelo aprendizado HITL diff --git a/docs/pt-BR/learn/llm-connections.mdx b/docs/pt-BR/learn/llm-connections.mdx index 1021050cb..6c09e7c97 100644 --- a/docs/pt-BR/learn/llm-connections.mdx +++ b/docs/pt-BR/learn/llm-connections.mdx @@ -7,7 +7,7 @@ mode: "wide" ## Conecte o CrewAI a LLMs -O CrewAI utiliza o LiteLLM para conectar-se a uma grande variedade de Modelos de Linguagem (LLMs). Essa integração proporciona grande versatilidade, permitindo que você utilize modelos de inúmeros provedores por meio de uma interface simples e unificada. +O CrewAI conecta-se a LLMs por meio de integrações nativas via SDK para os provedores mais populares (OpenAI, Anthropic, Google Gemini, Azure e AWS Bedrock), e usa o LiteLLM como alternativa flexível para todos os demais provedores. Por padrão, o CrewAI usa o modelo `gpt-4o-mini`. Isso é determinado pela variável de ambiente `OPENAI_MODEL_NAME`, que tem como padrão "gpt-4o-mini" se não for definida. @@ -40,6 +40,14 @@ O LiteLLM oferece suporte a uma ampla gama de provedores, incluindo, mas não se Para uma lista completa e sempre atualizada dos provedores suportados, consulte a [documentação de Provedores do LiteLLM](https://docs.litellm.ai/docs/providers). + + Para usar qualquer provedor não coberto por uma integração nativa, adicione o LiteLLM como dependência ao seu projeto: + ```bash + uv add 'crewai[litellm]' + ``` + Provedores nativos (OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock) usam seus próprios extras de SDK — consulte os [Exemplos de Configuração de Provedores](/pt-BR/concepts/llms#exemplos-de-configuração-de-provedores). + + ## Alterando a LLM Para utilizar uma LLM diferente com seus agentes CrewAI, você tem várias opções: diff --git a/docs/pt-BR/tools/automation/composiotool.mdx b/docs/pt-BR/tools/automation/composiotool.mdx index eb0db8578..60cce293a 100644 --- a/docs/pt-BR/tools/automation/composiotool.mdx +++ b/docs/pt-BR/tools/automation/composiotool.mdx @@ -11,84 +11,53 @@ mode: "wide" Composio é uma plataforma de integração que permite conectar seus agentes de IA a mais de 250 ferramentas. Os principais recursos incluem: - **Autenticação de Nível Empresarial**: Suporte integrado para OAuth, Chaves de API, JWT com atualização automática de token -- **Observabilidade Completa**: Logs detalhados de uso das ferramentas, registros de execução, e muito mais +- **Observabilidade Completa**: Logs detalhados de uso das ferramentas, carimbos de data/hora de execução e muito mais ## Instalação Para incorporar as ferramentas Composio em seu projeto, siga as instruções abaixo: ```shell -pip install composio-crewai +pip install composio composio-crewai pip install crewai ``` -Após a conclusão da instalação, execute `composio login` ou exporte sua chave de API do composio como `COMPOSIO_API_KEY`. Obtenha sua chave de API Composio [aqui](https://app.composio.dev) +Após concluir a instalação, defina sua chave de API do Composio como `COMPOSIO_API_KEY`. Obtenha sua chave de API do Composio [aqui](https://platform.composio.dev) ## Exemplo -O exemplo a seguir demonstra como inicializar a ferramenta e executar uma ação do github: +O exemplo a seguir demonstra como inicializar a ferramenta e executar uma ação do GitHub: -1. Inicialize o conjunto de ferramentas Composio +1. Inicialize o Composio com o Provider do CrewAI ```python Code -from composio_crewai import ComposioToolSet, App, Action +from composio_crewai import ComposioProvider +from composio import Composio from crewai import Agent, Task, Crew -toolset = ComposioToolSet() +composio = Composio(provider=ComposioProvider()) ``` -2. Conecte sua conta do GitHub +2. Crie uma nova sessão Composio e recupere as ferramentas -```shell CLI -composio add github -``` -```python Code -request = toolset.initiate_connection(app=App.GITHUB) -print(f"Open this URL to authenticate: {request.redirectUrl}") +```python +session = composio.create( + user_id="your-user-id", + toolkits=["gmail", "github"] # optional, default is all toolkits +) +tools = session.tools() ``` +Leia mais sobre sessões e gerenciamento de usuários [aqui](https://docs.composio.dev/docs/configuring-sessions) -3. Obtenha ferramentas +3. Autenticação manual dos usuários -- Recuperando todas as ferramentas de um app (não recomendado em produção): +O Composio autentica automaticamente os usuários durante a sessão de chat do agente. No entanto, você também pode autenticar o usuário manualmente chamando o método `authorize`. ```python Code -tools = toolset.get_tools(apps=[App.GITHUB]) +connection_request = session.authorize("github") +print(f"Open this URL to authenticate: {connection_request.redirect_url}") ``` -- Filtrando ferramentas com base em tags: -```python Code -tag = "users" - -filtered_action_enums = toolset.find_actions_by_tags( - App.GITHUB, - tags=[tag], -) - -tools = toolset.get_tools(actions=filtered_action_enums) -``` - -- Filtrando ferramentas com base no caso de uso: -```python Code -use_case = "Star a repository on GitHub" - -filtered_action_enums = toolset.find_actions_by_use_case( - App.GITHUB, use_case=use_case, advanced=False -) - -tools = toolset.get_tools(actions=filtered_action_enums) -``` -Defina `advanced` como True para obter ações para casos de uso complexos - -- Usando ferramentas específicas: - -Neste exemplo, usaremos a ação `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` do app GitHub. -```python Code -tools = toolset.get_tools( - actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER] -) -``` -Saiba mais sobre como filtrar ações [aqui](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions) - 4. Defina o agente ```python Code @@ -116,4 +85,4 @@ crew = Crew(agents=[crewai_agent], tasks=[task]) crew.kickoff() ``` -* Uma lista mais detalhada de ferramentas pode ser encontrada [aqui](https://app.composio.dev) \ No newline at end of file +* Uma lista mais detalhada de ferramentas pode ser encontrada [aqui](https://docs.composio.dev/toolkits) \ No newline at end of file diff --git a/lib/crewai-files/pyproject.toml b/lib/crewai-files/pyproject.toml index c53a1c1ff..3ca357622 100644 --- a/lib/crewai-files/pyproject.toml +++ b/lib/crewai-files/pyproject.toml @@ -8,8 +8,8 @@ authors = [ ] requires-python = ">=3.10, <3.14" dependencies = [ - "Pillow~=10.4.0", - "pypdf~=4.0.0", + "Pillow~=12.1.1", + "pypdf~=6.7.5", "python-magic>=0.4.27", "aiocache~=0.12.3", "aiofiles~=24.1.0", diff --git a/lib/crewai-files/src/crewai_files/__init__.py b/lib/crewai-files/src/crewai_files/__init__.py index e457867d1..33db66b65 100644 --- a/lib/crewai-files/src/crewai_files/__init__.py +++ b/lib/crewai-files/src/crewai_files/__init__.py @@ -152,4 +152,4 @@ __all__ = [ "wrap_file_source", ] -__version__ = "1.9.3" +__version__ = "1.10.1" diff --git a/lib/crewai-tools/pyproject.toml b/lib/crewai-tools/pyproject.toml index a683b9967..17b7c71b5 100644 --- a/lib/crewai-tools/pyproject.toml +++ b/lib/crewai-tools/pyproject.toml @@ -8,12 +8,10 @@ authors = [ ] requires-python = ">=3.10, <3.14" dependencies = [ - "lancedb~=0.5.4", "pytube~=15.0.0", "requests~=2.32.5", "docker~=7.1.0", - "crewai==1.9.3", - "lancedb~=0.5.4", + "crewai==1.10.1", "tiktoken~=0.8.0", "beautifulsoup4~=4.13.4", "python-docx~=1.2.0", @@ -110,7 +108,7 @@ stagehand = [ "stagehand>=0.4.1", ] github = [ - "gitpython==3.1.38", + "gitpython>=3.1.41,<4", "PyGithub==1.59.1", ] rag = [ diff --git a/lib/crewai-tools/src/crewai_tools/__init__.py b/lib/crewai-tools/src/crewai_tools/__init__.py index 5aded17a7..a8a469904 100644 --- a/lib/crewai-tools/src/crewai_tools/__init__.py +++ b/lib/crewai-tools/src/crewai_tools/__init__.py @@ -291,4 +291,4 @@ __all__ = [ "ZapierActionTools", ] -__version__ = "1.9.3" +__version__ = "1.10.1" diff --git a/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_search_tool.py index 415810c1b..b24057231 100644 --- a/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_search_tool.py +++ b/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_search_tool.py @@ -10,6 +10,7 @@ from pydantic import BaseModel, Field from pydantic.types import StringConstraints import requests + load_dotenv() diff --git a/lib/crewai-tools/src/crewai_tools/tools/multion_tool/example.py b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/example.py index 28354efa3..00646a0d4 100644 --- a/lib/crewai-tools/src/crewai_tools/tools/multion_tool/example.py +++ b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/example.py @@ -1,7 +1,7 @@ import os from crewai import Agent, Crew, Task -from multion_tool import MultiOnTool # type: ignore[import-not-found] +from multion_tool import MultiOnTool # type: ignore[import-not-found] os.environ["OPENAI_API_KEY"] = "Your Key" diff --git a/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/example.py b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/example.py index a14df60df..4b1215792 100644 --- a/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/example.py +++ b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/example.py @@ -17,11 +17,11 @@ Usage: import os +from crewai import Agent, Crew, Process, Task from crewai.utilities.printer import Printer from dotenv import load_dotenv from stagehand.schemas import AvailableModel # type: ignore[import-untyped] -from crewai import Agent, Crew, Process, Task from crewai_tools import StagehandTool diff --git a/lib/crewai-tools/tool.specs.json b/lib/crewai-tools/tool.specs.json index 1e32b2d6c..4e3ffad2a 100644 --- a/lib/crewai-tools/tool.specs.json +++ b/lib/crewai-tools/tool.specs.json @@ -20117,18 +20117,6 @@ "humanized_name": "Web Automation Tool", "init_params_schema": { "$defs": { - "AvailableModel": { - "enum": [ - "gpt-4o", - "gpt-4o-mini", - "claude-3-5-sonnet-latest", - "claude-3-7-sonnet-latest", - "computer-use-preview", - "gemini-2.0-flash" - ], - "title": "AvailableModel", - "type": "string" - }, "EnvVar": { "properties": { "default": { @@ -20206,17 +20194,6 @@ "default": null, "title": "Model Api Key" }, - "model_name": { - "anyOf": [ - { - "$ref": "#/$defs/AvailableModel" - }, - { - "type": "null" - } - ], - "default": "claude-3-7-sonnet-latest" - }, "project_id": { "anyOf": [ { diff --git a/lib/crewai/pyproject.toml b/lib/crewai/pyproject.toml index 7a295251a..2a51dae01 100644 --- a/lib/crewai/pyproject.toml +++ b/lib/crewai/pyproject.toml @@ -21,11 +21,13 @@ dependencies = [ "opentelemetry-exporter-otlp-proto-http~=1.34.0", # Data Handling "chromadb~=1.1.0", - "tokenizers~=0.20.3", + "tokenizers>=0.21,<1", "openpyxl~=3.1.5", # Authentication and Security "python-dotenv~=1.1.1", "pyjwt>=2.9.0,<3", + # TUI + "textual>=7.5.0", # Configuration and Utils "click~=8.1.7", "appdirs~=1.4.4", @@ -36,10 +38,12 @@ dependencies = [ "json5~=0.10.0", "portalocker~=2.7.0", "pydantic-settings~=2.10.1", + "httpx~=0.28.1", "mcp~=1.26.0", "uv~=0.9.13", "aiosqlite~=0.21.0", "pyyaml~=6.0", + "lancedb>=0.29.2", ] [project.urls] @@ -50,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI" [project.optional-dependencies] tools = [ - "crewai-tools==1.9.3", + "crewai-tools==1.10.1", ] embeddings = [ "tiktoken~=0.8.0" @@ -63,7 +67,7 @@ openpyxl = [ ] mem0 = ["mem0ai~=0.1.94"] docling = [ - "docling~=2.63.0", + "docling~=2.75.0", ] qdrant = [ "qdrant-client[fastembed]~=1.14.3", @@ -85,7 +89,7 @@ bedrock = [ "boto3~=1.40.45", ] google-genai = [ - "google-genai~=1.49.0", + "google-genai~=1.65.0", ] azure-ai-inference = [ "azure-ai-inference~=1.0.0b9", diff --git a/lib/crewai/src/crewai/__init__.py b/lib/crewai/src/crewai/__init__.py index b410be7e5..46c1f8f95 100644 --- a/lib/crewai/src/crewai/__init__.py +++ b/lib/crewai/src/crewai/__init__.py @@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None: _suppress_pydantic_deprecation_warnings() -__version__ = "1.9.3" +__version__ = "1.10.1" _telemetry_submitted = False @@ -71,6 +71,25 @@ def _track_install_async() -> None: _track_install_async() + +_LAZY_IMPORTS: dict[str, tuple[str, str]] = { + "Memory": ("crewai.memory.unified_memory", "Memory"), +} + + +def __getattr__(name: str) -> Any: + """Lazily import heavy modules (e.g. Memory → lancedb) on first access.""" + if name in _LAZY_IMPORTS: + module_path, attr = _LAZY_IMPORTS[name] + import importlib + + mod = importlib.import_module(module_path) + val = getattr(mod, attr) + globals()[name] = val + return val + raise AttributeError(f"module 'crewai' has no attribute {name!r}") + + __all__ = [ "LLM", "Agent", @@ -80,6 +99,7 @@ __all__ = [ "Flow", "Knowledge", "LLMGuardrail", + "Memory", "Process", "Task", "TaskOutput", diff --git a/lib/crewai/src/crewai/a2a/utils/agent_card.py b/lib/crewai/src/crewai/a2a/utils/agent_card.py index c548cd1e7..45819bebd 100644 --- a/lib/crewai/src/crewai/a2a/utils/agent_card.py +++ b/lib/crewai/src/crewai/a2a/utils/agent_card.py @@ -4,6 +4,7 @@ from __future__ import annotations import asyncio from collections.abc import MutableMapping +import concurrent.futures from functools import lru_cache import ssl import time @@ -138,14 +139,17 @@ def fetch_agent_card( ttl_hash = int(time.time() // cache_ttl) return _fetch_agent_card_cached(endpoint, auth_hash, timeout, ttl_hash) - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + coro = afetch_agent_card(endpoint=endpoint, auth=auth, timeout=timeout) try: - return loop.run_until_complete( - afetch_agent_card(endpoint=endpoint, auth=auth, timeout=timeout) - ) - finally: - loop.close() + asyncio.get_running_loop() + has_running_loop = True + except RuntimeError: + has_running_loop = False + + if has_running_loop: + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool: + return pool.submit(asyncio.run, coro).result() + return asyncio.run(coro) async def afetch_agent_card( @@ -203,14 +207,17 @@ def _fetch_agent_card_cached( """Cached sync version of fetch_agent_card.""" auth = _auth_store.get(auth_hash) - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + coro = _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout) try: - return loop.run_until_complete( - _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout) - ) - finally: - loop.close() + asyncio.get_running_loop() + has_running_loop = True + except RuntimeError: + has_running_loop = False + + if has_running_loop: + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool: + return pool.submit(asyncio.run, coro).result() + return asyncio.run(coro) @cached(ttl=300, serializer=PickleSerializer()) # type: ignore[untyped-decorator] diff --git a/lib/crewai/src/crewai/a2a/utils/delegation.py b/lib/crewai/src/crewai/a2a/utils/delegation.py index cfcf51f36..3a6795c34 100644 --- a/lib/crewai/src/crewai/a2a/utils/delegation.py +++ b/lib/crewai/src/crewai/a2a/utils/delegation.py @@ -5,6 +5,7 @@ from __future__ import annotations import asyncio import base64 from collections.abc import AsyncIterator, Callable, MutableMapping +import concurrent.futures from contextlib import asynccontextmanager import logging from typing import TYPE_CHECKING, Any, Final, Literal @@ -194,56 +195,43 @@ def execute_a2a_delegation( Returns: TaskStateResult with status, result/error, history, and agent_card. - - Raises: - RuntimeError: If called from an async context with a running event loop. """ + coro = aexecute_a2a_delegation( + endpoint=endpoint, + auth=auth, + timeout=timeout, + task_description=task_description, + context=context, + context_id=context_id, + task_id=task_id, + reference_task_ids=reference_task_ids, + metadata=metadata, + extensions=extensions, + conversation_history=conversation_history, + agent_id=agent_id, + agent_role=agent_role, + agent_branch=agent_branch, + response_model=response_model, + turn_number=turn_number, + updates=updates, + from_task=from_task, + from_agent=from_agent, + skill_id=skill_id, + client_extensions=client_extensions, + transport=transport, + accepted_output_modes=accepted_output_modes, + input_files=input_files, + ) try: asyncio.get_running_loop() - raise RuntimeError( - "execute_a2a_delegation() cannot be called from an async context. " - "Use 'await aexecute_a2a_delegation()' instead." - ) - except RuntimeError as e: - if "no running event loop" not in str(e).lower(): - raise + has_running_loop = True + except RuntimeError: + has_running_loop = False - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - try: - return loop.run_until_complete( - aexecute_a2a_delegation( - endpoint=endpoint, - auth=auth, - timeout=timeout, - task_description=task_description, - context=context, - context_id=context_id, - task_id=task_id, - reference_task_ids=reference_task_ids, - metadata=metadata, - extensions=extensions, - conversation_history=conversation_history, - agent_id=agent_id, - agent_role=agent_role, - agent_branch=agent_branch, - response_model=response_model, - turn_number=turn_number, - updates=updates, - from_task=from_task, - from_agent=from_agent, - skill_id=skill_id, - client_extensions=client_extensions, - transport=transport, - accepted_output_modes=accepted_output_modes, - input_files=input_files, - ) - ) - finally: - try: - loop.run_until_complete(loop.shutdown_asyncgens()) - finally: - loop.close() + if has_running_loop: + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool: + return pool.submit(asyncio.run, coro).result() + return asyncio.run(coro) async def aexecute_a2a_delegation( diff --git a/lib/crewai/src/crewai/agent/core.py b/lib/crewai/src/crewai/agent/core.py index ff97e093d..39d807c42 100644 --- a/lib/crewai/src/crewai/agent/core.py +++ b/lib/crewai/src/crewai/agent/core.py @@ -9,11 +9,9 @@ import time from typing import ( TYPE_CHECKING, Any, - Final, Literal, cast, ) -from urllib.parse import urlparse from pydantic import ( BaseModel, @@ -63,17 +61,8 @@ from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.lite_agent_output import LiteAgentOutput from crewai.llms.base_llm import BaseLLM -from crewai.mcp import ( - MCPClient, - MCPServerConfig, - MCPServerHTTP, - MCPServerSSE, - MCPServerStdio, -) -from crewai.mcp.transports.http import HTTPTransport -from crewai.mcp.transports.sse import SSETransport -from crewai.mcp.transports.stdio import StdioTransport -from crewai.memory.contextual.contextual_memory import ContextualMemory +from crewai.mcp import MCPServerConfig +from crewai.mcp.tool_resolver import MCPToolResolver from crewai.rag.embeddings.types import EmbedderConfig from crewai.security.fingerprint import Fingerprint from crewai.skills.loader import activate_skill, discover_skills @@ -116,18 +105,8 @@ if TYPE_CHECKING: from crewai.utilities.types import LLMMessage -# MCP Connection timeout constants (in seconds) -MCP_CONNECTION_TIMEOUT: Final[int] = 10 -MCP_TOOL_EXECUTION_TIMEOUT: Final[int] = 30 -MCP_DISCOVERY_TIMEOUT: Final[int] = 15 -MCP_MAX_RETRIES: Final[int] = 3 - _passthrough_exceptions: tuple[type[Exception], ...] = () -# Simple in-memory cache for MCP tool schemas (duration: 5 minutes) -_mcp_schema_cache: dict[str, Any] = {} -_cache_ttl: Final[int] = 300 # 5 minutes - class Agent(BaseAgent): """Represents an agent in a system. @@ -159,7 +138,7 @@ class Agent(BaseAgent): model_config = ConfigDict() _times_executed: int = PrivateAttr(default=0) - _mcp_clients: list[Any] = PrivateAttr(default_factory=list) + _mcp_resolver: MCPToolResolver | None = PrivateAttr(default=None) _last_messages: list[LLMMessage] = PrivateAttr(default_factory=list) max_execution_time: int | None = Field( default=None, @@ -345,19 +324,12 @@ class Agent(BaseAgent): self.skills = resolved if resolved else None def _is_any_available_memory(self) -> bool: - """Check if any memory is available.""" - if not self.crew: - return False - - memory_attributes = [ - "memory", - "_short_term_memory", - "_long_term_memory", - "_entity_memory", - "_external_memory", - ] - - return any(getattr(self.crew, attr) for attr in memory_attributes) + """Check if unified memory is available (agent or crew).""" + if getattr(self, "memory", None): + return True + if self.crew and getattr(self.crew, "_memory", None): + return True + return False def _supports_native_tool_calling(self, tools: list[BaseTool]) -> bool: """Check if the LLM supports native function calling with the given tools. @@ -421,15 +393,16 @@ class Agent(BaseAgent): memory = "" try: - contextual_memory = ContextualMemory( - self.crew._short_term_memory, - self.crew._long_term_memory, - self.crew._entity_memory, - self.crew._external_memory, - agent=self, - task=task, + unified_memory = getattr(self, "memory", None) or ( + getattr(self.crew, "_memory", None) if self.crew else None ) - memory = contextual_memory.build_context_for_task(task, context or "") + if unified_memory is not None: + query = task.description + matches = unified_memory.recall(query, limit=5) + if matches: + memory = "Relevant memories:\n" + "\n".join( + m.format() for m in matches + ) if memory.strip() != "": task_prompt += self.i18n.slice("memory").format(memory=memory) @@ -660,17 +633,16 @@ class Agent(BaseAgent): memory = "" try: - contextual_memory = ContextualMemory( - self.crew._short_term_memory, - self.crew._long_term_memory, - self.crew._entity_memory, - self.crew._external_memory, - agent=self, - task=task, - ) - memory = await contextual_memory.abuild_context_for_task( - task, context or "" + unified_memory = getattr(self, "memory", None) or ( + getattr(self.crew, "_memory", None) if self.crew else None ) + if unified_memory is not None: + query = task.description + matches = unified_memory.recall(query, limit=5) + if matches: + memory = "Relevant memories:\n" + "\n".join( + m.format() for m in matches + ) if memory.strip() != "": task_prompt += self.i18n.slice("memory").format(memory=memory) @@ -910,7 +882,11 @@ class Agent(BaseAgent): respect_context_window=self.respect_context_window, request_within_rpm_limit=rpm_limit_fn, callbacks=[TokenCalcHandler(self._token_process)], - response_model=task.response_model if task else None, + response_model=( + task.response_model or task.output_pydantic or task.output_json + ) + if task + else None, ) def _update_executor_parameters( @@ -939,7 +915,11 @@ class Agent(BaseAgent): self.agent_executor.stop = stop_words self.agent_executor.tools_names = get_tool_names(tools) self.agent_executor.tools_description = render_text_description_and_args(tools) - self.agent_executor.response_model = task.response_model if task else None + self.agent_executor.response_model = ( + (task.response_model or task.output_pydantic or task.output_json) + if task + else None + ) self.agent_executor.tools_handler = self.tools_handler self.agent_executor.request_within_rpm_limit = rpm_limit_fn @@ -972,544 +952,17 @@ class Agent(BaseAgent): def get_mcp_tools(self, mcps: list[str | MCPServerConfig]) -> list[BaseTool]: """Convert MCP server references/configs to CrewAI tools. - Supports both string references (backwards compatible) and structured - configuration objects (MCPServerStdio, MCPServerHTTP, MCPServerSSE). - - Args: - mcps: List of MCP server references (strings) or configurations. - - Returns: - List of BaseTool instances from MCP servers. + Delegates to :class:`~crewai.mcp.tool_resolver.MCPToolResolver`. """ - all_tools = [] - clients = [] - - for mcp_config in mcps: - if isinstance(mcp_config, str): - tools = self._get_mcp_tools_from_string(mcp_config) - else: - tools, client = self._get_native_mcp_tools(mcp_config) - if client: - clients.append(client) - - all_tools.extend(tools) - - # Store clients for cleanup - self._mcp_clients.extend(clients) - return all_tools + self._cleanup_mcp_clients() + self._mcp_resolver = MCPToolResolver(agent=self, logger=self._logger) + return self._mcp_resolver.resolve(mcps) def _cleanup_mcp_clients(self) -> None: """Cleanup MCP client connections after task execution.""" - if not self._mcp_clients: - return - - async def _disconnect_all() -> None: - for client in self._mcp_clients: - if client and hasattr(client, "connected") and client.connected: - await client.disconnect() - - try: - asyncio.run(_disconnect_all()) - except Exception as e: - self._logger.log("error", f"Error during MCP client cleanup: {e}") - finally: - self._mcp_clients.clear() - - def _get_mcp_tools_from_string(self, mcp_ref: str) -> list[BaseTool]: - """Get tools from legacy string-based MCP references. - - This method maintains backwards compatibility with string-based - MCP references (https://... and crewai-amp:...). - - Args: - mcp_ref: String reference to MCP server. - - Returns: - List of BaseTool instances. - """ - if mcp_ref.startswith("crewai-amp:"): - return self._get_amp_mcp_tools(mcp_ref) - if mcp_ref.startswith("https://"): - return self._get_external_mcp_tools(mcp_ref) - return [] - - def _get_external_mcp_tools(self, mcp_ref: str) -> list[BaseTool]: - """Get tools from external HTTPS MCP server with graceful error handling.""" - from crewai.tools.mcp_tool_wrapper import MCPToolWrapper - - # Parse server URL and optional tool name - if "#" in mcp_ref: - server_url, specific_tool = mcp_ref.split("#", 1) - else: - server_url, specific_tool = mcp_ref, None - - server_params = {"url": server_url} - server_name = self._extract_server_name(server_url) - - try: - # Get tool schemas with timeout and error handling - tool_schemas = self._get_mcp_tool_schemas(server_params) - - if not tool_schemas: - self._logger.log( - "warning", f"No tools discovered from MCP server: {server_url}" - ) - return [] - - tools = [] - for tool_name, schema in tool_schemas.items(): - # Skip if specific tool requested and this isn't it - if specific_tool and tool_name != specific_tool: - continue - - try: - wrapper = MCPToolWrapper( - mcp_server_params=server_params, - tool_name=tool_name, - tool_schema=schema, - server_name=server_name, - ) - tools.append(wrapper) - except Exception as e: - self._logger.log( - "warning", - f"Failed to create MCP tool wrapper for {tool_name}: {e}", - ) - continue - - if specific_tool and not tools: - self._logger.log( - "warning", - f"Specific tool '{specific_tool}' not found on MCP server: {server_url}", - ) - - return cast(list[BaseTool], tools) - - except Exception as e: - self._logger.log( - "warning", f"Failed to connect to MCP server {server_url}: {e}" - ) - return [] - - def _get_native_mcp_tools( - self, mcp_config: MCPServerConfig - ) -> tuple[list[BaseTool], Any | None]: - """Get tools from MCP server using structured configuration. - - This method creates an MCP client based on the configuration type, - connects to the server, discovers tools, applies filtering, and - returns wrapped tools along with the client instance for cleanup. - - Args: - mcp_config: MCP server configuration (MCPServerStdio, MCPServerHTTP, or MCPServerSSE). - - Returns: - Tuple of (list of BaseTool instances, MCPClient instance for cleanup). - """ - from crewai.tools.base_tool import BaseTool - from crewai.tools.mcp_native_tool import MCPNativeTool - - transport: StdioTransport | HTTPTransport | SSETransport - if isinstance(mcp_config, MCPServerStdio): - transport = StdioTransport( - command=mcp_config.command, - args=mcp_config.args, - env=mcp_config.env, - ) - server_name = f"{mcp_config.command}_{'_'.join(mcp_config.args)}" - elif isinstance(mcp_config, MCPServerHTTP): - transport = HTTPTransport( - url=mcp_config.url, - headers=mcp_config.headers, - streamable=mcp_config.streamable, - ) - server_name = self._extract_server_name(mcp_config.url) - elif isinstance(mcp_config, MCPServerSSE): - transport = SSETransport( - url=mcp_config.url, - headers=mcp_config.headers, - ) - server_name = self._extract_server_name(mcp_config.url) - else: - raise ValueError(f"Unsupported MCP server config type: {type(mcp_config)}") - - client = MCPClient( - transport=transport, - cache_tools_list=mcp_config.cache_tools_list, - ) - - async def _setup_client_and_list_tools() -> list[dict[str, Any]]: - """Async helper to connect and list tools in same event loop.""" - - try: - if not client.connected: - await client.connect() - - tools_list = await client.list_tools() - - try: - await client.disconnect() - # Small delay to allow background tasks to finish cleanup - # This helps prevent "cancel scope in different task" errors - # when asyncio.run() closes the event loop - await asyncio.sleep(0.1) - except Exception as e: - self._logger.log("error", f"Error during disconnect: {e}") - - return tools_list - except Exception as e: - if client.connected: - await client.disconnect() - await asyncio.sleep(0.1) - raise RuntimeError( - f"Error during setup client and list tools: {e}" - ) from e - - try: - try: - asyncio.get_running_loop() - import concurrent.futures - - with concurrent.futures.ThreadPoolExecutor() as executor: - future = executor.submit( - asyncio.run, _setup_client_and_list_tools() - ) - tools_list = future.result() - except RuntimeError: - try: - tools_list = asyncio.run(_setup_client_and_list_tools()) - except RuntimeError as e: - error_msg = str(e).lower() - if "cancel scope" in error_msg or "task" in error_msg: - raise ConnectionError( - "MCP connection failed due to event loop cleanup issues. " - "This may be due to authentication errors or server unavailability." - ) from e - except asyncio.CancelledError as e: - raise ConnectionError( - "MCP connection was cancelled. This may indicate an authentication " - "error or server unavailability." - ) from e - - if mcp_config.tool_filter: - filtered_tools = [] - for tool in tools_list: - if callable(mcp_config.tool_filter): - try: - from crewai.mcp.filters import ToolFilterContext - - context = ToolFilterContext( - agent=self, - server_name=server_name, - run_context=None, - ) - if mcp_config.tool_filter(context, tool): # type: ignore[call-arg, arg-type] - filtered_tools.append(tool) - except (TypeError, AttributeError): - if mcp_config.tool_filter(tool): # type: ignore[call-arg, arg-type] - filtered_tools.append(tool) - else: - # Not callable - include tool - filtered_tools.append(tool) - tools_list = filtered_tools - - tools = [] - for tool_def in tools_list: - tool_name = tool_def.get("name", "") - if not tool_name: - continue - - # Convert inputSchema to Pydantic model if present - args_schema = None - if tool_def.get("inputSchema"): - args_schema = self._json_schema_to_pydantic( - tool_name, tool_def["inputSchema"] - ) - - tool_schema = { - "description": tool_def.get("description", ""), - "args_schema": args_schema, - } - - try: - native_tool = MCPNativeTool( - mcp_client=client, - tool_name=tool_name, - tool_schema=tool_schema, - server_name=server_name, - ) - tools.append(native_tool) - except Exception as e: - self._logger.log("error", f"Failed to create native MCP tool: {e}") - continue - - return cast(list[BaseTool], tools), client - except Exception as e: - if client.connected: - asyncio.run(client.disconnect()) - - raise RuntimeError(f"Failed to get native MCP tools: {e}") from e - - def _get_amp_mcp_tools(self, amp_ref: str) -> list[BaseTool]: - """Get tools from CrewAI AMP MCP marketplace.""" - # Parse: "crewai-amp:mcp-name" or "crewai-amp:mcp-name#tool_name" - amp_part = amp_ref.replace("crewai-amp:", "") - if "#" in amp_part: - mcp_name, specific_tool = amp_part.split("#", 1) - else: - mcp_name, specific_tool = amp_part, None - - # Call AMP API to get MCP server URLs - mcp_servers = self._fetch_amp_mcp_servers(mcp_name) - - tools = [] - for server_config in mcp_servers: - server_ref = server_config["url"] - if specific_tool: - server_ref += f"#{specific_tool}" - server_tools = self._get_external_mcp_tools(server_ref) - tools.extend(server_tools) - - return tools - - @staticmethod - def _extract_server_name(server_url: str) -> str: - """Extract clean server name from URL for tool prefixing.""" - - parsed = urlparse(server_url) - domain = parsed.netloc.replace(".", "_") - path = parsed.path.replace("/", "_").strip("_") - return f"{domain}_{path}" if path else domain - - def _get_mcp_tool_schemas( - self, server_params: dict[str, Any] - ) -> dict[str, dict[str, Any]]: - """Get tool schemas from MCP server for wrapper creation with caching.""" - server_url = server_params["url"] - - # Check cache first - cache_key = server_url - current_time = time.time() - - if cache_key in _mcp_schema_cache: - cached_data, cache_time = _mcp_schema_cache[cache_key] - if current_time - cache_time < _cache_ttl: - self._logger.log( - "debug", f"Using cached MCP tool schemas for {server_url}" - ) - return cached_data # type: ignore[no-any-return] - - try: - schemas = asyncio.run(self._get_mcp_tool_schemas_async(server_params)) - - # Cache successful results - _mcp_schema_cache[cache_key] = (schemas, current_time) - - return schemas - except Exception as e: - # Log warning but don't raise - this allows graceful degradation - self._logger.log( - "warning", f"Failed to get MCP tool schemas from {server_url}: {e}" - ) - return {} - - async def _get_mcp_tool_schemas_async( - self, server_params: dict[str, Any] - ) -> dict[str, dict[str, Any]]: - """Async implementation of MCP tool schema retrieval with timeouts and retries.""" - server_url = server_params["url"] - return await self._retry_mcp_discovery( - self._discover_mcp_tools_with_timeout, server_url - ) - - async def _retry_mcp_discovery( - self, operation_func: Any, server_url: str - ) -> dict[str, dict[str, Any]]: - """Retry MCP discovery operation with exponential backoff, avoiding try-except in loop.""" - last_error = None - - for attempt in range(MCP_MAX_RETRIES): - # Execute single attempt outside try-except loop structure - result, error, should_retry = await self._attempt_mcp_discovery( - operation_func, server_url - ) - - # Success case - return immediately - if result is not None: - return result - - # Non-retryable error - raise immediately - if not should_retry: - raise RuntimeError(error) - - # Retryable error - continue with backoff - last_error = error - if attempt < MCP_MAX_RETRIES - 1: - wait_time = 2**attempt # Exponential backoff - await asyncio.sleep(wait_time) - - raise RuntimeError( - f"Failed to discover MCP tools after {MCP_MAX_RETRIES} attempts: {last_error}" - ) - - @staticmethod - async def _attempt_mcp_discovery( - operation_func: Any, server_url: str - ) -> tuple[dict[str, dict[str, Any]] | None, str, bool]: - """Attempt single MCP discovery operation and return (result, error_message, should_retry).""" - try: - result = await operation_func(server_url) - return result, "", False - - except ImportError: - return ( - None, - "MCP library not available. Please install with: pip install mcp", - False, - ) - - except asyncio.TimeoutError: - return ( - None, - f"MCP discovery timed out after {MCP_DISCOVERY_TIMEOUT} seconds", - True, - ) - - except Exception as e: - error_str = str(e).lower() - - # Classify errors as retryable or non-retryable - if "authentication" in error_str or "unauthorized" in error_str: - return None, f"Authentication failed for MCP server: {e!s}", False - if "connection" in error_str or "network" in error_str: - return None, f"Network connection failed: {e!s}", True - if "json" in error_str or "parsing" in error_str: - return None, f"Server response parsing error: {e!s}", True - return None, f"MCP discovery error: {e!s}", False - - async def _discover_mcp_tools_with_timeout( - self, server_url: str - ) -> dict[str, dict[str, Any]]: - """Discover MCP tools with timeout wrapper.""" - return await asyncio.wait_for( - self._discover_mcp_tools(server_url), timeout=MCP_DISCOVERY_TIMEOUT - ) - - async def _discover_mcp_tools(self, server_url: str) -> dict[str, dict[str, Any]]: - """Discover tools from MCP server with proper timeout handling.""" - from mcp import ClientSession - from mcp.client.streamable_http import streamablehttp_client - - async with streamablehttp_client(server_url) as (read, write, _): - async with ClientSession(read, write) as session: - # Initialize the connection with timeout - await asyncio.wait_for( - session.initialize(), timeout=MCP_CONNECTION_TIMEOUT - ) - - # List available tools with timeout - tools_result = await asyncio.wait_for( - session.list_tools(), - timeout=MCP_DISCOVERY_TIMEOUT - MCP_CONNECTION_TIMEOUT, - ) - - schemas = {} - for tool in tools_result.tools: - args_schema = None - if hasattr(tool, "inputSchema") and tool.inputSchema: - args_schema = self._json_schema_to_pydantic( - sanitize_tool_name(tool.name), tool.inputSchema - ) - - schemas[sanitize_tool_name(tool.name)] = { - "description": getattr(tool, "description", ""), - "args_schema": args_schema, - } - return schemas - - def _json_schema_to_pydantic( - self, tool_name: str, json_schema: dict[str, Any] - ) -> type: - """Convert JSON Schema to Pydantic model for tool arguments. - - Args: - tool_name: Name of the tool (used for model naming) - json_schema: JSON Schema dict with 'properties', 'required', etc. - - Returns: - Pydantic BaseModel class - """ - from pydantic import Field, create_model - - properties = json_schema.get("properties", {}) - required_fields = json_schema.get("required", []) - - field_definitions: dict[str, Any] = {} - - for field_name, field_schema in properties.items(): - field_type = self._json_type_to_python(field_schema) - field_description = field_schema.get("description", "") - - is_required = field_name in required_fields - - if is_required: - field_definitions[field_name] = ( - field_type, - Field(..., description=field_description), - ) - else: - field_definitions[field_name] = ( - field_type | None, - Field(default=None, description=field_description), - ) - - model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema" - return create_model(model_name, **field_definitions) # type: ignore[no-any-return] - - def _json_type_to_python(self, field_schema: dict[str, Any]) -> type: - """Convert JSON Schema type to Python type. - - Args: - field_schema: JSON Schema field definition - - Returns: - Python type - """ - - json_type = field_schema.get("type") - - if "anyOf" in field_schema: - types: list[type] = [] - for option in field_schema["anyOf"]: - if "const" in option: - types.append(str) - else: - types.append(self._json_type_to_python(option)) - unique_types = list(set(types)) - if len(unique_types) > 1: - result: Any = unique_types[0] - for t in unique_types[1:]: - result = result | t - return result # type: ignore[no-any-return] - return unique_types[0] - - type_mapping: dict[str | None, type] = { - "string": str, - "number": float, - "integer": int, - "boolean": bool, - "array": list, - "object": dict, - } - - return type_mapping.get(json_type, Any) - - @staticmethod - def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict[str, Any]]: - """Fetch MCP server configurations from CrewAI AMP API.""" - # TODO: Implement AMP API call to "integrations/mcps" endpoint - # Should return list of server configs with URLs - return [] + if self._mcp_resolver is not None: + self._mcp_resolver.cleanup() + self._mcp_resolver = None @staticmethod def get_multimodal_tools() -> Sequence[BaseTool]: @@ -1741,15 +1194,32 @@ class Agent(BaseAgent): # Process platform apps and MCP tools if self.apps: platform_tools = self.get_platform_tools(self.apps) - if platform_tools and self.tools is not None: + if platform_tools: + if self.tools is None: + self.tools = [] self.tools.extend(platform_tools) if self.mcps: mcps = self.get_mcp_tools(self.mcps) - if mcps and self.tools is not None: + if mcps: + if self.tools is None: + self.tools = [] self.tools.extend(mcps) # Prepare tools raw_tools: list[BaseTool] = self.tools or [] + + # Inject memory tools for standalone kickoff (crew path handles its own) + agent_memory = getattr(self, "memory", None) + if agent_memory is not None: + from crewai.tools.memory_tools import create_memory_tools + + existing_names = {sanitize_tool_name(t.name) for t in raw_tools} + raw_tools.extend( + mt + for mt in create_memory_tools(agent_memory) + if sanitize_tool_name(mt.name) not in existing_names + ) + parsed_tools = parse_tools(raw_tools) # Build agent_info for backward-compatible event emission @@ -1824,6 +1294,49 @@ class Agent(BaseAgent): if input_files: all_files.update(input_files) + # Inject memory context for standalone kickoff (recall before execution) + if agent_memory is not None: + try: + crewai_event_bus.emit( + self, + event=MemoryRetrievalStartedEvent( + task_id=None, + source_type="agent_kickoff", + from_agent=self, + ), + ) + start_time = time.time() + matches = agent_memory.recall(formatted_messages, limit=20) + memory_block = "" + if matches: + memory_block = "Relevant memories:\n" + "\n".join( + m.format() for m in matches + ) + if memory_block: + formatted_messages += "\n\n" + self.i18n.slice("memory").format( + memory=memory_block + ) + crewai_event_bus.emit( + self, + event=MemoryRetrievalCompletedEvent( + task_id=None, + memory_content=memory_block, + retrieval_time_ms=(time.time() - start_time) * 1000, + source_type="agent_kickoff", + from_agent=self, + ), + ) + except Exception as e: + crewai_event_bus.emit( + self, + event=MemoryRetrievalFailedEvent( + task_id=None, + source_type="agent_kickoff", + from_agent=self, + error=str(e), + ), + ) + # Build the input dict for the executor inputs: dict[str, Any] = { "input": formatted_messages, @@ -1894,6 +1407,9 @@ class Agent(BaseAgent): response_format=response_format, ) + # Save to memory after execution (passive save) + self._save_kickoff_to_memory(messages, output.raw) + crewai_event_bus.emit( self, event=LiteAgentExecutionCompletedEvent( @@ -1914,6 +1430,32 @@ class Agent(BaseAgent): ) raise + def _save_kickoff_to_memory( + self, messages: str | list[LLMMessage], output_text: str + ) -> None: + """Save kickoff result to memory. No-op if agent has no memory.""" + agent_memory = getattr(self, "memory", None) + if agent_memory is None: + return + try: + if isinstance(messages, str): + input_str = messages + else: + input_str = ( + "\n".join( + str(msg.get("content", "")) + for msg in messages + if msg.get("content") + ) + or "User request" + ) + raw = f"Input: {input_str}\nAgent: {self.role}\nResult: {output_text}" + extracted = agent_memory.extract_memories(raw) + if extracted: + agent_memory.remember_many(extracted) + except Exception as e: + self._logger.log("error", f"Failed to save kickoff result to memory: {e}") + def _execute_and_build_output( self, executor: AgentExecutor, @@ -2196,6 +1738,9 @@ class Agent(BaseAgent): response_format=response_format, ) + # Save to memory after async execution (passive save) + self._save_kickoff_to_memory(messages, output.raw) + crewai_event_bus.emit( self, event=LiteAgentExecutionCompletedEvent( diff --git a/lib/crewai/src/crewai/agents/agent_builder/base_agent.py b/lib/crewai/src/crewai/agents/agent_builder/base_agent.py index c45dad6b1..1f8e77654 100644 --- a/lib/crewai/src/crewai/agents/agent_builder/base_agent.py +++ b/lib/crewai/src/crewai/agents/agent_builder/base_agent.py @@ -5,7 +5,8 @@ from collections.abc import Callable from copy import copy as shallow_copy from hashlib import md5 from pathlib import Path -from typing import Any, Literal +import re +from typing import Any, Final, Literal import uuid from pydantic import ( @@ -38,6 +39,11 @@ from crewai.utilities.rpm_controller import RPMController from crewai.utilities.string_utils import interpolate_only +_SLUG_RE: Final[re.Pattern[str]] = re.compile( + r"^(?:crewai-amp:)?[a-zA-Z0-9][a-zA-Z0-9_-]*(?:#\w+)?$" +) + + PlatformApp = Literal[ "asana", "box", @@ -199,7 +205,15 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta): ) mcps: list[str | MCPServerConfig] | None = Field( default=None, - description="List of MCP server references. Supports 'https://server.com/path' for external servers and 'crewai-amp:mcp-name' for AMP marketplace. Use '#tool_name' suffix for specific tools.", + description="List of MCP server references. Supports 'https://server.com/path' for external servers and bare slugs like 'notion' for connected MCP integrations. Use '#tool_name' suffix for specific tools.", + ) + memory: Any = Field( + default=None, + description=( + "Enable agent memory. Pass True for default Memory(), " + "or a Memory/MemoryScope/MemorySlice instance for custom configuration. " + "If not set, falls back to crew memory." + ), ) skills: list[Path | Skill] | None = Field( default=None, @@ -274,14 +288,16 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta): validated_mcps: list[str | MCPServerConfig] = [] for mcp in mcps: if isinstance(mcp, str): - if mcp.startswith(("https://", "crewai-amp:")): + if mcp.startswith("https://"): + validated_mcps.append(mcp) + elif _SLUG_RE.match(mcp): validated_mcps.append(mcp) else: raise ValueError( - f"Invalid MCP reference: {mcp}. " - "String references must start with 'https://' or 'crewai-amp:'" + f"Invalid MCP reference: {mcp!r}. " + "String references must be an 'https://' URL or a valid " + "slug (e.g. 'notion', 'notion#search', 'crewai-amp:notion')." ) - elif isinstance(mcp, (MCPServerConfig)): validated_mcps.append(mcp) else: @@ -335,6 +351,17 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta): self._token_process = TokenProcess() return self + @model_validator(mode="after") + def resolve_memory(self) -> Self: + """Resolve memory field: True creates a default Memory(), instance is used as-is.""" + if self.memory is True: + from crewai.memory.unified_memory import Memory + + self.memory = Memory() + elif self.memory is False: + self.memory = None + return self + @property def key(self) -> str: source = [ diff --git a/lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py b/lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py index 03787c802..1abfb6e5a 100644 --- a/lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py +++ b/lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py @@ -1,13 +1,8 @@ from __future__ import annotations -import time from typing import TYPE_CHECKING from crewai.agents.parser import AgentFinish -from crewai.memory.entity.entity_memory_item import EntityMemoryItem -from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem -from crewai.utilities.converter import ConverterError -from crewai.utilities.evaluators.task_evaluator import TaskEvaluator from crewai.utilities.printer import Printer from crewai.utilities.string_utils import sanitize_tool_name @@ -30,110 +25,29 @@ class CrewAgentExecutorMixin: _i18n: I18N _printer: Printer = Printer() - def _create_short_term_memory(self, output: AgentFinish) -> None: - """Create and save a short-term memory item if conditions are met.""" + def _save_to_memory(self, output: AgentFinish) -> None: + """Save task result to unified memory (memory or crew._memory).""" + memory = getattr(self.agent, "memory", None) or ( + getattr(self.crew, "_memory", None) if self.crew else None + ) + if memory is None or not self.task or getattr(memory, "_read_only", False): + return if ( - self.crew - and self.agent - and self.task - and f"Action: {sanitize_tool_name('Delegate work to coworker')}" - not in output.text + f"Action: {sanitize_tool_name('Delegate work to coworker')}" + in output.text ): - try: - if ( - hasattr(self.crew, "_short_term_memory") - and self.crew._short_term_memory - ): - self.crew._short_term_memory.save( - value=output.text, - metadata={ - "observation": self.task.description, - }, - ) - except Exception as e: - self.agent._logger.log( - "error", f"Failed to add to short term memory: {e}" - ) - - def _create_external_memory(self, output: AgentFinish) -> None: - """Create and save a external-term memory item if conditions are met.""" - if ( - self.crew - and self.agent - and self.task - and hasattr(self.crew, "_external_memory") - and self.crew._external_memory - ): - try: - self.crew._external_memory.save( - value=output.text, - metadata={ - "description": self.task.description, - "messages": self.messages, - }, - ) - except Exception as e: - self.agent._logger.log( - "error", f"Failed to add to external memory: {e}" - ) - - def _create_long_term_memory(self, output: AgentFinish) -> None: - """Create and save long-term and entity memory items based on evaluation.""" - if ( - self.crew - and self.crew._long_term_memory - and self.crew._entity_memory - and self.task - and self.agent - ): - try: - ltm_agent = TaskEvaluator(self.agent) - evaluation = ltm_agent.evaluate(self.task, output.text) - - if isinstance(evaluation, ConverterError): - return - - long_term_memory = LongTermMemoryItem( - task=self.task.description, - agent=self.agent.role, - quality=evaluation.quality, - datetime=str(time.time()), - expected_output=self.task.expected_output, - metadata={ - "suggestions": evaluation.suggestions, - "quality": evaluation.quality, - }, - ) - self.crew._long_term_memory.save(long_term_memory) - - entity_memories = [ - EntityMemoryItem( - name=entity.name, - type=entity.type, - description=entity.description, - relationships="\n".join( - [f"- {r}" for r in entity.relationships] - ), - ) - for entity in evaluation.entities - ] - if entity_memories: - self.crew._entity_memory.save(entity_memories) - except AttributeError as e: - self.agent._logger.log( - "error", f"Missing attributes for long term memory: {e}" - ) - except Exception as e: - self.agent._logger.log( - "error", f"Failed to add to long term memory: {e}" - ) - elif ( - self.crew - and self.crew._long_term_memory - and self.crew._entity_memory is None - ): - if self.agent and self.agent.verbose: - self._printer.print( - content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.", - color="bold_yellow", - ) + return + try: + raw = ( + f"Task: {self.task.description}\n" + f"Agent: {self.agent.role}\n" + f"Expected result: {self.task.expected_output}\n" + f"Result: {output.text}" + ) + extracted = memory.extract_memories(raw) + if extracted: + memory.remember_many(extracted, agent_role=self.agent.role) + except Exception as e: + self.agent._logger.log( + "error", f"Failed to save to memory: {e}" + ) diff --git a/lib/crewai/src/crewai/agents/cache/__init__.py b/lib/crewai/src/crewai/agents/cache/__init__.py index d18771ca3..6cc557fd9 100644 --- a/lib/crewai/src/crewai/agents/cache/__init__.py +++ b/lib/crewai/src/crewai/agents/cache/__init__.py @@ -1,5 +1,4 @@ from crewai.agents.cache.cache_handler import CacheHandler - __all__ = ["CacheHandler"] diff --git a/lib/crewai/src/crewai/agents/crew_agent_executor.py b/lib/crewai/src/crewai/agents/crew_agent_executor.py index c7adcbe09..ac1cccbeb 100644 --- a/lib/crewai/src/crewai/agents/crew_agent_executor.py +++ b/lib/crewai/src/crewai/agents/crew_agent_executor.py @@ -6,7 +6,11 @@ and memory management. from __future__ import annotations +import asyncio from collections.abc import Callable +import contextvars +from concurrent.futures import ThreadPoolExecutor, as_completed +import inspect import logging from typing import TYPE_CHECKING, Any, Literal, cast @@ -47,6 +51,7 @@ from crewai.utilities.agent_utils import ( handle_unknown_error, has_reached_max_iterations, is_context_length_exceeded, + parse_tool_call_args, process_llm_response, track_delegation_if_needed, ) @@ -234,9 +239,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): if self.ask_for_human_input: formatted_answer = self._handle_human_feedback(formatted_answer) - self._create_short_term_memory(formatted_answer) - self._create_long_term_memory(formatted_answer) - self._create_external_memory(formatted_answer) + self._save_to_memory(formatted_answer) return {"output": formatted_answer.output} def _inject_multimodal_files(self, inputs: dict[str, Any] | None = None) -> None: @@ -485,8 +488,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): # No tools available, fall back to simple LLM call return self._invoke_loop_native_no_tools() - openai_tools, available_functions = convert_tools_to_openai_schema( - self.original_tools + openai_tools, available_functions, self._tool_name_mapping = ( + convert_tools_to_openai_schema(self.original_tools) ) while True: @@ -687,30 +690,141 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): Returns: AgentFinish if tool has result_as_answer=True, None otherwise. """ - from datetime import datetime - import json - - from crewai.events import crewai_event_bus - from crewai.events.types.tool_usage_events import ( - ToolUsageErrorEvent, - ToolUsageFinishedEvent, - ToolUsageStartedEvent, - ) - if not tool_calls: return None - # Only process the FIRST tool call for sequential execution with reflection - tool_call = tool_calls[0] + parsed_calls = [ + parsed + for tool_call in tool_calls + if (parsed := self._parse_native_tool_call(tool_call)) is not None + ] + if not parsed_calls: + return None - # Extract tool call info - handle OpenAI-style, Anthropic-style, and Gemini-style + original_tools_by_name: dict[str, Any] = dict(self._tool_name_mapping) + + if len(parsed_calls) > 1: + has_result_as_answer_in_batch = any( + bool( + original_tools_by_name.get(func_name) + and getattr( + original_tools_by_name.get(func_name), "result_as_answer", False + ) + ) + for _, func_name, _ in parsed_calls + ) + has_max_usage_count_in_batch = any( + bool( + original_tools_by_name.get(func_name) + and getattr( + original_tools_by_name.get(func_name), + "max_usage_count", + None, + ) + is not None + ) + for _, func_name, _ in parsed_calls + ) + + # Preserve historical sequential behavior for result_as_answer batches. + # Also avoid threading around usage counters for max_usage_count tools. + if has_result_as_answer_in_batch or has_max_usage_count_in_batch: + logger.debug( + "Skipping parallel native execution because batch includes result_as_answer or max_usage_count tool" + ) + else: + execution_plan: list[ + tuple[str, str, str | dict[str, Any], Any | None] + ] = [] + for call_id, func_name, func_args in parsed_calls: + original_tool = original_tools_by_name.get(func_name) + execution_plan.append( + (call_id, func_name, func_args, original_tool) + ) + + self._append_assistant_tool_calls_message( + [ + (call_id, func_name, func_args) + for call_id, func_name, func_args, _ in execution_plan + ] + ) + + max_workers = min(8, len(execution_plan)) + ordered_results: list[dict[str, Any] | None] = [None] * len( + execution_plan + ) + with ThreadPoolExecutor(max_workers=max_workers) as pool: + futures = { + pool.submit( + contextvars.copy_context().run, + self._execute_single_native_tool_call, + call_id=call_id, + func_name=func_name, + func_args=func_args, + available_functions=available_functions, + original_tool=original_tool, + should_execute=True, + ): idx + for idx, ( + call_id, + func_name, + func_args, + original_tool, + ) in enumerate(execution_plan) + } + for future in as_completed(futures): + idx = futures[future] + ordered_results[idx] = future.result() + + for execution_result in ordered_results: + if not execution_result: + continue + tool_finish = self._append_tool_result_and_check_finality( + execution_result + ) + if tool_finish: + return tool_finish + + reasoning_prompt = self._i18n.slice("post_tool_reasoning") + reasoning_message: LLMMessage = { + "role": "user", + "content": reasoning_prompt, + } + self.messages.append(reasoning_message) + return None + + # Sequential behavior: process only first tool call, then force reflection. + call_id, func_name, func_args = parsed_calls[0] + self._append_assistant_tool_calls_message([(call_id, func_name, func_args)]) + + execution_result = self._execute_single_native_tool_call( + call_id=call_id, + func_name=func_name, + func_args=func_args, + available_functions=available_functions, + original_tool=original_tools_by_name.get(func_name), + should_execute=True, + ) + tool_finish = self._append_tool_result_and_check_finality(execution_result) + if tool_finish: + return tool_finish + + reasoning_prompt = self._i18n.slice("post_tool_reasoning") + reasoning_message = { + "role": "user", + "content": reasoning_prompt, + } + self.messages.append(reasoning_message) + return None + + def _parse_native_tool_call( + self, tool_call: Any + ) -> tuple[str, str, str | dict[str, Any]] | None: if hasattr(tool_call, "function"): - # OpenAI-style: has .function.name and .function.arguments call_id = getattr(tool_call, "id", f"call_{id(tool_call)}") func_name = sanitize_tool_name(tool_call.function.name) - func_args = tool_call.function.arguments - elif hasattr(tool_call, "function_call") and tool_call.function_call: - # Gemini-style: has .function_call.name and .function_call.args + return call_id, func_name, tool_call.function.arguments + if hasattr(tool_call, "function_call") and tool_call.function_call: call_id = f"call_{id(tool_call)}" func_name = sanitize_tool_name(tool_call.function_call.name) func_args = ( @@ -718,13 +832,12 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): if tool_call.function_call.args else {} ) - elif hasattr(tool_call, "name") and hasattr(tool_call, "input"): - # Anthropic format: has .name and .input (ToolUseBlock) + return call_id, func_name, func_args + if hasattr(tool_call, "name") and hasattr(tool_call, "input"): call_id = getattr(tool_call, "id", f"call_{id(tool_call)}") func_name = sanitize_tool_name(tool_call.name) - func_args = tool_call.input # Already a dict in Anthropic - elif isinstance(tool_call, dict): - # Support OpenAI "id", Bedrock "toolUseId", or generate one + return call_id, func_name, tool_call.input + if isinstance(tool_call, dict): call_id = ( tool_call.get("id") or tool_call.get("toolUseId") @@ -735,10 +848,15 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): func_info.get("name", "") or tool_call.get("name", "") ) func_args = func_info.get("arguments", "{}") or tool_call.get("input", {}) - else: - return None + return call_id, func_name, func_args + return None + + def _append_assistant_tool_calls_message( + self, + parsed_calls: list[tuple[str, str, str | dict[str, Any]]], + ) -> None: + import json - # Append assistant message with single tool call assistant_message: LLMMessage = { "role": "assistant", "content": None, @@ -753,42 +871,54 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): else json.dumps(func_args), }, } + for call_id, func_name, func_args in parsed_calls ], } - self.messages.append(assistant_message) - # Parse arguments for the single tool call - if isinstance(func_args, str): - try: - args_dict = json.loads(func_args) - except json.JSONDecodeError: - args_dict = {} - else: - args_dict = func_args + def _execute_single_native_tool_call( + self, + *, + call_id: str, + func_name: str, + func_args: str | dict[str, Any], + available_functions: dict[str, Callable[..., Any]], + original_tool: Any | None = None, + should_execute: bool = True, + ) -> dict[str, Any]: + from datetime import datetime + import json - agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown" + from crewai.events.types.tool_usage_events import ( + ToolUsageErrorEvent, + ToolUsageFinishedEvent, + ToolUsageStartedEvent, + ) - # Find original tool by matching sanitized name (needed for cache_function and result_as_answer) + args_dict, parse_error = parse_tool_call_args(func_args, func_name, call_id, original_tool) + if parse_error is not None: + return parse_error - original_tool = None - for tool in self.original_tools or []: - if sanitize_tool_name(tool.name) == func_name: - original_tool = tool - break + if original_tool is None: + for tool in self.original_tools or []: + if sanitize_tool_name(tool.name) == func_name: + original_tool = tool + break - # Check if tool has reached max usage count max_usage_reached = False - if original_tool: - if ( - hasattr(original_tool, "max_usage_count") - and original_tool.max_usage_count is not None - and original_tool.current_usage_count >= original_tool.max_usage_count - ): - max_usage_reached = True + if not should_execute and original_tool: + max_usage_reached = True + elif ( + should_execute + and original_tool + and (max_count := getattr(original_tool, "max_usage_count", None)) + is not None + and getattr(original_tool, "current_usage_count", 0) >= max_count + ): + max_usage_reached = True - # Check cache before executing from_cache = False + result: str = "Tool not found" input_str = json.dumps(args_dict) if args_dict else "" if self.tools_handler and self.tools_handler.cache: cached_result = self.tools_handler.cache.read( @@ -802,7 +932,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): ) from_cache = True - # Emit tool usage started event + agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown" started_at = datetime.now() crewai_event_bus.emit( self, @@ -818,14 +948,18 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): track_delegation_if_needed(func_name, args_dict, self.task) - # Find the structured tool for hook context structured_tool: CrewStructuredTool | None = None - for structured in self.tools or []: - if sanitize_tool_name(structured.name) == func_name: - structured_tool = structured - break + if original_tool is not None: + for structured in self.tools or []: + if getattr(structured, "_original_tool", None) is original_tool: + structured_tool = structured + break + if structured_tool is None: + for structured in self.tools or []: + if sanitize_tool_name(structured.name) == func_name: + structured_tool = structured + break - # Execute before_tool_call hooks hook_blocked = False before_hook_context = ToolCallHookContext( tool_name=func_name, @@ -849,58 +983,48 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): color="red", ) - # If hook blocked execution, set result and skip tool execution if hook_blocked: result = f"Tool execution blocked by hook. Tool: {func_name}" - # Execute the tool (only if not cached, not at max usage, and not blocked by hook) - elif not from_cache and not max_usage_reached: - result = "Tool not found" - if func_name in available_functions: - try: - tool_func = available_functions[func_name] - raw_result = tool_func(**args_dict) - - # Add to cache after successful execution (before string conversion) - if self.tools_handler and self.tools_handler.cache: - should_cache = True - if ( - original_tool - and hasattr(original_tool, "cache_function") - and callable(original_tool.cache_function) - ): - should_cache = original_tool.cache_function( - args_dict, raw_result - ) - if should_cache: - self.tools_handler.cache.add( - tool=func_name, input=input_str, output=raw_result - ) - - # Convert to string for message - result = ( - str(raw_result) - if not isinstance(raw_result, str) - else raw_result - ) - except Exception as e: - result = f"Error executing tool: {e}" - if self.task: - self.task.increment_tools_errors() - crewai_event_bus.emit( - self, - event=ToolUsageErrorEvent( - tool_name=func_name, - tool_args=args_dict, - from_agent=self.agent, - from_task=self.task, - agent_key=agent_key, - error=e, - ), - ) - error_event_emitted = True elif max_usage_reached and original_tool: - # Return error message when max usage limit is reached result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore." + elif not from_cache and func_name in available_functions: + try: + raw_result = available_functions[func_name](**args_dict) + + if self.tools_handler and self.tools_handler.cache: + should_cache = True + if ( + original_tool + and hasattr(original_tool, "cache_function") + and callable(original_tool.cache_function) + ): + should_cache = original_tool.cache_function( + args_dict, raw_result + ) + if should_cache: + self.tools_handler.cache.add( + tool=func_name, input=input_str, output=raw_result + ) + + result = ( + str(raw_result) if not isinstance(raw_result, str) else raw_result + ) + except Exception as e: + result = f"Error executing tool: {e}" + if self.task: + self.task.increment_tools_errors() + crewai_event_bus.emit( + self, + event=ToolUsageErrorEvent( + tool_name=func_name, + tool_args=args_dict, + from_agent=self.agent, + from_task=self.task, + agent_key=agent_key, + error=e, + ), + ) + error_event_emitted = True after_hook_context = ToolCallHookContext( tool_name=func_name, @@ -940,7 +1064,23 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): ), ) - # Append tool result message + return { + "call_id": call_id, + "func_name": func_name, + "result": result, + "from_cache": from_cache, + "original_tool": original_tool, + } + + def _append_tool_result_and_check_finality( + self, execution_result: dict[str, Any] + ) -> AgentFinish | None: + call_id = cast(str, execution_result["call_id"]) + func_name = cast(str, execution_result["func_name"]) + result = cast(str, execution_result["result"]) + from_cache = cast(bool, execution_result["from_cache"]) + original_tool = execution_result["original_tool"] + tool_message: LLMMessage = { "role": "tool", "tool_call_id": call_id, @@ -949,7 +1089,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): } self.messages.append(tool_message) - # Log the tool execution if self.agent and self.agent.verbose: cache_info = " (from cache)" if from_cache else "" self._printer.print( @@ -962,20 +1101,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): and hasattr(original_tool, "result_as_answer") and original_tool.result_as_answer ): - # Return immediately with tool result as final answer return AgentFinish( thought="Tool result is the final answer", output=result, text=result, ) - - # Inject post-tool reasoning prompt to enforce analysis - reasoning_prompt = self._i18n.slice("post_tool_reasoning") - reasoning_message: LLMMessage = { - "role": "user", - "content": reasoning_prompt, - } - self.messages.append(reasoning_message) return None async def ainvoke(self, inputs: dict[str, Any]) -> dict[str, Any]: @@ -1011,9 +1141,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): if self.ask_for_human_input: formatted_answer = await self._ahandle_human_feedback(formatted_answer) - self._create_short_term_memory(formatted_answer) - self._create_long_term_memory(formatted_answer) - self._create_external_memory(formatted_answer) + self._save_to_memory(formatted_answer) return {"output": formatted_answer.output} async def _ainvoke_loop(self) -> AgentFinish: @@ -1137,7 +1265,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): formatted_answer, tool_result ) - self._invoke_step_callback(formatted_answer) # type: ignore[arg-type] + await self._ainvoke_step_callback(formatted_answer) # type: ignore[arg-type] self._append_message(formatted_answer.text) # type: ignore[union-attr] except OutputParserError as e: @@ -1190,8 +1318,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): if not self.original_tools: return await self._ainvoke_loop_native_no_tools() - openai_tools, available_functions = convert_tools_to_openai_schema( - self.original_tools + openai_tools, available_functions, self._tool_name_mapping = ( + convert_tools_to_openai_schema(self.original_tools) ) while True: @@ -1252,7 +1380,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): output=answer, text=answer, ) - self._invoke_step_callback(formatted_answer) + await self._ainvoke_step_callback(formatted_answer) self._append_message(answer) # Save final answer to messages self._show_logs(formatted_answer) return formatted_answer @@ -1264,7 +1392,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): output=answer, text=output_json, ) - self._invoke_step_callback(formatted_answer) + await self._ainvoke_step_callback(formatted_answer) self._append_message(output_json) self._show_logs(formatted_answer) return formatted_answer @@ -1275,7 +1403,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): output=str(answer), text=str(answer), ) - self._invoke_step_callback(formatted_answer) + await self._ainvoke_step_callback(formatted_answer) self._append_message(str(answer)) # Save final answer to messages self._show_logs(formatted_answer) return formatted_answer @@ -1369,13 +1497,28 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): def _invoke_step_callback( self, formatted_answer: AgentAction | AgentFinish ) -> None: - """Invoke step callback. + """Invoke step callback (sync context). Args: formatted_answer: Current agent response. """ if self.step_callback: - self.step_callback(formatted_answer) + cb_result = self.step_callback(formatted_answer) + if inspect.iscoroutine(cb_result): + asyncio.run(cb_result) + + async def _ainvoke_step_callback( + self, formatted_answer: AgentAction | AgentFinish + ) -> None: + """Invoke step callback (async context). + + Args: + formatted_answer: Current agent response. + """ + if self.step_callback: + cb_result = self.step_callback(formatted_answer) + if inspect.iscoroutine(cb_result): + await cb_result def _append_message( self, text: str, role: Literal["user", "assistant", "system"] = "assistant" diff --git a/lib/crewai/src/crewai/cli/authentication/__init__.py b/lib/crewai/src/crewai/cli/authentication/__init__.py index 687ccdfa9..98070be42 100644 --- a/lib/crewai/src/crewai/cli/authentication/__init__.py +++ b/lib/crewai/src/crewai/cli/authentication/__init__.py @@ -1,5 +1,4 @@ from crewai.cli.authentication.main import AuthenticationCommand - __all__ = ["AuthenticationCommand"] diff --git a/lib/crewai/src/crewai/cli/authentication/main.py b/lib/crewai/src/crewai/cli/authentication/main.py index 996fd7c63..7bbda61d5 100644 --- a/lib/crewai/src/crewai/cli/authentication/main.py +++ b/lib/crewai/src/crewai/cli/authentication/main.py @@ -2,8 +2,8 @@ import time from typing import TYPE_CHECKING, Any, TypeVar, cast import webbrowser +import httpx from pydantic import BaseModel, Field -import requests from rich.console import Console from crewai.cli.authentication.utils import validate_jwt_token @@ -98,7 +98,7 @@ class AuthenticationCommand: "scope": " ".join(self.oauth2_provider.get_oauth_scopes()), "audience": self.oauth2_provider.get_audience(), } - response = requests.post( + response = httpx.post( url=self.oauth2_provider.get_authorize_url(), data=device_code_payload, timeout=20, @@ -130,7 +130,7 @@ class AuthenticationCommand: attempts = 0 while True and attempts < 10: - response = requests.post( + response = httpx.post( self.oauth2_provider.get_token_url(), data=token_payload, timeout=30 ) token_data = response.json() @@ -149,7 +149,7 @@ class AuthenticationCommand: return if token_data["error"] not in ("authorization_pending", "slow_down"): - raise requests.HTTPError( + raise httpx.HTTPError( token_data.get("error_description") or token_data.get("error") ) diff --git a/lib/crewai/src/crewai/cli/cli.py b/lib/crewai/src/crewai/cli/cli.py index a8f9571cc..32c8a00bb 100644 --- a/lib/crewai/src/crewai/cli/cli.py +++ b/lib/crewai/src/crewai/cli/cli.py @@ -1,6 +1,7 @@ from importlib.metadata import version as get_version import os import subprocess +from typing import Any import click @@ -179,9 +180,19 @@ def log_tasks_outputs() -> None: @crewai.command() -@click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory") -@click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory") -@click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory") +@click.option("-m", "--memory", is_flag=True, help="Reset MEMORY") +@click.option( + "-l", "--long", is_flag=True, hidden=True, + help="[Deprecated: use --memory] Reset memory", +) +@click.option( + "-s", "--short", is_flag=True, hidden=True, + help="[Deprecated: use --memory] Reset memory", +) +@click.option( + "-e", "--entities", is_flag=True, hidden=True, + help="[Deprecated: use --memory] Reset memory", +) @click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage") @click.option( "-akn", "--agent-knowledge", is_flag=True, help="Reset AGENT KNOWLEDGE storage" @@ -191,6 +202,7 @@ def log_tasks_outputs() -> None: ) @click.option("-a", "--all", is_flag=True, help="Reset ALL memories") def reset_memories( + memory: bool, long: bool, short: bool, entities: bool, @@ -200,13 +212,22 @@ def reset_memories( all: bool, ) -> None: """ - Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs, knowledge, agent_knowledge). This will delete all the data saved. + Reset the crew memories (memory, knowledge, agent_knowledge, kickoff_outputs). This will delete all the data saved. """ try: + # Treat legacy flags as --memory with a deprecation warning + if long or short or entities: + legacy_used = [ + f for f, v in [("--long", long), ("--short", short), ("--entities", entities)] if v + ] + click.echo( + f"Warning: {', '.join(legacy_used)} {'is' if len(legacy_used) == 1 else 'are'} " + "deprecated. Use --memory (-m) instead. All memory is now unified." + ) + memory = True + memory_types = [ - long, - short, - entities, + memory, knowledge, agent_knowledge, kickoff_outputs, @@ -218,12 +239,73 @@ def reset_memories( ) return reset_memories_command( - long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all + memory, knowledge, agent_knowledge, kickoff_outputs, all ) except Exception as e: click.echo(f"An error occurred while resetting memories: {e}", err=True) +@crewai.command() +@click.option( + "--storage-path", + type=str, + default=None, + help="Path to LanceDB memory directory. If omitted, uses ./.crewai/memory.", +) +@click.option( + "--embedder-provider", + type=str, + default=None, + help="Embedder provider for recall queries (e.g. openai, google-vertex, cohere, ollama).", +) +@click.option( + "--embedder-model", + type=str, + default=None, + help="Embedder model name (e.g. text-embedding-3-small, gemini-embedding-001).", +) +@click.option( + "--embedder-config", + type=str, + default=None, + help='Full embedder config as JSON (e.g. \'{"provider": "cohere", "config": {"model_name": "embed-v4.0"}}\').', +) +def memory( + storage_path: str | None, + embedder_provider: str | None, + embedder_model: str | None, + embedder_config: str | None, +) -> None: + """Open the Memory TUI to browse scopes and recall memories.""" + try: + from crewai.cli.memory_tui import MemoryTUI + except ImportError as exc: + click.echo( + "Textual is required for the memory TUI but could not be imported. " + "Try reinstalling crewai or: pip install textual" + ) + raise SystemExit(1) from exc + + # Build embedder spec from CLI flags. + embedder_spec: dict[str, Any] | None = None + if embedder_config: + import json as _json + + try: + embedder_spec = _json.loads(embedder_config) + except _json.JSONDecodeError as exc: + click.echo(f"Invalid --embedder-config JSON: {exc}") + raise SystemExit(1) from exc + elif embedder_provider: + cfg: dict[str, str] = {} + if embedder_model: + cfg["model_name"] = embedder_model + embedder_spec = {"provider": embedder_provider, "config": cfg} + + app = MemoryTUI(storage_path=storage_path, embedder_config=embedder_spec) + app.run() + + @crewai.command() @click.option( "-n", diff --git a/lib/crewai/src/crewai/cli/command.py b/lib/crewai/src/crewai/cli/command.py index 3f85318fb..139f69373 100644 --- a/lib/crewai/src/crewai/cli/command.py +++ b/lib/crewai/src/crewai/cli/command.py @@ -1,5 +1,6 @@ -import requests -from requests.exceptions import JSONDecodeError +import json + +import httpx from rich.console import Console from crewai.cli.authentication.token import get_auth_token @@ -30,16 +31,16 @@ class PlusAPIMixin: console.print("Run 'crewai login' to sign up/login.", style="bold green") raise SystemExit from None - def _validate_response(self, response: requests.Response) -> None: + def _validate_response(self, response: httpx.Response) -> None: """ Handle and display error messages from API responses. Args: - response (requests.Response): The response from the Plus API + response (httpx.Response): The response from the Plus API """ try: json_response = response.json() - except (JSONDecodeError, ValueError): + except (json.JSONDecodeError, ValueError): console.print( "Failed to parse response from Enterprise API failed. Details:", style="bold red", @@ -62,7 +63,7 @@ class PlusAPIMixin: ) raise SystemExit - if not response.ok: + if not response.is_success: console.print( "Request to Enterprise API failed. Details:", style="bold red" ) diff --git a/lib/crewai/src/crewai/cli/constants.py b/lib/crewai/src/crewai/cli/constants.py index 4de0d0082..2ef8dcc7f 100644 --- a/lib/crewai/src/crewai/cli/constants.py +++ b/lib/crewai/src/crewai/cli/constants.py @@ -69,7 +69,7 @@ ENV_VARS: dict[str, list[dict[str, Any]]] = { }, { "prompt": "Enter your AWS Region Name (press Enter to skip)", - "key_name": "AWS_REGION_NAME", + "key_name": "AWS_DEFAULT_REGION", }, ], "azure": [ diff --git a/lib/crewai/src/crewai/cli/create_crew.py b/lib/crewai/src/crewai/cli/create_crew.py index 7f4fe2e6e..9bca7c499 100644 --- a/lib/crewai/src/crewai/cli/create_crew.py +++ b/lib/crewai/src/crewai/cli/create_crew.py @@ -143,7 +143,7 @@ def create_folder_structure( (folder_path / "src" / folder_name).mkdir(parents=True) (folder_path / "src" / folder_name / "tools").mkdir(parents=True) (folder_path / "src" / folder_name / "config").mkdir(parents=True) - + # Copy AGENTS.md to project root (top-level projects only) package_dir = Path(__file__).parent agents_md_src = package_dir / "templates" / "AGENTS.md" diff --git a/lib/crewai/src/crewai/cli/create_flow.py b/lib/crewai/src/crewai/cli/create_flow.py index 76c68db32..2156d422c 100644 --- a/lib/crewai/src/crewai/cli/create_flow.py +++ b/lib/crewai/src/crewai/cli/create_flow.py @@ -1,5 +1,5 @@ -import shutil from pathlib import Path +import shutil import click diff --git a/lib/crewai/src/crewai/cli/enterprise/main.py b/lib/crewai/src/crewai/cli/enterprise/main.py index 2a73f1ae0..395de418b 100644 --- a/lib/crewai/src/crewai/cli/enterprise/main.py +++ b/lib/crewai/src/crewai/cli/enterprise/main.py @@ -1,7 +1,7 @@ +import json from typing import Any, cast -import requests -from requests.exceptions import JSONDecodeError, RequestException +import httpx from rich.console import Console from crewai.cli.authentication.main import Oauth2Settings, ProviderFactory @@ -47,12 +47,12 @@ class EnterpriseConfigureCommand(BaseCommand): "User-Agent": f"CrewAI-CLI/{get_crewai_version()}", "X-Crewai-Version": get_crewai_version(), } - response = requests.get(oauth_endpoint, timeout=30, headers=headers) + response = httpx.get(oauth_endpoint, timeout=30, headers=headers) response.raise_for_status() try: oauth_config = response.json() - except JSONDecodeError as e: + except json.JSONDecodeError as e: raise ValueError(f"Invalid JSON response from {oauth_endpoint}") from e self._validate_oauth_config(oauth_config) @@ -62,7 +62,7 @@ class EnterpriseConfigureCommand(BaseCommand): ) return cast(dict[str, Any], oauth_config) - except RequestException as e: + except httpx.HTTPError as e: raise ValueError(f"Failed to connect to enterprise URL: {e!s}") from e except Exception as e: raise ValueError(f"Error fetching OAuth2 configuration: {e!s}") from e diff --git a/lib/crewai/src/crewai/cli/memory_tui.py b/lib/crewai/src/crewai/cli/memory_tui.py new file mode 100644 index 000000000..9dd91a42c --- /dev/null +++ b/lib/crewai/src/crewai/cli/memory_tui.py @@ -0,0 +1,410 @@ +"""Textual TUI for browsing and recalling unified memory.""" + +from __future__ import annotations + +import asyncio +from typing import Any + +from textual.app import App, ComposeResult +from textual.containers import Horizontal, Vertical +from textual.widgets import Footer, Header, Input, OptionList, Static, Tree + + +# -- CrewAI brand palette -- +_PRIMARY = "#eb6658" # coral +_SECONDARY = "#1F7982" # teal +_TERTIARY = "#ffffff" # white + + +def _format_scope_info(info: Any) -> str: + """Format ScopeInfo with Rich markup.""" + return ( + f"[bold {_PRIMARY}]{info.path}[/]\n\n" + f"[dim]Records:[/] [bold]{info.record_count}[/]\n" + f"[dim]Categories:[/] {', '.join(info.categories) or 'none'}\n" + f"[dim]Oldest:[/] {info.oldest_record or '-'}\n" + f"[dim]Newest:[/] {info.newest_record or '-'}\n" + f"[dim]Children:[/] {', '.join(info.child_scopes) or 'none'}" + ) + + +class MemoryTUI(App[None]): + """TUI to browse memory scopes and run recall queries.""" + + TITLE = "CrewAI Memory" + SUB_TITLE = "Browse scopes and recall memories" + + CSS = f""" + Header {{ + background: {_PRIMARY}; + color: {_TERTIARY}; + }} + Footer {{ + background: {_SECONDARY}; + color: {_TERTIARY}; + }} + Footer > .footer-key--key {{ + background: {_PRIMARY}; + color: {_TERTIARY}; + }} + Horizontal {{ + height: 1fr; + }} + #scope-tree {{ + width: 30%; + padding: 1 2; + background: {_SECONDARY} 8%; + border-right: solid {_SECONDARY}; + }} + #scope-tree:focus > .tree--cursor {{ + background: {_SECONDARY}; + color: {_TERTIARY}; + }} + #scope-tree > .tree--guides {{ + color: {_SECONDARY} 50%; + }} + #scope-tree > .tree--guides-hover {{ + color: {_PRIMARY}; + }} + #scope-tree > .tree--guides-selected {{ + color: {_SECONDARY}; + }} + #right-panel {{ + width: 70%; + padding: 0 1; + }} + #info-panel {{ + height: 2fr; + padding: 1 2; + overflow-y: auto; + border: round {_SECONDARY}; + }} + #info-panel:focus {{ + border: round {_PRIMARY}; + }} + #info-panel LoadingIndicator {{ + color: {_PRIMARY}; + }} + #entry-list {{ + height: 1fr; + border: round {_SECONDARY}; + padding: 0 1; + scrollbar-color: {_PRIMARY}; + }} + #entry-list:focus {{ + border: round {_PRIMARY}; + }} + #entry-list > .option-list--option-highlighted {{ + background: {_SECONDARY}; + color: {_TERTIARY}; + }} + #recall-input {{ + margin: 0 1 1 1; + border: tall {_SECONDARY}; + }} + #recall-input:focus {{ + border: tall {_PRIMARY}; + }} + """ + + def __init__( + self, + storage_path: str | None = None, + embedder_config: dict[str, Any] | None = None, + ) -> None: + super().__init__() + self._memory: Any = None + self._init_error: str | None = None + self._selected_scope: str = "/" + self._entries: list[Any] = [] + self._view_mode: str = "list" # "list" | "recall" + self._recall_matches: list[Any] = [] + self._last_scope_info: Any = None + self._custom_embedder = embedder_config is not None + try: + from crewai.memory.storage.lancedb_storage import LanceDBStorage + from crewai.memory.unified_memory import Memory + + storage = LanceDBStorage(path=storage_path) if storage_path else LanceDBStorage() + embedder = None + if embedder_config is not None: + from crewai.rag.embeddings.factory import build_embedder + + embedder = build_embedder(embedder_config) + self._memory = Memory(storage=storage, embedder=embedder) if embedder else Memory(storage=storage) + except Exception as e: + self._init_error = str(e) + + def compose(self) -> ComposeResult: + yield Header(show_clock=False) + with Horizontal(): + yield self._build_scope_tree() + initial = ( + self._init_error + if self._init_error + else "Select a scope or type a recall query." + ) + with Vertical(id="right-panel"): + yield Static(initial, id="info-panel") + yield OptionList(id="entry-list") + yield Input( + placeholder="Type a query and press Enter to recall...", + id="recall-input", + ) + yield Footer() + + def on_mount(self) -> None: + """Set initial border titles on mounted widgets.""" + self.query_one("#info-panel", Static).border_title = "Detail" + self.query_one("#entry-list", OptionList).border_title = "Entries" + + def _build_scope_tree(self) -> Tree[str]: + tree: Tree[str] = Tree("/", id="scope-tree") + if self._memory is None: + tree.root.data = "/" + tree.root.label = "/ (0 records)" + return tree + info = self._memory.info("/") + tree.root.label = f"/ ({info.record_count} records)" + tree.root.data = "/" + self._add_children(tree.root, "/", depth=0, max_depth=3) + tree.root.expand() + return tree + + def _add_children( + self, + parent_node: Tree.Node[str], + path: str, + depth: int, + max_depth: int, + ) -> None: + if depth >= max_depth or self._memory is None: + return + info = self._memory.info(path) + for child in info.child_scopes: + child_info = self._memory.info(child) + label = f"{child} ({child_info.record_count})" + node = parent_node.add(label, data=child) + self._add_children(node, child, depth + 1, max_depth) + + # -- Populating the OptionList ------------------------------------------- + + def _populate_entry_list(self) -> None: + """Clear the OptionList and fill it with the current scope's entries.""" + option_list = self.query_one("#entry-list", OptionList) + option_list.clear_options() + for record in self._entries: + date_str = record.created_at.strftime("%Y-%m-%d") + preview = ( + (record.content[:80] + "…") + if len(record.content) > 80 + else record.content + ) + label = ( + f"{date_str} " + f"[bold]{record.importance:.1f}[/] " + f"{preview}" + ) + option_list.add_option(label) + + def _populate_recall_list(self) -> None: + """Clear the OptionList and fill it with the current recall matches.""" + option_list = self.query_one("#entry-list", OptionList) + option_list.clear_options() + if not self._recall_matches: + return + for m in self._recall_matches: + preview = ( + (m.record.content[:80] + "…") + if len(m.record.content) > 80 + else m.record.content + ) + label = ( + f"[bold]\\[{m.score:.2f}][/] " + f"{preview} " + f"[dim]scope={m.record.scope}[/]" + ) + option_list.add_option(label) + + # -- Detail rendering ---------------------------------------------------- + + def _format_record_detail(self, record: Any, context_line: str = "") -> str: + """Format a full MemoryRecord as Rich markup for the detail view. + + Args: + record: A MemoryRecord instance. + context_line: Optional header line shown above the fields + (e.g. "Entry 3 of 47"). + + Returns: + A Rich-markup string with all meaningful record fields. + """ + sep = f"[bold {_PRIMARY}]{'─' * 44}[/]" + lines: list[str] = [] + + if context_line: + lines.append(context_line) + lines.append("") + + # -- Fields block -- + lines.append(f"[dim]ID:[/] {record.id}") + lines.append(f"[dim]Scope:[/] [bold]{record.scope}[/]") + lines.append(f"[dim]Importance:[/] [bold]{record.importance:.2f}[/]") + lines.append( + f"[dim]Created:[/] " + f"{record.created_at.strftime('%Y-%m-%d %H:%M:%S')}" + ) + lines.append( + f"[dim]Last accessed:[/] " + f"{record.last_accessed.strftime('%Y-%m-%d %H:%M:%S')}" + ) + lines.append( + f"[dim]Categories:[/] " + f"{', '.join(record.categories) if record.categories else 'none'}" + ) + lines.append(f"[dim]Source:[/] {record.source or '-'}") + lines.append(f"[dim]Private:[/] {'Yes' if record.private else 'No'}") + + # -- Content block -- + lines.append(f"\n{sep}") + lines.append("[bold]Content[/]\n") + lines.append(record.content) + + # -- Metadata block -- + if record.metadata: + lines.append(f"\n{sep}") + lines.append("[bold]Metadata[/]\n") + for k, v in record.metadata.items(): + lines.append(f"[dim]{k}:[/] {v}") + + return "\n".join(lines) + + # -- Event handlers ------------------------------------------------------ + + def on_tree_node_selected(self, event: Tree.NodeSelected[str]) -> None: + """Load entries for the selected scope and populate the OptionList.""" + path = event.node.data if event.node.data is not None else "/" + self._selected_scope = path + self._view_mode = "list" + panel = self.query_one("#info-panel", Static) + if self._memory is None: + panel.update(self._init_error or "No memory loaded.") + return + display_limit = 1000 + info = self._memory.info(path) + self._last_scope_info = info + self._entries = self._memory.list_records(scope=path, limit=display_limit) + panel.update(_format_scope_info(info)) + panel.border_title = "Detail" + entry_list = self.query_one("#entry-list", OptionList) + capped = info.record_count > display_limit + count_label = ( + f"Entries (showing {display_limit} of {info.record_count} — display limit)" + if capped + else f"Entries ({len(self._entries)})" + ) + entry_list.border_title = count_label + self._populate_entry_list() + + def on_option_list_option_highlighted( + self, event: OptionList.OptionHighlighted + ) -> None: + """Live-update the info panel with the detail of the highlighted entry.""" + panel = self.query_one("#info-panel", Static) + idx = event.option_index + + if self._view_mode == "list": + if idx < len(self._entries): + record = self._entries[idx] + total = len(self._entries) + context = ( + f"[bold {_PRIMARY}]Entry {idx + 1} of {total}[/] " + f"[dim]in[/] [bold]{self._selected_scope}[/]" + ) + panel.border_title = f"Entry {idx + 1} of {total}" + panel.update(self._format_record_detail(record, context_line=context)) + + elif self._view_mode == "recall": + if idx < len(self._recall_matches): + match = self._recall_matches[idx] + total = len(self._recall_matches) + panel.border_title = f"Match {idx + 1} of {total}" + score_color = _PRIMARY if match.score >= 0.5 else "dim" + header_lines: list[str] = [ + f"[bold {_PRIMARY}]Recall Match {idx + 1} of {total}[/]\n", + f"[dim]Score:[/] [{score_color}][bold]{match.score:.2f}[/][/]", + ( + f"[dim]Match reasons:[/] " + f"{', '.join(match.match_reasons) if match.match_reasons else '-'}" + ), + ( + f"[dim]Evidence gaps:[/] " + f"{', '.join(match.evidence_gaps) if match.evidence_gaps else 'none'}" + ), + f"\n[bold {_PRIMARY}]{'─' * 44}[/]", + ] + record_detail = self._format_record_detail(match.record) + header_lines.append(record_detail) + panel.update("\n".join(header_lines)) + + def on_input_submitted(self, event: Input.Submitted) -> None: + query = event.value.strip() + if not query: + return + if self._memory is None: + panel = self.query_one("#info-panel", Static) + panel.update(self._init_error or "No memory loaded. Cannot recall.") + return + self.run_worker(self._do_recall(query), exclusive=True) + + async def _do_recall(self, query: str) -> None: + """Execute a recall query and display results in the OptionList.""" + panel = self.query_one("#info-panel", Static) + panel.loading = True + try: + scope = ( + self._selected_scope + if self._selected_scope != "/" + else None + ) + loop = asyncio.get_event_loop() + matches = await loop.run_in_executor( + None, + lambda: self._memory.recall( + query, scope=scope, limit=10, depth="deep" + ), + ) + self._recall_matches = matches or [] + self._view_mode = "recall" + + if not self._recall_matches: + panel.update("[dim]No memories found.[/]") + self.query_one("#entry-list", OptionList).clear_options() + return + + info_lines: list[str] = [] + info_lines.append( + "[dim italic]Searched the full dataset" + + (f" within [bold]{scope}[/]" if scope else "") + + " using the recall flow (semantic + recency + importance).[/]\n" + ) + if not self._custom_embedder: + info_lines.append( + "[dim italic]Note: Using default OpenAI embedder. " + "If memories were created with a different embedder, " + "pass --embedder-provider to match.[/]\n" + ) + info_lines.append( + f"[bold]Recall Results[/] [dim]" + f"({len(self._recall_matches)} matches)[/]\n" + f"[dim]Navigate the list below to view details.[/]" + ) + panel.update("\n".join(info_lines)) + panel.border_title = "Recall Detail" + entry_list = self.query_one("#entry-list", OptionList) + entry_list.border_title = f"Recall Results ({len(self._recall_matches)})" + self._populate_recall_list() + except Exception as e: + panel.update(f"[bold red]Error:[/] {e}") + finally: + panel.loading = False diff --git a/lib/crewai/src/crewai/cli/organization/main.py b/lib/crewai/src/crewai/cli/organization/main.py index 4ee954698..fe61ec202 100644 --- a/lib/crewai/src/crewai/cli/organization/main.py +++ b/lib/crewai/src/crewai/cli/organization/main.py @@ -1,4 +1,4 @@ -from requests import HTTPError +from httpx import HTTPStatusError from rich.console import Console from rich.table import Table @@ -10,11 +10,11 @@ console = Console() class OrganizationCommand(BaseCommand, PlusAPIMixin): - def __init__(self): + def __init__(self) -> None: BaseCommand.__init__(self) PlusAPIMixin.__init__(self, telemetry=self._telemetry) - def list(self): + def list(self) -> None: try: response = self.plus_api_client.get_organizations() response.raise_for_status() @@ -33,7 +33,7 @@ class OrganizationCommand(BaseCommand, PlusAPIMixin): table.add_row(org["name"], org["uuid"]) console.print(table) - except HTTPError as e: + except HTTPStatusError as e: if e.response.status_code == 401: console.print( "You are not logged in to any organization. Use 'crewai login' to login.", @@ -50,7 +50,7 @@ class OrganizationCommand(BaseCommand, PlusAPIMixin): ) raise SystemExit(1) from e - def switch(self, org_id): + def switch(self, org_id: str) -> None: try: response = self.plus_api_client.get_organizations() response.raise_for_status() @@ -72,7 +72,7 @@ class OrganizationCommand(BaseCommand, PlusAPIMixin): f"Successfully switched to {org['name']} ({org['uuid']})", style="bold green", ) - except HTTPError as e: + except HTTPStatusError as e: if e.response.status_code == 401: console.print( "You are not logged in to any organization. Use 'crewai login' to login.", @@ -87,7 +87,7 @@ class OrganizationCommand(BaseCommand, PlusAPIMixin): console.print(f"Failed to switch organization: {e!s}", style="bold red") raise SystemExit(1) from e - def current(self): + def current(self) -> None: settings = Settings() if settings.org_uuid: console.print( diff --git a/lib/crewai/src/crewai/cli/plus_api.py b/lib/crewai/src/crewai/cli/plus_api.py index e07d44d10..e32e5220d 100644 --- a/lib/crewai/src/crewai/cli/plus_api.py +++ b/lib/crewai/src/crewai/cli/plus_api.py @@ -3,7 +3,6 @@ from typing import Any from urllib.parse import urljoin import httpx -import requests from crewai.cli.config import Settings from crewai.cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL @@ -23,14 +22,15 @@ class PlusAPI: EPHEMERAL_TRACING_RESOURCE = "/crewai_plus/api/v1/tracing/ephemeral" INTEGRATIONS_RESOURCE = "/crewai_plus/api/v1/integrations" - def __init__(self, api_key: str) -> None: + def __init__(self, api_key: str | None = None) -> None: self.api_key = api_key self.headers = { - "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", "User-Agent": f"CrewAI-CLI/{get_crewai_version()}", "X-Crewai-Version": get_crewai_version(), } + if api_key: + self.headers["Authorization"] = f"Bearer {api_key}" settings = Settings() if settings.org_uuid: self.headers["X-Crewai-Organization-Id"] = settings.org_uuid @@ -43,16 +43,21 @@ class PlusAPI: def _make_request( self, method: str, endpoint: str, **kwargs: Any - ) -> requests.Response: + ) -> httpx.Response: url = urljoin(self.base_url, endpoint) - session = requests.Session() - session.trust_env = False - return session.request(method, url, headers=self.headers, **kwargs) + verify = kwargs.pop("verify", True) + with httpx.Client(trust_env=False, verify=verify) as client: + return client.request(method, url, headers=self.headers, **kwargs) - def login_to_tool_repository(self) -> requests.Response: - return self._make_request("POST", f"{self.TOOLS_RESOURCE}/login") + def login_to_tool_repository( + self, user_identifier: str | None = None + ) -> httpx.Response: + payload = {} + if user_identifier: + payload["user_identifier"] = user_identifier + return self._make_request("POST", f"{self.TOOLS_RESOURCE}/login", json=payload) - def get_tool(self, handle: str) -> requests.Response: + def get_tool(self, handle: str) -> httpx.Response: return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}") async def get_agent(self, handle: str) -> httpx.Response: @@ -68,7 +73,7 @@ class PlusAPI: description: str | None, encoded_file: str, available_exports: list[dict[str, Any]] | None = None, - ) -> requests.Response: + ) -> httpx.Response: params = { "handle": handle, "public": is_public, @@ -79,54 +84,52 @@ class PlusAPI: } return self._make_request("POST", f"{self.TOOLS_RESOURCE}", json=params) - def deploy_by_name(self, project_name: str) -> requests.Response: + def deploy_by_name(self, project_name: str) -> httpx.Response: return self._make_request( "POST", f"{self.CREWS_RESOURCE}/by-name/{project_name}/deploy" ) - def deploy_by_uuid(self, uuid: str) -> requests.Response: + def deploy_by_uuid(self, uuid: str) -> httpx.Response: return self._make_request("POST", f"{self.CREWS_RESOURCE}/{uuid}/deploy") - def crew_status_by_name(self, project_name: str) -> requests.Response: + def crew_status_by_name(self, project_name: str) -> httpx.Response: return self._make_request( "GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/status" ) - def crew_status_by_uuid(self, uuid: str) -> requests.Response: + def crew_status_by_uuid(self, uuid: str) -> httpx.Response: return self._make_request("GET", f"{self.CREWS_RESOURCE}/{uuid}/status") def crew_by_name( self, project_name: str, log_type: str = "deployment" - ) -> requests.Response: + ) -> httpx.Response: return self._make_request( "GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/logs/{log_type}" ) - def crew_by_uuid( - self, uuid: str, log_type: str = "deployment" - ) -> requests.Response: + def crew_by_uuid(self, uuid: str, log_type: str = "deployment") -> httpx.Response: return self._make_request( "GET", f"{self.CREWS_RESOURCE}/{uuid}/logs/{log_type}" ) - def delete_crew_by_name(self, project_name: str) -> requests.Response: + def delete_crew_by_name(self, project_name: str) -> httpx.Response: return self._make_request( "DELETE", f"{self.CREWS_RESOURCE}/by-name/{project_name}" ) - def delete_crew_by_uuid(self, uuid: str) -> requests.Response: + def delete_crew_by_uuid(self, uuid: str) -> httpx.Response: return self._make_request("DELETE", f"{self.CREWS_RESOURCE}/{uuid}") - def list_crews(self) -> requests.Response: + def list_crews(self) -> httpx.Response: return self._make_request("GET", self.CREWS_RESOURCE) - def create_crew(self, payload: dict[str, Any]) -> requests.Response: + def create_crew(self, payload: dict[str, Any]) -> httpx.Response: return self._make_request("POST", self.CREWS_RESOURCE, json=payload) - def get_organizations(self) -> requests.Response: + def get_organizations(self) -> httpx.Response: return self._make_request("GET", self.ORGANIZATIONS_RESOURCE) - def initialize_trace_batch(self, payload: dict[str, Any]) -> requests.Response: + def initialize_trace_batch(self, payload: dict[str, Any]) -> httpx.Response: return self._make_request( "POST", f"{self.TRACING_RESOURCE}/batches", @@ -136,7 +139,7 @@ class PlusAPI: def initialize_ephemeral_trace_batch( self, payload: dict[str, Any] - ) -> requests.Response: + ) -> httpx.Response: return self._make_request( "POST", f"{self.EPHEMERAL_TRACING_RESOURCE}/batches", @@ -145,7 +148,7 @@ class PlusAPI: def send_trace_events( self, trace_batch_id: str, payload: dict[str, Any] - ) -> requests.Response: + ) -> httpx.Response: return self._make_request( "POST", f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/events", @@ -155,7 +158,7 @@ class PlusAPI: def send_ephemeral_trace_events( self, trace_batch_id: str, payload: dict[str, Any] - ) -> requests.Response: + ) -> httpx.Response: return self._make_request( "POST", f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/events", @@ -165,7 +168,7 @@ class PlusAPI: def finalize_trace_batch( self, trace_batch_id: str, payload: dict[str, Any] - ) -> requests.Response: + ) -> httpx.Response: return self._make_request( "PATCH", f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/finalize", @@ -175,7 +178,7 @@ class PlusAPI: def finalize_ephemeral_trace_batch( self, trace_batch_id: str, payload: dict[str, Any] - ) -> requests.Response: + ) -> httpx.Response: return self._make_request( "PATCH", f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/finalize", @@ -185,7 +188,7 @@ class PlusAPI: def mark_trace_batch_as_failed( self, trace_batch_id: str, error_message: str - ) -> requests.Response: + ) -> httpx.Response: return self._make_request( "PATCH", f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}", @@ -193,13 +196,20 @@ class PlusAPI: timeout=30, ) - def get_triggers(self) -> requests.Response: + def get_mcp_configs(self, slugs: list[str]) -> httpx.Response: + """Get MCP server configurations for the given slugs.""" + return self._make_request( + "GET", + f"{self.INTEGRATIONS_RESOURCE}/mcp_configs", + params={"slugs": ",".join(slugs)}, + timeout=30, + ) + + def get_triggers(self) -> httpx.Response: """Get all available triggers from integrations.""" return self._make_request("GET", f"{self.INTEGRATIONS_RESOURCE}/apps") - def get_trigger_payload( - self, app_slug: str, trigger_slug: str - ) -> requests.Response: + def get_trigger_payload(self, app_slug: str, trigger_slug: str) -> httpx.Response: """Get sample payload for a specific trigger.""" return self._make_request( "GET", f"{self.INTEGRATIONS_RESOURCE}/{app_slug}/{trigger_slug}/payload" diff --git a/lib/crewai/src/crewai/cli/provider.py b/lib/crewai/src/crewai/cli/provider.py index 6de337b85..1f1e4ec40 100644 --- a/lib/crewai/src/crewai/cli/provider.py +++ b/lib/crewai/src/crewai/cli/provider.py @@ -8,7 +8,7 @@ from typing import Any import certifi import click -import requests +import httpx from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS @@ -165,20 +165,20 @@ def fetch_provider_data(cache_file: Path) -> dict[str, Any] | None: ssl_config = os.environ["SSL_CERT_FILE"] = certifi.where() try: - response = requests.get(JSON_URL, stream=True, timeout=60, verify=ssl_config) - response.raise_for_status() - data = download_data(response) - with open(cache_file, "w") as f: - json.dump(data, f) - return data - except requests.RequestException as e: + with httpx.stream("GET", JSON_URL, timeout=60, verify=ssl_config) as response: + response.raise_for_status() + data = download_data(response) + with open(cache_file, "w") as f: + json.dump(data, f) + return data + except httpx.HTTPError as e: click.secho(f"Error fetching provider data: {e}", fg="red") except json.JSONDecodeError: click.secho("Error parsing provider data. Invalid JSON format.", fg="red") return None -def download_data(response: requests.Response) -> dict[str, Any]: +def download_data(response: httpx.Response) -> dict[str, Any]: """Downloads data from a given HTTP response and returns the JSON content. Args: @@ -194,7 +194,7 @@ def download_data(response: requests.Response) -> dict[str, Any]: with click.progressbar( length=total_size, label="Downloading", show_pos=True ) as bar: - for chunk in response.iter_content(block_size): + for chunk in response.iter_bytes(block_size): if chunk: data_chunks.append(chunk) bar.update(len(chunk)) diff --git a/lib/crewai/src/crewai/cli/reset_memories_command.py b/lib/crewai/src/crewai/cli/reset_memories_command.py index 494744731..85971f94f 100644 --- a/lib/crewai/src/crewai/cli/reset_memories_command.py +++ b/lib/crewai/src/crewai/cli/reset_memories_command.py @@ -2,43 +2,61 @@ import subprocess import click -from crewai.cli.utils import get_crews +from crewai.cli.utils import get_crews, get_flows +from crewai.flow import Flow + + +def _reset_flow_memory(flow: Flow) -> None: + """Reset memory for a single flow instance. + + Handles Memory, MemoryScope (both have .reset()), and MemorySlice + (delegates to the underlying ._memory). Silently succeeds when the + storage directory does not exist yet (nothing to reset). + + Args: + flow: The flow instance whose memory should be reset. + """ + mem = flow.memory + if mem is None: + return + try: + if hasattr(mem, "reset"): + mem.reset() + elif hasattr(mem, "_memory") and hasattr(mem._memory, "reset"): + mem._memory.reset() + except (FileNotFoundError, OSError): + pass def reset_memories_command( - long, - short, - entity, - knowledge, - agent_knowledge, - kickoff_outputs, - all, + memory: bool, + knowledge: bool, + agent_knowledge: bool, + kickoff_outputs: bool, + all: bool, ) -> None: - """ - Reset the crew memories. + """Reset the crew and flow memories. Args: - long (bool): Whether to reset the long-term memory. - short (bool): Whether to reset the short-term memory. - entity (bool): Whether to reset the entity memory. - kickoff_outputs (bool): Whether to reset the latest kickoff task outputs. - all (bool): Whether to reset all memories. - knowledge (bool): Whether to reset the knowledge. - agent_knowledge (bool): Whether to reset the agents knowledge. + memory: Whether to reset the unified memory. + knowledge: Whether to reset the knowledge. + agent_knowledge: Whether to reset the agents knowledge. + kickoff_outputs: Whether to reset the latest kickoff task outputs. + all: Whether to reset all memories. """ - try: - if not any( - [long, short, entity, kickoff_outputs, knowledge, agent_knowledge, all] - ): + if not any([memory, kickoff_outputs, knowledge, agent_knowledge, all]): click.echo( "No memory type specified. Please specify at least one type to reset." ) return crews = get_crews() - if not crews: - raise ValueError("No crew found.") + flows = get_flows() + + if not crews and not flows: + raise ValueError("No crew or flow found.") + for crew in crews: if all: crew.reset_memories(command_type="all") @@ -46,20 +64,10 @@ def reset_memories_command( f"[Crew ({crew.name if crew.name else crew.id})] Reset memories command has been completed." ) continue - if long: - crew.reset_memories(command_type="long") + if memory: + crew.reset_memories(command_type="memory") click.echo( - f"[Crew ({crew.name if crew.name else crew.id})] Long term memory has been reset." - ) - if short: - crew.reset_memories(command_type="short") - click.echo( - f"[Crew ({crew.name if crew.name else crew.id})] Short term memory has been reset." - ) - if entity: - crew.reset_memories(command_type="entity") - click.echo( - f"[Crew ({crew.name if crew.name else crew.id})] Entity memory has been reset." + f"[Crew ({crew.name if crew.name else crew.id})] Memory has been reset." ) if kickoff_outputs: crew.reset_memories(command_type="kickoff_outputs") @@ -77,6 +85,20 @@ def reset_memories_command( f"[Crew ({crew.name if crew.name else crew.id})] Agents knowledge has been reset." ) + for flow in flows: + flow_name = flow.name or flow.__class__.__name__ + if all: + _reset_flow_memory(flow) + click.echo( + f"[Flow ({flow_name})] Reset memories command has been completed." + ) + continue + if memory: + _reset_flow_memory(flow) + click.echo( + f"[Flow ({flow_name})] Memory has been reset." + ) + except subprocess.CalledProcessError as e: click.echo(f"An error occurred while resetting the memories: {e}", err=True) click.echo(e.output, err=True) diff --git a/lib/crewai/src/crewai/cli/templates/crew/crew.py b/lib/crewai/src/crewai/cli/templates/crew/crew.py index 43a2608a4..758d324df 100644 --- a/lib/crewai/src/crewai/cli/templates/crew/crew.py +++ b/lib/crewai/src/crewai/cli/templates/crew/crew.py @@ -1,7 +1,6 @@ from crewai import Agent, Crew, Process, Task from crewai.project import CrewBase, agent, crew, task from crewai.agents.agent_builder.base_agent import BaseAgent -from typing import List # If you want to run a snippet of code before or after the crew starts, # you can use the @before_kickoff and @after_kickoff decorators # https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators @@ -10,8 +9,8 @@ from typing import List class {{crew_name}}(): """{{crew_name}} crew""" - agents: List[BaseAgent] - tasks: List[Task] + agents: list[BaseAgent] + tasks: list[Task] # Learn more about YAML configuration files here: # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended diff --git a/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml b/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml index 301c6e553..de884767c 100644 --- a/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml +++ b/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml @@ -5,7 +5,7 @@ description = "{{name}} using crewAI" authors = [{ name = "Your Name", email = "you@example.com" }] requires-python = ">=3.10,<3.14" dependencies = [ - "crewai[tools]==1.9.3" + "crewai[tools]==1.10.1" ] [project.scripts] diff --git a/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py b/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py index 8c3358097..a3feceb77 100644 --- a/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py +++ b/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py @@ -1,5 +1,3 @@ -from typing import List - from crewai import Agent, Crew, Process, Task from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.project import CrewBase, agent, crew, task @@ -13,8 +11,8 @@ from crewai.project import CrewBase, agent, crew, task class PoemCrew: """Poem Crew""" - agents: List[BaseAgent] - tasks: List[Task] + agents: list[BaseAgent] + tasks: list[Task] # Learn more about YAML configuration files here: # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended diff --git a/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml b/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml index d80f05bee..cfc68f74b 100644 --- a/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml +++ b/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml @@ -5,7 +5,7 @@ description = "{{name}} using crewAI" authors = [{ name = "Your Name", email = "you@example.com" }] requires-python = ">=3.10,<3.14" dependencies = [ - "crewai[tools]==1.9.3" + "crewai[tools]==1.10.1" ] [project.scripts] diff --git a/lib/crewai/src/crewai/cli/templates/tool/pyproject.toml b/lib/crewai/src/crewai/cli/templates/tool/pyproject.toml index 61d4343b9..0e8c784f0 100644 --- a/lib/crewai/src/crewai/cli/templates/tool/pyproject.toml +++ b/lib/crewai/src/crewai/cli/templates/tool/pyproject.toml @@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}" readme = "README.md" requires-python = ">=3.10,<3.14" dependencies = [ - "crewai[tools]>=0.203.1" + "crewai[tools]==1.10.1" ] [tool.crewai] diff --git a/lib/crewai/src/crewai/cli/tools/main.py b/lib/crewai/src/crewai/cli/tools/main.py index e2dd21dde..0a9f68af0 100644 --- a/lib/crewai/src/crewai/cli/tools/main.py +++ b/lib/crewai/src/crewai/cli/tools/main.py @@ -23,6 +23,7 @@ from crewai.cli.utils import ( tree_copy, tree_find_and_replace, ) +from crewai.events.listeners.tracing.utils import get_user_id console = Console() @@ -169,7 +170,9 @@ class ToolCommand(BaseCommand, PlusAPIMixin): console.print(f"Successfully installed {handle}", style="bold green") def login(self) -> None: - login_response = self.plus_api_client.login_to_tool_repository() + login_response = self.plus_api_client.login_to_tool_repository( + user_identifier=get_user_id() + ) if login_response.status_code != 200: console.print( diff --git a/lib/crewai/src/crewai/cli/utils.py b/lib/crewai/src/crewai/cli/utils.py index b73f9f76b..6ee181ea1 100644 --- a/lib/crewai/src/crewai/cli/utils.py +++ b/lib/crewai/src/crewai/cli/utils.py @@ -386,6 +386,109 @@ def fetch_crews(module_attr: Any) -> list[Crew]: return crew_instances +def get_flow_instance(module_attr: Any) -> Flow | None: + """Check if a module attribute is a user-defined Flow subclass and return an instance. + + Args: + module_attr: An attribute from a loaded module. + + Returns: + A Flow instance if the attribute is a valid user-defined Flow subclass, + None otherwise. + """ + if ( + isinstance(module_attr, type) + and issubclass(module_attr, Flow) + and module_attr is not Flow + ): + try: + return module_attr() + except Exception: + return None + return None + + +_SKIP_DIRS = frozenset( + {".venv", "venv", ".git", "__pycache__", "node_modules", ".tox", ".nox"} +) + + +def get_flows(flow_path: str = "main.py") -> list[Flow]: + """Get the flow instances from project files. + + Walks the project directory looking for files matching ``flow_path`` + (default ``main.py``), loads each module, and extracts Flow subclass + instances. Directories that are clearly not user source code (virtual + environments, ``.git``, etc.) are pruned to avoid noisy import errors. + + Args: + flow_path: Filename to search for (default ``main.py``). + + Returns: + A list of discovered Flow instances. + """ + flow_instances: list[Flow] = [] + try: + current_dir = os.getcwd() + if current_dir not in sys.path: + sys.path.insert(0, current_dir) + + src_dir = os.path.join(current_dir, "src") + if os.path.isdir(src_dir) and src_dir not in sys.path: + sys.path.insert(0, src_dir) + + search_paths = [".", "src"] if os.path.isdir("src") else ["."] + + for search_path in search_paths: + for root, dirs, files in os.walk(search_path): + dirs[:] = [ + d + for d in dirs + if d not in _SKIP_DIRS and not d.startswith(".") + ] + if flow_path in files and "cli/templates" not in root: + file_os_path = os.path.join(root, flow_path) + try: + spec = importlib.util.spec_from_file_location( + "flow_module", file_os_path + ) + if not spec or not spec.loader: + continue + + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + + try: + spec.loader.exec_module(module) + + for attr_name in dir(module): + module_attr = getattr(module, attr_name) + try: + if flow_instance := get_flow_instance( + module_attr + ): + flow_instances.append(flow_instance) + except Exception: # noqa: S112 + continue + + if flow_instances: + break + + except Exception: # noqa: S112 + continue + + except (ImportError, AttributeError): + continue + + if flow_instances: + break + + except Exception: # noqa: S110 + pass + + return flow_instances + + def is_valid_tool(obj: Any) -> bool: from crewai.tools.base_tool import Tool diff --git a/lib/crewai/src/crewai/crew.py b/lib/crewai/src/crewai/crew.py index 2788170eb..2f9fd5aad 100644 --- a/lib/crewai/src/crewai/crew.py +++ b/lib/crewai/src/crewai/crew.py @@ -84,10 +84,6 @@ from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.llm import LLM from crewai.llms.base_llm import BaseLLM -from crewai.memory.entity.entity_memory import EntityMemory -from crewai.memory.external.external_memory import ExternalMemory -from crewai.memory.long_term.long_term_memory import LongTermMemory -from crewai.memory.short_term.short_term_memory import ShortTermMemory from crewai.process import Process from crewai.rag.embeddings.types import EmbedderConfig from crewai.rag.types import SearchResult @@ -175,10 +171,7 @@ class Crew(FlowTrackable, BaseModel): _logger: Logger = PrivateAttr() _file_handler: FileHandler = PrivateAttr() _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default_factory=CacheHandler) - _short_term_memory: InstanceOf[ShortTermMemory] | None = PrivateAttr() - _long_term_memory: InstanceOf[LongTermMemory] | None = PrivateAttr() - _entity_memory: InstanceOf[EntityMemory] | None = PrivateAttr() - _external_memory: InstanceOf[ExternalMemory] | None = PrivateAttr() + _memory: Any = PrivateAttr(default=None) # Unified Memory | MemoryScope _train: bool | None = PrivateAttr(default=False) _train_iteration: int | None = PrivateAttr() _inputs: dict[str, Any] | None = PrivateAttr(default=None) @@ -196,25 +189,12 @@ class Crew(FlowTrackable, BaseModel): agents: list[BaseAgent] = Field(default_factory=list) process: Process = Field(default=Process.sequential) verbose: bool = Field(default=False) - memory: bool = Field( + memory: bool | Any = Field( default=False, - description="If crew should use memory to store memories of it's execution", - ) - short_term_memory: InstanceOf[ShortTermMemory] | None = Field( - default=None, - description="An Instance of the ShortTermMemory to be used by the Crew", - ) - long_term_memory: InstanceOf[LongTermMemory] | None = Field( - default=None, - description="An Instance of the LongTermMemory to be used by the Crew", - ) - entity_memory: InstanceOf[EntityMemory] | None = Field( - default=None, - description="An Instance of the EntityMemory to be used by the Crew", - ) - external_memory: InstanceOf[ExternalMemory] | None = Field( - default=None, - description="An Instance of the ExternalMemory to be used by the Crew", + description=( + "Enable crew memory. Pass True for default Memory(), " + "or a Memory/MemoryScope/MemorySlice instance for custom configuration." + ), ) embedder: EmbedderConfig | None = Field( default=None, @@ -377,31 +357,23 @@ class Crew(FlowTrackable, BaseModel): return self - def _initialize_default_memories(self) -> None: - self._long_term_memory = self._long_term_memory or LongTermMemory() - self._short_term_memory = self._short_term_memory or ShortTermMemory( - crew=self, - embedder_config=self.embedder, - ) - self._entity_memory = self.entity_memory or EntityMemory( - crew=self, embedder_config=self.embedder - ) - @model_validator(mode="after") def create_crew_memory(self) -> Crew: - """Initialize private memory attributes.""" - self._external_memory = ( - # External memory does not support a default value since it was - # designed to be managed entirely externally - self.external_memory.set_crew(self) if self.external_memory else None - ) + """Initialize unified memory, respecting crew embedder config.""" + if self.memory is True: + from crewai.memory.unified_memory import Memory - self._long_term_memory = self.long_term_memory - self._short_term_memory = self.short_term_memory - self._entity_memory = self.entity_memory + embedder = None + if self.embedder is not None: + from crewai.rag.embeddings.factory import build_embedder - if self.memory: - self._initialize_default_memories() + embedder = build_embedder(cast(dict[str, Any], self.embedder)) + self._memory = Memory(embedder=embedder) + elif self.memory: + # User passed a Memory / MemoryScope / MemorySlice instance + self._memory = self.memory + else: + self._memory = None return self @@ -773,6 +745,9 @@ class Crew(FlowTrackable, BaseModel): ) raise finally: + # Ensure all background memory saves complete before returning + if self._memory is not None and hasattr(self._memory, "drain_writes"): + self._memory.drain_writes() clear_files(self.id) detach(token) @@ -1328,6 +1303,11 @@ class Crew(FlowTrackable, BaseModel): if agent and (hasattr(agent, "mcps") and getattr(agent, "mcps", None)): tools = self._add_mcp_tools(task, tools) + # Add memory tools if memory is available (agent or crew level) + resolved_memory = getattr(agent, "memory", None) or self._memory + if resolved_memory is not None: + tools = self._add_memory_tools(tools, resolved_memory) + files = get_all_files(self.id, task.id) if files: supported_types: list[str] = [] @@ -1435,6 +1415,20 @@ class Crew(FlowTrackable, BaseModel): return self._merge_tools(tools, cast(list[BaseTool], code_tools)) return tools + def _add_memory_tools(self, tools: list[BaseTool], memory: Any) -> list[BaseTool]: + """Add recall and remember tools when memory is available. + + Args: + tools: Current list of tools. + memory: The resolved Memory, MemoryScope, or MemorySlice instance. + + Returns: + Updated list with memory tools added. + """ + from crewai.tools.memory_tools import create_memory_tools + + return self._merge_tools(tools, create_memory_tools(memory)) + def _add_file_tools( self, tools: list[BaseTool], files: dict[str, Any] ) -> list[BaseTool]: @@ -1679,10 +1673,7 @@ class Crew(FlowTrackable, BaseModel): "_execution_span", "_file_handler", "_cache_handler", - "_short_term_memory", - "_long_term_memory", - "_entity_memory", - "_external_memory", + "_memory", "agents", "tasks", "knowledge_sources", @@ -1716,18 +1707,8 @@ class Crew(FlowTrackable, BaseModel): copied_data = self.model_dump(exclude=exclude) copied_data = {k: v for k, v in copied_data.items() if v is not None} - if self.short_term_memory: - copied_data["short_term_memory"] = self.short_term_memory.model_copy( - deep=True - ) - if self.long_term_memory: - copied_data["long_term_memory"] = self.long_term_memory.model_copy( - deep=True - ) - if self.entity_memory: - copied_data["entity_memory"] = self.entity_memory.model_copy(deep=True) - if self.external_memory: - copied_data["external_memory"] = self.external_memory.model_copy(deep=True) + if getattr(self, "_memory", None): + copied_data["memory"] = self._memory copied_data.pop("agents", None) copied_data.pop("tasks", None) @@ -1858,23 +1839,24 @@ class Crew(FlowTrackable, BaseModel): Args: command_type: Type of memory to reset. - Valid options: 'long', 'short', 'entity', 'knowledge', 'agent_knowledge' - 'kickoff_outputs', or 'all' + Valid options: 'memory', 'knowledge', 'agent_knowledge', + 'kickoff_outputs', or 'all'. Legacy names 'long', 'short', + 'entity', 'external' are treated as 'memory'. Raises: ValueError: If an invalid command type is provided. RuntimeError: If memory reset operation fails. """ + legacy_memory = frozenset(["long", "short", "entity", "external"]) + if command_type in legacy_memory: + command_type = "memory" valid_types = frozenset( [ - "long", - "short", - "entity", + "memory", "knowledge", "agent_knowledge", "kickoff_outputs", "all", - "external", ] ) @@ -1980,25 +1962,10 @@ class Crew(FlowTrackable, BaseModel): ) + agent_knowledges return { - "short": { - "system": getattr(self, "_short_term_memory", None), + "memory": { + "system": getattr(self, "_memory", None), "reset": default_reset, - "name": "Short Term", - }, - "entity": { - "system": getattr(self, "_entity_memory", None), - "reset": default_reset, - "name": "Entity", - }, - "external": { - "system": getattr(self, "_external_memory", None), - "reset": default_reset, - "name": "External", - }, - "long": { - "system": getattr(self, "_long_term_memory", None), - "reset": default_reset, - "name": "Long Term", + "name": "Memory", }, "kickoff_outputs": { "system": getattr(self, "_task_output_handler", None), diff --git a/lib/crewai/src/crewai/crews/__init__.py b/lib/crewai/src/crewai/crews/__init__.py index 8b46d5c2b..10bee3117 100644 --- a/lib/crewai/src/crewai/crews/__init__.py +++ b/lib/crewai/src/crewai/crews/__init__.py @@ -1,5 +1,4 @@ from crewai.crews.crew_output import CrewOutput - __all__ = ["CrewOutput"] diff --git a/lib/crewai/src/crewai/events/__init__.py b/lib/crewai/src/crewai/events/__init__.py index e18893fc6..bcdafe49a 100644 --- a/lib/crewai/src/crewai/events/__init__.py +++ b/lib/crewai/src/crewai/events/__init__.py @@ -63,6 +63,7 @@ from crewai.events.types.logging_events import ( AgentLogsStartedEvent, ) from crewai.events.types.mcp_events import ( + MCPConfigFetchFailedEvent, MCPConnectionCompletedEvent, MCPConnectionFailedEvent, MCPConnectionStartedEvent, @@ -173,6 +174,7 @@ __all__ = [ "LiteAgentExecutionCompletedEvent", "LiteAgentExecutionErrorEvent", "LiteAgentExecutionStartedEvent", + "MCPConfigFetchFailedEvent", "MCPConnectionCompletedEvent", "MCPConnectionFailedEvent", "MCPConnectionStartedEvent", diff --git a/lib/crewai/src/crewai/events/base_event_listener.py b/lib/crewai/src/crewai/events/base_event_listener.py index 2319c9f97..c0187674b 100644 --- a/lib/crewai/src/crewai/events/base_event_listener.py +++ b/lib/crewai/src/crewai/events/base_event_listener.py @@ -23,4 +23,3 @@ class BaseEventListener(ABC): Args: crewai_event_bus: The event bus to register listeners on. """ - pass diff --git a/lib/crewai/src/crewai/events/event_listener.py b/lib/crewai/src/crewai/events/event_listener.py index 5f22d0188..09dc25316 100644 --- a/lib/crewai/src/crewai/events/event_listener.py +++ b/lib/crewai/src/crewai/events/event_listener.py @@ -68,6 +68,7 @@ from crewai.events.types.logging_events import ( AgentLogsStartedEvent, ) from crewai.events.types.mcp_events import ( + MCPConfigFetchFailedEvent, MCPConnectionCompletedEvent, MCPConnectionFailedEvent, MCPConnectionStartedEvent, @@ -665,6 +666,16 @@ class EventListener(BaseEventListener): event.error_type, ) + @crewai_event_bus.on(MCPConfigFetchFailedEvent) + def on_mcp_config_fetch_failed( + _: Any, event: MCPConfigFetchFailedEvent + ) -> None: + self.formatter.handle_mcp_config_fetch_failed( + event.slug, + event.error, + event.error_type, + ) + @crewai_event_bus.on(MCPToolExecutionStartedEvent) def on_mcp_tool_execution_started( _: Any, event: MCPToolExecutionStartedEvent diff --git a/lib/crewai/src/crewai/events/event_types.py b/lib/crewai/src/crewai/events/event_types.py index 5fca4bd7d..63b6cdfc8 100644 --- a/lib/crewai/src/crewai/events/event_types.py +++ b/lib/crewai/src/crewai/events/event_types.py @@ -67,6 +67,7 @@ from crewai.events.types.llm_guardrail_events import ( LLMGuardrailStartedEvent, ) from crewai.events.types.mcp_events import ( + MCPConfigFetchFailedEvent, MCPConnectionCompletedEvent, MCPConnectionFailedEvent, MCPConnectionStartedEvent, @@ -181,4 +182,5 @@ EventTypes = ( | MCPToolExecutionStartedEvent | MCPToolExecutionCompletedEvent | MCPToolExecutionFailedEvent + | MCPConfigFetchFailedEvent ) diff --git a/lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py b/lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py index 6c45f63ef..da25792fb 100644 --- a/lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py +++ b/lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py @@ -15,6 +15,7 @@ from crewai.cli.plus_api import PlusAPI from crewai.cli.version import get_crewai_version from crewai.events.listeners.tracing.types import TraceEvent from crewai.events.listeners.tracing.utils import ( + get_user_id, is_tracing_enabled_in_context, should_auto_collect_first_time_traces, ) @@ -67,7 +68,7 @@ class TraceBatchManager: api_key=get_auth_token(), ) except AuthError: - self.plus_api = PlusAPI(api_key="") + self.plus_api = PlusAPI() self.ephemeral_trace_url = None def initialize_batch( @@ -120,7 +121,6 @@ class TraceBatchManager: payload = { "trace_id": self.current_batch.batch_id, "execution_type": execution_metadata.get("execution_type", "crew"), - "user_identifier": execution_metadata.get("user_context", None), "execution_context": { "crew_fingerprint": execution_metadata.get("crew_fingerprint"), "crew_name": execution_metadata.get("crew_name", None), @@ -140,6 +140,7 @@ class TraceBatchManager: } if use_ephemeral: payload["ephemeral_trace_id"] = self.current_batch.batch_id + payload["user_identifier"] = get_user_id() response = ( self.plus_api.initialize_ephemeral_trace_batch(payload) diff --git a/lib/crewai/src/crewai/events/types/flow_events.py b/lib/crewai/src/crewai/events/types/flow_events.py index 826722762..3eea1bbdd 100644 --- a/lib/crewai/src/crewai/events/types/flow_events.py +++ b/lib/crewai/src/crewai/events/types/flow_events.py @@ -120,6 +120,52 @@ class FlowPlotEvent(FlowEvent): type: str = "flow_plot" +class FlowInputRequestedEvent(FlowEvent): + """Event emitted when a flow requests user input via ``Flow.ask()``. + + This event is emitted before the flow suspends waiting for user input, + allowing UI frameworks and observability tools to know when a flow + needs user interaction. + + Attributes: + flow_name: Name of the flow requesting input. + method_name: Name of the flow method that called ``ask()``. + message: The question or prompt being shown to the user. + metadata: Optional metadata sent with the question (e.g., user ID, + channel, session context). + """ + + method_name: str + message: str + metadata: dict[str, Any] | None = None + type: str = "flow_input_requested" + + +class FlowInputReceivedEvent(FlowEvent): + """Event emitted when user input is received after ``Flow.ask()``. + + This event is emitted after the user provides input (or the request + times out), allowing UI frameworks and observability tools to track + input collection. + + Attributes: + flow_name: Name of the flow that received input. + method_name: Name of the flow method that called ``ask()``. + message: The original question or prompt. + response: The user's response, or None if timed out / unavailable. + metadata: Optional metadata sent with the question. + response_metadata: Optional metadata from the provider about the + response (e.g., who responded, thread ID, timestamps). + """ + + method_name: str + message: str + response: str | None = None + metadata: dict[str, Any] | None = None + response_metadata: dict[str, Any] | None = None + type: str = "flow_input_received" + + class HumanFeedbackRequestedEvent(FlowEvent): """Event emitted when human feedback is requested. diff --git a/lib/crewai/src/crewai/events/types/llm_events.py b/lib/crewai/src/crewai/events/types/llm_events.py index 87087f100..73d743804 100644 --- a/lib/crewai/src/crewai/events/types/llm_events.py +++ b/lib/crewai/src/crewai/events/types/llm_events.py @@ -86,3 +86,11 @@ class LLMStreamChunkEvent(LLMEventBase): tool_call: ToolCall | None = None call_type: LLMCallType | None = None response_id: str | None = None + + +class LLMThinkingChunkEvent(LLMEventBase): + """Event emitted when a thinking/reasoning chunk is received from a thinking model""" + + type: str = "llm_thinking_chunk" + chunk: str + response_id: str | None = None diff --git a/lib/crewai/src/crewai/events/types/mcp_events.py b/lib/crewai/src/crewai/events/types/mcp_events.py index d360aa62a..d6ca9b99a 100644 --- a/lib/crewai/src/crewai/events/types/mcp_events.py +++ b/lib/crewai/src/crewai/events/types/mcp_events.py @@ -83,3 +83,16 @@ class MCPToolExecutionFailedEvent(MCPEvent): error_type: str | None = None # "timeout", "validation", "server_error", etc. started_at: datetime | None = None failed_at: datetime | None = None + + +class MCPConfigFetchFailedEvent(BaseEvent): + """Event emitted when fetching an AMP MCP server config fails. + + This covers cases where the slug is not connected, the API call + failed, or native MCP resolution failed after config was fetched. + """ + + type: str = "mcp_config_fetch_failed" + slug: str + error: str + error_type: str | None = None # "not_connected", "api_error", "connection_failed" diff --git a/lib/crewai/src/crewai/events/utils/console_formatter.py b/lib/crewai/src/crewai/events/utils/console_formatter.py index 4d3b71495..77cc76f4b 100644 --- a/lib/crewai/src/crewai/events/utils/console_formatter.py +++ b/lib/crewai/src/crewai/events/utils/console_formatter.py @@ -170,16 +170,16 @@ To enable tracing, do any one of these: """Create standardized status content with consistent formatting.""" content = Text() content.append(f"{title}\n", style=f"{status_style} bold") - content.append("Name: \n", style="white") + content.append("Name: ", style="white") content.append(f"{name}\n", style=status_style) for label, value in fields.items(): - content.append(f"{label}: \n", style="white") + content.append(f"{label}: ", style="white") content.append( f"{value}\n", style=fields.get(f"{label}_style", status_style) ) if tool_args: - content.append("Tool Args: \n", style="white") + content.append("Tool Args: ", style="white") content.append(f"{tool_args}\n", style=status_style) return content @@ -737,6 +737,27 @@ To enable tracing, do any one of these: self.print_panel(content, title, style) + @staticmethod + def _simplify_tools_field(fields: dict[str, Any]) -> dict[str, Any]: + """Simplify the tools field to show only tool names instead of full definitions. + + Args: + fields: Dictionary of fields that may contain a 'tools' key with + full tool objects. + + Returns: + The fields dictionary with 'tools' replaced by a comma-separated + string of tool names. + """ + if "tools" in fields: + tools = fields["tools"] + if tools: + tool_names = [getattr(t, "name", str(t)) for t in tools] + fields["tools"] = ", ".join(tool_names) if tool_names else "None" + else: + fields["tools"] = "None" + return fields + def handle_lite_agent_execution( self, lite_agent_role: str, @@ -748,6 +769,8 @@ To enable tracing, do any one of these: if not self.verbose: return + fields = self._simplify_tools_field(fields) + if status == "started": self.create_lite_agent_branch(lite_agent_role) if fields: @@ -1489,6 +1512,34 @@ To enable tracing, do any one of these: self.print(panel) self.print() + def handle_mcp_config_fetch_failed( + self, + slug: str, + error: str = "", + error_type: str | None = None, + ) -> None: + """Handle MCP config fetch failed event (AMP resolution failures).""" + if not self.verbose: + return + + content = Text() + content.append("MCP Config Fetch Failed\n\n", style="red bold") + content.append("Server: ", style="white") + content.append(f"{slug}\n", style="red") + + if error_type: + content.append("Error Type: ", style="white") + content.append(f"{error_type}\n", style="red") + + if error: + content.append("\nError: ", style="white bold") + error_preview = error[:500] + "..." if len(error) > 500 else error + content.append(f"{error_preview}\n", style="red") + + panel = self.create_panel(content, "❌ MCP Config Failed", "red") + self.print(panel) + self.print() + def handle_mcp_tool_execution_started( self, server_name: str, diff --git a/lib/crewai/src/crewai/experimental/agent_executor.py b/lib/crewai/src/crewai/experimental/agent_executor.py index 9f2fecb25..b0662f6c6 100644 --- a/lib/crewai/src/crewai/experimental/agent_executor.py +++ b/lib/crewai/src/crewai/experimental/agent_executor.py @@ -1,7 +1,11 @@ from __future__ import annotations +import asyncio +import contextvars from collections.abc import Callable, Coroutine +from concurrent.futures import ThreadPoolExecutor, as_completed from datetime import datetime +import inspect import json import threading from typing import TYPE_CHECKING, Any, Literal, cast @@ -49,6 +53,8 @@ from crewai.hooks.types import ( BeforeLLMCallHookCallable, BeforeLLMCallHookType, ) +from crewai.tools.base_tool import BaseTool +from crewai.tools.structured_tool import CrewStructuredTool from crewai.utilities.agent_utils import ( convert_tools_to_openai_schema, enforce_rpm_limit, @@ -63,6 +69,7 @@ from crewai.utilities.agent_utils import ( has_reached_max_iterations, is_context_length_exceeded, is_inside_event_loop, + parse_tool_call_args, process_llm_response, track_delegation_if_needed, ) @@ -81,8 +88,6 @@ if TYPE_CHECKING: from crewai.crew import Crew from crewai.llms.base_llm import BaseLLM from crewai.task import Task - from crewai.tools.base_tool import BaseTool - from crewai.tools.structured_tool import CrewStructuredTool from crewai.tools.tool_types import ToolResult from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult @@ -298,6 +303,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): super().__init__( suppress_flow_events=True, tracing=current_tracing if current_tracing else None, + max_method_calls=self.max_iter * 10, ) self._flow_initialized = True @@ -317,7 +323,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): def _setup_native_tools(self) -> None: """Convert tools to OpenAI schema format for native function calling.""" if self.original_tools: - self._openai_tools, self._available_functions = ( + self._openai_tools, self._available_functions, self._tool_name_mapping = ( convert_tools_to_openai_schema(self.original_tools) ) @@ -399,7 +405,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): self._setup_native_tools() return "initialized" - @listen("force_final_answer") + @listen("max_iterations_exceeded") def force_final_answer(self) -> Literal["agent_finished"]: """Force agent to provide final answer when max iterations exceeded.""" formatted_answer = handle_max_iterations_exceeded( @@ -590,21 +596,19 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): def execute_tool_action(self) -> Literal["tool_completed", "tool_result_is_final"]: """Execute the tool action and handle the result.""" + action = cast(AgentAction, self.state.current_answer) + + fingerprint_context = {} + if ( + self.agent + and hasattr(self.agent, "security_config") + and hasattr(self.agent.security_config, "fingerprint") + ): + fingerprint_context = { + "agent_fingerprint": str(self.agent.security_config.fingerprint) + } + try: - action = cast(AgentAction, self.state.current_answer) - - # Extract fingerprint context for tool execution - fingerprint_context = {} - if ( - self.agent - and hasattr(self.agent, "security_config") - and hasattr(self.agent.security_config, "fingerprint") - ): - fingerprint_context = { - "agent_fingerprint": str(self.agent.security_config.fingerprint) - } - - # Execute the tool tool_result = execute_tool_and_check_finality( agent_action=action, fingerprint_context=fingerprint_context, @@ -618,24 +622,19 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): function_calling_llm=self.function_calling_llm, crew=self.crew, ) + except Exception as e: + if self.agent and self.agent.verbose: + self._printer.print( + content=f"Error in tool execution: {e}", color="red" + ) + if self.task: + self.task.increment_tools_errors() - # Handle agent action and append observation to messages - result = self._handle_agent_action(action, tool_result) - self.state.current_answer = result + error_observation = f"\nObservation: Error executing tool: {e}" + action.text += error_observation + action.result = str(e) + self._append_message_to_state(action.text) - # Invoke step callback if configured - self._invoke_step_callback(result) - - # Append result message to conversation state - if hasattr(result, "text"): - self._append_message_to_state(result.text) - - # Check if tool result became a final answer (result_as_answer flag) - if isinstance(result, AgentFinish): - self.state.is_finished = True - return "tool_result_is_final" - - # Inject post-tool reasoning prompt to enforce analysis reasoning_prompt = self._i18n.slice("post_tool_reasoning") reasoning_message: LLMMessage = { "role": "user", @@ -645,12 +644,26 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): return "tool_completed" - except Exception as e: - error_text = Text() - error_text.append("❌ Error in tool execution: ", style="red bold") - error_text.append(str(e), style="red") - self._console.print(error_text) - raise + result = self._handle_agent_action(action, tool_result) + self.state.current_answer = result + + self._invoke_step_callback(result) + + if hasattr(result, "text"): + self._append_message_to_state(result.text) + + if isinstance(result, AgentFinish): + self.state.is_finished = True + return "tool_result_is_final" + + reasoning_prompt = self._i18n.slice("post_tool_reasoning") + reasoning_message_post: LLMMessage = { + "role": "user", + "content": reasoning_prompt, + } + self.state.messages.append(reasoning_message_post) + + return "tool_completed" @listen("native_tool_calls") def execute_native_tool( @@ -668,9 +681,12 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): if not self.state.pending_tool_calls: return "native_tool_completed" + pending_tool_calls = list(self.state.pending_tool_calls) + self.state.pending_tool_calls.clear() + # Group all tool calls into a single assistant message tool_calls_to_report = [] - for tool_call in self.state.pending_tool_calls: + for tool_call in pending_tool_calls: info = extract_tool_call_info(tool_call) if not info: continue @@ -695,202 +711,99 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): "content": None, "tool_calls": tool_calls_to_report, } - if all( - type(tc).__qualname__ == "Part" for tc in self.state.pending_tool_calls - ): - assistant_message["raw_tool_call_parts"] = list( - self.state.pending_tool_calls - ) + if all(type(tc).__qualname__ == "Part" for tc in pending_tool_calls): + assistant_message["raw_tool_call_parts"] = list(pending_tool_calls) self.state.messages.append(assistant_message) - # Now execute each tool - while self.state.pending_tool_calls: - tool_call = self.state.pending_tool_calls.pop(0) - info = extract_tool_call_info(tool_call) - if not info: - continue + runnable_tool_calls = [ + tool_call + for tool_call in pending_tool_calls + if extract_tool_call_info(tool_call) is not None + ] + should_parallelize = self._should_parallelize_native_tool_calls( + runnable_tool_calls + ) - call_id, func_name, func_args = info - - # Parse arguments - if isinstance(func_args, str): - try: - args_dict = json.loads(func_args) - except json.JSONDecodeError: - args_dict = {} - else: - args_dict = func_args - - # Get agent_key for event tracking - agent_key = ( - getattr(self.agent, "key", "unknown") if self.agent else "unknown" - ) - - # Find original tool by matching sanitized name (needed for cache_function and result_as_answer) - original_tool = None - for tool in self.original_tools or []: - if sanitize_tool_name(tool.name) == func_name: - original_tool = tool - break - - # Check if tool has reached max usage count - max_usage_reached = False - if ( - original_tool - and original_tool.max_usage_count is not None - and original_tool.current_usage_count >= original_tool.max_usage_count - ): - max_usage_reached = True - - # Check cache before executing - from_cache = False - input_str = json.dumps(args_dict) if args_dict else "" - if self.tools_handler and self.tools_handler.cache: - cached_result = self.tools_handler.cache.read( - tool=func_name, input=input_str + execution_results: list[dict[str, Any]] = [] + if should_parallelize: + max_workers = min(8, len(runnable_tool_calls)) + with ThreadPoolExecutor(max_workers=max_workers) as pool: + future_to_idx = { + pool.submit(contextvars.copy_context().run, self._execute_single_native_tool_call, tool_call): idx + for idx, tool_call in enumerate(runnable_tool_calls) + } + ordered_results: list[dict[str, Any] | None] = [None] * len( + runnable_tool_calls ) - if cached_result is not None: - result = ( - str(cached_result) - if not isinstance(cached_result, str) - else cached_result - ) - from_cache = True - - # Emit tool usage started event - started_at = datetime.now() - crewai_event_bus.emit( - self, - event=ToolUsageStartedEvent( - tool_name=func_name, - tool_args=args_dict, - from_agent=self.agent, - from_task=self.task, - agent_key=agent_key, - ), - ) - error_event_emitted = False - - track_delegation_if_needed(func_name, args_dict, self.task) - - structured_tool: CrewStructuredTool | None = None - for structured in self.tools or []: - if sanitize_tool_name(structured.name) == func_name: - structured_tool = structured - break - - hook_blocked = False - before_hook_context = ToolCallHookContext( - tool_name=func_name, - tool_input=args_dict, - tool=structured_tool, # type: ignore[arg-type] - agent=self.agent, - task=self.task, - crew=self.crew, - ) - before_hooks = get_before_tool_call_hooks() - try: - for hook in before_hooks: - hook_result = hook(before_hook_context) - if hook_result is False: - hook_blocked = True - break - except Exception as hook_error: - if self.agent.verbose: - self._printer.print( - content=f"Error in before_tool_call hook: {hook_error}", - color="red", - ) - - if hook_blocked: - result = f"Tool execution blocked by hook. Tool: {func_name}" - elif not from_cache and not max_usage_reached: - result = "Tool not found" - if func_name in self._available_functions: + for future in as_completed(future_to_idx): + idx = future_to_idx[future] try: - tool_func = self._available_functions[func_name] - raw_result = tool_func(**args_dict) - - # Add to cache after successful execution (before string conversion) - if self.tools_handler and self.tools_handler.cache: - should_cache = True - if original_tool: - should_cache = original_tool.cache_function( - args_dict, raw_result - ) - if should_cache: - self.tools_handler.cache.add( - tool=func_name, input=input_str, output=raw_result - ) - - # Convert to string for message - result = ( - str(raw_result) - if not isinstance(raw_result, str) - else raw_result - ) + ordered_results[idx] = future.result() except Exception as e: - result = f"Error executing tool: {e}" - if self.task: - self.task.increment_tools_errors() - # Emit tool usage error event - crewai_event_bus.emit( - self, - event=ToolUsageErrorEvent( - tool_name=func_name, - tool_args=args_dict, - from_agent=self.agent, - from_task=self.task, - agent_key=agent_key, - error=e, - ), - ) - error_event_emitted = True - elif max_usage_reached and original_tool: - # Return error message when max usage limit is reached - result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore." + tool_call = runnable_tool_calls[idx] + info = extract_tool_call_info(tool_call) + call_id = info[0] if info else "unknown" + func_name = info[1] if info else "unknown" + ordered_results[idx] = { + "call_id": call_id, + "func_name": func_name, + "result": f"Error executing tool: {e}", + "from_cache": False, + "original_tool": None, + } + execution_results = [ + result for result in ordered_results if result is not None + ] + else: + # Execute sequentially so result_as_answer tools can short-circuit + # immediately without running remaining calls. + for tool_call in runnable_tool_calls: + execution_result = self._execute_single_native_tool_call(tool_call) + call_id = cast(str, execution_result["call_id"]) + func_name = cast(str, execution_result["func_name"]) + result = cast(str, execution_result["result"]) + from_cache = cast(bool, execution_result["from_cache"]) + original_tool = execution_result["original_tool"] - # Execute after_tool_call hooks (even if blocked, to allow logging/monitoring) - after_hook_context = ToolCallHookContext( - tool_name=func_name, - tool_input=args_dict, - tool=structured_tool, # type: ignore[arg-type] - agent=self.agent, - task=self.task, - crew=self.crew, - tool_result=result, - ) - after_hooks = get_after_tool_call_hooks() - try: - for after_hook in after_hooks: - after_hook_result = after_hook(after_hook_context) - if after_hook_result is not None: - result = after_hook_result - after_hook_context.tool_result = result - except Exception as hook_error: - if self.agent.verbose: + tool_message: LLMMessage = { + "role": "tool", + "tool_call_id": call_id, + "name": func_name, + "content": result, + } + self.state.messages.append(tool_message) + + # Log the tool execution + if self.agent and self.agent.verbose: + cache_info = " (from cache)" if from_cache else "" self._printer.print( - content=f"Error in after_tool_call hook: {hook_error}", - color="red", + content=f"Tool {func_name} executed with result{cache_info}: {result[:200]}...", + color="green", ) - if not error_event_emitted: - crewai_event_bus.emit( - self, - event=ToolUsageFinishedEvent( + if ( + original_tool + and hasattr(original_tool, "result_as_answer") + and original_tool.result_as_answer + ): + self.state.current_answer = AgentFinish( + thought="Tool result is the final answer", output=result, - tool_name=func_name, - tool_args=args_dict, - from_agent=self.agent, - from_task=self.task, - agent_key=agent_key, - started_at=started_at, - finished_at=datetime.now(), - ), - ) + text=result, + ) + self.state.is_finished = True + return "tool_result_is_final" - # Append tool result message - tool_message: LLMMessage = { + return "native_tool_completed" + + for execution_result in execution_results: + call_id = cast(str, execution_result["call_id"]) + func_name = cast(str, execution_result["func_name"]) + result = cast(str, execution_result["result"]) + from_cache = cast(bool, execution_result["from_cache"]) + original_tool = execution_result["original_tool"] + + tool_message = { "role": "tool", "tool_call_id": call_id, "name": func_name, @@ -922,6 +835,249 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): return "native_tool_completed" + def _should_parallelize_native_tool_calls(self, tool_calls: list[Any]) -> bool: + """Determine if native tool calls are safe to run in parallel.""" + if len(tool_calls) <= 1: + return False + + for tool_call in tool_calls: + info = extract_tool_call_info(tool_call) + if not info: + continue + _, func_name, _ = info + + mapping = getattr(self, "_tool_name_mapping", None) + original_tool: BaseTool | None = None + if mapping and func_name in mapping: + mapped = mapping[func_name] + if isinstance(mapped, BaseTool): + original_tool = mapped + if original_tool is None: + for tool in self.original_tools or []: + if sanitize_tool_name(tool.name) == func_name: + original_tool = tool + break + + if not original_tool: + continue + + if getattr(original_tool, "result_as_answer", False): + return False + if getattr(original_tool, "max_usage_count", None) is not None: + return False + + return True + + def _execute_single_native_tool_call(self, tool_call: Any) -> dict[str, Any]: + """Execute a single native tool call and return metadata/result.""" + info = extract_tool_call_info(tool_call) + if not info: + call_id = ( + getattr(tool_call, "id", None) + or (tool_call.get("id") if isinstance(tool_call, dict) else None) + or "unknown" + ) + return { + "call_id": call_id, + "func_name": "unknown", + "result": "Error: Invalid native tool call format", + "from_cache": False, + "original_tool": None, + } + + call_id, func_name, func_args = info + + # Parse arguments + parsed_args, parse_error = parse_tool_call_args(func_args, func_name, call_id) + if parse_error is not None: + return parse_error + args_dict: dict[str, Any] = parsed_args or {} + + # Get agent_key for event tracking + agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown" + + original_tool: BaseTool | None = None + mapping = getattr(self, "_tool_name_mapping", None) + if mapping and func_name in mapping: + mapped = mapping[func_name] + if isinstance(mapped, BaseTool): + original_tool = mapped + if original_tool is None: + for tool in self.original_tools or []: + if sanitize_tool_name(tool.name) == func_name: + original_tool = tool + break + + # Check if tool has reached max usage count + max_usage_reached = False + if ( + original_tool + and original_tool.max_usage_count is not None + and original_tool.current_usage_count >= original_tool.max_usage_count + ): + max_usage_reached = True + + # Check cache before executing + from_cache = False + input_str = json.dumps(args_dict) if args_dict else "" + if self.tools_handler and self.tools_handler.cache: + cached_result = self.tools_handler.cache.read( + tool=func_name, input=input_str + ) + if cached_result is not None: + result = ( + str(cached_result) + if not isinstance(cached_result, str) + else cached_result + ) + from_cache = True + + # Emit tool usage started event + started_at = datetime.now() + crewai_event_bus.emit( + self, + event=ToolUsageStartedEvent( + tool_name=func_name, + tool_args=args_dict, + from_agent=self.agent, + from_task=self.task, + agent_key=agent_key, + ), + ) + error_event_emitted = False + + track_delegation_if_needed(func_name, args_dict, self.task) + + structured_tool: CrewStructuredTool | None = None + if original_tool is not None: + for structured in self.tools or []: + if getattr(structured, "_original_tool", None) is original_tool: + structured_tool = structured + break + if structured_tool is None: + for structured in self.tools or []: + if sanitize_tool_name(structured.name) == func_name: + structured_tool = structured + break + + hook_blocked = False + before_hook_context = ToolCallHookContext( + tool_name=func_name, + tool_input=args_dict, + tool=structured_tool, # type: ignore[arg-type] + agent=self.agent, + task=self.task, + crew=self.crew, + ) + before_hooks = get_before_tool_call_hooks() + try: + for hook in before_hooks: + hook_result = hook(before_hook_context) + if hook_result is False: + hook_blocked = True + break + except Exception as hook_error: + if self.agent.verbose: + self._printer.print( + content=f"Error in before_tool_call hook: {hook_error}", + color="red", + ) + + if hook_blocked: + result = f"Tool execution blocked by hook. Tool: {func_name}" + elif not from_cache and not max_usage_reached: + result = "Tool not found" + if func_name in self._available_functions: + try: + tool_func = self._available_functions[func_name] + raw_result = tool_func(**args_dict) + + # Add to cache after successful execution (before string conversion) + if self.tools_handler and self.tools_handler.cache: + should_cache = True + if original_tool: + should_cache = original_tool.cache_function( + args_dict, raw_result + ) + if should_cache: + self.tools_handler.cache.add( + tool=func_name, input=input_str, output=raw_result + ) + + # Convert to string for message + result = ( + str(raw_result) + if not isinstance(raw_result, str) + else raw_result + ) + except Exception as e: + result = f"Error executing tool: {e}" + if self.task: + self.task.increment_tools_errors() + # Emit tool usage error event + crewai_event_bus.emit( + self, + event=ToolUsageErrorEvent( + tool_name=func_name, + tool_args=args_dict, + from_agent=self.agent, + from_task=self.task, + agent_key=agent_key, + error=e, + ), + ) + error_event_emitted = True + elif max_usage_reached and original_tool: + # Return error message when max usage limit is reached + result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore." + + # Execute after_tool_call hooks (even if blocked, to allow logging/monitoring) + after_hook_context = ToolCallHookContext( + tool_name=func_name, + tool_input=args_dict, + tool=structured_tool, # type: ignore[arg-type] + agent=self.agent, + task=self.task, + crew=self.crew, + tool_result=result, + ) + after_hooks = get_after_tool_call_hooks() + try: + for after_hook in after_hooks: + after_hook_result = after_hook(after_hook_context) + if after_hook_result is not None: + result = after_hook_result + after_hook_context.tool_result = result + except Exception as hook_error: + if self.agent.verbose: + self._printer.print( + content=f"Error in after_tool_call hook: {hook_error}", + color="red", + ) + + if not error_event_emitted: + crewai_event_bus.emit( + self, + event=ToolUsageFinishedEvent( + output=result, + tool_name=func_name, + tool_args=args_dict, + from_agent=self.agent, + from_task=self.task, + agent_key=agent_key, + started_at=started_at, + finished_at=datetime.now(), + ), + ) + + return { + "call_id": call_id, + "func_name": func_name, + "result": result, + "from_cache": from_cache, + "original_tool": original_tool, + } + def _extract_tool_name(self, tool_call: Any) -> str: """Extract tool name from various tool call formats.""" if hasattr(tool_call, "function"): @@ -954,11 +1110,11 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): def check_max_iterations( self, ) -> Literal[ - "force_final_answer", "continue_reasoning", "continue_reasoning_native" + "max_iterations_exceeded", "continue_reasoning", "continue_reasoning_native" ]: """Check if max iterations reached before proceeding with reasoning.""" if has_reached_max_iterations(self.state.iterations, self.max_iter): - return "force_final_answer" + return "max_iterations_exceeded" if self.state.use_native_tools: return "continue_reasoning_native" return "continue_reasoning" @@ -1106,9 +1262,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): if self.state.ask_for_human_input: formatted_answer = self._handle_human_feedback(formatted_answer) - self._create_short_term_memory(formatted_answer) - self._create_long_term_memory(formatted_answer) - self._create_external_memory(formatted_answer) + self._save_to_memory(formatted_answer) return {"output": formatted_answer.output} @@ -1191,9 +1345,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): if self.state.ask_for_human_input: formatted_answer = await self._ahandle_human_feedback(formatted_answer) - self._create_short_term_memory(formatted_answer) - self._create_long_term_memory(formatted_answer) - self._create_external_memory(formatted_answer) + self._save_to_memory(formatted_answer) return {"output": formatted_answer.output} @@ -1256,7 +1408,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin): formatted_answer: Current agent response. """ if self.step_callback: - self.step_callback(formatted_answer) + cb_result = self.step_callback(formatted_answer) + if inspect.iscoroutine(cb_result): + asyncio.run(cb_result) def _append_message_to_state( self, text: str, role: Literal["user", "assistant", "system"] = "assistant" diff --git a/lib/crewai/src/crewai/flow/__init__.py b/lib/crewai/src/crewai/flow/__init__.py index 2e31d9220..ec4a3ac5e 100644 --- a/lib/crewai/src/crewai/flow/__init__.py +++ b/lib/crewai/src/crewai/flow/__init__.py @@ -7,6 +7,7 @@ from crewai.flow.async_feedback import ( from crewai.flow.flow import Flow, and_, listen, or_, router, start from crewai.flow.flow_config import flow_config from crewai.flow.human_feedback import HumanFeedbackResult, human_feedback +from crewai.flow.input_provider import InputProvider, InputResponse from crewai.flow.persistence import persist from crewai.flow.visualization import ( FlowStructure, @@ -22,6 +23,8 @@ __all__ = [ "HumanFeedbackPending", "HumanFeedbackProvider", "HumanFeedbackResult", + "InputProvider", + "InputResponse", "PendingFeedbackContext", "and_", "build_flow_structure", diff --git a/lib/crewai/src/crewai/flow/async_feedback/providers.py b/lib/crewai/src/crewai/flow/async_feedback/providers.py index e86c0a747..65055d650 100644 --- a/lib/crewai/src/crewai/flow/async_feedback/providers.py +++ b/lib/crewai/src/crewai/flow/async_feedback/providers.py @@ -1,7 +1,8 @@ -"""Default provider implementations for human feedback. +"""Default provider implementations for human feedback and user input. This module provides the ConsoleProvider, which is the default synchronous -provider that collects feedback via console input. +provider that collects both feedback (for ``@human_feedback``) and user input +(for ``Flow.ask()``) via console. """ from __future__ import annotations @@ -16,20 +17,23 @@ if TYPE_CHECKING: class ConsoleProvider: - """Default synchronous console-based feedback provider. + """Default synchronous console-based provider for feedback and input. This provider blocks execution and waits for console input from the user. - It displays the method output with formatting and prompts for feedback. + It serves two purposes: + + - **Feedback** (``request_feedback``): Used by ``@human_feedback`` to + display method output and collect review feedback. + - **Input** (``request_input``): Used by ``Flow.ask()`` to prompt the + user with a question and collect a response. This is the default provider used when no custom provider is specified - in the @human_feedback decorator. + in the ``@human_feedback`` decorator or on the Flow's ``input_provider``. - Example: + Example (feedback): ```python from crewai.flow.async_feedback import ConsoleProvider - - # Explicitly use console provider @human_feedback( message="Review this:", provider=ConsoleProvider(), @@ -37,9 +41,20 @@ class ConsoleProvider: def my_method(self): return "Content to review" ``` + + Example (input): + ```python + from crewai.flow import Flow, start + + class MyFlow(Flow): + @start() + def gather_info(self): + topic = self.ask("What topic should we research?") + return topic + ``` """ - def __init__(self, verbose: bool = True): + def __init__(self, verbose: bool = True) -> None: """Initialize the console provider. Args: @@ -124,3 +139,55 @@ class ConsoleProvider: finally: # Resume live updates formatter.resume_live_updates() + + def request_input( + self, + message: str, + flow: Flow[Any], + metadata: dict[str, Any] | None = None, + ) -> str | None: + """Request user input via console (blocking). + + Displays the prompt message with formatting and waits for the user + to type their response. Used by ``Flow.ask()``. + + Unlike ``request_feedback``, this method does not display an + "OUTPUT FOR REVIEW" panel or emit feedback-specific events (those + are handled by ``ask()`` itself). + + Args: + message: The question or prompt to display to the user. + flow: The Flow instance requesting input. + metadata: Optional metadata from the caller. Ignored by the + console provider (console has no concept of user routing). + + Returns: + The user's input as a stripped string. Returns empty string + if user presses Enter without input. Never returns None + (console input is always available). + """ + from crewai.events.event_listener import event_listener + + # Pause live updates during human input + formatter = event_listener.formatter + formatter.pause_live_updates() + + try: + console = formatter.console + + if self.verbose: + console.print() + console.print(message, style="yellow") + console.print() + + response = input(">>> \n").strip() + else: + response = input(f"{message} ").strip() + + # Add line break after input so formatter output starts clean + console.print() + + return response + finally: + # Resume live updates + formatter.resume_live_updates() diff --git a/lib/crewai/src/crewai/flow/flow.py b/lib/crewai/src/crewai/flow/flow.py index f9f6843aa..e8ddc4765 100644 --- a/lib/crewai/src/crewai/flow/flow.py +++ b/lib/crewai/src/crewai/flow/flow.py @@ -10,13 +10,15 @@ import asyncio from collections.abc import ( Callable, ItemsView, + Iterable, Iterator, KeysView, Sequence, ValuesView, ) -from concurrent.futures import Future +from concurrent.futures import Future, ThreadPoolExecutor import copy +import enum import inspect import logging import threading @@ -27,8 +29,10 @@ from typing import ( Generic, Literal, ParamSpec, + SupportsIndex, TypeVar, cast, + overload, ) from uuid import uuid4 @@ -77,7 +81,12 @@ from crewai.flow.flow_wrappers import ( StartMethod, ) from crewai.flow.persistence.base import FlowPersistence -from crewai.flow.types import FlowExecutionData, FlowMethodName, PendingListenerKey +from crewai.flow.types import ( + FlowExecutionData, + FlowMethodName, + InputHistoryEntry, + PendingListenerKey, +) from crewai.flow.utils import ( _extract_all_methods, _extract_all_methods_recursive, @@ -416,13 +425,17 @@ def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition return {"type": AND_CONDITION, "conditions": processed_conditions} -class LockedListProxy(Generic[T]): +class LockedListProxy(list, Generic[T]): # type: ignore[type-arg] """Thread-safe proxy for list operations. - Wraps a list and uses a lock for all mutating operations. + Subclasses ``list`` so that ``isinstance(proxy, list)`` returns True, + which is required by libraries like LanceDB and Pydantic that do strict + type checks. All mutations go through the lock; reads delegate to the + underlying list. """ def __init__(self, lst: list[T], lock: threading.Lock) -> None: + super().__init__() # empty builtin list; all access goes through self._list self._list = lst self._lock = lock @@ -430,11 +443,11 @@ class LockedListProxy(Generic[T]): with self._lock: self._list.append(item) - def extend(self, items: list[T]) -> None: + def extend(self, items: Iterable[T]) -> None: with self._lock: self._list.extend(items) - def insert(self, index: int, item: T) -> None: + def insert(self, index: SupportsIndex, item: T) -> None: with self._lock: self._list.insert(index, item) @@ -442,7 +455,7 @@ class LockedListProxy(Generic[T]): with self._lock: self._list.remove(item) - def pop(self, index: int = -1) -> T: + def pop(self, index: SupportsIndex = -1) -> T: with self._lock: return self._list.pop(index) @@ -450,15 +463,23 @@ class LockedListProxy(Generic[T]): with self._lock: self._list.clear() - def __setitem__(self, index: int, value: T) -> None: + @overload + def __setitem__(self, index: SupportsIndex, value: T) -> None: ... + @overload + def __setitem__(self, index: slice, value: Iterable[T]) -> None: ... + def __setitem__(self, index: Any, value: Any) -> None: with self._lock: self._list[index] = value - def __delitem__(self, index: int) -> None: + def __delitem__(self, index: SupportsIndex | slice) -> None: with self._lock: del self._list[index] - def __getitem__(self, index: int) -> T: + @overload + def __getitem__(self, index: SupportsIndex) -> T: ... + @overload + def __getitem__(self, index: slice) -> list[T]: ... + def __getitem__(self, index: Any) -> Any: return self._list[index] def __len__(self) -> int: @@ -476,14 +497,31 @@ class LockedListProxy(Generic[T]): def __bool__(self) -> bool: return bool(self._list) + def __eq__(self, other: object) -> bool: + """Compare based on the underlying list contents.""" + if isinstance(other, LockedListProxy): + # Avoid deadlocks by acquiring locks in a consistent order. + first, second = (self, other) if id(self) <= id(other) else (other, self) + with first._lock: + with second._lock: + return first._list == second._list + with self._lock: + return self._list == other -class LockedDictProxy(Generic[T]): + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) + + +class LockedDictProxy(dict, Generic[T]): # type: ignore[type-arg] """Thread-safe proxy for dict operations. - Wraps a dict and uses a lock for all mutating operations. + Subclasses ``dict`` so that ``isinstance(proxy, dict)`` returns True, + which is required by libraries like Pydantic that do strict type checks. + All mutations go through the lock; reads delegate to the underlying dict. """ def __init__(self, d: dict[str, T], lock: threading.Lock) -> None: + super().__init__() # empty builtin dict; all access goes through self._dict self._dict = d self._lock = lock @@ -495,11 +533,11 @@ class LockedDictProxy(Generic[T]): with self._lock: del self._dict[key] - def pop(self, key: str, *default: T) -> T: + def pop(self, key: str, *default: T) -> T: # type: ignore[override] with self._lock: return self._dict.pop(key, *default) - def update(self, other: dict[str, T]) -> None: + def update(self, other: dict[str, T]) -> None: # type: ignore[override] with self._lock: self._dict.update(other) @@ -507,7 +545,7 @@ class LockedDictProxy(Generic[T]): with self._lock: self._dict.clear() - def setdefault(self, key: str, default: T) -> T: + def setdefault(self, key: str, default: T) -> T: # type: ignore[override] with self._lock: return self._dict.setdefault(key, default) @@ -523,16 +561,16 @@ class LockedDictProxy(Generic[T]): def __contains__(self, key: object) -> bool: return key in self._dict - def keys(self) -> KeysView[str]: + def keys(self) -> KeysView[str]: # type: ignore[override] return self._dict.keys() - def values(self) -> ValuesView[T]: + def values(self) -> ValuesView[T]: # type: ignore[override] return self._dict.values() - def items(self) -> ItemsView[str, T]: + def items(self) -> ItemsView[str, T]: # type: ignore[override] return self._dict.items() - def get(self, key: str, default: T | None = None) -> T | None: + def get(self, key: str, default: T | None = None) -> T | None: # type: ignore[override] return self._dict.get(key, default) def __repr__(self) -> str: @@ -541,6 +579,20 @@ class LockedDictProxy(Generic[T]): def __bool__(self) -> bool: return bool(self._dict) + def __eq__(self, other: object) -> bool: + """Compare based on the underlying dict contents.""" + if isinstance(other, LockedDictProxy): + # Avoid deadlocks by acquiring locks in a consistent order. + first, second = (self, other) if id(self) <= id(other) else (other, self) + with first._lock: + with second._lock: + return first._dict == second._dict + with self._lock: + return self._dict == other + + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) + class StateProxy(Generic[T]): """Proxy that provides thread-safe access to flow state. @@ -640,6 +692,7 @@ class FlowMeta(type): condition_type = getattr( attr_value, "__condition_type__", OR_CONDITION ) + if ( hasattr(attr_value, "__trigger_condition__") and attr_value.__trigger_condition__ is not None @@ -700,6 +753,10 @@ class Flow(Generic[T], metaclass=FlowMeta): name: str | None = None tracing: bool | None = None stream: bool = False + memory: Any = ( + None # Memory | MemoryScope | MemorySlice | None; auto-created if not set + ) + input_provider: Any = None # InputProvider | None; per-flow override for self.ask() def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]: class _FlowGeneric(cls): # type: ignore @@ -713,6 +770,7 @@ class Flow(Generic[T], metaclass=FlowMeta): persistence: FlowPersistence | None = None, tracing: bool | None = None, suppress_flow_events: bool = False, + max_method_calls: int = 100, **kwargs: Any, ) -> None: """Initialize a new Flow instance. @@ -721,6 +779,7 @@ class Flow(Generic[T], metaclass=FlowMeta): persistence: Optional persistence backend for storing flow states tracing: Whether to enable tracing. True=always enable, False=always disable, None=check environment/user settings suppress_flow_events: Whether to suppress flow event emissions (internal use) + max_method_calls: Maximum times a single method can be called per execution before raising RecursionError **kwargs: Additional state values to initialize or override """ # Initialize basic instance attributes @@ -736,6 +795,8 @@ class Flow(Generic[T], metaclass=FlowMeta): self._completed_methods: set[FlowMethodName] = ( set() ) # Track completed methods for reload + self._method_call_counts: dict[FlowMethodName, int] = {} + self._max_method_calls = max_method_calls self._persistence: FlowPersistence | None = persistence self._is_execution_resuming: bool = False self._event_futures: list[Future[None]] = [] @@ -746,6 +807,9 @@ class Flow(Generic[T], metaclass=FlowMeta): self._pending_feedback_context: PendingFeedbackContext | None = None self.suppress_flow_events: bool = suppress_flow_events + # User input history (for self.ask()) + self._input_history: list[InputHistoryEntry] = [] + # Initialize state with initial values self._state = self._create_initial_state() self.tracing = tracing @@ -767,6 +831,14 @@ class Flow(Generic[T], metaclass=FlowMeta): ), ) + # Auto-create memory if not provided at class or instance level. + # Internal flows (RecallFlow, EncodingFlow) set _skip_auto_memory + # to avoid creating a wasteful standalone Memory instance. + if self.memory is None and not getattr(self, "_skip_auto_memory", False): + from crewai.memory.unified_memory import Memory + + self.memory = Memory() + # Register all flow-related methods for method_name in dir(self): if not method_name.startswith("_"): @@ -777,6 +849,63 @@ class Flow(Generic[T], metaclass=FlowMeta): method = method.__get__(self, self.__class__) self._methods[method.__name__] = method + def recall(self, query: str, **kwargs: Any) -> Any: + """Recall relevant memories. Delegates to this flow's memory. + + Args: + query: Natural language query. + **kwargs: Passed to memory.recall (e.g. scope, categories, limit, depth). + + Returns: + Result of memory.recall(query, **kwargs). + + Raises: + ValueError: If no memory is configured for this flow. + """ + if self.memory is None: + raise ValueError("No memory configured for this flow") + return self.memory.recall(query, **kwargs) + + def remember(self, content: str | list[str], **kwargs: Any) -> Any: + """Store one or more items in memory. + + Pass a single string for synchronous save (returns the MemoryRecord). + Pass a list of strings for non-blocking batch save (returns immediately). + + Args: + content: Text or list of texts to remember. + **kwargs: Passed to memory.remember / remember_many + (e.g. scope, categories, metadata, importance). + + Returns: + MemoryRecord for single item, empty list for batch (background save). + + Raises: + ValueError: If no memory is configured for this flow. + """ + if self.memory is None: + raise ValueError("No memory configured for this flow") + if isinstance(content, list): + return self.memory.remember_many(content, **kwargs) + return self.memory.remember(content, **kwargs) + + def extract_memories(self, content: str) -> list[str]: + """Extract discrete memories from content. Delegates to this flow's memory. + + Args: + content: Raw text (e.g. task + result dump). + + Returns: + List of short, self-contained memory statements. + + Raises: + ValueError: If no memory is configured for this flow. + """ + if self.memory is None: + raise ValueError("No memory configured for this flow") + result: list[str] = self.memory.extract_memories(content) + return result + def _mark_or_listener_fired(self, listener_name: FlowMethodName) -> bool: """Mark an OR listener as fired atomically. @@ -1246,8 +1375,10 @@ class Flow(Generic[T], metaclass=FlowMeta): ValueError: If structured state model lacks 'id' field TypeError: If state is neither BaseModel nor dictionary """ + init_state = self.initial_state + # Handle case where initial_state is None but we have a type parameter - if self.initial_state is None and hasattr(self, "_initial_state_t"): + if init_state is None and hasattr(self, "_initial_state_t"): state_type = self._initial_state_t if isinstance(state_type, type): if issubclass(state_type, FlowState): @@ -1271,12 +1402,12 @@ class Flow(Generic[T], metaclass=FlowMeta): return cast(T, {"id": str(uuid4())}) # Handle case where no initial state is provided - if self.initial_state is None: + if init_state is None: return cast(T, {"id": str(uuid4())}) # Handle case where initial_state is a type (class) - if isinstance(self.initial_state, type): - state_class: type[T] = self.initial_state + if isinstance(init_state, type): + state_class = init_state if issubclass(state_class, FlowState): return state_class() if issubclass(state_class, BaseModel): @@ -1287,19 +1418,19 @@ class Flow(Generic[T], metaclass=FlowMeta): if not getattr(model_instance, "id", None): object.__setattr__(model_instance, "id", str(uuid4())) return model_instance - if self.initial_state is dict: + if init_state is dict: return cast(T, {"id": str(uuid4())}) # Handle dictionary instance case - if isinstance(self.initial_state, dict): - new_state = dict(self.initial_state) # Copy to avoid mutations + if isinstance(init_state, dict): + new_state = dict(init_state) # Copy to avoid mutations if "id" not in new_state: new_state["id"] = str(uuid4()) return cast(T, new_state) # Handle BaseModel instance case - if isinstance(self.initial_state, BaseModel): - model = cast(BaseModel, self.initial_state) + if isinstance(init_state, BaseModel): + model = cast(BaseModel, init_state) if not hasattr(model, "id"): raise ValueError("Flow state model must have an 'id' field") @@ -1613,7 +1744,12 @@ class Flow(Generic[T], metaclass=FlowMeta): async def _run_flow() -> Any: return await self.kickoff_async(inputs, input_files) - return asyncio.run(_run_flow()) + try: + asyncio.get_running_loop() + with ThreadPoolExecutor(max_workers=1) as pool: + return pool.submit(asyncio.run, _run_flow()).result() + except RuntimeError: + return asyncio.run(_run_flow()) async def kickoff_async( self, @@ -1697,9 +1833,15 @@ class Flow(Generic[T], metaclass=FlowMeta): self._method_outputs.clear() self._pending_and_listeners.clear() self._clear_or_listeners() + self._method_call_counts.clear() else: - # We're restoring from persistence, set the flag - self._is_execution_resuming = True + # Only enter resumption mode if there are completed methods to + # replay. When _completed_methods is empty (e.g. a pure + # state-reload via kickoff(inputs={"id": ...})), the flow + # executes from scratch and the flag would incorrectly + # suppress cyclic re-execution on the second iteration. + if self._completed_methods: + self._is_execution_resuming = True if inputs: # Override the id in the state if it exists in inputs @@ -1872,6 +2014,9 @@ class Flow(Generic[T], metaclass=FlowMeta): return final_output finally: + # Ensure all background memory saves complete before returning + if self.memory is not None and hasattr(self.memory, "drain_writes"): + self.memory.drain_writes() if request_id_token is not None: current_flow_request_id.reset(request_id_token) if flow_id_token is not None: @@ -2014,15 +2159,24 @@ class Flow(Generic[T], metaclass=FlowMeta): if future: self._event_futures.append(future) - if asyncio.iscoroutinefunction(method): - result = await method(*args, **kwargs) - else: - # Run sync methods in thread pool for isolation - # This allows Agent.kickoff() to work synchronously inside Flow methods - import contextvars + # Set method name in context so ask() can read it without + # stack inspection. Must happen before copy_context() so the + # value propagates into the thread pool for sync methods. + from crewai.flow.flow_context import current_flow_method_name - ctx = contextvars.copy_context() - result = await asyncio.to_thread(ctx.run, method, *args, **kwargs) + method_name_token = current_flow_method_name.set(method_name) + try: + if asyncio.iscoroutinefunction(method): + result = await method(*args, **kwargs) + else: + # Run sync methods in thread pool for isolation + # This allows Agent.kickoff() to work synchronously inside Flow methods + import contextvars + + ctx = contextvars.copy_context() + result = await asyncio.to_thread(ctx.run, method, *args, **kwargs) + finally: + current_flow_method_name.reset(method_name_token) # Auto-await coroutines returned from sync methods (enables AgentExecutor pattern) if asyncio.iscoroutine(result): @@ -2055,6 +2209,8 @@ class Flow(Generic[T], metaclass=FlowMeta): from crewai.flow.async_feedback.types import HumanFeedbackPending if isinstance(e, HumanFeedbackPending): + e.context.method_name = method_name + # Auto-save pending feedback (create default persistence if needed) if self._persistence is None: from crewai.flow.persistence import SQLiteFlowPersistence @@ -2154,14 +2310,23 @@ class Flow(Generic[T], metaclass=FlowMeta): router_name, router_input, current_triggering_event_id ) if router_result: # Only add non-None results - router_results.append(FlowMethodName(str(router_result))) + router_result_str = ( + router_result.value + if isinstance(router_result, enum.Enum) + else str(router_result) + ) + router_results.append(FlowMethodName(router_result_str)) # If this was a human_feedback router, map the outcome to the feedback if self.last_human_feedback is not None: - router_result_to_feedback[str(router_result)] = ( + router_result_to_feedback[router_result_str] = ( self.last_human_feedback ) current_trigger = ( - FlowMethodName(str(router_result)) + FlowMethodName( + router_result.value + if isinstance(router_result, enum.Enum) + else str(router_result) + ) if router_result is not None else FlowMethodName("") # Update for next iteration of router chain ) @@ -2410,6 +2575,16 @@ class Flow(Generic[T], metaclass=FlowMeta): - Skips execution if method was already completed (e.g., after reload) - Catches and logs any exceptions during execution, preventing individual listener failures from breaking the entire flow """ + count = self._method_call_counts.get(listener_name, 0) + 1 + if count > self._max_method_calls: + raise RecursionError( + f"Method '{listener_name}' has been called {self._max_method_calls} times in " + f"this flow execution, which indicates an infinite loop. " + f"This commonly happens when a @listen label matches the " + f"method's own name." + ) + self._method_call_counts[listener_name] = count + if listener_name in self._completed_methods: if self._is_execution_resuming: # During resumption, skip execution but continue listeners @@ -2428,8 +2603,12 @@ class Flow(Generic[T], metaclass=FlowMeta): return (None, None) # For cyclic flows, clear from completed to allow re-execution self._completed_methods.discard(listener_name) - # Also clear from fired OR listeners for cyclic flows - self._discard_or_listener(listener_name) + # Clear ALL fired OR listeners so they can fire again in the new cycle. + # This mirrors what _execute_start_method does for start-method cycles. + # Only discarding the individual listener is insufficient because + # downstream or_() listeners (e.g., method_a listening to + # or_(handler_a, handler_b)) would remain suppressed across iterations. + self._clear_or_listeners() try: method = self._methods[listener_name] @@ -2473,6 +2652,206 @@ class Flow(Generic[T], metaclass=FlowMeta): logger.error(f"Error executing listener {listener_name}: {e}") raise + # ── User Input (self.ask) ──────────────────────────────────────── + + def _resolve_input_provider(self) -> Any: + """Resolve the input provider using the priority chain. + + Resolution order: + 1. ``self.input_provider`` (per-flow override) + 2. ``flow_config.input_provider`` (global default) + 3. ``ConsoleInputProvider()`` (built-in fallback) + + Returns: + An object implementing the ``InputProvider`` protocol. + """ + from crewai.flow.async_feedback.providers import ConsoleProvider + from crewai.flow.flow_config import flow_config + + if self.input_provider is not None: + return self.input_provider + if flow_config.input_provider is not None: + return flow_config.input_provider + return ConsoleProvider() + + def _checkpoint_state_for_ask(self) -> None: + """Auto-checkpoint flow state before waiting for user input. + + If persistence is configured, saves the current state so that + ``self.state`` is recoverable even if the process crashes while + waiting for input. + + This is best-effort: if persistence is not configured, this is a no-op. + """ + if self._persistence is None: + return + try: + state_data = ( + self._state + if isinstance(self._state, dict) + else self._state.model_dump() + ) + self._persistence.save_state( + flow_uuid=self.flow_id, + method_name="_ask_checkpoint", + state_data=state_data, + ) + except Exception: + logger.debug("Failed to checkpoint state before ask()", exc_info=True) + + def ask( + self, + message: str, + timeout: float | None = None, + metadata: dict[str, Any] | None = None, + ) -> str | None: + """Request input from the user during flow execution. + + Blocks the current thread until the user provides input or the + timeout expires. Works in both sync and async flow methods (the + flow framework runs sync methods in a thread pool via + ``asyncio.to_thread``, so the event loop stays free). + + Timeout ensures flows always terminate. When timeout expires, + ``None`` is returned, enabling the pattern:: + + while (msg := self.ask("You: ", timeout=300)) is not None: + process(msg) + + Before waiting for input, the current ``self.state`` is automatically + checkpointed to persistence (if configured) for durability. + + Args: + message: The question or prompt to display to the user. + timeout: Maximum seconds to wait for input. ``None`` means + wait indefinitely. When timeout expires, returns ``None``. + Note: timeout is best-effort for the provider call -- + ``ask()`` returns ``None`` promptly, but the underlying + ``request_input()`` may continue running in a background + thread until it completes naturally. Network providers + should implement their own internal timeouts. + metadata: Optional metadata to send to the input provider, + such as user ID, channel, session context. The provider + can use this to route the question to the right recipient. + + Returns: + The user's input as a string, or ``None`` on timeout, disconnect, + or provider error. Empty string ``""`` means the user pressed + Enter without typing (intentional empty input). + + Example: + ```python + class MyFlow(Flow): + @start() + def gather_info(self): + topic = self.ask( + "What topic should we research?", + metadata={"user_id": "u123", "channel": "#research"}, + ) + if topic is None: + return "No input received" + return topic + ``` + """ + from concurrent.futures import ( + ThreadPoolExecutor, + TimeoutError as FuturesTimeoutError, + ) + from datetime import datetime + + from crewai.events.types.flow_events import ( + FlowInputReceivedEvent, + FlowInputRequestedEvent, + ) + from crewai.flow.flow_context import current_flow_method_name + from crewai.flow.input_provider import InputResponse + + method_name = current_flow_method_name.get("unknown") + + # Emit input requested event + crewai_event_bus.emit( + self, + FlowInputRequestedEvent( + type="flow_input_requested", + flow_name=self.name or self.__class__.__name__, + method_name=method_name, + message=message, + metadata=metadata, + ), + ) + + # Auto-checkpoint state before waiting + self._checkpoint_state_for_ask() + + provider = self._resolve_input_provider() + raw: str | InputResponse | None = None + + try: + if timeout is not None: + # Manual executor management to avoid shutdown(wait=True) + # deadlock when the provider call outlives the timeout. + executor = ThreadPoolExecutor(max_workers=1) + future = executor.submit( + provider.request_input, message, self, metadata + ) + try: + raw = future.result(timeout=timeout) + except FuturesTimeoutError: + future.cancel() + raw = None + finally: + # wait=False so we don't block if the provider is still + # running (e.g. input() stuck waiting for user). + # cancel_futures=True cleans up any queued-but-not-started tasks. + executor.shutdown(wait=False, cancel_futures=True) + else: + raw = provider.request_input(message, self, metadata=metadata) + except KeyboardInterrupt: + raise + except Exception: + logger.debug("Input provider error in ask()", exc_info=True) + raw = None + + # Normalize provider response: str, InputResponse, or None + response: str | None = None + response_metadata: dict[str, Any] | None = None + + if isinstance(raw, InputResponse): + response = raw.text + response_metadata = raw.metadata + elif isinstance(raw, str): + response = raw + else: + response = None + + # Record in history + self._input_history.append( + { + "message": message, + "response": response, + "method_name": method_name, + "timestamp": datetime.now(), + "metadata": metadata, + "response_metadata": response_metadata, + } + ) + + # Emit input received event + crewai_event_bus.emit( + self, + FlowInputReceivedEvent( + type="flow_input_received", + flow_name=self.name or self.__class__.__name__, + method_name=method_name, + message=message, + response=response, + metadata=metadata, + response_metadata=response_metadata, + ), + ) + + return response + def _request_human_feedback( self, message: str, diff --git a/lib/crewai/src/crewai/flow/flow_config.py b/lib/crewai/src/crewai/flow/flow_config.py index 8684cc3cf..a4a6bfbe4 100644 --- a/lib/crewai/src/crewai/flow/flow_config.py +++ b/lib/crewai/src/crewai/flow/flow_config.py @@ -11,6 +11,7 @@ from typing import TYPE_CHECKING, Any if TYPE_CHECKING: from crewai.flow.async_feedback.types import HumanFeedbackProvider + from crewai.flow.input_provider import InputProvider class FlowConfig: @@ -20,10 +21,15 @@ class FlowConfig: hitl_provider: The human-in-the-loop feedback provider. Defaults to None (uses console input). Can be overridden by deployments at startup. + input_provider: The input provider used by ``Flow.ask()``. + Defaults to None (uses ``ConsoleProvider``). + Can be overridden by + deployments at startup. """ def __init__(self) -> None: self._hitl_provider: HumanFeedbackProvider | None = None + self._input_provider: InputProvider | None = None @property def hitl_provider(self) -> Any: @@ -35,6 +41,32 @@ class FlowConfig: """Set the HITL provider.""" self._hitl_provider = provider + @property + def input_provider(self) -> Any: + """Get the configured input provider for ``Flow.ask()``. + + Returns: + The configured InputProvider instance, or None if not set + (in which case ``ConsoleInputProvider`` is used as default). + """ + return self._input_provider + + @input_provider.setter + def input_provider(self, provider: Any) -> None: + """Set the input provider for ``Flow.ask()``. + + Args: + provider: An object implementing the ``InputProvider`` protocol. + + Example: + ```python + from crewai.flow import flow_config + + flow_config.input_provider = WebSocketInputProvider(...) + ``` + """ + self._input_provider = provider + # Singleton instance flow_config = FlowConfig() diff --git a/lib/crewai/src/crewai/flow/flow_context.py b/lib/crewai/src/crewai/flow/flow_context.py index ae9bd69f9..0ff6cf973 100644 --- a/lib/crewai/src/crewai/flow/flow_context.py +++ b/lib/crewai/src/crewai/flow/flow_context.py @@ -14,3 +14,7 @@ current_flow_request_id: contextvars.ContextVar[str | None] = contextvars.Contex current_flow_id: contextvars.ContextVar[str | None] = contextvars.ContextVar( "flow_id", default=None ) + +current_flow_method_name: contextvars.ContextVar[str] = contextvars.ContextVar( + "flow_method_name", default="unknown" +) diff --git a/lib/crewai/src/crewai/flow/human_feedback.py b/lib/crewai/src/crewai/flow/human_feedback.py index f5f2c9a14..4a191da99 100644 --- a/lib/crewai/src/crewai/flow/human_feedback.py +++ b/lib/crewai/src/crewai/flow/human_feedback.py @@ -62,6 +62,8 @@ from datetime import datetime from functools import wraps from typing import TYPE_CHECKING, Any, TypeVar +from pydantic import BaseModel, Field + from crewai.flow.flow_wrappers import FlowMethod @@ -132,10 +134,12 @@ class HumanFeedbackConfig: message: str emit: Sequence[str] | None = None - llm: str | BaseLLM | None = None + llm: str | BaseLLM | None = "gpt-4o-mini" default_outcome: str | None = None metadata: dict[str, Any] | None = None provider: HumanFeedbackProvider | None = None + learn: bool = False + learn_source: str = "hitl" class HumanFeedbackMethod(FlowMethod[Any, Any]): @@ -155,13 +159,36 @@ class HumanFeedbackMethod(FlowMethod[Any, Any]): __human_feedback_config__: HumanFeedbackConfig | None = None +class PreReviewResult(BaseModel): + """Structured output from the HITL pre-review LLM call.""" + + improved_output: str = Field( + description="The improved version of the output with past human feedback lessons applied.", + ) + + +class DistilledLessons(BaseModel): + """Structured output from the HITL lesson distillation LLM call.""" + + lessons: list[str] = Field( + default_factory=list, + description=( + "Generalizable lessons extracted from the human feedback. " + "Each lesson should be a reusable rule or preference. " + "Return an empty list if the feedback contains no generalizable guidance." + ), + ) + + def human_feedback( message: str, emit: Sequence[str] | None = None, - llm: str | BaseLLM | None = None, + llm: str | BaseLLM | None = "gpt-4o-mini", default_outcome: str | None = None, metadata: dict[str, Any] | None = None, provider: HumanFeedbackProvider | None = None, + learn: bool = False, + learn_source: str = "hitl" ) -> Callable[[F], F]: """Decorator for Flow methods that require human feedback. @@ -256,7 +283,9 @@ def human_feedback( if not llm: raise ValueError( "llm is required when emit is specified. " - "Provide an LLM model string (e.g., 'gpt-4o-mini') or a BaseLLM instance." + "Provide an LLM model string (e.g., 'gpt-4o-mini') or a BaseLLM instance. " + "See the CrewAI Human-in-the-Loop (HITL) documentation for more information: " + "https://docs.crewai.com/en/learn/human-feedback-in-flows" ) if default_outcome is not None and default_outcome not in emit: raise ValueError( @@ -269,6 +298,101 @@ def human_feedback( def decorator(func: F) -> F: """Inner decorator that wraps the function.""" + # -- HITL learning helpers (only used when learn=True) -------- + + def _get_hitl_prompt(key: str) -> str: + """Read a HITL prompt from the i18n translations.""" + from crewai.utilities.i18n import get_i18n + + return get_i18n().slice(key) + + def _resolve_llm_instance() -> Any: + """Resolve the ``llm`` parameter to a BaseLLM instance. + + Uses the SAME model specified in the decorator so pre-review, + distillation, and outcome collapsing all share one model. + """ + if llm is None: + from crewai.llm import LLM + + return LLM(model="gpt-4o-mini") + if isinstance(llm, str): + from crewai.llm import LLM + + return LLM(model=llm) + return llm # already a BaseLLM instance + + def _pre_review_with_lessons( + flow_instance: Flow[Any], method_output: Any + ) -> Any: + """Recall past HITL lessons and use LLM to pre-review the output.""" + try: + query = f"human feedback lessons for {func.__name__}: {method_output!s}" + matches = flow_instance.memory.recall( + query, source=learn_source + ) + if not matches: + return method_output + + lessons = "\n".join(f"- {m.record.content}" for m in matches) + llm_inst = _resolve_llm_instance() + prompt = _get_hitl_prompt("hitl_pre_review_user").format( + output=str(method_output), + lessons=lessons, + ) + messages = [ + {"role": "system", "content": _get_hitl_prompt("hitl_pre_review_system")}, + {"role": "user", "content": prompt}, + ] + if getattr(llm_inst, "supports_function_calling", lambda: False)(): + response = llm_inst.call(messages, response_model=PreReviewResult) + if isinstance(response, PreReviewResult): + return response.improved_output + return PreReviewResult.model_validate(response).improved_output + reviewed = llm_inst.call(messages) + return reviewed if isinstance(reviewed, str) else str(reviewed) + except Exception: + return method_output # fallback to raw output on any failure + + def _distill_and_store_lessons( + flow_instance: Flow[Any], method_output: Any, raw_feedback: str + ) -> None: + """Extract generalizable lessons from output + feedback, store in memory.""" + try: + llm_inst = _resolve_llm_instance() + prompt = _get_hitl_prompt("hitl_distill_user").format( + method_name=func.__name__, + output=str(method_output), + feedback=raw_feedback, + ) + messages = [ + {"role": "system", "content": _get_hitl_prompt("hitl_distill_system")}, + {"role": "user", "content": prompt}, + ] + + lessons: list[str] = [] + if getattr(llm_inst, "supports_function_calling", lambda: False)(): + response = llm_inst.call(messages, response_model=DistilledLessons) + if isinstance(response, DistilledLessons): + lessons = response.lessons + else: + lessons = DistilledLessons.model_validate(response).lessons + else: + response = llm_inst.call(messages) + if isinstance(response, str): + lessons = [ + line.strip("- ").strip() + for line in response.strip().split("\n") + if line.strip() and line.strip() != "NONE" + ] + + if lessons: + flow_instance.memory.remember_many(lessons, source=learn_source) + except Exception: # noqa: S110 + pass # non-critical: don't fail the flow because lesson storage failed + + # -- Core feedback helpers ------------------------------------ + def _request_feedback(flow_instance: Flow[Any], method_output: Any) -> str: """Request feedback using provider or default console.""" from crewai.flow.async_feedback.types import PendingFeedbackContext @@ -353,28 +477,40 @@ def human_feedback( # Async wrapper @wraps(func) async def async_wrapper(self: Flow[Any], *args: Any, **kwargs: Any) -> Any: - # Execute the original method method_output = await func(self, *args, **kwargs) - # Request human feedback (may raise HumanFeedbackPending) - raw_feedback = _request_feedback(self, method_output) + # Pre-review: apply past HITL lessons before human sees it + if learn and getattr(self, "memory", None) is not None: + method_output = _pre_review_with_lessons(self, method_output) - # Process and return - return _process_feedback(self, method_output, raw_feedback) + raw_feedback = _request_feedback(self, method_output) + result = _process_feedback(self, method_output, raw_feedback) + + # Distill: extract lessons from output + feedback, store in memory + if learn and getattr(self, "memory", None) is not None and raw_feedback.strip(): + _distill_and_store_lessons(self, method_output, raw_feedback) + + return result wrapper: Any = async_wrapper else: # Sync wrapper @wraps(func) def sync_wrapper(self: Flow[Any], *args: Any, **kwargs: Any) -> Any: - # Execute the original method method_output = func(self, *args, **kwargs) - # Request human feedback (may raise HumanFeedbackPending) - raw_feedback = _request_feedback(self, method_output) + # Pre-review: apply past HITL lessons before human sees it + if learn and getattr(self, "memory", None) is not None: + method_output = _pre_review_with_lessons(self, method_output) - # Process and return - return _process_feedback(self, method_output, raw_feedback) + raw_feedback = _request_feedback(self, method_output) + result = _process_feedback(self, method_output, raw_feedback) + + # Distill: extract lessons from output + feedback, store in memory + if learn and getattr(self, "memory", None) is not None and raw_feedback.strip(): + _distill_and_store_lessons(self, method_output, raw_feedback) + + return result wrapper = sync_wrapper @@ -397,6 +533,8 @@ def human_feedback( default_outcome=default_outcome, metadata=metadata, provider=provider, + learn=learn, + learn_source=learn_source ) wrapper.__is_flow_method__ = True diff --git a/lib/crewai/src/crewai/flow/input_provider.py b/lib/crewai/src/crewai/flow/input_provider.py new file mode 100644 index 000000000..20799abbe --- /dev/null +++ b/lib/crewai/src/crewai/flow/input_provider.py @@ -0,0 +1,151 @@ +"""Input provider protocol for Flow.ask(). + +This module provides the InputProvider protocol and InputResponse dataclass +used by Flow.ask() to request input from users during flow execution. + +The default implementation is ``ConsoleProvider`` (from +``crewai.flow.async_feedback.providers``), which serves both feedback +and input collection via console. + +Example (default console input): + ```python + from crewai.flow import Flow, start + + + class MyFlow(Flow): + @start() + def gather_info(self): + topic = self.ask("What topic should we research?") + return topic + ``` + +Example (custom provider with metadata): + ```python + from crewai.flow import Flow, start + from crewai.flow.input_provider import InputProvider, InputResponse + + + class SlackProvider: + def request_input(self, message, flow, metadata=None): + channel = metadata.get("channel", "#general") if metadata else "#general" + thread = self.post_question(channel, message) + reply = self.wait_for_reply(thread) + return InputResponse( + text=reply.text, + metadata={"responded_by": reply.user_id, "thread_id": thread.id}, + ) + + + class MyFlow(Flow): + input_provider = SlackProvider() + + @start() + def gather_info(self): + topic = self.ask("What topic?", metadata={"channel": "#research"}) + return topic + ``` +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable + + +if TYPE_CHECKING: + from crewai.flow.flow import Flow + + +@dataclass +class InputResponse: + """Response from an InputProvider, optionally carrying metadata. + + Simple providers can just return a string from ``request_input()``. + Providers that need to send metadata back (e.g., who responded, + thread ID, external timestamps) return an ``InputResponse`` instead. + + ``ask()`` normalizes both cases -- callers always get ``str | None``. + The response metadata is stored in ``_input_history`` and emitted + in ``FlowInputReceivedEvent``. + + Attributes: + text: The user's input text, or None if unavailable. + metadata: Optional metadata from the provider about the response + (e.g., who responded, thread ID, timestamps). + + Example: + ```python + class MyProvider: + def request_input(self, message, flow, metadata=None): + response = get_response_from_external_system(message) + return InputResponse( + text=response.text, + metadata={"responded_by": response.user_id}, + ) + ``` + """ + + text: str | None + metadata: dict[str, Any] | None = field(default=None) + + +@runtime_checkable +class InputProvider(Protocol): + """Protocol for user input collection strategies. + + Implement this protocol to create custom input providers that integrate + with external systems like websockets, web UIs, Slack, or custom APIs. + + The default provider is ``ConsoleProvider``, which blocks waiting for + console input via Python's built-in ``input()`` function. + + Providers are always synchronous. The flow framework runs sync methods + in a thread pool (via ``asyncio.to_thread``), so ``ask()`` never blocks + the event loop even inside async flow methods. + + Providers can return either: + - ``str | None`` for simple cases (no response metadata) + - ``InputResponse`` when they need to send metadata back with the answer + + Example (simple): + ```python + class SimpleProvider: + def request_input(self, message: str, flow: Flow) -> str | None: + return input(message) + ``` + + Example (with metadata): + ```python + class SlackProvider: + def request_input(self, message, flow, metadata=None): + channel = metadata.get("channel") if metadata else "#general" + reply = self.post_and_wait(channel, message) + return InputResponse( + text=reply.text, + metadata={"responded_by": reply.user_id}, + ) + ``` + """ + + def request_input( + self, + message: str, + flow: Flow[Any], + metadata: dict[str, Any] | None = None, + ) -> str | InputResponse | None: + """Request input from the user. + + Args: + message: The question or prompt to display to the user. + flow: The Flow instance requesting input. Can be used to + access flow state, name, or other context. + metadata: Optional metadata from the caller, such as user ID, + channel, session context, etc. Providers can use this to + route the question to the right recipient. + + Returns: + The user's input as a string, an ``InputResponse`` with text + and optional response metadata, or None if input is unavailable + (e.g., user cancelled, connection dropped). + """ + ... diff --git a/lib/crewai/src/crewai/flow/types.py b/lib/crewai/src/crewai/flow/types.py index 024de41df..65ed3a995 100644 --- a/lib/crewai/src/crewai/flow/types.py +++ b/lib/crewai/src/crewai/flow/types.py @@ -4,6 +4,7 @@ This module contains TypedDict definitions and type aliases used throughout the Flow system. """ +from datetime import datetime from typing import ( Annotated, Any, @@ -101,6 +102,30 @@ class FlowData(TypedDict): flow_methods_attributes: list[FlowMethodData] +class InputHistoryEntry(TypedDict): + """A single entry in the flow's input history from ``self.ask()``. + + Each call to ``Flow.ask()`` appends one entry recording the question, + the user's response, which method asked, and any metadata exchanged + between the caller and the input provider. + + Attributes: + message: The question or prompt that was displayed to the user. + response: The user's response, or None on timeout/error. + method_name: The flow method that called ``ask()``. + timestamp: When the input was received. + metadata: Metadata sent with the question (caller to provider). + response_metadata: Metadata received with the answer (provider to caller). + """ + + message: str + response: str | None + method_name: str + timestamp: datetime + metadata: dict[str, Any] | None + response_metadata: dict[str, Any] | None + + class FlowExecutionData(TypedDict): """Flow execution data. diff --git a/lib/crewai/src/crewai/lite_agent.py b/lib/crewai/src/crewai/lite_agent.py index ba66dded9..66b710890 100644 --- a/lib/crewai/src/crewai/lite_agent.py +++ b/lib/crewai/src/crewai/lite_agent.py @@ -5,6 +5,7 @@ from collections.abc import Callable from functools import wraps import inspect import json +import time from types import MethodType from typing import ( TYPE_CHECKING, @@ -49,9 +50,19 @@ from crewai.events.types.agent_events import ( LiteAgentExecutionStartedEvent, ) from crewai.events.types.logging_events import AgentLogsExecutionEvent +from crewai.events.types.memory_events import ( + MemoryRetrievalCompletedEvent, + MemoryRetrievalFailedEvent, + MemoryRetrievalStartedEvent, +) from crewai.flow.flow_trackable import FlowTrackable from crewai.hooks.llm_hooks import get_after_llm_call_hooks, get_before_llm_call_hooks -from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType +from crewai.hooks.types import ( + AfterLLMCallHookCallable, + AfterLLMCallHookType, + BeforeLLMCallHookCallable, + BeforeLLMCallHookType, +) from crewai.lite_agent_output import LiteAgentOutput from crewai.llm import LLM from crewai.llms.base_llm import BaseLLM @@ -244,6 +255,10 @@ class LiteAgent(FlowTrackable, BaseModel): description="A2A (Agent-to-Agent) configuration for delegating tasks to remote agents. " "Can be a single A2AConfig/A2AClientConfig/A2AServerConfig, or a list of configurations.", ) + memory: bool | Any | None = Field( + default=None, + description="If True, use default Memory(). If Memory/MemoryScope/MemorySlice, use it for recall and remember.", + ) tools_results: list[dict[str, Any]] = Field( default_factory=list, description="Results of the tools used by the agent." ) @@ -260,12 +275,13 @@ class LiteAgent(FlowTrackable, BaseModel): _guardrail: GuardrailCallable | None = PrivateAttr(default=None) _guardrail_retry_count: int = PrivateAttr(default=0) _callbacks: list[TokenCalcHandler] = PrivateAttr(default_factory=list) - _before_llm_call_hooks: list[BeforeLLMCallHookType] = PrivateAttr( - default_factory=get_before_llm_call_hooks + _before_llm_call_hooks: list[BeforeLLMCallHookType | BeforeLLMCallHookCallable] = ( + PrivateAttr(default_factory=get_before_llm_call_hooks) ) - _after_llm_call_hooks: list[AfterLLMCallHookType] = PrivateAttr( - default_factory=get_after_llm_call_hooks + _after_llm_call_hooks: list[AfterLLMCallHookType | AfterLLMCallHookCallable] = ( + PrivateAttr(default_factory=get_after_llm_call_hooks) ) + _memory: Any = PrivateAttr(default=None) @model_validator(mode="after") def emit_deprecation_warning(self) -> Self: @@ -363,6 +379,19 @@ class LiteAgent(FlowTrackable, BaseModel): return self + @model_validator(mode="after") + def resolve_memory(self) -> Self: + """Resolve memory field to _memory: default Memory() when True, else user instance or None.""" + if self.memory is True: + from crewai.memory.unified_memory import Memory + + object.__setattr__(self, "_memory", Memory()) + elif self.memory is not None and self.memory is not False: + object.__setattr__(self, "_memory", self.memory) + else: + object.__setattr__(self, "_memory", None) + return self + @field_validator("guardrail", mode="before") @classmethod def validate_guardrail_function( @@ -416,12 +445,16 @@ class LiteAgent(FlowTrackable, BaseModel): return self.role @property - def before_llm_call_hooks(self) -> list[BeforeLLMCallHookType]: + def before_llm_call_hooks( + self, + ) -> list[BeforeLLMCallHookType | BeforeLLMCallHookCallable]: """Get the before_llm_call hooks for this agent.""" return self._before_llm_call_hooks @property - def after_llm_call_hooks(self) -> list[AfterLLMCallHookType]: + def after_llm_call_hooks( + self, + ) -> list[AfterLLMCallHookType | AfterLLMCallHookCallable]: """Get the after_llm_call hooks for this agent.""" return self._after_llm_call_hooks @@ -455,6 +488,20 @@ class LiteAgent(FlowTrackable, BaseModel): Returns: LiteAgentOutput: The result of the agent execution. """ + # Inject memory tools once if memory is configured (mirrors Agent._prepare_kickoff) + if self._memory is not None: + from crewai.tools.memory_tools import create_memory_tools + from crewai.utilities.string_utils import sanitize_tool_name + + existing_names = {sanitize_tool_name(t.name) for t in self._parsed_tools} + memory_tools = [ + mt + for mt in create_memory_tools(self._memory) + if sanitize_tool_name(mt.name) not in existing_names + ] + if memory_tools: + self._parsed_tools = self._parsed_tools + parse_tools(memory_tools) + # Create agent info for event emission agent_info = { "id": self.id, @@ -474,6 +521,7 @@ class LiteAgent(FlowTrackable, BaseModel): self._messages = self._format_messages( messages, response_format=response_format, input_files=input_files ) + self._inject_memory_context() return self._execute_core( agent_info=agent_info, response_format=response_format @@ -496,6 +544,77 @@ class LiteAgent(FlowTrackable, BaseModel): ) raise e + def _get_last_user_content(self) -> str: + """Get the last user message content from _messages for recall/input.""" + for msg in reversed(self._messages): + if msg.get("role") == "user": + content = msg.get("content") + return content if isinstance(content, str) else "" + return "" + + def _inject_memory_context(self) -> None: + """Recall relevant memories and append to the system message. No-op if _memory is None.""" + if self._memory is None: + return + query = self._get_last_user_content() + crewai_event_bus.emit( + self, + event=MemoryRetrievalStartedEvent( + task_id=None, + source_type="lite_agent", + ), + ) + start_time = time.time() + memory_block = "" + try: + matches = self._memory.recall(query, limit=10) + if matches: + memory_block = "Relevant memories:\n" + "\n".join( + f"- {m.record.content}" for m in matches + ) + if memory_block: + formatted = self.i18n.slice("memory").format(memory=memory_block) + if self._messages and self._messages[0].get("role") == "system": + existing_content = self._messages[0].get("content", "") + if not isinstance(existing_content, str): + existing_content = "" + self._messages[0]["content"] = existing_content + "\n\n" + formatted + crewai_event_bus.emit( + self, + event=MemoryRetrievalCompletedEvent( + task_id=None, + memory_content=memory_block, + retrieval_time_ms=(time.time() - start_time) * 1000, + source_type="lite_agent", + ), + ) + except Exception as e: + crewai_event_bus.emit( + self, + event=MemoryRetrievalFailedEvent( + task_id=None, + source_type="lite_agent", + error=str(e), + ), + ) + + def _save_to_memory(self, output_text: str) -> None: + """Extract discrete memories from the run and remember each. No-op if _memory is None or read-only.""" + if self._memory is None or getattr(self._memory, "_read_only", False): + return + input_str = self._get_last_user_content() or "User request" + try: + raw = f"Input: {input_str}\nAgent: {self.role}\nResult: {output_text}" + extracted = self._memory.extract_memories(raw) + if extracted: + self._memory.remember_many(extracted, agent_role=self.role) + except Exception as e: + if self.verbose: + self._printer.print( + content=f"Failed to save to memory: {e}", + color="yellow", + ) + def _execute_core( self, agent_info: dict[str, Any], response_format: type[BaseModel] | None = None ) -> LiteAgentOutput: @@ -510,11 +629,20 @@ class LiteAgent(FlowTrackable, BaseModel): ) # Execute the agent using invoke loop - agent_finish = self._invoke_loop() + active_response_format = response_format or self.response_format + agent_finish = self._invoke_loop(response_model=active_response_format) + if self._memory is not None: + output_text = ( + agent_finish.output.model_dump_json() + if isinstance(agent_finish.output, BaseModel) + else agent_finish.output + ) + self._save_to_memory(output_text) formatted_result: BaseModel | None = None - active_response_format = response_format or self.response_format - if active_response_format: + if isinstance(agent_finish.output, BaseModel): + formatted_result = agent_finish.output + elif active_response_format: try: model_schema = generate_model_description(active_response_format) schema = json.dumps(model_schema, indent=2) @@ -546,8 +674,13 @@ class LiteAgent(FlowTrackable, BaseModel): usage_metrics = self._token_process.get_summary() # Create output + raw_output = ( + agent_finish.output.model_dump_json() + if isinstance(agent_finish.output, BaseModel) + else agent_finish.output + ) output = LiteAgentOutput( - raw=agent_finish.output, + raw=raw_output, pydantic=formatted_result, agent_role=self.role, usage_metrics=usage_metrics.model_dump() if usage_metrics else None, @@ -724,10 +857,15 @@ class LiteAgent(FlowTrackable, BaseModel): return formatted_messages - def _invoke_loop(self) -> AgentFinish: + def _invoke_loop( + self, response_model: type[BaseModel] | None = None + ) -> AgentFinish: """ Run the agent's thought process until it reaches a conclusion or max iterations. + Args: + response_model: Optional Pydantic model for native structured output. + Returns: AgentFinish: The final result of the agent execution. """ @@ -756,12 +894,19 @@ class LiteAgent(FlowTrackable, BaseModel): printer=self._printer, from_agent=self, executor_context=self, + response_model=response_model, verbose=self.verbose, ) except Exception as e: raise e + if isinstance(answer, BaseModel): + formatted_answer = AgentFinish( + thought="", output=answer, text=answer.model_dump_json() + ) + break + formatted_answer = process_llm_response( cast(str, answer), self.use_stop_words ) @@ -787,7 +932,7 @@ class LiteAgent(FlowTrackable, BaseModel): ) self._append_message(formatted_answer.text, role="assistant") - except OutputParserError as e: # noqa: PERF203 + except OutputParserError as e: if self.verbose: self._printer.print( content="Failed to parse LLM output. Retrying...", diff --git a/lib/crewai/src/crewai/llm.py b/lib/crewai/src/crewai/llm.py index 902a3d310..8a4ac2edd 100644 --- a/lib/crewai/src/crewai/llm.py +++ b/lib/crewai/src/crewai/llm.py @@ -419,8 +419,22 @@ class LLM(BaseLLM): # FALLBACK to LiteLLM if not LITELLM_AVAILABLE: - logger.error("LiteLLM is not available, falling back to LiteLLM") - raise ImportError("Fallback to LiteLLM is not available") from None + native_list = ", ".join(SUPPORTED_NATIVE_PROVIDERS) + error_msg = ( + f"Unable to initialize LLM with model '{model}'. " + f"The model did not match any supported native provider " + f"({native_list}), and the LiteLLM fallback package is not " + f"installed.\n\n" + f"To fix this, either:\n" + f" 1. Install LiteLLM for broad model support: " + f"uv add 'crewai[litellm]'\n" + f"or\n" + f"pip install litellm\n\n" + f"For more details, see: " + f"https://docs.crewai.com/en/learn/llm-connections" + ) + logger.error(error_msg) + raise ImportError(error_msg) from None instance = object.__new__(cls) super(LLM, instance).__init__(model=model, is_litellm=True, **kwargs) diff --git a/lib/crewai/src/crewai/llms/base_llm.py b/lib/crewai/src/crewai/llms/base_llm.py index dcb261fd7..1ab710706 100644 --- a/lib/crewai/src/crewai/llms/base_llm.py +++ b/lib/crewai/src/crewai/llms/base_llm.py @@ -26,6 +26,7 @@ from crewai.events.types.llm_events import ( LLMCallStartedEvent, LLMCallType, LLMStreamChunkEvent, + LLMThinkingChunkEvent, ) from crewai.events.types.tool_usage_events import ( ToolUsageErrorEvent, @@ -368,9 +369,6 @@ class BaseLLM(ABC): """Emit LLM call started event.""" from crewai.utilities.serialization import to_serializable - if not hasattr(crewai_event_bus, "emit"): - raise ValueError("crewai_event_bus does not have an emit method") from None - crewai_event_bus.emit( self, event=LLMCallStartedEvent( @@ -416,9 +414,6 @@ class BaseLLM(ABC): from_agent: Agent | None = None, ) -> None: """Emit LLM call failed event.""" - if not hasattr(crewai_event_bus, "emit"): - raise ValueError("crewai_event_bus does not have an emit method") from None - crewai_event_bus.emit( self, event=LLMCallFailedEvent( @@ -449,9 +444,6 @@ class BaseLLM(ABC): call_type: The type of LLM call (LLM_CALL or TOOL_CALL). response_id: Unique ID for a particular LLM response, chunks have same response_id. """ - if not hasattr(crewai_event_bus, "emit"): - raise ValueError("crewai_event_bus does not have an emit method") from None - crewai_event_bus.emit( self, event=LLMStreamChunkEvent( @@ -465,6 +457,32 @@ class BaseLLM(ABC): ), ) + def _emit_thinking_chunk_event( + self, + chunk: str, + from_task: Task | None = None, + from_agent: Agent | None = None, + response_id: str | None = None, + ) -> None: + """Emit thinking/reasoning chunk event from a thinking model. + + Args: + chunk: The thinking text content. + from_task: The task that initiated the call. + from_agent: The agent that initiated the call. + response_id: Unique ID for a particular LLM response. + """ + crewai_event_bus.emit( + self, + event=LLMThinkingChunkEvent( + chunk=chunk, + from_task=from_task, + from_agent=from_agent, + response_id=response_id, + call_id=get_current_call_id(), + ), + ) + def _handle_tool_execution( self, function_name: str, diff --git a/lib/crewai/src/crewai/llms/providers/bedrock/completion.py b/lib/crewai/src/crewai/llms/providers/bedrock/completion.py index 47946d949..c707be3af 100644 --- a/lib/crewai/src/crewai/llms/providers/bedrock/completion.py +++ b/lib/crewai/src/crewai/llms/providers/bedrock/completion.py @@ -234,7 +234,7 @@ class BedrockCompletion(BaseLLM): aws_access_key_id: str | None = None, aws_secret_access_key: str | None = None, aws_session_token: str | None = None, - region_name: str = "us-east-1", + region_name: str | None = None, temperature: float | None = None, max_tokens: int | None = None, top_p: float | None = None, @@ -287,15 +287,6 @@ class BedrockCompletion(BaseLLM): **kwargs, ) - # Initialize Bedrock client with proper configuration - session = Session( - aws_access_key_id=aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID"), - aws_secret_access_key=aws_secret_access_key - or os.getenv("AWS_SECRET_ACCESS_KEY"), - aws_session_token=aws_session_token or os.getenv("AWS_SESSION_TOKEN"), - region_name=region_name, - ) - # Configure client with timeouts and retries following AWS best practices config = Config( read_timeout=300, @@ -306,8 +297,12 @@ class BedrockCompletion(BaseLLM): tcp_keepalive=True, ) - self.client = session.client("bedrock-runtime", config=config) - self.region_name = region_name + self.region_name = ( + region_name + or os.getenv("AWS_DEFAULT_REGION") + or os.getenv("AWS_REGION_NAME") + or "us-east-1" + ) self.aws_access_key_id = aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID") self.aws_secret_access_key = aws_secret_access_key or os.getenv( @@ -315,6 +310,16 @@ class BedrockCompletion(BaseLLM): ) self.aws_session_token = aws_session_token or os.getenv("AWS_SESSION_TOKEN") + # Initialize Bedrock client with proper configuration + session = Session( + aws_access_key_id=self.aws_access_key_id, + aws_secret_access_key=self.aws_secret_access_key, + aws_session_token=self.aws_session_token, + region_name=self.region_name, + ) + + self.client = session.client("bedrock-runtime", config=config) + self._async_exit_stack = AsyncExitStack() if AIOBOTOCORE_AVAILABLE else None self._async_client_initialized = False diff --git a/lib/crewai/src/crewai/llms/providers/gemini/completion.py b/lib/crewai/src/crewai/llms/providers/gemini/completion.py index 14603b7d2..fd0530abe 100644 --- a/lib/crewai/src/crewai/llms/providers/gemini/completion.py +++ b/lib/crewai/src/crewai/llms/providers/gemini/completion.py @@ -61,6 +61,7 @@ class GeminiCompletion(BaseLLM): interceptor: BaseInterceptor[Any, Any] | None = None, use_vertexai: bool | None = None, response_format: type[BaseModel] | None = None, + thinking_config: types.ThinkingConfig | None = None, **kwargs: Any, ): """Initialize Google Gemini chat completion client. @@ -93,6 +94,10 @@ class GeminiCompletion(BaseLLM): api_version="v1" is automatically configured. response_format: Pydantic model for structured output. Used as default when response_model is not passed to call()/acall() methods. + thinking_config: ThinkingConfig for thinking models (gemini-2.5+, gemini-3+). + Controls thought output via include_thoughts, thinking_budget, + and thinking_level. When None, thinking models automatically + get include_thoughts=True so thought content is surfaced. **kwargs: Additional parameters """ if interceptor is not None: @@ -139,6 +144,14 @@ class GeminiCompletion(BaseLLM): version_match and float(version_match.group(1)) >= 2.0 ) + self.thinking_config = thinking_config + if ( + self.thinking_config is None + and version_match + and float(version_match.group(1)) >= 2.5 + ): + self.thinking_config = types.ThinkingConfig(include_thoughts=True) + @property def stop(self) -> list[str]: """Get stop sequences sent to the API.""" @@ -520,6 +533,9 @@ class GeminiCompletion(BaseLLM): if self.safety_settings: config_params["safety_settings"] = self.safety_settings + if self.thinking_config is not None: + config_params["thinking_config"] = self.thinking_config + return types.GenerateContentConfig(**config_params) def _convert_tools_for_interference( # type: ignore[override] @@ -618,9 +634,17 @@ class GeminiCompletion(BaseLLM): function_response_part = types.Part.from_function_response( name=tool_name, response=response_data ) - contents.append( - types.Content(role="user", parts=[function_response_part]) - ) + if ( + contents + and contents[-1].role == "user" + and contents[-1].parts + and contents[-1].parts[-1].function_response is not None + ): + contents[-1].parts.append(function_response_part) + else: + contents.append( + types.Content(role="user", parts=[function_response_part]) + ) elif role == "assistant" and message.get("tool_calls"): raw_parts: list[Any] | None = message.get("raw_tool_call_parts") if raw_parts and all(isinstance(p, types.Part) for p in raw_parts): @@ -894,7 +918,7 @@ class GeminiCompletion(BaseLLM): content = self._extract_text_from_response(response) effective_response_model = None if self.tools else response_model - if not effective_response_model: + if not response_model: content = self._apply_stop_words(content) return self._finalize_completion_response( @@ -931,15 +955,6 @@ class GeminiCompletion(BaseLLM): if chunk.usage_metadata: usage_data = self._extract_token_usage(chunk) - if chunk.text: - full_response += chunk.text - self._emit_stream_chunk_event( - chunk=chunk.text, - from_task=from_task, - from_agent=from_agent, - response_id=response_id, - ) - if chunk.candidates: candidate = chunk.candidates[0] if candidate.content and candidate.content.parts: @@ -976,6 +991,21 @@ class GeminiCompletion(BaseLLM): call_type=LLMCallType.TOOL_CALL, response_id=response_id, ) + elif part.thought and part.text: + self._emit_thinking_chunk_event( + chunk=part.text, + from_task=from_task, + from_agent=from_agent, + response_id=response_id, + ) + elif part.text: + full_response += part.text + self._emit_stream_chunk_event( + chunk=part.text, + from_task=from_task, + from_agent=from_agent, + response_id=response_id, + ) return full_response, function_calls, usage_data @@ -1329,7 +1359,7 @@ class GeminiCompletion(BaseLLM): text_parts = [ part.text for part in candidate.content.parts - if hasattr(part, "text") and part.text + if part.text and not part.thought ] return "".join(text_parts) diff --git a/lib/crewai/src/crewai/mcp/__init__.py b/lib/crewai/src/crewai/mcp/__init__.py index 282cb1f56..e078919fd 100644 --- a/lib/crewai/src/crewai/mcp/__init__.py +++ b/lib/crewai/src/crewai/mcp/__init__.py @@ -18,6 +18,7 @@ from crewai.mcp.filters import ( create_dynamic_tool_filter, create_static_tool_filter, ) +from crewai.mcp.tool_resolver import MCPToolResolver from crewai.mcp.transports.base import BaseTransport, TransportType @@ -28,6 +29,7 @@ __all__ = [ "MCPServerHTTP", "MCPServerSSE", "MCPServerStdio", + "MCPToolResolver", "StaticToolFilter", "ToolFilter", "ToolFilterContext", diff --git a/lib/crewai/src/crewai/mcp/client.py b/lib/crewai/src/crewai/mcp/client.py index f608933f6..2b5d75371 100644 --- a/lib/crewai/src/crewai/mcp/client.py +++ b/lib/crewai/src/crewai/mcp/client.py @@ -6,7 +6,7 @@ from contextlib import AsyncExitStack from datetime import datetime import logging import time -from typing import Any +from typing import Any, NamedTuple from typing_extensions import Self @@ -34,6 +34,13 @@ from crewai.mcp.transports.stdio import StdioTransport from crewai.utilities.string_utils import sanitize_tool_name +class _MCPToolResult(NamedTuple): + """Internal result from an MCP tool call, carrying the ``isError`` flag.""" + + content: str + is_error: bool + + # MCP Connection timeout constants (in seconds) MCP_CONNECTION_TIMEOUT = 30 # Increased for slow servers MCP_TOOL_EXECUTION_TIMEOUT = 30 @@ -420,6 +427,7 @@ class MCPClient: return [ { "name": sanitize_tool_name(tool.name), + "original_name": tool.name, "description": getattr(tool, "description", ""), "inputSchema": getattr(tool, "inputSchema", {}), } @@ -461,29 +469,46 @@ class MCPClient: ) try: - result = await self._retry_operation( + tool_result: _MCPToolResult = await self._retry_operation( lambda: self._call_tool_impl(tool_name, cleaned_arguments), timeout=self.execution_timeout, ) - completed_at = datetime.now() - execution_duration_ms = (completed_at - started_at).total_seconds() * 1000 - crewai_event_bus.emit( - self, - MCPToolExecutionCompletedEvent( - server_name=server_name, - server_url=server_url, - transport_type=transport_type, - tool_name=tool_name, - tool_args=cleaned_arguments, - result=result, - started_at=started_at, - completed_at=completed_at, - execution_duration_ms=execution_duration_ms, - ), - ) + finished_at = datetime.now() + execution_duration_ms = (finished_at - started_at).total_seconds() * 1000 - return result + if tool_result.is_error: + crewai_event_bus.emit( + self, + MCPToolExecutionFailedEvent( + server_name=server_name, + server_url=server_url, + transport_type=transport_type, + tool_name=tool_name, + tool_args=cleaned_arguments, + error=tool_result.content, + error_type="tool_error", + started_at=started_at, + failed_at=finished_at, + ), + ) + else: + crewai_event_bus.emit( + self, + MCPToolExecutionCompletedEvent( + server_name=server_name, + server_url=server_url, + transport_type=transport_type, + tool_name=tool_name, + tool_args=cleaned_arguments, + result=tool_result.content, + started_at=started_at, + completed_at=finished_at, + execution_duration_ms=execution_duration_ms, + ), + ) + + return tool_result.content except Exception as e: failed_at = datetime.now() error_type = ( @@ -564,23 +589,27 @@ class MCPClient: return cleaned - async def _call_tool_impl(self, tool_name: str, arguments: dict[str, Any]) -> Any: + async def _call_tool_impl( + self, tool_name: str, arguments: dict[str, Any] + ) -> _MCPToolResult: """Internal implementation of call_tool.""" result = await asyncio.wait_for( self.session.call_tool(tool_name, arguments), timeout=self.execution_timeout, ) + is_error = getattr(result, "isError", False) or False + # Extract result content if hasattr(result, "content") and result.content: if isinstance(result.content, list) and len(result.content) > 0: content_item = result.content[0] if hasattr(content_item, "text"): - return str(content_item.text) - return str(content_item) - return str(result.content) + return _MCPToolResult(str(content_item.text), is_error) + return _MCPToolResult(str(content_item), is_error) + return _MCPToolResult(str(result.content), is_error) - return str(result) + return _MCPToolResult(str(result), is_error) async def list_prompts(self) -> list[dict[str, Any]]: """List available prompts from MCP server. diff --git a/lib/crewai/src/crewai/mcp/tool_resolver.py b/lib/crewai/src/crewai/mcp/tool_resolver.py new file mode 100644 index 000000000..34af189f2 --- /dev/null +++ b/lib/crewai/src/crewai/mcp/tool_resolver.py @@ -0,0 +1,592 @@ +"""MCP tool resolution for CrewAI agents. + +This module extracts all MCP-related tool resolution logic from the Agent class +into a standalone MCPToolResolver. It handles three flavours of MCP reference: + + 1. Native configs: MCPServerStdio / MCPServerHTTP / MCPServerSSE objects. + 2. HTTPS URLs: e.g. "https://mcp.example.com/api" + 3. AMP references: e.g. "notion" or "notion#search" (legacy "crewai-amp:" prefix also works) +""" + +from __future__ import annotations + +import asyncio +import time +from typing import TYPE_CHECKING, Any, Final, cast +from urllib.parse import urlparse + +from crewai.mcp.client import MCPClient +from crewai.mcp.config import ( + MCPServerConfig, + MCPServerHTTP, + MCPServerSSE, + MCPServerStdio, +) +from crewai.mcp.transports.http import HTTPTransport +from crewai.mcp.transports.sse import SSETransport +from crewai.mcp.transports.stdio import StdioTransport + + +if TYPE_CHECKING: + from crewai.tools.base_tool import BaseTool + from crewai.utilities.logger import Logger + +MCP_CONNECTION_TIMEOUT: Final[int] = 10 +MCP_TOOL_EXECUTION_TIMEOUT: Final[int] = 30 +MCP_DISCOVERY_TIMEOUT: Final[int] = 15 +MCP_MAX_RETRIES: Final[int] = 3 + +_mcp_schema_cache: dict[str, Any] = {} +_cache_ttl: Final[int] = 300 # 5 minutes + + +class MCPToolResolver: + """Resolves MCP server references / configs into CrewAI ``BaseTool`` instances. + + Typical lifecycle:: + + resolver = MCPToolResolver(agent=my_agent, logger=my_agent._logger) + tools = resolver.resolve(my_agent.mcps) + # … agent executes tasks using *tools* … + resolver.cleanup() + + The resolver owns the MCP client connections it creates and is responsible + for tearing them down via :meth:`cleanup`. + """ + + def __init__(self, agent: Any, logger: Logger) -> None: + self._agent = agent + self._logger = logger + self._clients: list[Any] = [] + + @property + def clients(self) -> list[Any]: + return list(self._clients) + + def resolve(self, mcps: list[str | MCPServerConfig]) -> list[BaseTool]: + """Convert MCP server references/configs to CrewAI tools.""" + all_tools: list[BaseTool] = [] + amp_refs: list[tuple[str, str | None]] = [] + + for mcp_config in mcps: + if isinstance(mcp_config, str) and mcp_config.startswith("https://"): + all_tools.extend(self._resolve_external(mcp_config)) + elif isinstance(mcp_config, str): + amp_refs.append(self._parse_amp_ref(mcp_config)) + else: + tools, client = self._resolve_native(mcp_config) + all_tools.extend(tools) + if client: + self._clients.append(client) + + if amp_refs: + tools, clients = self._resolve_amp(amp_refs) + all_tools.extend(tools) + self._clients.extend(clients) + + return all_tools + + def cleanup(self) -> None: + """Disconnect all MCP client connections.""" + if not self._clients: + return + + async def _disconnect_all() -> None: + for client in self._clients: + if client and hasattr(client, "connected") and client.connected: + await client.disconnect() + + try: + asyncio.run(_disconnect_all()) + except Exception as e: + self._logger.log("error", f"Error during MCP client cleanup: {e}") + finally: + self._clients.clear() + + @staticmethod + def _parse_amp_ref(mcp_config: str) -> tuple[str, str | None]: + """Parse an AMP reference into *(slug, optional tool name)*. + + Accepts both bare slugs (``"notion"``, ``"notion#search"``) and the + legacy ``"crewai-amp:notion"`` form. + """ + bare = mcp_config.removeprefix("crewai-amp:") + slug, _, specific_tool = bare.partition("#") + return slug, specific_tool or None + + def _resolve_amp( + self, amp_refs: list[tuple[str, str | None]] + ) -> tuple[list[BaseTool], list[Any]]: + """Fetch AMP configs in bulk and return their tools and clients. + + Resolves each unique slug only once (single connection per server), + then applies per-ref tool filters to select specific tools. + """ + from crewai.events.event_bus import crewai_event_bus + from crewai.events.types.mcp_events import MCPConfigFetchFailedEvent + + unique_slugs = list(dict.fromkeys(slug for slug, _ in amp_refs)) + amp_configs_map = self._fetch_amp_mcp_configs(unique_slugs) + + all_tools: list[BaseTool] = [] + all_clients: list[Any] = [] + + resolved_cache: dict[str, tuple[list[BaseTool], Any | None]] = {} + + for slug in unique_slugs: + config_dict = amp_configs_map.get(slug) + if not config_dict: + crewai_event_bus.emit( + self, + MCPConfigFetchFailedEvent( + slug=slug, + error=f"Config for '{slug}' not found. Make sure it is connected in your account.", + error_type="not_connected", + ), + ) + continue + + mcp_server_config = self._build_mcp_config_from_dict(config_dict) + + try: + tools, client = self._resolve_native(mcp_server_config) + resolved_cache[slug] = (tools, client) + if client: + all_clients.append(client) + except Exception as e: + crewai_event_bus.emit( + self, + MCPConfigFetchFailedEvent( + slug=slug, + error=str(e), + error_type="connection_failed", + ), + ) + + for slug, specific_tool in amp_refs: + cached = resolved_cache.get(slug) + if not cached: + continue + + slug_tools, _ = cached + if specific_tool: + all_tools.extend( + t for t in slug_tools if t.name.endswith(f"_{specific_tool}") + ) + else: + all_tools.extend(slug_tools) + + return all_tools, all_clients + + def _fetch_amp_mcp_configs(self, slugs: list[str]) -> dict[str, dict[str, Any]]: + """Fetch MCP server configurations via CrewAI+ API. + + Sends a GET request to the CrewAI+ mcps/configs endpoint with + comma-separated slugs. CrewAI+ proxies the request to crewai-oauth. + + API-level failures return ``{}``; individual slugs will then + surface as ``MCPConfigFetchFailedEvent`` in :meth:`_resolve_amp`. + """ + import httpx + + try: + from crewai_tools.tools.crewai_platform_tools.misc import ( + get_platform_integration_token, + ) + + from crewai.cli.plus_api import PlusAPI + + plus_api = PlusAPI(api_key=get_platform_integration_token()) + response = plus_api.get_mcp_configs(slugs) + + if response.status_code == 200: + configs: dict[str, dict[str, Any]] = response.json().get("configs", {}) + return configs + + self._logger.log( + "debug", + f"Failed to fetch MCP configs: HTTP {response.status_code}", + ) + return {} + + except httpx.HTTPError as e: + self._logger.log("debug", f"Failed to fetch MCP configs: {e}") + return {} + except Exception as e: + self._logger.log("debug", f"Cannot fetch AMP MCP configs: {e}") + return {} + + def _resolve_external(self, mcp_ref: str) -> list[BaseTool]: + """Resolve an HTTPS MCP server URL into tools.""" + from crewai.tools.mcp_tool_wrapper import MCPToolWrapper + + if "#" in mcp_ref: + server_url, specific_tool = mcp_ref.split("#", 1) + else: + server_url, specific_tool = mcp_ref, None + + server_params = {"url": server_url} + server_name = self._extract_server_name(server_url) + + try: + tool_schemas = self._get_mcp_tool_schemas(server_params) + + if not tool_schemas: + self._logger.log( + "warning", f"No tools discovered from MCP server: {server_url}" + ) + return [] + + tools = [] + for tool_name, schema in tool_schemas.items(): + if specific_tool and tool_name != specific_tool: + continue + + try: + wrapper = MCPToolWrapper( + mcp_server_params=server_params, + tool_name=tool_name, + tool_schema=schema, + server_name=server_name, + ) + tools.append(wrapper) + except Exception as e: + self._logger.log( + "warning", + f"Failed to create MCP tool wrapper for {tool_name}: {e}", + ) + continue + + if specific_tool and not tools: + self._logger.log( + "warning", + f"Specific tool '{specific_tool}' not found on MCP server: {server_url}", + ) + + return cast(list[BaseTool], tools) + + except Exception as e: + self._logger.log( + "warning", f"Failed to connect to MCP server {server_url}: {e}" + ) + return [] + + def _resolve_native( + self, mcp_config: MCPServerConfig + ) -> tuple[list[BaseTool], Any | None]: + """Resolve an ``MCPServerConfig`` into tools, returning the client for cleanup.""" + from crewai.tools.base_tool import BaseTool + from crewai.tools.mcp_native_tool import MCPNativeTool + + transport: StdioTransport | HTTPTransport | SSETransport + if isinstance(mcp_config, MCPServerStdio): + transport = StdioTransport( + command=mcp_config.command, + args=mcp_config.args, + env=mcp_config.env, + ) + server_name = f"{mcp_config.command}_{'_'.join(mcp_config.args)}" + elif isinstance(mcp_config, MCPServerHTTP): + transport = HTTPTransport( + url=mcp_config.url, + headers=mcp_config.headers, + streamable=mcp_config.streamable, + ) + server_name = self._extract_server_name(mcp_config.url) + elif isinstance(mcp_config, MCPServerSSE): + transport = SSETransport( + url=mcp_config.url, + headers=mcp_config.headers, + ) + server_name = self._extract_server_name(mcp_config.url) + else: + raise ValueError(f"Unsupported MCP server config type: {type(mcp_config)}") + + client = MCPClient( + transport=transport, + cache_tools_list=mcp_config.cache_tools_list, + ) + + async def _setup_client_and_list_tools() -> list[dict[str, Any]]: + try: + if not client.connected: + await client.connect() + + tools_list = await client.list_tools() + + try: + await client.disconnect() + await asyncio.sleep(0.1) + except Exception as e: + self._logger.log("error", f"Error during disconnect: {e}") + + return tools_list + except Exception as e: + if client.connected: + await client.disconnect() + await asyncio.sleep(0.1) + raise RuntimeError( + f"Error during setup client and list tools: {e}" + ) from e + + try: + try: + asyncio.get_running_loop() + import concurrent.futures + + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit( + asyncio.run, _setup_client_and_list_tools() + ) + tools_list = future.result() + except RuntimeError: + try: + tools_list = asyncio.run(_setup_client_and_list_tools()) + except RuntimeError as e: + error_msg = str(e).lower() + if "cancel scope" in error_msg or "task" in error_msg: + raise ConnectionError( + "MCP connection failed due to event loop cleanup issues. " + "This may be due to authentication errors or server unavailability." + ) from e + except asyncio.CancelledError as e: + raise ConnectionError( + "MCP connection was cancelled. This may indicate an authentication " + "error or server unavailability." + ) from e + + if mcp_config.tool_filter: + filtered_tools = [] + for tool in tools_list: + if callable(mcp_config.tool_filter): + try: + from crewai.mcp.filters import ToolFilterContext + + context = ToolFilterContext( + agent=self._agent, + server_name=server_name, + run_context=None, + ) + if mcp_config.tool_filter(context, tool): # type: ignore[call-arg, arg-type] + filtered_tools.append(tool) + except (TypeError, AttributeError): + if mcp_config.tool_filter(tool): # type: ignore[call-arg, arg-type] + filtered_tools.append(tool) + else: + filtered_tools.append(tool) + tools_list = filtered_tools + + tools = [] + for tool_def in tools_list: + tool_name = tool_def.get("name", "") + original_tool_name = tool_def.get("original_name", tool_name) + if not tool_name: + continue + + args_schema = None + if tool_def.get("inputSchema"): + args_schema = self._json_schema_to_pydantic( + tool_name, tool_def["inputSchema"] + ) + + tool_schema = { + "description": tool_def.get("description", ""), + "args_schema": args_schema, + } + + try: + native_tool = MCPNativeTool( + mcp_client=client, + tool_name=tool_name, + tool_schema=tool_schema, + server_name=server_name, + original_tool_name=original_tool_name, + ) + tools.append(native_tool) + except Exception as e: + self._logger.log("error", f"Failed to create native MCP tool: {e}") + continue + + return cast(list[BaseTool], tools), client + except Exception as e: + if client.connected: + asyncio.run(client.disconnect()) + + raise RuntimeError(f"Failed to get native MCP tools: {e}") from e + + @staticmethod + def _build_mcp_config_from_dict( + config_dict: dict[str, Any], + ) -> MCPServerConfig: + """Convert a config dict from crewai-oauth into an MCPServerConfig.""" + config_type = config_dict.get("type", "http") + + if config_type == "sse": + return MCPServerSSE( + url=config_dict["url"], + headers=config_dict.get("headers"), + cache_tools_list=config_dict.get("cache_tools_list", False), + ) + + return MCPServerHTTP( + url=config_dict["url"], + headers=config_dict.get("headers"), + streamable=config_dict.get("streamable", True), + cache_tools_list=config_dict.get("cache_tools_list", False), + ) + + @staticmethod + def _extract_server_name(server_url: str) -> str: + """Extract clean server name from URL for tool prefixing.""" + parsed = urlparse(server_url) + domain = parsed.netloc.replace(".", "_") + path = parsed.path.replace("/", "_").strip("_") + return f"{domain}_{path}" if path else domain + + def _get_mcp_tool_schemas( + self, server_params: dict[str, Any] + ) -> dict[str, dict[str, Any]]: + """Get tool schemas from MCP server with caching.""" + server_url = server_params["url"] + + cache_key = server_url + current_time = time.time() + + if cache_key in _mcp_schema_cache: + cached_data, cache_time = _mcp_schema_cache[cache_key] + if current_time - cache_time < _cache_ttl: + self._logger.log( + "debug", f"Using cached MCP tool schemas for {server_url}" + ) + return cached_data # type: ignore[no-any-return] + + try: + schemas = asyncio.run(self._get_mcp_tool_schemas_async(server_params)) + _mcp_schema_cache[cache_key] = (schemas, current_time) + return schemas + except Exception as e: + self._logger.log( + "warning", f"Failed to get MCP tool schemas from {server_url}: {e}" + ) + return {} + + async def _get_mcp_tool_schemas_async( + self, server_params: dict[str, Any] + ) -> dict[str, dict[str, Any]]: + """Async implementation of MCP tool schema retrieval.""" + server_url = server_params["url"] + return await self._retry_mcp_discovery( + self._discover_mcp_tools_with_timeout, server_url + ) + + async def _retry_mcp_discovery( + self, operation_func: Any, server_url: str + ) -> dict[str, dict[str, Any]]: + """Retry MCP discovery with exponential backoff.""" + last_error = None + + for attempt in range(MCP_MAX_RETRIES): + result, error, should_retry = await self._attempt_mcp_discovery( + operation_func, server_url + ) + + if result is not None: + return result + + if not should_retry: + raise RuntimeError(error) + + last_error = error + if attempt < MCP_MAX_RETRIES - 1: + wait_time = 2**attempt + await asyncio.sleep(wait_time) + + raise RuntimeError( + f"Failed to discover MCP tools after {MCP_MAX_RETRIES} attempts: {last_error}" + ) + + @staticmethod + async def _attempt_mcp_discovery( + operation_func: Any, server_url: str + ) -> tuple[dict[str, dict[str, Any]] | None, str, bool]: + """Attempt single MCP discovery; returns *(result, error_message, should_retry)*.""" + try: + result = await operation_func(server_url) + return result, "", False + + except ImportError: + return ( + None, + "MCP library not available. Please install with: pip install mcp", + False, + ) + + except asyncio.TimeoutError: + return ( + None, + f"MCP discovery timed out after {MCP_DISCOVERY_TIMEOUT} seconds", + True, + ) + + except Exception as e: + error_str = str(e).lower() + + if "authentication" in error_str or "unauthorized" in error_str: + return None, f"Authentication failed for MCP server: {e!s}", False + if "connection" in error_str or "network" in error_str: + return None, f"Network connection failed: {e!s}", True + if "json" in error_str or "parsing" in error_str: + return None, f"Server response parsing error: {e!s}", True + return None, f"MCP discovery error: {e!s}", False + + async def _discover_mcp_tools_with_timeout( + self, server_url: str + ) -> dict[str, dict[str, Any]]: + """Discover MCP tools with timeout wrapper.""" + return await asyncio.wait_for( + self._discover_mcp_tools(server_url), timeout=MCP_DISCOVERY_TIMEOUT + ) + + async def _discover_mcp_tools(self, server_url: str) -> dict[str, dict[str, Any]]: + """Discover tools from an MCP server (HTTPS / streamable-HTTP path).""" + from mcp import ClientSession + from mcp.client.streamable_http import streamablehttp_client + + from crewai.utilities.string_utils import sanitize_tool_name + + async with streamablehttp_client(server_url) as (read, write, _): + async with ClientSession(read, write) as session: + await asyncio.wait_for( + session.initialize(), timeout=MCP_CONNECTION_TIMEOUT + ) + + tools_result = await asyncio.wait_for( + session.list_tools(), + timeout=MCP_DISCOVERY_TIMEOUT - MCP_CONNECTION_TIMEOUT, + ) + + schemas = {} + for tool in tools_result.tools: + args_schema = None + if hasattr(tool, "inputSchema") and tool.inputSchema: + args_schema = self._json_schema_to_pydantic( + sanitize_tool_name(tool.name), tool.inputSchema + ) + + schemas[sanitize_tool_name(tool.name)] = { + "description": getattr(tool, "description", ""), + "args_schema": args_schema, + } + return schemas + + @staticmethod + def _json_schema_to_pydantic(tool_name: str, json_schema: dict[str, Any]) -> type: + """Convert JSON Schema to a Pydantic model for tool arguments.""" + from crewai.utilities.pydantic_schema_utils import create_model_from_schema + + model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema" + return create_model_from_schema( + json_schema, + model_name=model_name, + enrich_descriptions=True, + ) diff --git a/lib/crewai/src/crewai/memory/__init__.py b/lib/crewai/src/crewai/memory/__init__.py index 1109aef0a..eb7b140b9 100644 --- a/lib/crewai/src/crewai/memory/__init__.py +++ b/lib/crewai/src/crewai/memory/__init__.py @@ -1,13 +1,53 @@ -from crewai.memory.entity.entity_memory import EntityMemory -from crewai.memory.external.external_memory import ExternalMemory -from crewai.memory.long_term.long_term_memory import LongTermMemory -from crewai.memory.short_term.short_term_memory import ShortTermMemory +"""Memory module: unified Memory with LLM analysis and pluggable storage. +Heavy dependencies are lazily imported so that +``import crewai`` does not initialise at runtime — critical for +Celery pre-fork and similar deployment patterns. +""" + +from __future__ import annotations + +from typing import Any + +from crewai.memory.memory_scope import MemoryScope, MemorySlice +from crewai.memory.types import ( + MemoryMatch, + MemoryRecord, + ScopeInfo, + compute_composite_score, + embed_text, + embed_texts, +) + + +_LAZY_IMPORTS: dict[str, tuple[str, str]] = { + "Memory": ("crewai.memory.unified_memory", "Memory"), + "EncodingFlow": ("crewai.memory.encoding_flow", "EncodingFlow"), +} + + +def __getattr__(name: str) -> Any: + """Lazily import Memory / EncodingFlow to avoid pulling in lancedb at import time.""" + if name in _LAZY_IMPORTS: + import importlib + + module_path, attr = _LAZY_IMPORTS[name] + mod = importlib.import_module(module_path) + val = getattr(mod, attr) + globals()[name] = val + return val + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") __all__ = [ - "EntityMemory", - "ExternalMemory", - "LongTermMemory", - "ShortTermMemory", + "EncodingFlow", + "Memory", + "MemoryMatch", + "MemoryRecord", + "MemoryScope", + "MemorySlice", + "ScopeInfo", + "compute_composite_score", + "embed_text", + "embed_texts", ] diff --git a/lib/crewai/src/crewai/memory/analyze.py b/lib/crewai/src/crewai/memory/analyze.py new file mode 100644 index 000000000..88a200f82 --- /dev/null +++ b/lib/crewai/src/crewai/memory/analyze.py @@ -0,0 +1,371 @@ +"""LLM-powered analysis for memory save and recall.""" + +from __future__ import annotations + +import json +import logging +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from crewai.memory.types import MemoryRecord, ScopeInfo +from crewai.utilities.i18n import get_i18n + + +_logger = logging.getLogger(__name__) + + +class ExtractedMetadata(BaseModel): + """Fixed schema for LLM-extracted metadata (OpenAI requires additionalProperties: false).""" + + model_config = ConfigDict(extra="forbid") + + entities: list[str] = Field( + default_factory=list, + description="Entities (people, orgs, places) mentioned in the content.", + ) + dates: list[str] = Field( + default_factory=list, + description="Dates or time references in the content.", + ) + topics: list[str] = Field( + default_factory=list, + description="Topics or themes in the content.", + ) + + +class MemoryAnalysis(BaseModel): + """LLM output for analyzing content before saving to memory.""" + + suggested_scope: str = Field( + description="Best matching existing scope or new path (e.g. /company/decisions).", + ) + categories: list[str] = Field( + default_factory=list, + description="Categories for the memory (prefer existing, add new if needed).", + ) + importance: float = Field( + default=0.5, + ge=0.0, + le=1.0, + description="Importance score from 0.0 to 1.0.", + ) + extracted_metadata: ExtractedMetadata = Field( + default_factory=ExtractedMetadata, + description="Entities, dates, topics extracted from the content.", + ) + + +class QueryAnalysis(BaseModel): + """LLM output for analyzing a recall query.""" + + keywords: list[str] = Field( + default_factory=list, + description="Key entities or keywords for filtering.", + ) + suggested_scopes: list[str] = Field( + default_factory=list, + description="Scope paths to search (subset of available scopes).", + ) + complexity: str = Field( + default="simple", + description="One of 'simple' (single fact) or 'complex' (aggregation/reasoning).", + ) + recall_queries: list[str] = Field( + default_factory=list, + description=( + "1-3 short, targeted search phrases distilled from the query. " + "Each should be a concise question or keyword phrase optimized " + "for semantic vector search. If the query is already short and " + "focused, return it as a single item." + ), + ) + time_filter: str | None = Field( + default=None, + description=( + "If the query references a specific time period (e.g. 'last week', " + "'yesterday', 'in January'), return an ISO 8601 date string representing " + "the earliest date that results should match (e.g. '2026-02-01'). " + "Return null if no time constraint is implied." + ), + ) + + +class ExtractedMemories(BaseModel): + """LLM output for extracting discrete memories from raw content.""" + + memories: list[str] = Field( + default_factory=list, + description="List of discrete, self-contained memory statements extracted from the content.", + ) + + +class ConsolidationAction(BaseModel): + """A single action in a consolidation plan.""" + + model_config = ConfigDict(extra="forbid") + + action: str = Field( + description="One of 'keep', 'update', or 'delete'.", + ) + record_id: str = Field( + description="ID of the existing record this action applies to.", + ) + new_content: str | None = Field( + default=None, + description="Updated content text. Required when action is 'update'.", + ) + reason: str = Field( + default="", + description="Brief reason for this action.", + ) + + +class ConsolidationPlan(BaseModel): + """LLM output for consolidating new content with existing memories.""" + + model_config = ConfigDict(extra="forbid") + + actions: list[ConsolidationAction] = Field( + default_factory=list, + description="Actions to take on existing records (keep/update/delete).", + ) + insert_new: bool = Field( + default=True, + description="Whether to also insert the new content as a separate record.", + ) + insert_reason: str = Field( + default="", + description="Why the new content should or should not be inserted.", + ) + + +def _get_prompt(key: str) -> str: + """Retrieve a memory prompt from the i18n translations. + + Args: + key: The prompt key under the "memory" section. + + Returns: + The prompt string. + """ + return get_i18n().memory(key) + + +def extract_memories_from_content(content: str, llm: Any) -> list[str]: + """Use the LLM to extract discrete memory statements from raw content. + + This is a pure helper: it does NOT store anything. Callers should call + memory.remember() on each returned string to persist them. + + On LLM failure, returns the full content as a single memory so callers + still persist something rather than dropping the output. + + Args: + content: Raw text (e.g. task description + result dump). + llm: The LLM instance to use. + + Returns: + List of short, self-contained memory statements (or [content] on failure). + """ + if not (content or "").strip(): + return [] + user = _get_prompt("extract_memories_user").format(content=content) + messages = [ + {"role": "system", "content": _get_prompt("extract_memories_system")}, + {"role": "user", "content": user}, + ] + try: + if getattr(llm, "supports_function_calling", lambda: False)(): + response = llm.call(messages, response_model=ExtractedMemories) + if isinstance(response, ExtractedMemories): + return response.memories + return ExtractedMemories.model_validate(response).memories + response = llm.call(messages) + if isinstance(response, ExtractedMemories): + return response.memories + if isinstance(response, str): + data = json.loads(response) + return ExtractedMemories.model_validate(data).memories + return ExtractedMemories.model_validate(response).memories + except Exception as e: + _logger.warning( + "Memory extraction failed, storing full content as single memory: %s", + e, + exc_info=False, + ) + return [content] + + +def analyze_query( + query: str, + available_scopes: list[str], + scope_info: ScopeInfo | None, + llm: Any, +) -> QueryAnalysis: + """Use the LLM to analyze a recall query. + + On LLM failure, returns safe defaults so recall degrades to plain vector search. + + Args: + query: The user's recall query. + available_scopes: Scope paths that exist in the store. + scope_info: Optional info about the current scope. + llm: The LLM instance to use. + + Returns: + QueryAnalysis with keywords, suggested_scopes, complexity, recall_queries, time_filter. + """ + scope_desc = "" + if scope_info: + scope_desc = f"Current scope has {scope_info.record_count} records, categories: {scope_info.categories}" + user = _get_prompt("query_user").format( + query=query, + available_scopes=available_scopes or ["/"], + scope_desc=scope_desc, + ) + messages = [ + {"role": "system", "content": _get_prompt("query_system")}, + {"role": "user", "content": user}, + ] + try: + if getattr(llm, "supports_function_calling", lambda: False)(): + response = llm.call(messages, response_model=QueryAnalysis) + if isinstance(response, QueryAnalysis): + return response + return QueryAnalysis.model_validate(response) + response = llm.call(messages) + if isinstance(response, QueryAnalysis): + return response + if isinstance(response, str): + data = json.loads(response) + return QueryAnalysis.model_validate(data) + return QueryAnalysis.model_validate(response) + except Exception as e: + _logger.warning( + "Query analysis failed, using defaults (complexity=simple): %s", + e, + exc_info=False, + ) + scopes = (available_scopes or ["/"])[:5] + return QueryAnalysis( + keywords=[], + suggested_scopes=scopes, + complexity="simple", + recall_queries=[query], + ) + + +_SAVE_DEFAULTS = MemoryAnalysis( + suggested_scope="/", + categories=[], + importance=0.5, + extracted_metadata=ExtractedMetadata(), +) + + +def analyze_for_save( + content: str, + existing_scopes: list[str], + existing_categories: list[str], + llm: Any, +) -> MemoryAnalysis: + """Infer scope, categories, importance, and metadata for a single memory. + + Uses the small ``MemoryAnalysis`` schema (4 fields) for fast LLM response. + On failure, returns safe defaults so the memory still gets persisted. + + Args: + content: The memory content to analyze. + existing_scopes: Current scope paths in the memory store. + existing_categories: Current categories in use. + llm: The LLM instance to use. + + Returns: + MemoryAnalysis with suggested_scope, categories, importance, extracted_metadata. + """ + user = _get_prompt("save_user").format( + content=content, + existing_scopes=existing_scopes or ["/"], + existing_categories=existing_categories or [], + ) + messages = [ + {"role": "system", "content": _get_prompt("save_system")}, + {"role": "user", "content": user}, + ] + try: + if getattr(llm, "supports_function_calling", lambda: False)(): + response = llm.call(messages, response_model=MemoryAnalysis) + if isinstance(response, MemoryAnalysis): + return response + return MemoryAnalysis.model_validate(response) + response = llm.call(messages) + if isinstance(response, MemoryAnalysis): + return response + if isinstance(response, str): + data = json.loads(response) + return MemoryAnalysis.model_validate(data) + return MemoryAnalysis.model_validate(response) + except Exception as e: + _logger.warning( + "Memory save analysis failed, using defaults: %s", e, exc_info=False, + ) + return _SAVE_DEFAULTS + + +_CONSOLIDATION_DEFAULT = ConsolidationPlan(actions=[], insert_new=True) + + +def analyze_for_consolidation( + new_content: str, + existing_records: list[MemoryRecord], + llm: Any, +) -> ConsolidationPlan: + """Decide insert/update/delete for a single memory against similar existing records. + + Uses the small ``ConsolidationPlan`` schema (3 fields) for fast LLM response. + On failure, returns a safe default (insert_new=True) so the memory still gets persisted. + + Args: + new_content: The new content to store. + existing_records: Existing records that are semantically similar. + llm: The LLM instance to use. + + Returns: + ConsolidationPlan with actions per record and whether to insert the new content. + """ + if not existing_records: + return ConsolidationPlan(actions=[], insert_new=True) + records_lines: list[str] = [] + for r in existing_records: + created = r.created_at.isoformat() if r.created_at else "" + records_lines.append( + f"- id={r.id} | scope={r.scope} | importance={r.importance:.2f} | created={created}\n" + f" content: {r.content[:200]}{'...' if len(r.content) > 200 else ''}" + ) + user = _get_prompt("consolidation_user").format( + new_content=new_content, + records_summary="\n\n".join(records_lines), + ) + messages = [ + {"role": "system", "content": _get_prompt("consolidation_system")}, + {"role": "user", "content": user}, + ] + try: + if getattr(llm, "supports_function_calling", lambda: False)(): + response = llm.call(messages, response_model=ConsolidationPlan) + if isinstance(response, ConsolidationPlan): + return response + return ConsolidationPlan.model_validate(response) + response = llm.call(messages) + if isinstance(response, ConsolidationPlan): + return response + if isinstance(response, str): + data = json.loads(response) + return ConsolidationPlan.model_validate(data) + return ConsolidationPlan.model_validate(response) + except Exception as e: + _logger.warning( + "Consolidation analysis failed, defaulting to insert: %s", e, exc_info=False, + ) + return _CONSOLIDATION_DEFAULT diff --git a/lib/crewai/src/crewai/memory/contextual/__init__.py b/lib/crewai/src/crewai/memory/contextual/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lib/crewai/src/crewai/memory/contextual/contextual_memory.py b/lib/crewai/src/crewai/memory/contextual/contextual_memory.py deleted file mode 100644 index 5e35d4f2f..000000000 --- a/lib/crewai/src/crewai/memory/contextual/contextual_memory.py +++ /dev/null @@ -1,254 +0,0 @@ -from __future__ import annotations - -import asyncio -from typing import TYPE_CHECKING - -from crewai.memory import ( - EntityMemory, - ExternalMemory, - LongTermMemory, - ShortTermMemory, -) - - -if TYPE_CHECKING: - from crewai.agent import Agent - from crewai.task import Task - - -class ContextualMemory: - """Aggregates and retrieves context from multiple memory sources.""" - - def __init__( - self, - stm: ShortTermMemory, - ltm: LongTermMemory, - em: EntityMemory, - exm: ExternalMemory, - agent: Agent | None = None, - task: Task | None = None, - ) -> None: - self.stm = stm - self.ltm = ltm - self.em = em - self.exm = exm - self.agent = agent - self.task = task - - if self.stm is not None: - self.stm.agent = self.agent - self.stm.task = self.task - if self.ltm is not None: - self.ltm.agent = self.agent - self.ltm.task = self.task - if self.em is not None: - self.em.agent = self.agent - self.em.task = self.task - if self.exm is not None: - self.exm.agent = self.agent - self.exm.task = self.task - - def build_context_for_task(self, task: Task, context: str) -> str: - """Build contextual information for a task synchronously. - - Args: - task: The task to build context for. - context: Additional context string. - - Returns: - Formatted context string from all memory sources. - """ - query = f"{task.description} {context}".strip() - - if query == "": - return "" - - context_parts = [ - self._fetch_ltm_context(task.description), - self._fetch_stm_context(query), - self._fetch_entity_context(query), - self._fetch_external_context(query), - ] - return "\n".join(filter(None, context_parts)) - - async def abuild_context_for_task(self, task: Task, context: str) -> str: - """Build contextual information for a task asynchronously. - - Args: - task: The task to build context for. - context: Additional context string. - - Returns: - Formatted context string from all memory sources. - """ - query = f"{task.description} {context}".strip() - - if query == "": - return "" - - # Fetch all contexts concurrently - results = await asyncio.gather( - self._afetch_ltm_context(task.description), - self._afetch_stm_context(query), - self._afetch_entity_context(query), - self._afetch_external_context(query), - ) - - return "\n".join(filter(None, results)) - - def _fetch_stm_context(self, query: str) -> str: - """ - Fetches recent relevant insights from STM related to the task's description and expected_output, - formatted as bullet points. - """ - - if self.stm is None: - return "" - - stm_results = self.stm.search(query) - formatted_results = "\n".join( - [f"- {result['content']}" for result in stm_results] - ) - return f"Recent Insights:\n{formatted_results}" if stm_results else "" - - def _fetch_ltm_context(self, task: str) -> str | None: - """ - Fetches historical data or insights from LTM that are relevant to the task's description and expected_output, - formatted as bullet points. - """ - - if self.ltm is None: - return "" - - ltm_results = self.ltm.search(task, latest_n=2) - if not ltm_results: - return None - - formatted_results = [ - suggestion - for result in ltm_results - for suggestion in result["metadata"]["suggestions"] - ] - formatted_results = list(dict.fromkeys(formatted_results)) - formatted_results = "\n".join([f"- {result}" for result in formatted_results]) # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]") - - return f"Historical Data:\n{formatted_results}" if ltm_results else "" - - def _fetch_entity_context(self, query: str) -> str: - """ - Fetches relevant entity information from Entity Memory related to the task's description and expected_output, - formatted as bullet points. - """ - if self.em is None: - return "" - - em_results = self.em.search(query) - formatted_results = "\n".join( - [f"- {result['content']}" for result in em_results] - ) - return f"Entities:\n{formatted_results}" if em_results else "" - - def _fetch_external_context(self, query: str) -> str: - """ - Fetches and formats relevant information from External Memory. - Args: - query (str): The search query to find relevant information. - Returns: - str: Formatted information as bullet points, or an empty string if none found. - """ - if self.exm is None: - return "" - - external_memories = self.exm.search(query) - - if not external_memories: - return "" - - formatted_memories = "\n".join( - f"- {result['content']}" for result in external_memories - ) - return f"External memories:\n{formatted_memories}" - - async def _afetch_stm_context(self, query: str) -> str: - """Fetch recent relevant insights from STM asynchronously. - - Args: - query: The search query. - - Returns: - Formatted insights as bullet points, or empty string if none found. - """ - if self.stm is None: - return "" - - stm_results = await self.stm.asearch(query) - formatted_results = "\n".join( - [f"- {result['content']}" for result in stm_results] - ) - return f"Recent Insights:\n{formatted_results}" if stm_results else "" - - async def _afetch_ltm_context(self, task: str) -> str | None: - """Fetch historical data from LTM asynchronously. - - Args: - task: The task description to search for. - - Returns: - Formatted historical data as bullet points, or None if none found. - """ - if self.ltm is None: - return "" - - ltm_results = await self.ltm.asearch(task, latest_n=2) - if not ltm_results: - return None - - formatted_results = [ - suggestion - for result in ltm_results - for suggestion in result["metadata"]["suggestions"] - ] - formatted_results = list(dict.fromkeys(formatted_results)) - formatted_results = "\n".join([f"- {result}" for result in formatted_results]) # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]") - - return f"Historical Data:\n{formatted_results}" if ltm_results else "" - - async def _afetch_entity_context(self, query: str) -> str: - """Fetch relevant entity information asynchronously. - - Args: - query: The search query. - - Returns: - Formatted entity information as bullet points, or empty string if none found. - """ - if self.em is None: - return "" - - em_results = await self.em.asearch(query) - formatted_results = "\n".join( - [f"- {result['content']}" for result in em_results] - ) - return f"Entities:\n{formatted_results}" if em_results else "" - - async def _afetch_external_context(self, query: str) -> str: - """Fetch relevant information from External Memory asynchronously. - - Args: - query: The search query. - - Returns: - Formatted information as bullet points, or empty string if none found. - """ - if self.exm is None: - return "" - - external_memories = await self.exm.asearch(query) - - if not external_memories: - return "" - - formatted_memories = "\n".join( - f"- {result['content']}" for result in external_memories - ) - return f"External memories:\n{formatted_memories}" diff --git a/lib/crewai/src/crewai/memory/encoding_flow.py b/lib/crewai/src/crewai/memory/encoding_flow.py new file mode 100644 index 000000000..6792cb4bd --- /dev/null +++ b/lib/crewai/src/crewai/memory/encoding_flow.py @@ -0,0 +1,444 @@ +"""Batch-native encoding flow: full save pipeline for one or more memories. + +Orchestrates the encoding side of memory in a single Flow with 5 steps: +1. Batch embed (ONE embedder call for all items) +2. Intra-batch dedup (cosine matrix, drop near-exact duplicates) +3. Parallel find similar (concurrent storage searches) +4. Parallel analyze (N concurrent LLM calls -- field resolution + consolidation) +5. Execute plans (batch re-embed updates + bulk insert) +""" + +from __future__ import annotations + +from concurrent.futures import Future, ThreadPoolExecutor +from datetime import datetime +import math +from typing import Any +from uuid import uuid4 + +from pydantic import BaseModel, Field + +from crewai.flow.flow import Flow, listen, start +from crewai.memory.analyze import ( + ConsolidationPlan, + MemoryAnalysis, + analyze_for_consolidation, + analyze_for_save, +) +from crewai.memory.types import MemoryConfig, MemoryRecord, embed_texts + + +# --------------------------------------------------------------------------- +# State models +# --------------------------------------------------------------------------- + + +class ItemState(BaseModel): + """Per-item tracking within a batch.""" + + content: str = "" + # Caller-provided (None = infer via LLM) + scope: str | None = None + categories: list[str] | None = None + metadata: dict[str, Any] | None = None + importance: float | None = None + source: str | None = None + private: bool = False + # Resolved values + resolved_scope: str = "/" + resolved_categories: list[str] = Field(default_factory=list) + resolved_metadata: dict[str, Any] = Field(default_factory=dict) + resolved_importance: float = 0.5 + resolved_source: str | None = None + resolved_private: bool = False + # Embedding + embedding: list[float] = Field(default_factory=list) + # Intra-batch dedup + dropped: bool = False + # Consolidation + similar_records: list[MemoryRecord] = Field(default_factory=list) + top_similarity: float = 0.0 + plan: ConsolidationPlan | None = None + result_record: MemoryRecord | None = None + + +class EncodingState(BaseModel): + """Batch-level state for the encoding flow.""" + + id: str = Field(default_factory=lambda: str(uuid4())) + items: list[ItemState] = Field(default_factory=list) + # Aggregate stats + records_inserted: int = 0 + records_updated: int = 0 + records_deleted: int = 0 + items_dropped_dedup: int = 0 + + +# --------------------------------------------------------------------------- +# Flow +# --------------------------------------------------------------------------- + + +class EncodingFlow(Flow[EncodingState]): + """Batch-native encoding pipeline for memory.remember() / remember_many(). + + Processes N items through 5 sequential steps, maximising parallelism: + - ONE embedder call for all items + - N concurrent storage searches + - N concurrent individual LLM calls (field resolution + consolidation) + - ONE batch re-embed for updates + ONE bulk storage write + """ + + _skip_auto_memory: bool = True + + initial_state = EncodingState + + def __init__( + self, + storage: Any, + llm: Any, + embedder: Any, + config: MemoryConfig | None = None, + ) -> None: + super().__init__(suppress_flow_events=True) + self._storage = storage + self._llm = llm + self._embedder = embedder + self._config = config or MemoryConfig() + + # ------------------------------------------------------------------ + # Step 1: Batch embed (ONE embedder call) + # ------------------------------------------------------------------ + + @start() + def batch_embed(self) -> None: + """Embed all items in a single embedder call.""" + items = list(self.state.items) + texts = [item.content for item in items] + embeddings = embed_texts(self._embedder, texts) + for item, emb in zip(items, embeddings, strict=False): + item.embedding = emb + + # ------------------------------------------------------------------ + # Step 2: Intra-batch dedup (cosine similarity matrix) + # ------------------------------------------------------------------ + + @listen(batch_embed) + def intra_batch_dedup(self) -> None: + """Drop near-exact duplicates within the batch.""" + items = list(self.state.items) + if len(items) <= 1: + return + + threshold = self._config.batch_dedup_threshold + n = len(items) + for j in range(1, n): + if items[j].dropped or not items[j].embedding: + continue + for i in range(j): + if items[i].dropped or not items[i].embedding: + continue + sim = self._cosine_similarity(items[i].embedding, items[j].embedding) + if sim >= threshold: + items[j].dropped = True + self.state.items_dropped_dedup += 1 + break + + @staticmethod + def _cosine_similarity(a: list[float], b: list[float]) -> float: + """Compute cosine similarity between two vectors.""" + if len(a) != len(b) or not a: + return 0.0 + dot = sum(x * y for x, y in zip(a, b, strict=False)) + norm_a = math.sqrt(sum(x * x for x in a)) + norm_b = math.sqrt(sum(x * x for x in b)) + if norm_a == 0.0 or norm_b == 0.0: + return 0.0 + return dot / (norm_a * norm_b) + + # ------------------------------------------------------------------ + # Step 3: Parallel find similar (concurrent storage searches) + # ------------------------------------------------------------------ + + @listen(intra_batch_dedup) + def parallel_find_similar(self) -> None: + """Search storage for similar records, concurrently for all active items.""" + items = list(self.state.items) + active = [(i, item) for i, item in enumerate(items) if not item.dropped and item.embedding] + + if not active: + return + + def _search_one(item: ItemState) -> list[tuple[MemoryRecord, float]]: + scope_prefix = item.scope if item.scope and item.scope.strip("/") else None + return self._storage.search( + item.embedding, + scope_prefix=scope_prefix, + categories=None, + limit=self._config.consolidation_limit, + min_score=0.0, + ) + + if len(active) == 1: + _, item = active[0] + raw = _search_one(item) + item.similar_records = [r for r, _ in raw] + item.top_similarity = float(raw[0][1]) if raw else 0.0 + else: + with ThreadPoolExecutor(max_workers=min(len(active), 8)) as pool: + futures = [(i, item, pool.submit(_search_one, item)) for i, item in active] + for _, item, future in futures: + raw = future.result() + item.similar_records = [r for r, _ in raw] + item.top_similarity = float(raw[0][1]) if raw else 0.0 + + # ------------------------------------------------------------------ + # Step 4: Parallel analyze (N concurrent LLM calls) + # ------------------------------------------------------------------ + + @listen(parallel_find_similar) + def parallel_analyze(self) -> None: + """Field resolution + consolidation via parallel individual LLM calls. + + Classifies each active item into one of four groups: + - Group A: fields provided + no similar records -> fast insert, 0 LLM calls. + - Group B: fields provided + similar records above threshold -> 1 consolidation call. + - Group C: fields missing + no similar records -> 1 field-resolution call. + - Group D: fields missing + similar records above threshold -> 2 concurrent calls. + + All LLM calls across all items run in parallel via ThreadPoolExecutor. + """ + items = list(self.state.items) + threshold = self._config.consolidation_threshold + + # Pre-fetch scope/category info (shared across all field-resolution calls) + any_needs_fields = any( + not it.dropped + and (it.scope is None or it.categories is None or it.importance is None) + for it in items + ) + existing_scopes: list[str] = [] + existing_categories: list[str] = [] + if any_needs_fields: + existing_scopes = self._storage.list_scopes("/") or ["/"] + existing_categories = list( + self._storage.list_categories(scope_prefix=None).keys() + ) + + # Classify items and submit LLM calls + save_futures: dict[int, Future[MemoryAnalysis]] = {} + consol_futures: dict[int, Future[ConsolidationPlan]] = {} + + pool = ThreadPoolExecutor(max_workers=10) + try: + for i, item in enumerate(items): + if item.dropped: + continue + + fields_provided = ( + item.scope is not None + and item.categories is not None + and item.importance is not None + ) + has_similar = item.top_similarity >= threshold + + if fields_provided and not has_similar: + # Group A: fast path + self._apply_defaults(item) + item.plan = ConsolidationPlan(actions=[], insert_new=True) + elif fields_provided and has_similar: + # Group B: consolidation only + self._apply_defaults(item) + consol_futures[i] = pool.submit( + analyze_for_consolidation, + item.content, list(item.similar_records), self._llm, + ) + elif not fields_provided and not has_similar: + # Group C: field resolution only + save_futures[i] = pool.submit( + analyze_for_save, + item.content, existing_scopes, existing_categories, self._llm, + ) + else: + # Group D: both in parallel + save_futures[i] = pool.submit( + analyze_for_save, + item.content, existing_scopes, existing_categories, self._llm, + ) + consol_futures[i] = pool.submit( + analyze_for_consolidation, + item.content, list(item.similar_records), self._llm, + ) + + # Collect field-resolution results + for i, future in save_futures.items(): + analysis = future.result() + item = items[i] + item.resolved_scope = item.scope or analysis.suggested_scope or "/" + item.resolved_categories = ( + item.categories + if item.categories is not None + else analysis.categories + ) + item.resolved_importance = ( + item.importance + if item.importance is not None + else analysis.importance + ) + item.resolved_metadata = dict( + item.metadata or {}, + **( + analysis.extracted_metadata.model_dump() + if analysis.extracted_metadata + else {} + ), + ) + item.resolved_source = item.source + item.resolved_private = item.private + # If no consolidation future, it's Group C -> insert + if i not in consol_futures: + item.plan = ConsolidationPlan(actions=[], insert_new=True) + + # Collect consolidation results + for i, future in consol_futures.items(): + items[i].plan = future.result() + finally: + pool.shutdown(wait=False) + + def _apply_defaults(self, item: ItemState) -> None: + """Apply caller values with config defaults (fast path).""" + item.resolved_scope = item.scope or "/" + item.resolved_categories = item.categories or [] + item.resolved_metadata = item.metadata or {} + item.resolved_importance = ( + item.importance + if item.importance is not None + else self._config.default_importance + ) + item.resolved_source = item.source + item.resolved_private = item.private + + # ------------------------------------------------------------------ + # Step 5: Execute plans (batch re-embed + bulk insert) + # ------------------------------------------------------------------ + + @listen(parallel_analyze) + def execute_plans(self) -> None: + """Apply all consolidation plans with batch re-embedding and bulk insert. + + Actions are deduplicated across items before applying: when multiple + items reference the same existing record (e.g. both want to delete it), + only the first action is applied. This prevents LanceDB commit + conflicts from two operations targeting the same record. + """ + items = list(self.state.items) + now = datetime.utcnow() + + # --- Deduplicate actions across all items --- + # Multiple items may reference the same existing record (because their + # similar_records overlap). Collect one action per record_id, first wins. + # Also build a map from record_id to the original MemoryRecord for updates. + dedup_deletes: set[str] = set() # record_ids to delete + dedup_updates: dict[str, tuple[int, str]] = {} # record_id -> (item_idx, new_content) + all_similar: dict[str, MemoryRecord] = {} # record_id -> MemoryRecord + + for i, item in enumerate(items): + if item.dropped or item.plan is None: + continue + for r in item.similar_records: + if r.id not in all_similar: + all_similar[r.id] = r + for action in item.plan.actions: + rid = action.record_id + if action.action == "delete" and rid not in dedup_deletes and rid not in dedup_updates: + dedup_deletes.add(rid) + elif action.action == "update" and action.new_content and rid not in dedup_deletes and rid not in dedup_updates: + dedup_updates[rid] = (i, action.new_content) + + # --- Batch re-embed all update contents in ONE call --- + update_list = list(dedup_updates.items()) # [(record_id, (item_idx, new_content)), ...] + update_embeddings: list[list[float]] = [] + if update_list: + update_contents = [content for _, (_, content) in update_list] + update_embeddings = embed_texts(self._embedder, update_contents) + + update_emb_map: dict[str, list[float]] = {} + for (rid, _), emb in zip(update_list, update_embeddings, strict=False): + update_emb_map[rid] = emb + + # --- Apply all storage mutations under one lock --- + # Hold the write lock for the entire delete + update + insert sequence + # so no other pipeline can interleave and cause version conflicts. + # The lock is reentrant (RLock), so the individual storage methods + # can re-acquire it without deadlocking. + # Collect records to insert (outside lock -- pure data assembly) + to_insert: list[tuple[int, MemoryRecord]] = [] + for i, item in enumerate(items): + if item.dropped or item.plan is None: + continue + if item.plan.insert_new: + to_insert.append((i, MemoryRecord( + content=item.content, + scope=item.resolved_scope, + categories=item.resolved_categories, + metadata=item.resolved_metadata, + importance=item.resolved_importance, + embedding=item.embedding if item.embedding else None, + source=item.resolved_source, + private=item.resolved_private, + ))) + + # All storage mutations under one lock so no other pipeline can + # interleave and cause version conflicts. The lock is reentrant + # (RLock) so the individual storage methods re-acquire it safely. + updated_records: dict[str, MemoryRecord] = {} + with self._storage.write_lock: + if dedup_deletes: + self._storage.delete(record_ids=list(dedup_deletes)) + self.state.records_deleted += len(dedup_deletes) + + for rid, (_item_idx, new_content) in dedup_updates.items(): + existing = all_similar.get(rid) + if existing is not None: + new_emb = update_emb_map.get(rid, []) + updated = MemoryRecord( + id=existing.id, + content=new_content, + scope=existing.scope, + categories=existing.categories, + metadata=existing.metadata, + importance=existing.importance, + created_at=existing.created_at, + last_accessed=now, + embedding=new_emb if new_emb else existing.embedding, + ) + self._storage.update(updated) + self.state.records_updated += 1 + updated_records[rid] = updated + + if to_insert: + records = [r for _, r in to_insert] + self._storage.save(records) + self.state.records_inserted += len(records) + for idx, record in to_insert: + items[idx].result_record = record + + # Set result_record for non-insert items (after lock, using updated_records) + for _i, item in enumerate(items): + if item.dropped or item.plan is None or item.plan.insert_new: + continue + if item.result_record is not None: + continue + first_updated = next( + ( + updated_records[a.record_id] + for a in item.plan.actions + if a.action == "update" and a.record_id in updated_records + ), + None, + ) + item.result_record = ( + first_updated + if first_updated is not None + else (item.similar_records[0] if item.similar_records else None) + ) diff --git a/lib/crewai/src/crewai/memory/entity/__init__.py b/lib/crewai/src/crewai/memory/entity/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lib/crewai/src/crewai/memory/entity/entity_memory.py b/lib/crewai/src/crewai/memory/entity/entity_memory.py deleted file mode 100644 index b3e3a568b..000000000 --- a/lib/crewai/src/crewai/memory/entity/entity_memory.py +++ /dev/null @@ -1,404 +0,0 @@ -import time -from typing import Any - -from pydantic import PrivateAttr - -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemoryQueryCompletedEvent, - MemoryQueryFailedEvent, - MemoryQueryStartedEvent, - MemorySaveCompletedEvent, - MemorySaveFailedEvent, - MemorySaveStartedEvent, -) -from crewai.memory.entity.entity_memory_item import EntityMemoryItem -from crewai.memory.memory import Memory -from crewai.memory.storage.rag_storage import RAGStorage - - -class EntityMemory(Memory): - """ - EntityMemory class for managing structured information about entities - and their relationships using SQLite storage. - Inherits from the Memory class. - """ - - _memory_provider: str | None = PrivateAttr() - - def __init__( - self, - crew: Any = None, - embedder_config: Any = None, - storage: Any = None, - path: str | None = None, - ) -> None: - memory_provider = None - if embedder_config and isinstance(embedder_config, dict): - memory_provider = embedder_config.get("provider") - - if memory_provider == "mem0": - try: - from crewai.memory.storage.mem0_storage import Mem0Storage - except ImportError as e: - raise ImportError( - "Mem0 is not installed. Please install it with `pip install mem0ai`." - ) from e - config = ( - embedder_config.get("config") - if embedder_config and isinstance(embedder_config, dict) - else None - ) - storage = Mem0Storage(type="short_term", crew=crew, config=config) # type: ignore[no-untyped-call] - else: - storage = ( - storage - if storage - else RAGStorage( - type="entities", - allow_reset=True, - embedder_config=embedder_config, - crew=crew, - path=path, - ) - ) - - super().__init__(storage=storage) - self._memory_provider = memory_provider - - def save( - self, - value: EntityMemoryItem | list[EntityMemoryItem], - metadata: dict[str, Any] | None = None, - ) -> None: - """Saves one or more entity items into the SQLite storage. - - Args: - value: Single EntityMemoryItem or list of EntityMemoryItems to save. - metadata: Optional metadata dict (included for supertype compatibility but not used). - - Notes: - The metadata parameter is included to satisfy the supertype signature but is not - used - entity metadata is extracted from the EntityMemoryItem objects themselves. - """ - - if not value: - return - - items = value if isinstance(value, list) else [value] - is_batch = len(items) > 1 - - metadata = {"entity_count": len(items)} if is_batch else items[0].metadata - crewai_event_bus.emit( - self, - event=MemorySaveStartedEvent( - metadata=metadata, - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - saved_count = 0 - errors = [] - - def save_single_item(item: EntityMemoryItem) -> tuple[bool, str | None]: - """Save a single item and return success status.""" - try: - if self._memory_provider == "mem0": - data = f""" - Remember details about the following entity: - Name: {item.name} - Type: {item.type} - Entity Description: {item.description} - """ - else: - data = f"{item.name}({item.type}): {item.description}" - - super(EntityMemory, self).save(data, item.metadata) - return True, None - except Exception as e: - return False, f"{item.name}: {e!s}" - - try: - for item in items: - success, error = save_single_item(item) - if success: - saved_count += 1 - else: - errors.append(error) - - if is_batch: - emit_value = f"Saved {saved_count} entities" - metadata = {"entity_count": saved_count, "errors": errors} - else: - emit_value = f"{items[0].name}({items[0].type}): {items[0].description}" - metadata = items[0].metadata - - crewai_event_bus.emit( - self, - event=MemorySaveCompletedEvent( - value=emit_value, - metadata=metadata, - save_time_ms=(time.time() - start_time) * 1000, - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - if errors: - raise Exception( - f"Partial save: {len(errors)} failed out of {len(items)}" - ) - - except Exception as e: - fail_metadata = ( - {"entity_count": len(items), "saved": saved_count} - if is_batch - else items[0].metadata - ) - crewai_event_bus.emit( - self, - event=MemorySaveFailedEvent( - metadata=fail_metadata, - error=str(e), - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - raise - - def search( - self, - query: str, - limit: int = 5, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search entity memory for relevant entries. - - Args: - query: The search query. - limit: Maximum number of results to return. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching memory entries. - """ - crewai_event_bus.emit( - self, - event=MemoryQueryStartedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - results = super().search( - query=query, limit=limit, score_threshold=score_threshold - ) - - crewai_event_bus.emit( - self, - event=MemoryQueryCompletedEvent( - query=query, - results=results, - limit=limit, - score_threshold=score_threshold, - query_time_ms=(time.time() - start_time) * 1000, - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - return results - except Exception as e: - crewai_event_bus.emit( - self, - event=MemoryQueryFailedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - error=str(e), - source_type="entity_memory", - ), - ) - raise - - async def asave( - self, - value: EntityMemoryItem | list[EntityMemoryItem], - metadata: dict[str, Any] | None = None, - ) -> None: - """Save entity items asynchronously. - - Args: - value: Single EntityMemoryItem or list of EntityMemoryItems to save. - metadata: Optional metadata dict (not used, for signature compatibility). - """ - if not value: - return - - items = value if isinstance(value, list) else [value] - is_batch = len(items) > 1 - - metadata = {"entity_count": len(items)} if is_batch else items[0].metadata - crewai_event_bus.emit( - self, - event=MemorySaveStartedEvent( - metadata=metadata, - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - saved_count = 0 - errors: list[str | None] = [] - - async def save_single_item(item: EntityMemoryItem) -> tuple[bool, str | None]: - """Save a single item asynchronously.""" - try: - if self._memory_provider == "mem0": - data = f""" - Remember details about the following entity: - Name: {item.name} - Type: {item.type} - Entity Description: {item.description} - """ - else: - data = f"{item.name}({item.type}): {item.description}" - - await super(EntityMemory, self).asave(data, item.metadata) - return True, None - except Exception as e: - return False, f"{item.name}: {e!s}" - - try: - for item in items: - success, error = await save_single_item(item) - if success: - saved_count += 1 - else: - errors.append(error) - - if is_batch: - emit_value = f"Saved {saved_count} entities" - metadata = {"entity_count": saved_count, "errors": errors} - else: - emit_value = f"{items[0].name}({items[0].type}): {items[0].description}" - metadata = items[0].metadata - - crewai_event_bus.emit( - self, - event=MemorySaveCompletedEvent( - value=emit_value, - metadata=metadata, - save_time_ms=(time.time() - start_time) * 1000, - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - if errors: - raise Exception( - f"Partial save: {len(errors)} failed out of {len(items)}" - ) - - except Exception as e: - fail_metadata = ( - {"entity_count": len(items), "saved": saved_count} - if is_batch - else items[0].metadata - ) - crewai_event_bus.emit( - self, - event=MemorySaveFailedEvent( - metadata=fail_metadata, - error=str(e), - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - raise - - async def asearch( - self, - query: str, - limit: int = 5, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search entity memory asynchronously. - - Args: - query: The search query. - limit: Maximum number of results to return. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching memory entries. - """ - crewai_event_bus.emit( - self, - event=MemoryQueryStartedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - results = await super().asearch( - query=query, limit=limit, score_threshold=score_threshold - ) - - crewai_event_bus.emit( - self, - event=MemoryQueryCompletedEvent( - query=query, - results=results, - limit=limit, - score_threshold=score_threshold, - query_time_ms=(time.time() - start_time) * 1000, - source_type="entity_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - return results - except Exception as e: - crewai_event_bus.emit( - self, - event=MemoryQueryFailedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - error=str(e), - source_type="entity_memory", - ), - ) - raise - - def reset(self) -> None: - try: - self.storage.reset() - except Exception as e: - raise Exception( - f"An error occurred while resetting the entity memory: {e}" - ) from e diff --git a/lib/crewai/src/crewai/memory/entity/entity_memory_item.py b/lib/crewai/src/crewai/memory/entity/entity_memory_item.py deleted file mode 100644 index 7e1ef1c0e..000000000 --- a/lib/crewai/src/crewai/memory/entity/entity_memory_item.py +++ /dev/null @@ -1,12 +0,0 @@ -class EntityMemoryItem: - def __init__( - self, - name: str, - type: str, - description: str, - relationships: str, - ): - self.name = name - self.type = type - self.description = description - self.metadata = {"relationships": relationships} diff --git a/lib/crewai/src/crewai/memory/external/__init__.py b/lib/crewai/src/crewai/memory/external/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lib/crewai/src/crewai/memory/external/external_memory.py b/lib/crewai/src/crewai/memory/external/external_memory.py deleted file mode 100644 index 6aedf0084..000000000 --- a/lib/crewai/src/crewai/memory/external/external_memory.py +++ /dev/null @@ -1,301 +0,0 @@ -from __future__ import annotations - -import time -from typing import TYPE_CHECKING, Any - -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemoryQueryCompletedEvent, - MemoryQueryFailedEvent, - MemoryQueryStartedEvent, - MemorySaveCompletedEvent, - MemorySaveFailedEvent, - MemorySaveStartedEvent, -) -from crewai.memory.external.external_memory_item import ExternalMemoryItem -from crewai.memory.memory import Memory -from crewai.memory.storage.interface import Storage -from crewai.rag.embeddings.types import ProviderSpec - - -if TYPE_CHECKING: - from crewai.memory.storage.mem0_storage import Mem0Storage - - -class ExternalMemory(Memory): - def __init__(self, storage: Storage | None = None, **data: Any): - super().__init__(storage=storage, **data) - - @staticmethod - def _configure_mem0(crew: Any, config: dict[str, Any]) -> Mem0Storage: - from crewai.memory.storage.mem0_storage import Mem0Storage - - return Mem0Storage(type="external", crew=crew, config=config) # type: ignore[no-untyped-call] - - @staticmethod - def external_supported_storages() -> dict[str, Any]: - return { - "mem0": ExternalMemory._configure_mem0, - } - - @staticmethod - def create_storage( - crew: Any, embedder_config: dict[str, Any] | ProviderSpec | None - ) -> Storage: - if not embedder_config: - raise ValueError("embedder_config is required") - - if "provider" not in embedder_config: - raise ValueError("embedder_config must include a 'provider' key") - - provider = embedder_config["provider"] - supported_storages = ExternalMemory.external_supported_storages() - if provider not in supported_storages: - raise ValueError(f"Provider {provider} not supported") - - storage: Storage = supported_storages[provider]( - crew, embedder_config.get("config", {}) - ) - return storage - - def save( - self, - value: Any, - metadata: dict[str, Any] | None = None, - ) -> None: - """Saves a value into the external storage.""" - crewai_event_bus.emit( - self, - event=MemorySaveStartedEvent( - value=value, - metadata=metadata, - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - item = ExternalMemoryItem( - value=value, - metadata=metadata, - agent=self.agent.role if self.agent else None, - ) - super().save(value=item.value, metadata=item.metadata) - - crewai_event_bus.emit( - self, - event=MemorySaveCompletedEvent( - value=value, - metadata=metadata, - save_time_ms=(time.time() - start_time) * 1000, - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - except Exception as e: - crewai_event_bus.emit( - self, - event=MemorySaveFailedEvent( - value=value, - metadata=metadata, - error=str(e), - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - raise - - def search( - self, - query: str, - limit: int = 5, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search external memory for relevant entries. - - Args: - query: The search query. - limit: Maximum number of results to return. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching memory entries. - """ - crewai_event_bus.emit( - self, - event=MemoryQueryStartedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - results = super().search( - query=query, limit=limit, score_threshold=score_threshold - ) - - crewai_event_bus.emit( - self, - event=MemoryQueryCompletedEvent( - query=query, - results=results, - limit=limit, - score_threshold=score_threshold, - query_time_ms=(time.time() - start_time) * 1000, - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - return results - except Exception as e: - crewai_event_bus.emit( - self, - event=MemoryQueryFailedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - error=str(e), - source_type="external_memory", - ), - ) - raise - - async def asave( - self, - value: Any, - metadata: dict[str, Any] | None = None, - ) -> None: - """Save a value to external memory asynchronously. - - Args: - value: The value to save. - metadata: Optional metadata to associate with the value. - """ - crewai_event_bus.emit( - self, - event=MemorySaveStartedEvent( - value=value, - metadata=metadata, - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - item = ExternalMemoryItem( - value=value, - metadata=metadata, - agent=self.agent.role if self.agent else None, - ) - await super().asave(value=item.value, metadata=item.metadata) - - crewai_event_bus.emit( - self, - event=MemorySaveCompletedEvent( - value=value, - metadata=metadata, - save_time_ms=(time.time() - start_time) * 1000, - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - except Exception as e: - crewai_event_bus.emit( - self, - event=MemorySaveFailedEvent( - value=value, - metadata=metadata, - error=str(e), - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - raise - - async def asearch( - self, - query: str, - limit: int = 5, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search external memory asynchronously. - - Args: - query: The search query. - limit: Maximum number of results to return. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching memory entries. - """ - crewai_event_bus.emit( - self, - event=MemoryQueryStartedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - results = await super().asearch( - query=query, limit=limit, score_threshold=score_threshold - ) - - crewai_event_bus.emit( - self, - event=MemoryQueryCompletedEvent( - query=query, - results=results, - limit=limit, - score_threshold=score_threshold, - query_time_ms=(time.time() - start_time) * 1000, - source_type="external_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - return results - except Exception as e: - crewai_event_bus.emit( - self, - event=MemoryQueryFailedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - error=str(e), - source_type="external_memory", - ), - ) - raise - - def reset(self) -> None: - self.storage.reset() - - def set_crew(self, crew: Any) -> ExternalMemory: - super().set_crew(crew) - - if not self.storage: - self.storage = self.create_storage(crew, self.embedder_config) # type: ignore[arg-type] - - return self diff --git a/lib/crewai/src/crewai/memory/external/external_memory_item.py b/lib/crewai/src/crewai/memory/external/external_memory_item.py deleted file mode 100644 index f66b16c3d..000000000 --- a/lib/crewai/src/crewai/memory/external/external_memory_item.py +++ /dev/null @@ -1,13 +0,0 @@ -from typing import Any - - -class ExternalMemoryItem: - def __init__( - self, - value: Any, - metadata: dict[str, Any] | None = None, - agent: str | None = None, - ): - self.value = value - self.metadata = metadata - self.agent = agent diff --git a/lib/crewai/src/crewai/memory/long_term/__init__.py b/lib/crewai/src/crewai/memory/long_term/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lib/crewai/src/crewai/memory/long_term/long_term_memory.py b/lib/crewai/src/crewai/memory/long_term/long_term_memory.py deleted file mode 100644 index 35ab12870..000000000 --- a/lib/crewai/src/crewai/memory/long_term/long_term_memory.py +++ /dev/null @@ -1,255 +0,0 @@ -import time -from typing import Any - -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemoryQueryCompletedEvent, - MemoryQueryFailedEvent, - MemoryQueryStartedEvent, - MemorySaveCompletedEvent, - MemorySaveFailedEvent, - MemorySaveStartedEvent, -) -from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem -from crewai.memory.memory import Memory -from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage - - -class LongTermMemory(Memory): - """ - LongTermMemory class for managing cross runs data related to overall crew's - execution and performance. - Inherits from the Memory class and utilizes an instance of a class that - adheres to the Storage for data storage, specifically working with - LongTermMemoryItem instances. - """ - - def __init__( - self, - storage: LTMSQLiteStorage | None = None, - path: str | None = None, - ) -> None: - if not storage: - storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage() - super().__init__(storage=storage) - - def save(self, item: LongTermMemoryItem) -> None: # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory" - crewai_event_bus.emit( - self, - event=MemorySaveStartedEvent( - value=item.task, - metadata=item.metadata, - agent_role=item.agent, - source_type="long_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - metadata = item.metadata - metadata.update( - {"agent": item.agent, "expected_output": item.expected_output} - ) - self.storage.save( - task_description=item.task, - score=metadata["quality"], - metadata=metadata, - datetime=item.datetime, - ) - - crewai_event_bus.emit( - self, - event=MemorySaveCompletedEvent( - value=item.task, - metadata=item.metadata, - agent_role=item.agent, - save_time_ms=(time.time() - start_time) * 1000, - source_type="long_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - except Exception as e: - crewai_event_bus.emit( - self, - event=MemorySaveFailedEvent( - value=item.task, - metadata=item.metadata, - agent_role=item.agent, - error=str(e), - source_type="long_term_memory", - ), - ) - raise - - def search( # type: ignore[override] - self, - task: str, - latest_n: int = 3, - ) -> list[dict[str, Any]]: - """Search long-term memory for relevant entries. - - Args: - task: The task description to search for. - latest_n: Maximum number of results to return. - - Returns: - List of matching memory entries. - """ - crewai_event_bus.emit( - self, - event=MemoryQueryStartedEvent( - query=task, - limit=latest_n, - source_type="long_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - results = self.storage.load(task, latest_n) - - crewai_event_bus.emit( - self, - event=MemoryQueryCompletedEvent( - query=task, - results=results, - limit=latest_n, - query_time_ms=(time.time() - start_time) * 1000, - source_type="long_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - return results or [] - except Exception as e: - crewai_event_bus.emit( - self, - event=MemoryQueryFailedEvent( - query=task, - limit=latest_n, - error=str(e), - source_type="long_term_memory", - ), - ) - raise - - async def asave(self, item: LongTermMemoryItem) -> None: # type: ignore[override] - """Save an item to long-term memory asynchronously. - - Args: - item: The LongTermMemoryItem to save. - """ - crewai_event_bus.emit( - self, - event=MemorySaveStartedEvent( - value=item.task, - metadata=item.metadata, - agent_role=item.agent, - source_type="long_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - metadata = item.metadata - metadata.update( - {"agent": item.agent, "expected_output": item.expected_output} - ) - await self.storage.asave( - task_description=item.task, - score=metadata["quality"], - metadata=metadata, - datetime=item.datetime, - ) - - crewai_event_bus.emit( - self, - event=MemorySaveCompletedEvent( - value=item.task, - metadata=item.metadata, - agent_role=item.agent, - save_time_ms=(time.time() - start_time) * 1000, - source_type="long_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - except Exception as e: - crewai_event_bus.emit( - self, - event=MemorySaveFailedEvent( - value=item.task, - metadata=item.metadata, - agent_role=item.agent, - error=str(e), - source_type="long_term_memory", - ), - ) - raise - - async def asearch( # type: ignore[override] - self, - task: str, - latest_n: int = 3, - ) -> list[dict[str, Any]]: - """Search long-term memory asynchronously. - - Args: - task: The task description to search for. - latest_n: Maximum number of results to return. - - Returns: - List of matching memory entries. - """ - crewai_event_bus.emit( - self, - event=MemoryQueryStartedEvent( - query=task, - limit=latest_n, - source_type="long_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - results = await self.storage.aload(task, latest_n) - - crewai_event_bus.emit( - self, - event=MemoryQueryCompletedEvent( - query=task, - results=results, - limit=latest_n, - query_time_ms=(time.time() - start_time) * 1000, - source_type="long_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - return results or [] - except Exception as e: - crewai_event_bus.emit( - self, - event=MemoryQueryFailedEvent( - query=task, - limit=latest_n, - error=str(e), - source_type="long_term_memory", - ), - ) - raise - - def reset(self) -> None: - """Reset long-term memory.""" - self.storage.reset() diff --git a/lib/crewai/src/crewai/memory/long_term/long_term_memory_item.py b/lib/crewai/src/crewai/memory/long_term/long_term_memory_item.py deleted file mode 100644 index 5196b2548..000000000 --- a/lib/crewai/src/crewai/memory/long_term/long_term_memory_item.py +++ /dev/null @@ -1,19 +0,0 @@ -from typing import Any - - -class LongTermMemoryItem: - def __init__( - self, - agent: str, - task: str, - expected_output: str, - datetime: str, - quality: int | float | None = None, - metadata: dict[str, Any] | None = None, - ): - self.task = task - self.agent = agent - self.quality = quality - self.datetime = datetime - self.expected_output = expected_output - self.metadata = metadata if metadata is not None else {} diff --git a/lib/crewai/src/crewai/memory/memory.py b/lib/crewai/src/crewai/memory/memory.py deleted file mode 100644 index fe90b8e3e..000000000 --- a/lib/crewai/src/crewai/memory/memory.py +++ /dev/null @@ -1,121 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING, Any - -from pydantic import BaseModel - -from crewai.rag.embeddings.types import EmbedderConfig - - -if TYPE_CHECKING: - from crewai.agent import Agent - from crewai.task import Task - - -class Memory(BaseModel): - """Base class for memory, supporting agent tags and generic metadata.""" - - embedder_config: EmbedderConfig | dict[str, Any] | None = None - crew: Any | None = None - - storage: Any - _agent: Agent | None = None - _task: Task | None = None - - def __init__(self, storage: Any, **data: Any): - super().__init__(storage=storage, **data) - - @property - def task(self) -> Task | None: - """Get the current task associated with this memory.""" - return self._task - - @task.setter - def task(self, task: Task | None) -> None: - """Set the current task associated with this memory.""" - self._task = task - - @property - def agent(self) -> Agent | None: - """Get the current agent associated with this memory.""" - return self._agent - - @agent.setter - def agent(self, agent: Agent | None) -> None: - """Set the current agent associated with this memory.""" - self._agent = agent - - def save( - self, - value: Any, - metadata: dict[str, Any] | None = None, - ) -> None: - """Save a value to memory. - - Args: - value: The value to save. - metadata: Optional metadata to associate with the value. - """ - metadata = metadata or {} - self.storage.save(value, metadata) - - async def asave( - self, - value: Any, - metadata: dict[str, Any] | None = None, - ) -> None: - """Save a value to memory asynchronously. - - Args: - value: The value to save. - metadata: Optional metadata to associate with the value. - """ - metadata = metadata or {} - await self.storage.asave(value, metadata) - - def search( - self, - query: str, - limit: int = 5, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search memory for relevant entries. - - Args: - query: The search query. - limit: Maximum number of results to return. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching memory entries. - """ - results: list[Any] = self.storage.search( - query=query, limit=limit, score_threshold=score_threshold - ) - return results - - async def asearch( - self, - query: str, - limit: int = 5, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search memory for relevant entries asynchronously. - - Args: - query: The search query. - limit: Maximum number of results to return. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching memory entries. - """ - results: list[Any] = await self.storage.asearch( - query=query, limit=limit, score_threshold=score_threshold - ) - return results - - def set_crew(self, crew: Any) -> Memory: - """Set the crew for this memory instance.""" - self.crew = crew - return self diff --git a/lib/crewai/src/crewai/memory/memory_scope.py b/lib/crewai/src/crewai/memory/memory_scope.py new file mode 100644 index 000000000..705ec07de --- /dev/null +++ b/lib/crewai/src/crewai/memory/memory_scope.py @@ -0,0 +1,272 @@ +"""Scoped and sliced views over unified Memory.""" + +from __future__ import annotations + +from datetime import datetime +from typing import TYPE_CHECKING, Any + + +if TYPE_CHECKING: + from crewai.memory.unified_memory import Memory + +from crewai.memory.types import ( + _RECALL_OVERSAMPLE_FACTOR, + MemoryMatch, + MemoryRecord, + ScopeInfo, +) + + +class MemoryScope: + """View of Memory restricted to a root path. All operations are scoped under that path.""" + + def __init__(self, memory: Memory, root_path: str) -> None: + """Initialize scope. + + Args: + memory: The underlying Memory instance. + root_path: Root path for this scope (e.g. /agent/1). + """ + self._memory = memory + self._root = root_path.rstrip("/") or "" + if self._root and not self._root.startswith("/"): + self._root = "/" + self._root + + def _scope_path(self, scope: str | None) -> str: + if not scope or scope == "/": + return self._root or "/" + s = scope.rstrip("/") + if not s.startswith("/"): + s = "/" + s + if not self._root: + return s + base = self._root.rstrip("/") + return f"{base}{s}" + + def remember( + self, + content: str, + scope: str | None = "/", + categories: list[str] | None = None, + metadata: dict[str, Any] | None = None, + importance: float | None = None, + source: str | None = None, + private: bool = False, + ) -> MemoryRecord: + """Remember content; scope is relative to this scope's root.""" + path = self._scope_path(scope) + return self._memory.remember( + content, + scope=path, + categories=categories, + metadata=metadata, + importance=importance, + source=source, + private=private, + ) + + def recall( + self, + query: str, + scope: str | None = None, + categories: list[str] | None = None, + limit: int = 10, + depth: str = "deep", + source: str | None = None, + include_private: bool = False, + ) -> list[MemoryMatch]: + """Recall within this scope (root path and below).""" + search_scope = self._scope_path(scope) if scope else (self._root or "/") + return self._memory.recall( + query, + scope=search_scope, + categories=categories, + limit=limit, + depth=depth, + source=source, + include_private=include_private, + ) + + def extract_memories(self, content: str) -> list[str]: + """Extract discrete memories from content; delegates to underlying Memory.""" + return self._memory.extract_memories(content) + + def forget( + self, + scope: str | None = None, + categories: list[str] | None = None, + older_than: datetime | None = None, + metadata_filter: dict[str, Any] | None = None, + record_ids: list[str] | None = None, + ) -> int: + """Forget within this scope.""" + prefix = self._scope_path(scope) if scope else (self._root or "/") + return self._memory.forget( + scope=prefix, + categories=categories, + older_than=older_than, + metadata_filter=metadata_filter, + record_ids=record_ids, + ) + + def list_scopes(self, path: str = "/") -> list[str]: + """List child scopes under path (relative to this scope's root).""" + full = self._scope_path(path) + return self._memory.list_scopes(full) + + def info(self, path: str = "/") -> ScopeInfo: + """Info for path under this scope.""" + full = self._scope_path(path) + return self._memory.info(full) + + def tree(self, path: str = "/", max_depth: int = 3) -> str: + """Tree under path within this scope.""" + full = self._scope_path(path) + return self._memory.tree(full, max_depth=max_depth) + + def list_categories(self, path: str | None = None) -> dict[str, int]: + """Categories in this scope; path None means this scope root.""" + full = self._scope_path(path) if path else (self._root or "/") + return self._memory.list_categories(full) + + def reset(self, scope: str | None = None) -> None: + """Reset within this scope.""" + prefix = self._scope_path(scope) if scope else (self._root or "/") + self._memory.reset(scope=prefix) + + def subscope(self, path: str) -> MemoryScope: + """Return a narrower scope under this scope.""" + child = path.strip("/") + if not child: + return MemoryScope(self._memory, self._root or "/") + base = self._root.rstrip("/") or "" + new_root = f"{base}/{child}" if base else f"/{child}" + return MemoryScope(self._memory, new_root) + + +class MemorySlice: + """View over multiple scopes: recall searches all, remember is a no-op when read_only.""" + + def __init__( + self, + memory: Memory, + scopes: list[str], + categories: list[str] | None = None, + read_only: bool = True, + ) -> None: + """Initialize slice. + + Args: + memory: The underlying Memory instance. + scopes: List of scope paths to include. + categories: Optional category filter for recall. + read_only: If True, remember() is a silent no-op. + """ + self._memory = memory + self._scopes = [s.rstrip("/") or "/" for s in scopes] + self._categories = categories + self._read_only = read_only + + def remember( + self, + content: str, + scope: str, + categories: list[str] | None = None, + metadata: dict[str, Any] | None = None, + importance: float | None = None, + source: str | None = None, + private: bool = False, + ) -> MemoryRecord | None: + """Remember into an explicit scope. No-op when read_only=True.""" + if self._read_only: + return None + return self._memory.remember( + content, + scope=scope, + categories=categories, + metadata=metadata, + importance=importance, + source=source, + private=private, + ) + + def recall( + self, + query: str, + scope: str | None = None, + categories: list[str] | None = None, + limit: int = 10, + depth: str = "deep", + source: str | None = None, + include_private: bool = False, + ) -> list[MemoryMatch]: + """Recall across all slice scopes; results merged and re-ranked.""" + cats = categories or self._categories + all_matches: list[MemoryMatch] = [] + for sc in self._scopes: + matches = self._memory.recall( + query, + scope=sc, + categories=cats, + limit=limit * _RECALL_OVERSAMPLE_FACTOR, + depth=depth, + source=source, + include_private=include_private, + ) + all_matches.extend(matches) + seen_ids: set[str] = set() + unique: list[MemoryMatch] = [] + for m in sorted(all_matches, key=lambda x: x.score, reverse=True): + if m.record.id not in seen_ids: + seen_ids.add(m.record.id) + unique.append(m) + if len(unique) >= limit: + break + return unique + + def extract_memories(self, content: str) -> list[str]: + """Extract discrete memories from content; delegates to underlying Memory.""" + return self._memory.extract_memories(content) + + def list_scopes(self, path: str = "/") -> list[str]: + """List scopes across all slice roots.""" + out: list[str] = [] + for sc in self._scopes: + full = f"{sc.rstrip('/')}{path}" if sc != "/" else path + out.extend(self._memory.list_scopes(full)) + return sorted(set(out)) + + def info(self, path: str = "/") -> ScopeInfo: + """Aggregate info across slice scopes (record counts summed).""" + total_records = 0 + all_categories: set[str] = set() + oldest: datetime | None = None + newest: datetime | None = None + children: list[str] = [] + for sc in self._scopes: + full = f"{sc.rstrip('/')}{path}" if sc != "/" else path + inf = self._memory.info(full) + total_records += inf.record_count + all_categories.update(inf.categories) + if inf.oldest_record: + oldest = inf.oldest_record if oldest is None else min(oldest, inf.oldest_record) + if inf.newest_record: + newest = inf.newest_record if newest is None else max(newest, inf.newest_record) + children.extend(inf.child_scopes) + return ScopeInfo( + path=path, + record_count=total_records, + categories=sorted(all_categories), + oldest_record=oldest, + newest_record=newest, + child_scopes=sorted(set(children)), + ) + + def list_categories(self, path: str | None = None) -> dict[str, int]: + """Categories and counts across slice scopes.""" + counts: dict[str, int] = {} + for sc in self._scopes: + full = (f"{sc.rstrip('/')}{path}" if sc != "/" else path) if path else sc + for k, v in self._memory.list_categories(full).items(): + counts[k] = counts.get(k, 0) + v + return counts diff --git a/lib/crewai/src/crewai/memory/recall_flow.py b/lib/crewai/src/crewai/memory/recall_flow.py new file mode 100644 index 000000000..e0f238861 --- /dev/null +++ b/lib/crewai/src/crewai/memory/recall_flow.py @@ -0,0 +1,358 @@ +"""RLM-inspired intelligent recall flow for memory retrieval. + +Implements adaptive-depth retrieval with: +- LLM query distillation into targeted sub-queries +- Time-based filtering from temporal hints +- Parallel multi-query, multi-scope search +- Confidence-based routing with iterative deepening (budget loop) +- Evidence gap tracking propagated to results +""" + +from __future__ import annotations + +from concurrent.futures import ThreadPoolExecutor, as_completed +from datetime import datetime +from typing import Any +from uuid import uuid4 + +from pydantic import BaseModel, Field + +from crewai.flow.flow import Flow, listen, router, start +from crewai.memory.analyze import QueryAnalysis, analyze_query +from crewai.memory.types import ( + _RECALL_OVERSAMPLE_FACTOR, + MemoryConfig, + MemoryMatch, + MemoryRecord, + compute_composite_score, + embed_texts, +) + + +class RecallState(BaseModel): + """State for the recall flow.""" + + id: str = Field(default_factory=lambda: str(uuid4())) + query: str = "" + scope: str | None = None + categories: list[str] | None = None + time_cutoff: datetime | None = None + source: str | None = None + include_private: bool = False + limit: int = 10 + query_embeddings: list[tuple[str, list[float]]] = Field(default_factory=list) + query_analysis: QueryAnalysis | None = None + candidate_scopes: list[str] = Field(default_factory=list) + chunk_findings: list[Any] = Field(default_factory=list) + evidence_gaps: list[str] = Field(default_factory=list) + confidence: float = 0.0 + final_results: list[MemoryMatch] = Field(default_factory=list) + exploration_budget: int = 1 + + +class RecallFlow(Flow[RecallState]): + """RLM-inspired intelligent memory recall flow. + + Analyzes the query via LLM to produce targeted sub-queries and filters, + embeds each sub-query, searches across candidate scopes in parallel, + and iteratively deepens exploration when confidence is low. + """ + + _skip_auto_memory: bool = True + + initial_state = RecallState + + def __init__( + self, + storage: Any, + llm: Any, + embedder: Any, + config: MemoryConfig | None = None, + ) -> None: + super().__init__(suppress_flow_events=True) + self._storage = storage + self._llm = llm + self._embedder = embedder + self._config = config or MemoryConfig() + + # ------------------------------------------------------------------ + # Helpers + # ------------------------------------------------------------------ + + def _merged_categories(self) -> list[str] | None: + """Return caller-supplied categories, or None if empty.""" + return self.state.categories or None + + def _do_search(self) -> list[dict[str, Any]]: + """Run parallel search across (embeddings x scopes) with filters. + + Populates ``state.chunk_findings`` and ``state.confidence``. + Returns the findings list. + """ + search_categories = self._merged_categories() + + def _search_one( + embedding: list[float], scope: str + ) -> tuple[str, list[tuple[MemoryRecord, float]]]: + raw = self._storage.search( + embedding, + scope_prefix=scope, + categories=search_categories, + limit=self.state.limit * _RECALL_OVERSAMPLE_FACTOR, + min_score=0.0, + ) + # Post-filter by time cutoff + if self.state.time_cutoff and raw: + raw = [ + (r, s) for r, s in raw if r.created_at >= self.state.time_cutoff + ] + # Privacy filter + if not self.state.include_private and raw: + raw = [ + (r, s) for r, s in raw + if not r.private or r.source == self.state.source + ] + return scope, raw + + # Build (embedding, scope) task list + tasks: list[tuple[list[float], str]] = [ + (embedding, scope) + for _query_text, embedding in self.state.query_embeddings + for scope in self.state.candidate_scopes + ] + + findings: list[dict[str, Any]] = [] + + if len(tasks) <= 1: + for emb, sc in tasks: + scope, results = _search_one(emb, sc) + if results: + top_composite, _ = compute_composite_score( + results[0][0], results[0][1], self._config + ) + findings.append({ + "scope": scope, + "results": results, + "top_score": top_composite, + }) + else: + with ThreadPoolExecutor(max_workers=min(len(tasks), 4)) as pool: + futures = { + pool.submit(_search_one, emb, sc): (emb, sc) + for emb, sc in tasks + } + for future in as_completed(futures): + scope, results = future.result() + if results: + top_composite, _ = compute_composite_score( + results[0][0], results[0][1], self._config + ) + findings.append({ + "scope": scope, + "results": results, + "top_score": top_composite, + }) + + self.state.chunk_findings = findings + self.state.confidence = max( + (f["top_score"] for f in findings), default=0.0 + ) + return findings + + # ------------------------------------------------------------------ + # Flow steps + # ------------------------------------------------------------------ + + @start() + def analyze_query_step(self) -> QueryAnalysis: + """Analyze the query, embed distilled sub-queries, extract filters. + + Short queries (below ``query_analysis_threshold`` characters) skip + the LLM call entirely and embed the raw query directly -- saving + ~1-3s per recall. Longer queries (e.g. full task descriptions) + benefit from LLM distillation into targeted sub-queries. + + Sub-queries are embedded in a single batch ``embed_texts()`` call + rather than sequential ``embed_text()`` calls. + """ + self.state.exploration_budget = self._config.exploration_budget + + query_len = len(self.state.query) + skip_llm = query_len < self._config.query_analysis_threshold + + if skip_llm: + # Short query: skip LLM, embed raw query directly + analysis = QueryAnalysis( + keywords=[], + suggested_scopes=[], + complexity="simple", + recall_queries=[self.state.query], + ) + self.state.query_analysis = analysis + else: + # Long query: use LLM to distill sub-queries and extract filters + available = self._storage.list_scopes(self.state.scope or "/") + if not available: + available = ["/"] + scope_info = ( + self._storage.get_scope_info(self.state.scope or "/") + if self.state.scope + else None + ) + analysis = analyze_query( + self.state.query, + available, + scope_info, + self._llm, + ) + self.state.query_analysis = analysis + + # Parse time_filter into a datetime cutoff + if analysis.time_filter: + try: + self.state.time_cutoff = datetime.fromisoformat(analysis.time_filter) + except ValueError: + pass + + # Batch-embed all sub-queries in ONE call + queries = analysis.recall_queries if analysis.recall_queries else [self.state.query] + queries = queries[:3] + embeddings = embed_texts(self._embedder, queries) + pairs: list[tuple[str, list[float]]] = [ + (q, emb) for q, emb in zip(queries, embeddings, strict=False) if emb + ] + if not pairs: + # Fallback: embed the raw query if distilled queries all failed + fallback_emb = embed_texts(self._embedder, [self.state.query]) + if fallback_emb and fallback_emb[0]: + pairs = [(self.state.query, fallback_emb[0])] + self.state.query_embeddings = pairs + return analysis + + @listen(analyze_query_step) + def filter_and_chunk(self) -> list[str]: + """Select candidate scopes based on LLM analysis.""" + analysis = self.state.query_analysis + scope_prefix = (self.state.scope or "/").rstrip("/") or "/" + if analysis and analysis.suggested_scopes: + candidates = [s for s in analysis.suggested_scopes if s] + else: + candidates = self._storage.list_scopes(scope_prefix) + if not candidates: + info = self._storage.get_scope_info(scope_prefix) + if info.record_count > 0: + candidates = [scope_prefix] + else: + candidates = [scope_prefix] + self.state.candidate_scopes = candidates[:20] + return self.state.candidate_scopes + + @listen(filter_and_chunk) + def search_chunks(self) -> list[Any]: + """Initial parallel search across (embeddings x scopes) with filters.""" + return self._do_search() + + @router(search_chunks) + def decide_depth(self) -> str: + """Route based on confidence, complexity, and remaining budget.""" + analysis = self.state.query_analysis + if ( + analysis + and analysis.complexity == "complex" + and self.state.confidence < self._config.complex_query_threshold + ): + if self.state.exploration_budget > 0: + return "explore_deeper" + if self.state.confidence >= self._config.confidence_threshold_high: + return "synthesize" + if ( + self.state.exploration_budget > 0 + and self.state.confidence < self._config.confidence_threshold_low + ): + return "explore_deeper" + return "synthesize" + + @listen("explore_deeper") + def recursive_exploration(self) -> list[Any]: + """Feed top results back to LLM for deeper context extraction. + + Decrements the exploration budget so the loop terminates. + """ + self.state.exploration_budget -= 1 + + enhanced = [] + for finding in self.state.chunk_findings: + if not finding.get("results"): + continue + content_parts = [r[0].content for r in finding["results"][:5]] + chunk_text = "\n---\n".join(content_parts) + prompt = ( + f"Query: {self.state.query}\n\n" + f"Relevant memory excerpts:\n{chunk_text}\n\n" + "Extract the most relevant information for the query. " + "If something is missing, say what's missing in one short line." + ) + try: + response = self._llm.call([{"role": "user", "content": prompt}]) + if isinstance(response, str) and "missing" in response.lower(): + self.state.evidence_gaps.append(response[:200]) + enhanced.append({ + "scope": finding["scope"], + "extraction": response, + "results": finding["results"], + }) + except Exception: + enhanced.append({ + "scope": finding["scope"], + "extraction": "", + "results": finding["results"], + }) + self.state.chunk_findings = enhanced + return enhanced + + @listen(recursive_exploration) + def re_search(self) -> list[Any]: + """Re-search after exploration to update confidence for the router loop.""" + return self._do_search() + + @router(re_search) + def re_decide_depth(self) -> str: + """Re-evaluate depth after re-search. Same logic as decide_depth.""" + return self.decide_depth() + + @listen("synthesize") + def synthesize_results(self) -> list[MemoryMatch]: + """Deduplicate, composite-score, rank, and attach evidence gaps.""" + seen_ids: set[str] = set() + matches: list[MemoryMatch] = [] + for finding in self.state.chunk_findings: + if not isinstance(finding, dict): + continue + results = finding.get("results", []) + if not isinstance(results, list): + continue + for item in results: + if isinstance(item, (list, tuple)) and len(item) >= 2: + record, score = item[0], item[1] + else: + continue + if isinstance(record, MemoryRecord) and record.id not in seen_ids: + seen_ids.add(record.id) + composite, reasons = compute_composite_score( + record, float(score), self._config + ) + matches.append( + MemoryMatch( + record=record, + score=composite, + match_reasons=reasons, + ) + ) + matches.sort(key=lambda m: m.score, reverse=True) + self.state.final_results = matches[: self.state.limit] + + # Attach evidence gaps to the first result so callers can inspect them + if self.state.evidence_gaps and self.state.final_results: + self.state.final_results[0].evidence_gaps = list(self.state.evidence_gaps) + + return self.state.final_results diff --git a/lib/crewai/src/crewai/memory/short_term/__init__.py b/lib/crewai/src/crewai/memory/short_term/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lib/crewai/src/crewai/memory/short_term/short_term_memory.py b/lib/crewai/src/crewai/memory/short_term/short_term_memory.py deleted file mode 100644 index c1663b4f5..000000000 --- a/lib/crewai/src/crewai/memory/short_term/short_term_memory.py +++ /dev/null @@ -1,318 +0,0 @@ -from __future__ import annotations - -import time -from typing import Any - -from pydantic import PrivateAttr - -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemoryQueryCompletedEvent, - MemoryQueryFailedEvent, - MemoryQueryStartedEvent, - MemorySaveCompletedEvent, - MemorySaveFailedEvent, - MemorySaveStartedEvent, -) -from crewai.memory.memory import Memory -from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem -from crewai.memory.storage.rag_storage import RAGStorage - - -class ShortTermMemory(Memory): - """ - ShortTermMemory class for managing transient data related to immediate tasks - and interactions. - Inherits from the Memory class and utilizes an instance of a class that - adheres to the Storage for data storage, specifically working with - MemoryItem instances. - """ - - _memory_provider: str | None = PrivateAttr() - - def __init__( - self, - crew: Any = None, - embedder_config: Any = None, - storage: Any = None, - path: str | None = None, - ) -> None: - memory_provider = None - if embedder_config and isinstance(embedder_config, dict): - memory_provider = embedder_config.get("provider") - - if memory_provider == "mem0": - try: - from crewai.memory.storage.mem0_storage import Mem0Storage - except ImportError as e: - raise ImportError( - "Mem0 is not installed. Please install it with `pip install mem0ai`." - ) from e - config = ( - embedder_config.get("config") - if embedder_config and isinstance(embedder_config, dict) - else None - ) - storage = Mem0Storage(type="short_term", crew=crew, config=config) # type: ignore[no-untyped-call] - else: - storage = ( - storage - if storage - else RAGStorage( - type="short_term", - embedder_config=embedder_config, - crew=crew, - path=path, - ) - ) - super().__init__(storage=storage) - self._memory_provider = memory_provider - - def save( - self, - value: Any, - metadata: dict[str, Any] | None = None, - ) -> None: - crewai_event_bus.emit( - self, - event=MemorySaveStartedEvent( - value=value, - metadata=metadata, - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - item = ShortTermMemoryItem( - data=value, - metadata=metadata, - agent=self.agent.role if self.agent else None, - ) - if self._memory_provider == "mem0": - item.data = ( - f"Remember the following insights from Agent run: {item.data}" - ) - - super().save(value=item.data, metadata=item.metadata) - - crewai_event_bus.emit( - self, - event=MemorySaveCompletedEvent( - value=value, - metadata=metadata, - # agent_role=agent, - save_time_ms=(time.time() - start_time) * 1000, - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - except Exception as e: - crewai_event_bus.emit( - self, - event=MemorySaveFailedEvent( - value=value, - metadata=metadata, - error=str(e), - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - raise - - def search( - self, - query: str, - limit: int = 5, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search short-term memory for relevant entries. - - Args: - query: The search query. - limit: Maximum number of results to return. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching memory entries. - """ - crewai_event_bus.emit( - self, - event=MemoryQueryStartedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - results = self.storage.search( - query=query, limit=limit, score_threshold=score_threshold - ) - - crewai_event_bus.emit( - self, - event=MemoryQueryCompletedEvent( - query=query, - results=results, - limit=limit, - score_threshold=score_threshold, - query_time_ms=(time.time() - start_time) * 1000, - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - return list(results) - except Exception as e: - crewai_event_bus.emit( - self, - event=MemoryQueryFailedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - error=str(e), - source_type="short_term_memory", - ), - ) - raise - - async def asave( - self, - value: Any, - metadata: dict[str, Any] | None = None, - ) -> None: - """Save a value to short-term memory asynchronously. - - Args: - value: The value to save. - metadata: Optional metadata to associate with the value. - """ - crewai_event_bus.emit( - self, - event=MemorySaveStartedEvent( - value=value, - metadata=metadata, - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - item = ShortTermMemoryItem( - data=value, - metadata=metadata, - agent=self.agent.role if self.agent else None, - ) - if self._memory_provider == "mem0": - item.data = ( - f"Remember the following insights from Agent run: {item.data}" - ) - - await super().asave(value=item.data, metadata=item.metadata) - - crewai_event_bus.emit( - self, - event=MemorySaveCompletedEvent( - value=value, - metadata=metadata, - save_time_ms=(time.time() - start_time) * 1000, - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - except Exception as e: - crewai_event_bus.emit( - self, - event=MemorySaveFailedEvent( - value=value, - metadata=metadata, - error=str(e), - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - raise - - async def asearch( - self, - query: str, - limit: int = 5, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search short-term memory asynchronously. - - Args: - query: The search query. - limit: Maximum number of results to return. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching memory entries. - """ - crewai_event_bus.emit( - self, - event=MemoryQueryStartedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - start_time = time.time() - try: - results = await self.storage.asearch( - query=query, limit=limit, score_threshold=score_threshold - ) - - crewai_event_bus.emit( - self, - event=MemoryQueryCompletedEvent( - query=query, - results=results, - limit=limit, - score_threshold=score_threshold, - query_time_ms=(time.time() - start_time) * 1000, - source_type="short_term_memory", - from_agent=self.agent, - from_task=self.task, - ), - ) - - return list(results) - except Exception as e: - crewai_event_bus.emit( - self, - event=MemoryQueryFailedEvent( - query=query, - limit=limit, - score_threshold=score_threshold, - error=str(e), - source_type="short_term_memory", - ), - ) - raise - - def reset(self) -> None: - try: - self.storage.reset() - except Exception as e: - raise Exception( - f"An error occurred while resetting the short-term memory: {e}" - ) from e diff --git a/lib/crewai/src/crewai/memory/short_term/short_term_memory_item.py b/lib/crewai/src/crewai/memory/short_term/short_term_memory_item.py deleted file mode 100644 index d04a291e1..000000000 --- a/lib/crewai/src/crewai/memory/short_term/short_term_memory_item.py +++ /dev/null @@ -1,13 +0,0 @@ -from typing import Any - - -class ShortTermMemoryItem: - def __init__( - self, - data: Any, - agent: str | None = None, - metadata: dict[str, Any] | None = None, - ): - self.data = data - self.agent = agent - self.metadata = metadata if metadata is not None else {} diff --git a/lib/crewai/src/crewai/memory/storage/backend.py b/lib/crewai/src/crewai/memory/storage/backend.py new file mode 100644 index 000000000..147b9e229 --- /dev/null +++ b/lib/crewai/src/crewai/memory/storage/backend.py @@ -0,0 +1,179 @@ +"""Storage backend protocol for the unified memory system.""" + +from __future__ import annotations + +from datetime import datetime +from typing import Any, Protocol, runtime_checkable + +from crewai.memory.types import MemoryRecord, ScopeInfo + + +@runtime_checkable +class StorageBackend(Protocol): + """Protocol for pluggable memory storage backends.""" + + def save(self, records: list[MemoryRecord]) -> None: + """Save memory records to storage. + + Args: + records: List of memory records to persist. + """ + ... + + def search( + self, + query_embedding: list[float], + scope_prefix: str | None = None, + categories: list[str] | None = None, + metadata_filter: dict[str, Any] | None = None, + limit: int = 10, + min_score: float = 0.0, + ) -> list[tuple[MemoryRecord, float]]: + """Search for memories by vector similarity with optional filters. + + Args: + query_embedding: Embedding vector for the query. + scope_prefix: Optional scope path prefix to filter results. + categories: Optional list of categories to filter by. + metadata_filter: Optional metadata key-value filter. + limit: Maximum number of results to return. + min_score: Minimum similarity score threshold. + + Returns: + List of (MemoryRecord, score) tuples ordered by relevance. + """ + ... + + def delete( + self, + scope_prefix: str | None = None, + categories: list[str] | None = None, + record_ids: list[str] | None = None, + older_than: datetime | None = None, + metadata_filter: dict[str, Any] | None = None, + ) -> int: + """Delete memories matching the given criteria. + + Args: + scope_prefix: Optional scope path prefix. + categories: Optional list of categories. + record_ids: Optional list of record IDs to delete. + older_than: Optional cutoff datetime (delete older records). + metadata_filter: Optional metadata key-value filter. + + Returns: + Number of records deleted. + """ + ... + + def update(self, record: MemoryRecord) -> None: + """Update an existing record. Replaces the record with the same ID.""" + ... + + def get_record(self, record_id: str) -> MemoryRecord | None: + """Return a single record by ID, or None if not found. + + Args: + record_id: The unique ID of the record. + + Returns: + The MemoryRecord, or None if no record with that ID exists. + """ + ... + + def list_records( + self, + scope_prefix: str | None = None, + limit: int = 200, + offset: int = 0, + ) -> list[MemoryRecord]: + """List records in a scope, newest first. + + Args: + scope_prefix: Optional scope path prefix to filter by. + limit: Maximum number of records to return. + offset: Number of records to skip (for pagination). + + Returns: + List of MemoryRecord, ordered by created_at descending. + """ + ... + + def get_scope_info(self, scope: str) -> ScopeInfo: + """Get information about a scope. + + Args: + scope: The scope path. + + Returns: + ScopeInfo with record count, categories, date range, child scopes. + """ + ... + + def list_scopes(self, parent: str = "/") -> list[str]: + """List immediate child scopes under a parent path. + + Args: + parent: Parent scope path (default root). + + Returns: + List of immediate child scope paths. + """ + ... + + def list_categories(self, scope_prefix: str | None = None) -> dict[str, int]: + """List categories and their counts within a scope. + + Args: + scope_prefix: Optional scope to limit to (None = global). + + Returns: + Mapping of category name to record count. + """ + ... + + def count(self, scope_prefix: str | None = None) -> int: + """Count records in scope (and subscopes). + + Args: + scope_prefix: Optional scope path (None = all). + + Returns: + Number of records. + """ + ... + + def reset(self, scope_prefix: str | None = None) -> None: + """Reset (delete all) memories in scope. + + Args: + scope_prefix: Optional scope path (None = reset all). + """ + ... + + async def asave(self, records: list[MemoryRecord]) -> None: + """Save memory records asynchronously.""" + ... + + async def asearch( + self, + query_embedding: list[float], + scope_prefix: str | None = None, + categories: list[str] | None = None, + metadata_filter: dict[str, Any] | None = None, + limit: int = 10, + min_score: float = 0.0, + ) -> list[tuple[MemoryRecord, float]]: + """Search for memories asynchronously.""" + ... + + async def adelete( + self, + scope_prefix: str | None = None, + categories: list[str] | None = None, + record_ids: list[str] | None = None, + older_than: datetime | None = None, + metadata_filter: dict[str, Any] | None = None, + ) -> int: + """Delete memories asynchronously.""" + ... diff --git a/lib/crewai/src/crewai/memory/storage/interface.py b/lib/crewai/src/crewai/memory/storage/interface.py deleted file mode 100644 index 90634bce7..000000000 --- a/lib/crewai/src/crewai/memory/storage/interface.py +++ /dev/null @@ -1,16 +0,0 @@ -from typing import Any - - -class Storage: - """Abstract base class defining the storage interface""" - - def save(self, value: Any, metadata: dict[str, Any]) -> None: - pass - - def search( - self, query: str, limit: int, score_threshold: float - ) -> dict[str, Any] | list[Any]: - return {} - - def reset(self) -> None: - pass diff --git a/lib/crewai/src/crewai/memory/storage/lancedb_storage.py b/lib/crewai/src/crewai/memory/storage/lancedb_storage.py new file mode 100644 index 000000000..e514edcac --- /dev/null +++ b/lib/crewai/src/crewai/memory/storage/lancedb_storage.py @@ -0,0 +1,655 @@ +"""LanceDB storage backend for the unified memory system.""" + +from __future__ import annotations + +from datetime import datetime +import json +import logging +import os +from pathlib import Path +import threading +import time +from typing import Any, ClassVar + +import lancedb + +from crewai.memory.types import MemoryRecord, ScopeInfo + + +_logger = logging.getLogger(__name__) + +# Default embedding vector dimensionality (matches OpenAI text-embedding-3-small). +# Used when creating new tables and for zero-vector placeholder scans. +# Callers can override via the ``vector_dim`` constructor parameter. +DEFAULT_VECTOR_DIM = 1536 + +# Safety cap on the number of rows returned by a single scan query. +# Prevents unbounded memory use when scanning large tables for scope info, +# listing, or deletion. Internal only -- not user-configurable. +_SCAN_ROWS_LIMIT = 50_000 + +# Retry settings for LanceDB commit conflicts (optimistic concurrency). +# Under heavy write load (many concurrent saves), the table version can +# advance rapidly. 5 retries with 0.2s base delay (0.2 + 0.4 + 0.8 + 1.6 + 3.2 = 6.2s max) +# gives enough headroom to catch up with version advancement. +_MAX_RETRIES = 5 +_RETRY_BASE_DELAY = 0.2 # seconds; doubles on each retry + + +class LanceDBStorage: + """LanceDB-backed storage for the unified memory system.""" + + # Class-level registry: maps resolved database path -> shared write lock. + # When multiple Memory instances (e.g. agent + crew) independently create + # LanceDBStorage pointing at the same directory, they share one lock so + # their writes don't conflict. + # Uses RLock (reentrant) so callers can hold the lock for a batch of + # operations while the individual methods re-acquire it without deadlocking. + _path_locks: ClassVar[dict[str, threading.RLock]] = {} + _path_locks_guard: ClassVar[threading.Lock] = threading.Lock() + + def __init__( + self, + path: str | Path | None = None, + table_name: str = "memories", + vector_dim: int | None = None, + compact_every: int = 100, + ) -> None: + """Initialize LanceDB storage. + + Args: + path: Directory path for the LanceDB database. Defaults to + ``$CREWAI_STORAGE_DIR/memory`` if the env var is set, + otherwise ``db_storage_path() / memory`` (platform data dir). + table_name: Name of the table for memory records. + vector_dim: Dimensionality of the embedding vector. When ``None`` + (default), the dimension is auto-detected from the existing + table schema or from the first saved embedding. + compact_every: Number of ``save()`` calls between automatic + background compactions. Each ``save()`` creates one new + fragment file; compaction merges them, keeping query + performance consistent. Set to 0 to disable. + """ + if path is None: + storage_dir = os.environ.get("CREWAI_STORAGE_DIR") + if storage_dir: + path = Path(storage_dir) / "memory" + else: + from crewai.utilities.paths import db_storage_path + + path = Path(db_storage_path()) / "memory" + self._path = Path(path) + self._path.mkdir(parents=True, exist_ok=True) + self._table_name = table_name + self._db = lancedb.connect(str(self._path)) + + # On macOS and Linux the default per-process open-file limit is 256. + # A LanceDB table stores one file per fragment (one fragment per save() + # call by default). With hundreds of fragments, a single full-table + # scan opens all of them simultaneously, exhausting the limit. + # Raise it proactively so scans on large tables never hit OS error 24. + try: + import resource + soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) + if soft < 4096: + resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 4096), hard)) + except Exception: # noqa: S110 + pass # Windows or already at the max hard limit — safe to ignore + + self._compact_every = compact_every + self._save_count = 0 + + # Get or create a shared write lock for this database path. + resolved = str(self._path.resolve()) + with LanceDBStorage._path_locks_guard: + if resolved not in LanceDBStorage._path_locks: + LanceDBStorage._path_locks[resolved] = threading.RLock() + self._write_lock = LanceDBStorage._path_locks[resolved] + + # Try to open an existing table and infer dimension from its schema. + # If no table exists yet, defer creation until the first save so the + # dimension can be auto-detected from the embedder's actual output. + try: + self._table: lancedb.table.Table | None = self._db.open_table(self._table_name) + self._vector_dim: int = self._infer_dim_from_table(self._table) + # Best-effort: create the scope index if it doesn't exist yet. + self._ensure_scope_index() + # Compact in the background if the table has accumulated many + # fragments from previous runs (each save() creates one). + self._compact_if_needed() + except Exception: + self._table = None + self._vector_dim = vector_dim or 0 # 0 = not yet known + + # Explicit dim provided: create the table immediately if it doesn't exist. + if self._table is None and vector_dim is not None: + self._vector_dim = vector_dim + self._table = self._create_table(vector_dim) + + @property + def write_lock(self) -> threading.RLock: + """The shared reentrant write lock for this database path. + + Callers can acquire this to hold the lock across multiple storage + operations (e.g. delete + update + save as one atomic batch). + Individual methods also acquire it internally, but since it's + reentrant (RLock), the same thread won't deadlock. + """ + return self._write_lock + + @staticmethod + def _infer_dim_from_table(table: lancedb.table.Table) -> int: + """Read vector dimension from an existing table's schema.""" + schema = table.schema + for field in schema: + if field.name == "vector": + try: + return field.type.list_size + except Exception: + break + return DEFAULT_VECTOR_DIM + + def _retry_write(self, op: str, *args: Any, **kwargs: Any) -> Any: + """Execute a table operation with retry on LanceDB commit conflicts. + + Args: + op: Method name on the table object (e.g. "add", "delete"). + *args, **kwargs: Passed to the table method. + + LanceDB uses optimistic concurrency: if two transactions overlap, + the second to commit fails with an ``OSError`` containing + "Commit conflict". This helper retries with exponential backoff, + refreshing the table reference before each retry so the retried + call uses the latest committed version (not a stale reference). + """ + delay = _RETRY_BASE_DELAY + for attempt in range(_MAX_RETRIES + 1): + try: + return getattr(self._table, op)(*args, **kwargs) + except OSError as e: # noqa: PERF203 + if "Commit conflict" not in str(e) or attempt >= _MAX_RETRIES: + raise + _logger.debug( + "LanceDB commit conflict on %s (attempt %d/%d), retrying in %.1fs", + op, attempt + 1, _MAX_RETRIES, delay, + ) + # Refresh table to pick up the latest version before retrying. + # The next getattr(self._table, op) will use the fresh table. + try: + self._table = self._db.open_table(self._table_name) + except Exception: # noqa: S110 + pass # table refresh is best-effort + time.sleep(delay) + delay *= 2 + return None # unreachable, but satisfies type checker + + def _create_table(self, vector_dim: int) -> lancedb.table.Table: + """Create a new table with the given vector dimension.""" + placeholder = [ + { + "id": "__schema_placeholder__", + "content": "", + "scope": "/", + "categories_str": "[]", + "metadata_str": "{}", + "importance": 0.5, + "created_at": datetime.utcnow().isoformat(), + "last_accessed": datetime.utcnow().isoformat(), + "source": "", + "private": False, + "vector": [0.0] * vector_dim, + } + ] + table = self._db.create_table(self._table_name, placeholder) + table.delete("id = '__schema_placeholder__'") + return table + + def _ensure_scope_index(self) -> None: + """Create a BTREE scalar index on the ``scope`` column if not present. + + A scalar index lets LanceDB skip a full table scan when filtering by + scope prefix, which is the hot path for ``list_records``, + ``get_scope_info``, and ``list_scopes``. The call is best-effort: + if the table is empty or the index already exists the exception is + swallowed silently. + """ + if self._table is None: + return + try: + self._table.create_scalar_index("scope", index_type="BTREE", replace=False) + except Exception: # noqa: S110 + pass # index already exists, table empty, or unsupported version + + # ------------------------------------------------------------------ + # Automatic background compaction + # ------------------------------------------------------------------ + + def _compact_if_needed(self) -> None: + """Spawn a background compaction on startup. + + Called whenever an existing table is opened so that fragments + accumulated in previous sessions are silently merged before the + first query. ``optimize()`` returns quickly when the table is + already compact, so the cost is negligible in the common case. + """ + if self._table is None or self._compact_every <= 0: + return + self._compact_async() + + def _compact_async(self) -> None: + """Fire-and-forget: compact the table in a daemon background thread.""" + threading.Thread( + target=self._compact_safe, + daemon=True, + name="lancedb-compact", + ).start() + + def _compact_safe(self) -> None: + """Run ``table.optimize()`` in a background thread, absorbing errors.""" + try: + if self._table is not None: + self._table.optimize() + # Refresh the scope index so new fragments are covered. + self._ensure_scope_index() + except Exception: + _logger.debug("LanceDB background compaction failed", exc_info=True) + + def _ensure_table(self, vector_dim: int | None = None) -> lancedb.table.Table: + """Return the table, creating it lazily if needed. + + Args: + vector_dim: Dimension hint (e.g. from the first embedding). + Falls back to the stored ``_vector_dim`` or ``DEFAULT_VECTOR_DIM``. + """ + if self._table is not None: + return self._table + dim = vector_dim or self._vector_dim or DEFAULT_VECTOR_DIM + self._vector_dim = dim + self._table = self._create_table(dim) + return self._table + + def _record_to_row(self, record: MemoryRecord) -> dict[str, Any]: + return { + "id": record.id, + "content": record.content, + "scope": record.scope, + "categories_str": json.dumps(record.categories), + "metadata_str": json.dumps(record.metadata), + "importance": record.importance, + "created_at": record.created_at.isoformat(), + "last_accessed": record.last_accessed.isoformat(), + "source": record.source or "", + "private": record.private, + "vector": record.embedding if record.embedding else [0.0] * self._vector_dim, + } + + def _row_to_record(self, row: dict[str, Any]) -> MemoryRecord: + def _parse_dt(val: Any) -> datetime: + if val is None: + return datetime.utcnow() + if isinstance(val, datetime): + return val + s = str(val) + return datetime.fromisoformat(s.replace("Z", "+00:00")) + + return MemoryRecord( + id=str(row["id"]), + content=str(row["content"]), + scope=str(row["scope"]), + categories=json.loads(row["categories_str"]) if row.get("categories_str") else [], + metadata=json.loads(row["metadata_str"]) if row.get("metadata_str") else {}, + importance=float(row.get("importance", 0.5)), + created_at=_parse_dt(row.get("created_at")), + last_accessed=_parse_dt(row.get("last_accessed")), + embedding=row.get("vector"), + source=row.get("source") or None, + private=bool(row.get("private", False)), + ) + + def save(self, records: list[MemoryRecord]) -> None: + if not records: + return + # Auto-detect dimension from the first real embedding. + dim = None + for r in records: + if r.embedding and len(r.embedding) > 0: + dim = len(r.embedding) + break + is_new_table = self._table is None + with self._write_lock: + self._ensure_table(vector_dim=dim) + rows = [self._record_to_row(r) for r in records] + for r in rows: + if r["vector"] is None or len(r["vector"]) != self._vector_dim: + r["vector"] = [0.0] * self._vector_dim + self._retry_write("add", rows) + # Create the scope index on the first save so it covers the initial dataset. + if is_new_table: + self._ensure_scope_index() + # Auto-compact every N saves so fragment files don't pile up. + self._save_count += 1 + if self._compact_every > 0 and self._save_count % self._compact_every == 0: + self._compact_async() + + def update(self, record: MemoryRecord) -> None: + """Update a record by ID. Preserves created_at, updates last_accessed.""" + with self._write_lock: + self._ensure_table() + safe_id = str(record.id).replace("'", "''") + self._retry_write("delete", f"id = '{safe_id}'") + row = self._record_to_row(record) + if row["vector"] is None or len(row["vector"]) != self._vector_dim: + row["vector"] = [0.0] * self._vector_dim + self._retry_write("add", [row]) + + def touch_records(self, record_ids: list[str]) -> None: + """Update last_accessed to now for the given record IDs. + + Uses a single batch ``table.update()`` call instead of N + delete-and-re-add cycles, which is both faster and avoids + unnecessary write amplification. + + Args: + record_ids: IDs of records to touch. + """ + if not record_ids or self._table is None: + return + with self._write_lock: + now = datetime.utcnow().isoformat() + safe_ids = [str(rid).replace("'", "''") for rid in record_ids] + ids_expr = ", ".join(f"'{rid}'" for rid in safe_ids) + self._retry_write( + "update", + where=f"id IN ({ids_expr})", + values={"last_accessed": now}, + ) + + def get_record(self, record_id: str) -> MemoryRecord | None: + """Return a single record by ID, or None if not found.""" + if self._table is None: + return None + safe_id = str(record_id).replace("'", "''") + rows = self._table.search().where(f"id = '{safe_id}'").limit(1).to_list() + if not rows: + return None + return self._row_to_record(rows[0]) + + def search( + self, + query_embedding: list[float], + scope_prefix: str | None = None, + categories: list[str] | None = None, + metadata_filter: dict[str, Any] | None = None, + limit: int = 10, + min_score: float = 0.0, + ) -> list[tuple[MemoryRecord, float]]: + if self._table is None: + return [] + query = self._table.search(query_embedding) + if scope_prefix is not None and scope_prefix.strip("/"): + prefix = scope_prefix.rstrip("/") + like_val = prefix + "%" + query = query.where(f"scope LIKE '{like_val}'") + results = query.limit(limit * 3 if (categories or metadata_filter) else limit).to_list() + out: list[tuple[MemoryRecord, float]] = [] + for row in results: + record = self._row_to_record(row) + if categories and not any(c in record.categories for c in categories): + continue + if metadata_filter and not all(record.metadata.get(k) == v for k, v in metadata_filter.items()): + continue + distance = row.get("_distance", 0.0) + score = 1.0 / (1.0 + float(distance)) if distance is not None else 1.0 + if score >= min_score: + out.append((record, score)) + if len(out) >= limit: + break + return out[:limit] + + def delete( + self, + scope_prefix: str | None = None, + categories: list[str] | None = None, + record_ids: list[str] | None = None, + older_than: datetime | None = None, + metadata_filter: dict[str, Any] | None = None, + ) -> int: + if self._table is None: + return 0 + with self._write_lock: + if record_ids and not (categories or metadata_filter): + before = self._table.count_rows() + ids_expr = ", ".join(f"'{rid}'" for rid in record_ids) + self._retry_write("delete", f"id IN ({ids_expr})") + return before - self._table.count_rows() + if categories or metadata_filter: + rows = self._scan_rows(scope_prefix) + to_delete: list[str] = [] + for row in rows: + record = self._row_to_record(row) + if categories and not any(c in record.categories for c in categories): + continue + if metadata_filter and not all(record.metadata.get(k) == v for k, v in metadata_filter.items()): + continue + if older_than and record.created_at >= older_than: + continue + to_delete.append(record.id) + if not to_delete: + return 0 + before = self._table.count_rows() + ids_expr = ", ".join(f"'{rid}'" for rid in to_delete) + self._retry_write("delete", f"id IN ({ids_expr})") + return before - self._table.count_rows() + conditions = [] + if scope_prefix is not None and scope_prefix.strip("/"): + prefix = scope_prefix.rstrip("/") + if not prefix.startswith("/"): + prefix = "/" + prefix + conditions.append(f"scope LIKE '{prefix}%' OR scope = '/'") + if older_than is not None: + conditions.append(f"created_at < '{older_than.isoformat()}'") + if not conditions: + before = self._table.count_rows() + self._retry_write("delete", "id != ''") + return before - self._table.count_rows() + where_expr = " AND ".join(conditions) + before = self._table.count_rows() + self._retry_write("delete", where_expr) + return before - self._table.count_rows() + + def _scan_rows( + self, + scope_prefix: str | None = None, + limit: int = _SCAN_ROWS_LIMIT, + columns: list[str] | None = None, + ) -> list[dict[str, Any]]: + """Scan rows optionally filtered by scope prefix. + + Uses a full table scan (no vector query) so the limit is applied after + the scope filter, not to ANN candidates before filtering. + + Args: + scope_prefix: Optional scope path prefix to filter by. + limit: Maximum number of rows to return (applied after filtering). + columns: Optional list of column names to fetch. Pass only the + columns you need for metadata operations to avoid reading the + heavy ``vector`` column unnecessarily. + """ + if self._table is None: + return [] + q = self._table.search() + if scope_prefix is not None and scope_prefix.strip("/"): + q = q.where(f"scope LIKE '{scope_prefix.rstrip('/')}%'") + if columns is not None: + q = q.select(columns) + return q.limit(limit).to_list() + + def list_records( + self, scope_prefix: str | None = None, limit: int = 200, offset: int = 0 + ) -> list[MemoryRecord]: + """List records in a scope, newest first. + + Args: + scope_prefix: Optional scope path prefix to filter by. + limit: Maximum number of records to return. + offset: Number of records to skip (for pagination). + + Returns: + List of MemoryRecord, ordered by created_at descending. + """ + rows = self._scan_rows(scope_prefix, limit=limit + offset) + records = [self._row_to_record(r) for r in rows] + records.sort(key=lambda r: r.created_at, reverse=True) + return records[offset : offset + limit] + + def get_scope_info(self, scope: str) -> ScopeInfo: + scope = scope.rstrip("/") or "/" + prefix = scope if scope != "/" else "" + if prefix and not prefix.startswith("/"): + prefix = "/" + prefix + rows = self._scan_rows( + prefix or None, + columns=["scope", "categories_str", "created_at"], + ) + if not rows: + return ScopeInfo( + path=scope or "/", + record_count=0, + categories=[], + oldest_record=None, + newest_record=None, + child_scopes=[], + ) + categories_set: set[str] = set() + oldest: datetime | None = None + newest: datetime | None = None + child_prefix = (prefix + "/") if prefix else "/" + children: set[str] = set() + for row in rows: + sc = str(row.get("scope", "")) + if child_prefix and sc.startswith(child_prefix): + rest = sc[len(child_prefix):] + first_component = rest.split("/", 1)[0] + if first_component: + children.add(child_prefix + first_component) + try: + cat_str = row.get("categories_str") or "[]" + categories_set.update(json.loads(cat_str)) + except Exception: # noqa: S110 + pass + created = row.get("created_at") + if created: + dt = datetime.fromisoformat(str(created).replace("Z", "+00:00")) if isinstance(created, str) else created + if isinstance(dt, datetime): + if oldest is None or dt < oldest: + oldest = dt + if newest is None or dt > newest: + newest = dt + return ScopeInfo( + path=scope or "/", + record_count=len(rows), + categories=sorted(categories_set), + oldest_record=oldest, + newest_record=newest, + child_scopes=sorted(children), + ) + + def list_scopes(self, parent: str = "/") -> list[str]: + parent = parent.rstrip("/") or "" + prefix = (parent + "/") if parent else "/" + rows = self._scan_rows(prefix if prefix != "/" else None, columns=["scope"]) + children: set[str] = set() + for row in rows: + sc = str(row.get("scope", "")) + if sc.startswith(prefix) and sc != (prefix.rstrip("/") or "/"): + rest = sc[len(prefix):] + first_component = rest.split("/", 1)[0] + if first_component: + children.add(prefix + first_component) + return sorted(children) + + def list_categories(self, scope_prefix: str | None = None) -> dict[str, int]: + rows = self._scan_rows(scope_prefix, columns=["categories_str"]) + counts: dict[str, int] = {} + for row in rows: + cat_str = row.get("categories_str") or "[]" + try: + parsed = json.loads(cat_str) + except Exception: # noqa: S112 + continue + for c in parsed: + counts[c] = counts.get(c, 0) + 1 + return counts + + def count(self, scope_prefix: str | None = None) -> int: + if self._table is None: + return 0 + if scope_prefix is None or scope_prefix.strip("/") == "": + return self._table.count_rows() + info = self.get_scope_info(scope_prefix) + return info.record_count + + def reset(self, scope_prefix: str | None = None) -> None: + if scope_prefix is None or scope_prefix.strip("/") == "": + if self._table is not None: + self._db.drop_table(self._table_name) + self._table = None + # Dimension is preserved; table will be recreated on next save. + return + if self._table is None: + return + prefix = scope_prefix.rstrip("/") + if prefix: + self._table.delete(f"scope >= '{prefix}' AND scope < '{prefix}/\uFFFF'") + + def optimize(self) -> None: + """Compact the table synchronously and refresh the scope index. + + Under normal usage this is called automatically in the background + (every ``compact_every`` saves and on startup when the table is + fragmented). Call this explicitly only when you need the compaction + to be complete before the next operation — for example immediately + after a large bulk import, before a latency-sensitive recall. + It is a no-op if the table does not exist. + """ + if self._table is None: + return + self._table.optimize() + self._ensure_scope_index() + + async def asave(self, records: list[MemoryRecord]) -> None: + self.save(records) + + async def asearch( + self, + query_embedding: list[float], + scope_prefix: str | None = None, + categories: list[str] | None = None, + metadata_filter: dict[str, Any] | None = None, + limit: int = 10, + min_score: float = 0.0, + ) -> list[tuple[MemoryRecord, float]]: + return self.search( + query_embedding, + scope_prefix=scope_prefix, + categories=categories, + metadata_filter=metadata_filter, + limit=limit, + min_score=min_score, + ) + + async def adelete( + self, + scope_prefix: str | None = None, + categories: list[str] | None = None, + record_ids: list[str] | None = None, + older_than: datetime | None = None, + metadata_filter: dict[str, Any] | None = None, + ) -> int: + return self.delete( + scope_prefix=scope_prefix, + categories=categories, + record_ids=record_ids, + older_than=older_than, + metadata_filter=metadata_filter, + ) diff --git a/lib/crewai/src/crewai/memory/storage/ltm_sqlite_storage.py b/lib/crewai/src/crewai/memory/storage/ltm_sqlite_storage.py deleted file mode 100644 index 2e64f416e..000000000 --- a/lib/crewai/src/crewai/memory/storage/ltm_sqlite_storage.py +++ /dev/null @@ -1,215 +0,0 @@ -import json -from pathlib import Path -import sqlite3 -from typing import Any - -import aiosqlite - -from crewai.utilities import Printer -from crewai.utilities.paths import db_storage_path - - -class LTMSQLiteStorage: - """SQLite storage class for long-term memory data.""" - - def __init__(self, db_path: str | None = None, verbose: bool = True) -> None: - """Initialize the SQLite storage. - - Args: - db_path: Optional path to the database file. - verbose: Whether to print error messages. - """ - if db_path is None: - db_path = str(Path(db_storage_path()) / "long_term_memory_storage.db") - self.db_path = db_path - self._verbose = verbose - self._printer: Printer = Printer() - Path(self.db_path).parent.mkdir(parents=True, exist_ok=True) - self._initialize_db() - - def _initialize_db(self) -> None: - """Initialize the SQLite database and create LTM table.""" - try: - with sqlite3.connect(self.db_path) as conn: - cursor = conn.cursor() - cursor.execute( - """ - CREATE TABLE IF NOT EXISTS long_term_memories ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - task_description TEXT, - metadata TEXT, - datetime TEXT, - score REAL - ) - """ - ) - - conn.commit() - except sqlite3.Error as e: - if self._verbose: - self._printer.print( - content=f"MEMORY ERROR: An error occurred during database initialization: {e}", - color="red", - ) - - def save( - self, - task_description: str, - metadata: dict[str, Any], - datetime: str, - score: int | float, - ) -> None: - """Saves data to the LTM table with error handling.""" - try: - with sqlite3.connect(self.db_path) as conn: - cursor = conn.cursor() - cursor.execute( - """ - INSERT INTO long_term_memories (task_description, metadata, datetime, score) - VALUES (?, ?, ?, ?) - """, - (task_description, json.dumps(metadata), datetime, score), - ) - conn.commit() - except sqlite3.Error as e: - if self._verbose: - self._printer.print( - content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}", - color="red", - ) - - def load(self, task_description: str, latest_n: int) -> list[dict[str, Any]] | None: - """Queries the LTM table by task description with error handling.""" - try: - with sqlite3.connect(self.db_path) as conn: - cursor = conn.cursor() - cursor.execute( - f""" - SELECT metadata, datetime, score - FROM long_term_memories - WHERE task_description = ? - ORDER BY datetime DESC, score ASC - LIMIT {latest_n} - """, # nosec # noqa: S608 - (task_description,), - ) - rows = cursor.fetchall() - if rows: - return [ - { - "metadata": json.loads(row[0]), - "datetime": row[1], - "score": row[2], - } - for row in rows - ] - - except sqlite3.Error as e: - if self._verbose: - self._printer.print( - content=f"MEMORY ERROR: An error occurred while querying LTM: {e}", - color="red", - ) - return None - - def reset(self) -> None: - """Resets the LTM table with error handling.""" - try: - with sqlite3.connect(self.db_path) as conn: - cursor = conn.cursor() - cursor.execute("DELETE FROM long_term_memories") - conn.commit() - - except sqlite3.Error as e: - if self._verbose: - self._printer.print( - content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}", - color="red", - ) - - async def asave( - self, - task_description: str, - metadata: dict[str, Any], - datetime: str, - score: int | float, - ) -> None: - """Save data to the LTM table asynchronously. - - Args: - task_description: Description of the task. - metadata: Metadata associated with the memory. - datetime: Timestamp of the memory. - score: Quality score of the memory. - """ - try: - async with aiosqlite.connect(self.db_path) as conn: - await conn.execute( - """ - INSERT INTO long_term_memories (task_description, metadata, datetime, score) - VALUES (?, ?, ?, ?) - """, - (task_description, json.dumps(metadata), datetime, score), - ) - await conn.commit() - except aiosqlite.Error as e: - if self._verbose: - self._printer.print( - content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}", - color="red", - ) - - async def aload( - self, task_description: str, latest_n: int - ) -> list[dict[str, Any]] | None: - """Query the LTM table by task description asynchronously. - - Args: - task_description: Description of the task to search for. - latest_n: Maximum number of results to return. - - Returns: - List of matching memory entries or None if error occurs. - """ - try: - async with aiosqlite.connect(self.db_path) as conn: - cursor = await conn.execute( - f""" - SELECT metadata, datetime, score - FROM long_term_memories - WHERE task_description = ? - ORDER BY datetime DESC, score ASC - LIMIT {latest_n} - """, # nosec # noqa: S608 - (task_description,), - ) - rows = await cursor.fetchall() - if rows: - return [ - { - "metadata": json.loads(row[0]), - "datetime": row[1], - "score": row[2], - } - for row in rows - ] - except aiosqlite.Error as e: - if self._verbose: - self._printer.print( - content=f"MEMORY ERROR: An error occurred while querying LTM: {e}", - color="red", - ) - return None - - async def areset(self) -> None: - """Reset the LTM table asynchronously.""" - try: - async with aiosqlite.connect(self.db_path) as conn: - await conn.execute("DELETE FROM long_term_memories") - await conn.commit() - except aiosqlite.Error as e: - if self._verbose: - self._printer.print( - content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}", - color="red", - ) diff --git a/lib/crewai/src/crewai/memory/storage/mem0_storage.py b/lib/crewai/src/crewai/memory/storage/mem0_storage.py deleted file mode 100644 index 73820ab11..000000000 --- a/lib/crewai/src/crewai/memory/storage/mem0_storage.py +++ /dev/null @@ -1,230 +0,0 @@ -from collections import defaultdict -from collections.abc import Iterable -import os -import re -from typing import Any - -from mem0 import Memory, MemoryClient # type: ignore[import-untyped,import-not-found] - -from crewai.memory.storage.interface import Storage -from crewai.rag.chromadb.utils import _sanitize_collection_name - - -MAX_AGENT_ID_LENGTH_MEM0 = 255 - - -class Mem0Storage(Storage): - """ - Extends Storage to handle embedding and searching across entities using Mem0. - """ - - def __init__(self, type, crew=None, config=None): - super().__init__() - - self._validate_type(type) - self.memory_type = type - self.crew = crew - self.config = config or {} - - self._extract_config_values() - self._initialize_memory() - - def _validate_type(self, type): - supported_types = {"short_term", "long_term", "entities", "external"} - if type not in supported_types: - raise ValueError( - f"Invalid type '{type}' for Mem0Storage. " - f"Must be one of: {', '.join(supported_types)}" - ) - - def _extract_config_values(self): - self.mem0_run_id = self.config.get("run_id") - self.includes = self.config.get("includes") - self.excludes = self.config.get("excludes") - self.custom_categories = self.config.get("custom_categories") - self.infer = self.config.get("infer", True) - - def _initialize_memory(self): - api_key = self.config.get("api_key") or os.getenv("MEM0_API_KEY") - org_id = self.config.get("org_id") - project_id = self.config.get("project_id") - local_config = self.config.get("local_mem0_config") - - if api_key: - self.memory = ( - MemoryClient(api_key=api_key, org_id=org_id, project_id=project_id) - if org_id and project_id - else MemoryClient(api_key=api_key) - ) - if self.custom_categories: - self.memory.update_project(custom_categories=self.custom_categories) - else: - self.memory = ( - Memory.from_config(local_config) - if local_config and len(local_config) - else Memory() - ) - - def _create_filter_for_search(self): - """ - Returns: - dict: A filter dictionary containing AND conditions for querying data. - - Includes user_id and agent_id if both are present. - - Includes user_id if only user_id is present. - - Includes agent_id if only agent_id is present. - - Includes run_id if memory_type is 'short_term' and - mem0_run_id is present. - """ - filter = defaultdict(list) - - if self.memory_type == "short_term" and self.mem0_run_id: - filter["AND"].append({"run_id": self.mem0_run_id}) - else: - user_id = self.config.get("user_id", "") - agent_id = self.config.get("agent_id", "") - - if user_id and agent_id: - filter["OR"].append({"user_id": user_id}) - filter["OR"].append({"agent_id": agent_id}) - elif user_id: - filter["AND"].append({"user_id": user_id}) - elif agent_id: - filter["AND"].append({"agent_id": agent_id}) - - return filter - - def save(self, value: Any, metadata: dict[str, Any]) -> None: - def _last_content(messages: Iterable[dict[str, Any]], role: str) -> str: - return next( - ( - m.get("content", "") - for m in reversed(list(messages)) - if m.get("role") == role - ), - "", - ) - - conversations = [] - messages = metadata.pop("messages", None) - if messages: - last_user = _last_content(messages, "user") - last_assistant = _last_content(messages, "assistant") - - if user_msg := self._get_user_message(last_user): - conversations.append({"role": "user", "content": user_msg}) - - if assistant_msg := self._get_assistant_message(last_assistant): - conversations.append({"role": "assistant", "content": assistant_msg}) - else: - conversations.append({"role": "assistant", "content": value}) - - user_id = self.config.get("user_id", "") - - base_metadata = { - "short_term": "short_term", - "long_term": "long_term", - "entities": "entity", - "external": "external", - } - - # Shared base params - params: dict[str, Any] = { - "metadata": {"type": base_metadata[self.memory_type], **metadata}, - "infer": self.infer, - } - - # MemoryClient-specific overrides - if isinstance(self.memory, MemoryClient): - params["includes"] = self.includes - params["excludes"] = self.excludes - params["output_format"] = "v1.1" - params["version"] = "v2" - - if self.memory_type == "short_term" and self.mem0_run_id: - params["run_id"] = self.mem0_run_id - - if user_id: - params["user_id"] = user_id - - if agent_id := self.config.get("agent_id", self._get_agent_name()): - params["agent_id"] = agent_id - - self.memory.add(conversations, **params) - - def search( - self, query: str, limit: int = 5, score_threshold: float = 0.6 - ) -> list[Any]: - params = { - "query": query, - "limit": limit, - "version": "v2", - "output_format": "v1.1", - } - - if user_id := self.config.get("user_id", ""): - params["user_id"] = user_id - - memory_type_map = { - "short_term": {"type": "short_term"}, - "long_term": {"type": "long_term"}, - "entities": {"type": "entity"}, - "external": {"type": "external"}, - } - - if self.memory_type in memory_type_map: - params["metadata"] = memory_type_map[self.memory_type] - if self.memory_type == "short_term": - params["run_id"] = self.mem0_run_id - - # Discard the filters for now since we create the filters - # automatically when the crew is created. - - params["filters"] = self._create_filter_for_search() - params["threshold"] = score_threshold - - if isinstance(self.memory, Memory): - del params["metadata"], params["version"], params["output_format"] - if params.get("run_id"): - del params["run_id"] - - results = self.memory.search(**params) - - # This makes it compatible for Contextual Memory to retrieve - for result in results["results"]: - result["content"] = result["memory"] - - return [r for r in results["results"]] - - def reset(self): - if self.memory: - self.memory.reset() - - def _sanitize_role(self, role: str) -> str: - """ - Sanitizes agent roles to ensure valid directory names. - """ - return role.replace("\n", "").replace(" ", "_").replace("/", "_") - - def _get_agent_name(self) -> str: - if not self.crew: - return "" - - agents = self.crew.agents - agents = [self._sanitize_role(agent.role) for agent in agents] - agents = "_".join(agents) - return _sanitize_collection_name( - name=agents, max_collection_length=MAX_AGENT_ID_LENGTH_MEM0 - ) - - def _get_assistant_message(self, text: str) -> str: - marker = "Final Answer:" - if marker in text: - return text.split(marker, 1)[1].strip() - return text - - def _get_user_message(self, text: str) -> str: - pattern = r"User message:\s*(.*)" - match = re.search(pattern, text) - if match: - return match.group(1).strip() - return text diff --git a/lib/crewai/src/crewai/memory/storage/rag_storage.py b/lib/crewai/src/crewai/memory/storage/rag_storage.py deleted file mode 100644 index b45cde55a..000000000 --- a/lib/crewai/src/crewai/memory/storage/rag_storage.py +++ /dev/null @@ -1,315 +0,0 @@ -from __future__ import annotations - -import logging -import traceback -from typing import TYPE_CHECKING, Any, cast -import warnings - -from crewai.rag.chromadb.config import ChromaDBConfig -from crewai.rag.chromadb.types import ChromaEmbeddingFunctionWrapper -from crewai.rag.config.utils import get_rag_client -from crewai.rag.embeddings.factory import build_embedder -from crewai.rag.factory import create_client -from crewai.rag.storage.base_rag_storage import BaseRAGStorage -from crewai.utilities.constants import MAX_FILE_NAME_LENGTH -from crewai.utilities.paths import db_storage_path - - -if TYPE_CHECKING: - from crewai.crew import Crew - from crewai.rag.core.base_client import BaseClient - from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider - from crewai.rag.embeddings.types import ProviderSpec - from crewai.rag.types import BaseRecord - - -class RAGStorage(BaseRAGStorage): - """ - Extends Storage to handle embeddings for memory entries, improving - search efficiency. - """ - - def __init__( - self, - type: str, - allow_reset: bool = True, - embedder_config: ProviderSpec | BaseEmbeddingsProvider[Any] | None = None, - crew: Crew | None = None, - path: str | None = None, - ) -> None: - super().__init__(type, allow_reset, embedder_config, crew) - crew_agents = crew.agents if crew else [] - sanitized_roles = [self._sanitize_role(agent.role) for agent in crew_agents] - agents_str = "_".join(sanitized_roles) - self.agents = agents_str - self.storage_file_name = self._build_storage_file_name(type, agents_str) - - self.type = type - self._client: BaseClient | None = None - - self.allow_reset = allow_reset - self.path = path - - warnings.filterwarnings( - "ignore", - message=r".*'model_fields'.*is deprecated.*", - module=r"^chromadb(\.|$)", - ) - - if self.embedder_config: - embedding_function = build_embedder(self.embedder_config) - - try: - _ = embedding_function(["test"]) - except Exception as e: - provider = ( - self.embedder_config["provider"] - if isinstance(self.embedder_config, dict) - else self.embedder_config.__class__.__name__.replace( - "Provider", "" - ).lower() - ) - raise ValueError( - f"Failed to initialize embedder. Please check your configuration or connection.\n" - f"Provider: {provider}\n" - f"Error: {e}" - ) from e - - batch_size = None - if ( - isinstance(self.embedder_config, dict) - and "config" in self.embedder_config - ): - nested_config = self.embedder_config["config"] - if isinstance(nested_config, dict): - batch_size = nested_config.get("batch_size") - - if batch_size is not None: - config = ChromaDBConfig( - embedding_function=cast( - ChromaEmbeddingFunctionWrapper, embedding_function - ), - batch_size=cast(int, batch_size), - ) - else: - config = ChromaDBConfig( - embedding_function=cast( - ChromaEmbeddingFunctionWrapper, embedding_function - ) - ) - - if self.path: - config.settings.persist_directory = self.path - - self._client = create_client(config) - - def _get_client(self) -> BaseClient: - """Get the appropriate client - instance-specific or global.""" - return self._client if self._client else get_rag_client() - - def _sanitize_role(self, role: str) -> str: - """ - Sanitizes agent roles to ensure valid directory names. - """ - return role.replace("\n", "").replace(" ", "_").replace("/", "_") - - @staticmethod - def _build_storage_file_name(type: str, file_name: str) -> str: - """ - Ensures file name does not exceed max allowed by OS - """ - base_path = f"{db_storage_path()}/{type}" - - if len(file_name) > MAX_FILE_NAME_LENGTH: - logging.warning( - f"Trimming file name from {len(file_name)} to {MAX_FILE_NAME_LENGTH} characters." - ) - file_name = file_name[:MAX_FILE_NAME_LENGTH] - - return f"{base_path}/{file_name}" - - def save(self, value: Any, metadata: dict[str, Any]) -> None: - """Save a value to storage. - - Args: - value: The value to save. - metadata: Metadata to associate with the value. - """ - try: - client = self._get_client() - collection_name = ( - f"memory_{self.type}_{self.agents}" - if self.agents - else f"memory_{self.type}" - ) - client.get_or_create_collection(collection_name=collection_name) - - document: BaseRecord = {"content": value} - if metadata: - document["metadata"] = metadata - - batch_size = None - if ( - self.embedder_config - and isinstance(self.embedder_config, dict) - and "config" in self.embedder_config - ): - nested_config = self.embedder_config["config"] - if isinstance(nested_config, dict): - batch_size = nested_config.get("batch_size") - - if batch_size is not None: - client.add_documents( - collection_name=collection_name, - documents=[document], - batch_size=cast(int, batch_size), - ) - else: - client.add_documents( - collection_name=collection_name, documents=[document] - ) - except Exception as e: - logging.error( - f"Error during {self.type} save: {e!s}\n{traceback.format_exc()}" - ) - - async def asave(self, value: Any, metadata: dict[str, Any]) -> None: - """Save a value to storage asynchronously. - - Args: - value: The value to save. - metadata: Metadata to associate with the value. - """ - try: - client = self._get_client() - collection_name = ( - f"memory_{self.type}_{self.agents}" - if self.agents - else f"memory_{self.type}" - ) - await client.aget_or_create_collection(collection_name=collection_name) - - document: BaseRecord = {"content": value} - if metadata: - document["metadata"] = metadata - - batch_size = None - if ( - self.embedder_config - and isinstance(self.embedder_config, dict) - and "config" in self.embedder_config - ): - nested_config = self.embedder_config["config"] - if isinstance(nested_config, dict): - batch_size = nested_config.get("batch_size") - - if batch_size is not None: - await client.aadd_documents( - collection_name=collection_name, - documents=[document], - batch_size=cast(int, batch_size), - ) - else: - await client.aadd_documents( - collection_name=collection_name, documents=[document] - ) - except Exception as e: - logging.error( - f"Error during {self.type} async save: {e!s}\n{traceback.format_exc()}" - ) - - def search( - self, - query: str, - limit: int = 5, - filter: dict[str, Any] | None = None, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search for matching entries in storage. - - Args: - query: The search query. - limit: Maximum number of results to return. - filter: Optional metadata filter. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching entries. - """ - try: - client = self._get_client() - collection_name = ( - f"memory_{self.type}_{self.agents}" - if self.agents - else f"memory_{self.type}" - ) - return client.search( - collection_name=collection_name, - query=query, - limit=limit, - metadata_filter=filter, - score_threshold=score_threshold, - ) - except Exception as e: - logging.error( - f"Error during {self.type} search: {e!s}\n{traceback.format_exc()}" - ) - return [] - - async def asearch( - self, - query: str, - limit: int = 5, - filter: dict[str, Any] | None = None, - score_threshold: float = 0.6, - ) -> list[Any]: - """Search for matching entries in storage asynchronously. - - Args: - query: The search query. - limit: Maximum number of results to return. - filter: Optional metadata filter. - score_threshold: Minimum similarity score for results. - - Returns: - List of matching entries. - """ - try: - client = self._get_client() - collection_name = ( - f"memory_{self.type}_{self.agents}" - if self.agents - else f"memory_{self.type}" - ) - return await client.asearch( - collection_name=collection_name, - query=query, - limit=limit, - metadata_filter=filter, - score_threshold=score_threshold, - ) - except Exception as e: - logging.error( - f"Error during {self.type} async search: {e!s}\n{traceback.format_exc()}" - ) - return [] - - def reset(self) -> None: - try: - client = self._get_client() - collection_name = ( - f"memory_{self.type}_{self.agents}" - if self.agents - else f"memory_{self.type}" - ) - client.delete_collection(collection_name=collection_name) - except Exception as e: - if "attempt to write a readonly database" in str( - e - ) or "does not exist" in str(e): - # Ignore readonly database and collection not found errors (already reset) - pass - else: - raise Exception( - f"An error occurred while resetting the {self.type} memory: {e}" - ) from e diff --git a/lib/crewai/src/crewai/memory/types.py b/lib/crewai/src/crewai/memory/types.py new file mode 100644 index 000000000..929e10092 --- /dev/null +++ b/lib/crewai/src/crewai/memory/types.py @@ -0,0 +1,385 @@ +"""Data types for the unified memory system.""" + +from __future__ import annotations + +from datetime import datetime +from typing import Any +from uuid import uuid4 + +from pydantic import BaseModel, Field + + +# When searching the vector store, we ask for more results than the caller +# requested so that post-search steps (composite scoring, deduplication, +# category filtering) have enough candidates to fill the final result set. +# For example, if the caller asks for 10 results and this is 2, we fetch 20 +# from the vector store and then trim down after scoring. +_RECALL_OVERSAMPLE_FACTOR = 2 + + +class MemoryRecord(BaseModel): + """A single memory entry stored in the memory system.""" + + id: str = Field( + default_factory=lambda: str(uuid4()), + description="Unique identifier for the memory record.", + ) + content: str = Field(description="The textual content of the memory.") + scope: str = Field( + default="/", + description="Hierarchical path organizing the memory (e.g. /company/team/user).", + ) + categories: list[str] = Field( + default_factory=list, + description="Categories or tags for the memory.", + ) + metadata: dict[str, Any] = Field( + default_factory=dict, + description="Arbitrary metadata associated with the memory.", + ) + importance: float = Field( + default=0.5, + ge=0.0, + le=1.0, + description="Importance score from 0.0 to 1.0, affects retrieval ranking.", + ) + created_at: datetime = Field( + default_factory=datetime.utcnow, + description="When the memory was created.", + ) + last_accessed: datetime = Field( + default_factory=datetime.utcnow, + description="When the memory was last accessed.", + ) + embedding: list[float] | None = Field( + default=None, + description="Vector embedding for semantic search. Computed on save if not provided.", + ) + source: str | None = Field( + default=None, + description=( + "Origin of this memory (e.g. user ID, session ID). " + "Used for provenance tracking and privacy filtering." + ), + ) + private: bool = Field( + default=False, + description=( + "If True, this memory is only visible to recall requests from the same source, " + "or when include_private=True is passed." + ), + ) + + +class MemoryMatch(BaseModel): + """A memory record with relevance score from a recall operation.""" + + record: MemoryRecord = Field(description="The matched memory record.") + score: float = Field( + description="Combined relevance score (semantic, recency, importance).", + ) + match_reasons: list[str] = Field( + default_factory=list, + description="Reasons for the match (e.g. semantic, recency, importance).", + ) + evidence_gaps: list[str] = Field( + default_factory=list, + description="Information the system looked for but could not find.", + ) + + def format(self) -> str: + """Format this match as a human-readable string including metadata. + + Returns: + A multi-line string with score, content, categories, and non-empty + metadata fields. + """ + lines = [f"- (score={self.score:.2f}) {self.record.content}"] + if self.record.categories: + lines.append(f" categories: {', '.join(self.record.categories)}") + if self.record.metadata: + for key, value in self.record.metadata.items(): + if value is not None: + lines.append(f" {key}: {value}") + return "\n".join(lines) + + +class ScopeInfo(BaseModel): + """Information about a scope in the memory hierarchy.""" + + path: str = Field(description="The scope path (e.g. /company/engineering).") + record_count: int = Field( + default=0, + description="Number of records in this scope (including subscopes if applicable).", + ) + categories: list[str] = Field( + default_factory=list, + description="Categories used in this scope.", + ) + oldest_record: datetime | None = Field( + default=None, + description="Timestamp of the oldest record in this scope.", + ) + newest_record: datetime | None = Field( + default=None, + description="Timestamp of the newest record in this scope.", + ) + child_scopes: list[str] = Field( + default_factory=list, + description="Immediate child scope paths.", + ) + + +class MemoryConfig(BaseModel): + """Internal configuration for memory scoring, consolidation, and recall behavior. + + Users configure these values via ``Memory(...)`` keyword arguments. + This model is not part of the public API -- it exists so that the config + can be passed as a single object to RecallFlow, EncodingFlow, and + compute_composite_score. + """ + + # -- Composite score weights -- + # The recall composite score is: + # semantic_weight * similarity + recency_weight * decay + importance_weight * importance + # These should sum to ~1.0 for intuitive 0-1 scoring. + + recency_weight: float = Field( + default=0.3, + ge=0.0, + le=1.0, + description=( + "Weight for recency in the composite relevance score. " + "Higher values favor recently created memories over older ones." + ), + ) + semantic_weight: float = Field( + default=0.5, + ge=0.0, + le=1.0, + description=( + "Weight for semantic similarity in the composite relevance score. " + "Higher values make recall rely more on vector-search closeness." + ), + ) + importance_weight: float = Field( + default=0.2, + ge=0.0, + le=1.0, + description=( + "Weight for explicit importance in the composite relevance score. " + "Higher values make high-importance memories surface more often." + ), + ) + recency_half_life_days: int = Field( + default=30, + ge=1, + description=( + "Number of days for the recency score to halve (exponential decay). " + "Lower values make memories lose relevance faster; higher values " + "keep old memories relevant longer." + ), + ) + + # -- Consolidation (on save) -- + + consolidation_threshold: float = Field( + default=0.85, + ge=0.0, + le=1.0, + description=( + "Semantic similarity above which the consolidation flow is triggered " + "when saving new content. The LLM then decides whether to merge, " + "update, or delete overlapping records. Set to 1.0 to disable." + ), + ) + consolidation_limit: int = Field( + default=5, + ge=1, + description=( + "Maximum number of existing records to compare against when checking " + "for consolidation during a save." + ), + ) + batch_dedup_threshold: float = Field( + default=0.98, + ge=0.0, + le=1.0, + description=( + "Cosine similarity threshold for dropping near-exact duplicates " + "within a single remember_many() batch. Only items with similarity " + ">= this value are dropped. Set very high (0.98) to avoid " + "discarding useful memories that are merely similar." + ), + ) + + # -- Save defaults -- + + default_importance: float = Field( + default=0.5, + ge=0.0, + le=1.0, + description=( + "Importance assigned to new memories when no explicit value is given " + "and the LLM analysis path is skipped (i.e. all fields provided by " + "the caller)." + ), + ) + + # -- Recall depth control -- + # The RecallFlow router uses these thresholds to decide between returning + # results immediately ("synthesize") and doing an extra LLM-driven + # exploration round ("explore_deeper"). + + confidence_threshold_high: float = Field( + default=0.8, + ge=0.0, + le=1.0, + description=( + "When recall confidence is at or above this value, results are " + "returned directly without deeper exploration." + ), + ) + confidence_threshold_low: float = Field( + default=0.5, + ge=0.0, + le=1.0, + description=( + "When recall confidence is below this value and exploration budget " + "remains, a deeper LLM-driven exploration round is triggered." + ), + ) + complex_query_threshold: float = Field( + default=0.7, + ge=0.0, + le=1.0, + description=( + "For queries classified as 'complex' by the LLM, deeper exploration " + "is triggered when confidence is below this value." + ), + ) + exploration_budget: int = Field( + default=1, + ge=0, + description=( + "Number of LLM-driven exploration rounds allowed during deep recall. " + "0 means recall always uses direct vector search only; higher values " + "allow more thorough but slower retrieval." + ), + ) + recall_oversample_factor: int = Field( + default=_RECALL_OVERSAMPLE_FACTOR, + ge=1, + description=( + "When searching the vector store, fetch this many times more results " + "than the caller requested so that post-search steps (composite " + "scoring, deduplication, category filtering) have enough candidates " + "to fill the final result set." + ), + ) + query_analysis_threshold: int = Field( + default=250, + ge=0, + description=( + "Character count threshold for LLM query analysis during deep recall. " + "Queries shorter than this are embedded directly without an LLM call " + "to distill sub-queries or infer scopes (saving ~1-3s). Longer queries " + "(e.g. full task descriptions) benefit from LLM distillation. " + "Set to 0 to always use LLM analysis." + ), + ) + + +def embed_text(embedder: Any, text: str) -> list[float]: + """Embed a single text string and return a list of floats. + + Args: + embedder: Callable that accepts a list of strings and returns embeddings. + text: The text to embed. + + Returns: + List of floats representing the embedding, or empty list on failure. + """ + if not text or not text.strip(): + return [] + result = embedder([text]) + if not result: + return [] + first = result[0] + if hasattr(first, "tolist"): + return list(first.tolist()) + if isinstance(first, list): + return [float(x) for x in first] + return list(first) + + +def embed_texts(embedder: Any, texts: list[str]) -> list[list[float]]: + """Embed multiple texts in a single API call. + + The embedder already accepts ``list[str]``, so this just calls it once + with the full batch and normalises the output format. + + Args: + embedder: Callable that accepts a list of strings and returns embeddings. + texts: List of texts to embed. + + Returns: + List of embeddings, one per input text. Empty texts produce empty lists. + """ + if not texts: + return [] + # Filter out empty texts, remembering their positions + valid: list[tuple[int, str]] = [ + (i, t) for i, t in enumerate(texts) if t and t.strip() + ] + if not valid: + return [[] for _ in texts] + + result = embedder([t for _, t in valid]) + embeddings: list[list[float]] = [[] for _ in texts] + for (orig_idx, _), emb in zip(valid, result, strict=False): + if hasattr(emb, "tolist"): + embeddings[orig_idx] = emb.tolist() + elif isinstance(emb, list): + embeddings[orig_idx] = [float(x) for x in emb] + else: + embeddings[orig_idx] = list(emb) + return embeddings + + +def compute_composite_score( + record: MemoryRecord, + semantic_score: float, + config: MemoryConfig, +) -> tuple[float, list[str]]: + """Compute a weighted composite relevance score from semantic, recency, and importance. + + composite = w_semantic * semantic + w_recency * decay + w_importance * importance + where decay = 0.5^(age_days / half_life_days). + + Args: + record: The memory record (provides created_at and importance). + semantic_score: Raw semantic similarity from vector search, in [0, 1]. + config: Weights and recency half-life. + + Returns: + Tuple of (composite_score, match_reasons). match_reasons includes + "semantic" always; "recency" if decay > 0.5; "importance" if record.importance > 0.5. + """ + age_seconds = (datetime.utcnow() - record.created_at).total_seconds() + age_days = max(age_seconds / 86400.0, 0.0) + decay = 0.5 ** (age_days / config.recency_half_life_days) + + composite = ( + config.semantic_weight * semantic_score + + config.recency_weight * decay + + config.importance_weight * record.importance + ) + + reasons: list[str] = ["semantic"] + if decay > 0.5: + reasons.append("recency") + if record.importance > 0.5: + reasons.append("importance") + + return composite, reasons diff --git a/lib/crewai/src/crewai/memory/unified_memory.py b/lib/crewai/src/crewai/memory/unified_memory.py new file mode 100644 index 000000000..cae9013bd --- /dev/null +++ b/lib/crewai/src/crewai/memory/unified_memory.py @@ -0,0 +1,869 @@ +"""Unified Memory class: single intelligent memory with LLM analysis and pluggable storage.""" + +from __future__ import annotations + +from concurrent.futures import Future, ThreadPoolExecutor +from datetime import datetime +import threading +import time +from typing import TYPE_CHECKING, Any, Literal + +from crewai.events.event_bus import crewai_event_bus +from crewai.events.types.memory_events import ( + MemoryQueryCompletedEvent, + MemoryQueryFailedEvent, + MemoryQueryStartedEvent, + MemorySaveCompletedEvent, + MemorySaveFailedEvent, + MemorySaveStartedEvent, +) +from crewai.llms.base_llm import BaseLLM +from crewai.memory.analyze import extract_memories_from_content +from crewai.memory.recall_flow import RecallFlow +from crewai.memory.storage.backend import StorageBackend +from crewai.memory.types import ( + MemoryConfig, + MemoryMatch, + MemoryRecord, + ScopeInfo, + compute_composite_score, + embed_text, +) +from crewai.rag.embeddings.factory import build_embedder +from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec + + +if TYPE_CHECKING: + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + +def _default_embedder() -> OpenAIEmbeddingFunction: + """Build default OpenAI embedder for memory.""" + spec: OpenAIProviderSpec = {"provider": "openai", "config": {}} + return build_embedder(spec) + + +class Memory: + """Unified memory: standalone, LLM-analyzed, with intelligent recall flow. + + Works without agent/crew. Uses LLM to infer scope, categories, importance on save. + Uses RecallFlow for adaptive-depth recall. Supports scope/slice views and + pluggable storage (LanceDB default). + """ + + def __init__( + self, + llm: BaseLLM | str = "gpt-4o-mini", + storage: StorageBackend | str = "lancedb", + embedder: Any = None, + # -- Scoring weights -- + # These three weights control how recall results are ranked. + # The composite score is: semantic_weight * similarity + recency_weight * decay + importance_weight * importance. + # They should sum to ~1.0 for intuitive scoring. + recency_weight: float = 0.3, + semantic_weight: float = 0.5, + importance_weight: float = 0.2, + # How quickly old memories lose relevance. The recency score halves every + # N days (exponential decay). Lower = faster forgetting; higher = longer relevance. + recency_half_life_days: int = 30, + # -- Consolidation -- + # When remembering new content, if an existing record has similarity >= this + # threshold, the LLM is asked to merge/update/delete. Set to 1.0 to disable. + consolidation_threshold: float = 0.85, + # Max existing records to compare against when checking for consolidation. + consolidation_limit: int = 5, + # -- Save defaults -- + # Importance assigned to new memories when no explicit value is given and + # the LLM analysis path is skipped (all fields provided by the caller). + default_importance: float = 0.5, + # -- Recall depth control -- + # These thresholds govern the RecallFlow router that decides between + # returning results immediately ("synthesize") vs. doing an extra + # LLM-driven exploration round ("explore_deeper"). + # confidence >= confidence_threshold_high => always synthesize + # confidence < confidence_threshold_low => explore deeper (if budget > 0) + # complex query + confidence < complex_query_threshold => explore deeper + confidence_threshold_high: float = 0.8, + confidence_threshold_low: float = 0.5, + complex_query_threshold: float = 0.7, + # How many LLM-driven exploration rounds the RecallFlow is allowed to run. + # 0 = always shallow (vector search only); higher = more thorough but slower. + exploration_budget: int = 1, + # Queries shorter than this skip LLM analysis (saving ~1-3s). + # Longer queries (full task descriptions) benefit from LLM distillation. + query_analysis_threshold: int = 200, + # When True, all write operations (remember, remember_many) are silently + # skipped. Useful for sharing a read-only view of memory across agents + # without any of them persisting new memories. + read_only: bool = False, + ) -> None: + """Initialize Memory. + + Args: + llm: LLM for analysis (model name or BaseLLM instance). + storage: Backend: "lancedb" or a StorageBackend instance. + embedder: Embedding callable, provider config dict, or None (default OpenAI). + recency_weight: Weight for recency in the composite relevance score. + semantic_weight: Weight for semantic similarity in the composite relevance score. + importance_weight: Weight for importance in the composite relevance score. + recency_half_life_days: Recency score halves every N days (exponential decay). + consolidation_threshold: Similarity above which consolidation is triggered on save. + consolidation_limit: Max existing records to compare during consolidation. + default_importance: Default importance when not provided or inferred. + confidence_threshold_high: Recall confidence above which results are returned directly. + confidence_threshold_low: Recall confidence below which deeper exploration is triggered. + complex_query_threshold: For complex queries, explore deeper below this confidence. + exploration_budget: Number of LLM-driven exploration rounds during deep recall. + query_analysis_threshold: Queries shorter than this skip LLM analysis during deep recall. + read_only: If True, remember() and remember_many() are silent no-ops. + """ + self._read_only = read_only + self._config = MemoryConfig( + recency_weight=recency_weight, + semantic_weight=semantic_weight, + importance_weight=importance_weight, + recency_half_life_days=recency_half_life_days, + consolidation_threshold=consolidation_threshold, + consolidation_limit=consolidation_limit, + default_importance=default_importance, + confidence_threshold_high=confidence_threshold_high, + confidence_threshold_low=confidence_threshold_low, + complex_query_threshold=complex_query_threshold, + exploration_budget=exploration_budget, + query_analysis_threshold=query_analysis_threshold, + ) + + # Store raw config for lazy initialization. LLM and embedder are only + # built on first access so that Memory() never fails at construction + # time (e.g. when auto-created by Flow without an API key set). + self._llm_config: BaseLLM | str = llm + self._llm_instance: BaseLLM | None = None if isinstance(llm, str) else llm + self._embedder_config: Any = embedder + self._embedder_instance: Any = ( + embedder + if (embedder is not None and not isinstance(embedder, dict)) + else None + ) + + if isinstance(storage, str): + from crewai.memory.storage.lancedb_storage import LanceDBStorage + + self._storage = LanceDBStorage() if storage == "lancedb" else LanceDBStorage(path=storage) + else: + self._storage = storage + + # Background save queue. max_workers=1 serializes saves to avoid + # concurrent storage mutations (two saves finding the same similar + # record and both trying to update/delete it). Within each save, + # the parallel LLM calls still run on their own thread pool. + self._save_pool = ThreadPoolExecutor( + max_workers=1, thread_name_prefix="memory-save" + ) + self._pending_saves: list[Future[Any]] = [] + self._pending_lock = threading.Lock() + + _MEMORY_DOCS_URL = "https://docs.crewai.com/concepts/memory" + + @property + def _llm(self) -> BaseLLM: + """Lazy LLM initialization -- only created when first needed.""" + if self._llm_instance is None: + from crewai.llm import LLM + + try: + model_name = ( + self._llm_config + if isinstance(self._llm_config, str) + else str(self._llm_config) + ) + self._llm_instance = LLM(model=model_name) + except Exception as e: + raise RuntimeError( + f"Memory requires an LLM for analysis but initialization failed: {e}\n\n" + "To fix this, do one of the following:\n" + " - Set OPENAI_API_KEY for the default model (gpt-4o-mini)\n" + ' - Pass a different model: Memory(llm="anthropic/claude-3-haiku-20240307")\n' + ' - Pass any LLM instance: Memory(llm=LLM(model="your-model"))\n' + " - To skip LLM analysis, pass all fields explicitly to remember()\n" + ' and use depth="shallow" for recall.\n\n' + f"Docs: {self._MEMORY_DOCS_URL}" + ) from e + return self._llm_instance + + @property + def _embedder(self) -> Any: + """Lazy embedder initialization -- only created when first needed.""" + if self._embedder_instance is None: + try: + if isinstance(self._embedder_config, dict): + self._embedder_instance = build_embedder(self._embedder_config) + else: + self._embedder_instance = _default_embedder() + except Exception as e: + raise RuntimeError( + f"Memory requires an embedder for vector search but initialization failed: {e}\n\n" + "To fix this, do one of the following:\n" + " - Set OPENAI_API_KEY for the default embedder (text-embedding-3-small)\n" + ' - Pass a different embedder: Memory(embedder={{"provider": "google", "config": {{...}}}})\n' + " - Pass a callable: Memory(embedder=my_embedding_function)\n\n" + f"Docs: {self._MEMORY_DOCS_URL}" + ) from e + return self._embedder_instance + + # ------------------------------------------------------------------ + # Background write queue + # ------------------------------------------------------------------ + + def _submit_save(self, fn: Any, *args: Any, **kwargs: Any) -> Future[Any]: + """Submit a save operation to the background thread pool. + + The future is tracked so that ``drain_writes()`` can wait for it. + If the pool has been shut down (e.g. after ``close()``), the save + runs synchronously as a fallback so late saves still succeed. + """ + try: + future: Future[Any] = self._save_pool.submit(fn, *args, **kwargs) + except RuntimeError: + # Pool shut down -- run synchronously as fallback + future = Future() + try: + result = fn(*args, **kwargs) + future.set_result(result) + except Exception as exc: + future.set_exception(exc) + return future + with self._pending_lock: + self._pending_saves.append(future) + future.add_done_callback(self._on_save_done) + return future + + def _on_save_done(self, future: Future[Any]) -> None: + """Remove a completed future from the pending list and emit failure event if needed. + + This callback must never raise -- it runs from the thread pool's + internal machinery during process shutdown when executors and the + event bus may already be closed. + """ + try: + with self._pending_lock: + try: + self._pending_saves.remove(future) + except ValueError: + pass # already removed + exc = future.exception() + if exc is not None: + crewai_event_bus.emit( + self, + MemorySaveFailedEvent( + value="background save", + error=str(exc), + source_type="unified_memory", + ), + ) + except Exception: # noqa: S110 + pass # swallow everything during shutdown + + def drain_writes(self) -> None: + """Block until all pending background saves have completed. + + Called automatically by ``recall()`` and should be called by the + crew at shutdown to ensure no saves are lost. + """ + with self._pending_lock: + pending = list(self._pending_saves) + for future in pending: + future.result() # blocks until done; re-raises exceptions + + def close(self) -> None: + """Drain pending saves and shut down the background thread pool.""" + self.drain_writes() + self._save_pool.shutdown(wait=True) + + def _encode_batch( + self, + contents: list[str], + scope: str | None = None, + categories: list[str] | None = None, + metadata: dict[str, Any] | None = None, + importance: float | None = None, + source: str | None = None, + private: bool = False, + ) -> list[MemoryRecord]: + """Run the batch EncodingFlow for one or more items. No event emission. + + This is the core encoding logic shared by ``remember()`` and + ``remember_many()``. Events are managed by the calling method. + """ + from crewai.memory.encoding_flow import EncodingFlow + + flow = EncodingFlow( + storage=self._storage, + llm=self._llm, + embedder=self._embedder, + config=self._config, + ) + items_input = [ + { + "content": c, + "scope": scope, + "categories": categories, + "metadata": metadata, + "importance": importance, + "source": source, + "private": private, + } + for c in contents + ] + flow.kickoff(inputs={"items": items_input}) + return [ + item.result_record + for item in flow.state.items + if not item.dropped and item.result_record is not None + ] + + def remember( + self, + content: str, + scope: str | None = None, + categories: list[str] | None = None, + metadata: dict[str, Any] | None = None, + importance: float | None = None, + source: str | None = None, + private: bool = False, + agent_role: str | None = None, + ) -> MemoryRecord | None: + """Store a single item in memory (synchronous). + + Routes through the same serialized save pool as ``remember_many`` + to prevent races, but blocks until the save completes so the caller + gets the ``MemoryRecord`` back immediately. + + Args: + content: Text to remember. + scope: Optional scope path; inferred if None. + categories: Optional categories; inferred if None. + metadata: Optional metadata; merged with LLM-extracted if inferred. + importance: Optional importance 0-1; inferred if None. + source: Optional provenance identifier (e.g. user ID, session ID). + private: If True, only visible to recall from the same source. + agent_role: Optional agent role for event metadata. + + Returns: + The created MemoryRecord, or None if this memory is read-only. + + Raises: + Exception: On save failure (events emitted). + """ + if self._read_only: + return None + _source_type = "unified_memory" + try: + crewai_event_bus.emit( + self, + MemorySaveStartedEvent( + value=content, + metadata=metadata, + source_type=_source_type, + ), + ) + start = time.perf_counter() + + # Submit through the save pool for proper serialization, + # then immediately wait for the result. + future = self._submit_save( + self._encode_batch, + [content], + scope, + categories, + metadata, + importance, + source, + private, + ) + records = future.result() + record = records[0] if records else None + + elapsed_ms = (time.perf_counter() - start) * 1000 + crewai_event_bus.emit( + self, + MemorySaveCompletedEvent( + value=content, + metadata=metadata or {}, + agent_role=agent_role, + save_time_ms=elapsed_ms, + source_type=_source_type, + ), + ) + return record + except Exception as e: + crewai_event_bus.emit( + self, + MemorySaveFailedEvent( + value=content, + metadata=metadata, + error=str(e), + source_type=_source_type, + ), + ) + raise + + def remember_many( + self, + contents: list[str], + scope: str | None = None, + categories: list[str] | None = None, + metadata: dict[str, Any] | None = None, + importance: float | None = None, + source: str | None = None, + private: bool = False, + agent_role: str | None = None, + ) -> list[MemoryRecord]: + """Store multiple items in memory (non-blocking). + + The encoding pipeline runs in a background thread. This method + returns immediately so the caller (e.g. agent) is not blocked. + A ``MemorySaveStartedEvent`` is emitted immediately; the + ``MemorySaveCompletedEvent`` is emitted when the background + save finishes. + + Any subsequent ``recall()`` call will automatically wait for + pending saves to complete before searching (read barrier). + + Args: + contents: List of text items to remember. + scope: Optional scope applied to all items. + categories: Optional categories applied to all items. + metadata: Optional metadata applied to all items. + importance: Optional importance applied to all items. + source: Optional provenance identifier applied to all items. + private: Privacy flag applied to all items. + agent_role: Optional agent role for event metadata. + + Returns: + Empty list (records are not available until the background save completes). + """ + if not contents or self._read_only: + return [] + + self._submit_save( + self._background_encode_batch, + contents, + scope, + categories, + metadata, + importance, + source, + private, + agent_role, + ) + return [] + + def _background_encode_batch( + self, + contents: list[str], + scope: str | None, + categories: list[str] | None, + metadata: dict[str, Any] | None, + importance: float | None, + source: str | None, + private: bool, + agent_role: str | None, + ) -> list[MemoryRecord]: + """Run the encoding pipeline in a background thread with event emission. + + Both started and completed events are emitted here (in the background + thread) so they pair correctly on the event bus scope stack. + + All ``emit`` calls are wrapped in try/except to handle the case where + the event bus shuts down before the background save finishes (e.g. + during process exit). + """ + try: + crewai_event_bus.emit( + self, + MemorySaveStartedEvent( + value=f"{len(contents)} memories (background)", + metadata=metadata, + source_type="unified_memory", + ), + ) + except RuntimeError: + pass # event bus shut down during process exit + + try: + start = time.perf_counter() + records = self._encode_batch( + contents, scope, categories, metadata, importance, source, private + ) + elapsed_ms = (time.perf_counter() - start) * 1000 + except RuntimeError: + # The encoding pipeline uses asyncio.run() -> to_thread() internally. + # If the process is shutting down, the default executor is closed and + # to_thread raises "cannot schedule new futures after shutdown". + # Silently abandon the save -- the process is exiting anyway. + return [] + + try: + crewai_event_bus.emit( + self, + MemorySaveCompletedEvent( + value=f"{len(records)} memories saved", + metadata=metadata or {}, + agent_role=agent_role, + save_time_ms=elapsed_ms, + source_type="unified_memory", + ), + ) + except RuntimeError: + pass # event bus shut down during process exit + return records + + def extract_memories(self, content: str) -> list[str]: + """Extract discrete memories from a raw content blob using the LLM. + + This is a pure helper -- it does NOT store anything. + Call remember() on each returned string to persist them. + + Args: + content: Raw text (e.g. task + result dump). + + Returns: + List of short, self-contained memory statements. + """ + return extract_memories_from_content(content, self._llm) + + def recall( + self, + query: str, + scope: str | None = None, + categories: list[str] | None = None, + limit: int = 10, + depth: Literal["shallow", "deep"] = "deep", + source: str | None = None, + include_private: bool = False, + ) -> list[MemoryMatch]: + """Retrieve relevant memories. + + ``shallow`` embeds the query directly and runs a single vector search. + ``deep`` (default) uses the RecallFlow: the LLM distills the query into + targeted sub-queries, selects scopes, searches in parallel, and applies + confidence-based routing for optional deeper exploration. + + Args: + query: Natural language query. + scope: Optional scope prefix to search within. + categories: Optional category filter. + limit: Max number of results. + depth: "shallow" for direct vector search, "deep" for intelligent flow. + source: Optional provenance filter. Private records are only visible + when this matches the record's source. + include_private: If True, all private records are visible regardless of source. + + Returns: + List of MemoryMatch, ordered by relevance. + """ + # Read barrier: wait for any pending background saves to finish + # so that the search sees all persisted records. + self.drain_writes() + + _source = "unified_memory" + try: + crewai_event_bus.emit( + self, + MemoryQueryStartedEvent( + query=query, + limit=limit, + score_threshold=None, + source_type=_source, + ), + ) + start = time.perf_counter() + + if depth == "shallow": + embedding = embed_text(self._embedder, query) + if not embedding: + results: list[MemoryMatch] = [] + else: + raw = self._storage.search( + embedding, + scope_prefix=scope, + categories=categories, + limit=limit, + min_score=0.0, + ) + # Privacy filter + if not include_private: + raw = [ + (r, s) + for r, s in raw + if not r.private or r.source == source + ] + results = [] + for r, s in raw: + composite, reasons = compute_composite_score(r, s, self._config) + results.append( + MemoryMatch( + record=r, + score=composite, + match_reasons=reasons, + ) + ) + results.sort(key=lambda m: m.score, reverse=True) + else: + flow = RecallFlow( + storage=self._storage, + llm=self._llm, + embedder=self._embedder, + config=self._config, + ) + flow.kickoff( + inputs={ + "query": query, + "scope": scope, + "categories": categories or [], + "limit": limit, + "source": source, + "include_private": include_private, + } + ) + results = flow.state.final_results + + # Update last_accessed for recalled records + if results: + try: + touch = getattr(self._storage, "touch_records", None) + if touch is not None: + touch([m.record.id for m in results]) + except Exception: # noqa: S110 + pass # Non-critical: don't fail recall because of touch + + elapsed_ms = (time.perf_counter() - start) * 1000 + crewai_event_bus.emit( + self, + MemoryQueryCompletedEvent( + query=query, + results=results, + limit=limit, + score_threshold=None, + query_time_ms=elapsed_ms, + source_type=_source, + ), + ) + return results + except Exception as e: + crewai_event_bus.emit( + self, + MemoryQueryFailedEvent( + query=query, + limit=limit, + score_threshold=None, + error=str(e), + source_type=_source, + ), + ) + raise + + def forget( + self, + scope: str | None = None, + categories: list[str] | None = None, + older_than: datetime | None = None, + metadata_filter: dict[str, Any] | None = None, + record_ids: list[str] | None = None, + ) -> int: + """Delete memories matching criteria. + + Returns: + Number of records deleted. + """ + return self._storage.delete( + scope_prefix=scope, + categories=categories, + record_ids=record_ids, + older_than=older_than, + metadata_filter=metadata_filter, + ) + + def update( + self, + record_id: str, + content: str | None = None, + scope: str | None = None, + categories: list[str] | None = None, + metadata: dict[str, Any] | None = None, + importance: float | None = None, + ) -> MemoryRecord: + """Update an existing memory record by ID. + + Args: + record_id: ID of the record to update. + content: New content; re-embedded if provided. + scope: New scope path. + categories: New categories. + metadata: New metadata. + importance: New importance score. + + Returns: + The updated MemoryRecord. + + Raises: + ValueError: If the record is not found. + """ + existing = self._storage.get_record(record_id) + if existing is None: + raise ValueError(f"Record not found: {record_id}") + now = datetime.utcnow() + updates: dict[str, Any] = {"last_accessed": now} + if content is not None: + updates["content"] = content + embedding = embed_text(self._embedder, content) + updates["embedding"] = embedding if embedding else existing.embedding + if scope is not None: + updates["scope"] = scope + if categories is not None: + updates["categories"] = categories + if metadata is not None: + updates["metadata"] = metadata + if importance is not None: + updates["importance"] = importance + updated = existing.model_copy(update=updates) + self._storage.update(updated) + return updated + + def scope(self, path: str) -> Any: + """Return a scoped view of this memory.""" + from crewai.memory.memory_scope import MemoryScope + + return MemoryScope(memory=self, root_path=path) + + def slice( + self, + scopes: list[str], + categories: list[str] | None = None, + read_only: bool = True, + ) -> Any: + """Return a multi-scope view (slice) of this memory.""" + from crewai.memory.memory_scope import MemorySlice + + return MemorySlice( + memory=self, + scopes=scopes, + categories=categories, + read_only=read_only, + ) + + def list_scopes(self, path: str = "/") -> list[str]: + """List immediate child scopes under path.""" + return self._storage.list_scopes(path) + + def list_records( + self, scope: str | None = None, limit: int = 200, offset: int = 0 + ) -> list[MemoryRecord]: + """List records in a scope, newest first. + + Args: + scope: Optional scope path prefix to filter by. + limit: Maximum number of records to return. + offset: Number of records to skip (for pagination). + """ + return self._storage.list_records( + scope_prefix=scope, limit=limit, offset=offset + ) + + def info(self, path: str = "/") -> ScopeInfo: + """Return scope info for path.""" + return self._storage.get_scope_info(path) + + def tree(self, path: str = "/", max_depth: int = 3) -> str: + """Return a formatted tree of scopes (string).""" + lines: list[str] = [] + + def _walk(p: str, depth: int, prefix: str) -> None: + if depth > max_depth: + return + info = self._storage.get_scope_info(p) + lines.append(f"{prefix}{p or '/'} ({info.record_count} records)") + for child in info.child_scopes[:20]: + _walk(child, depth + 1, prefix + " ") + + _walk(path.rstrip("/") or "/", 0, "") + return "\n".join(lines) if lines else f"{path or '/'} (0 records)" + + def list_categories(self, path: str | None = None) -> dict[str, int]: + """List categories and counts; path=None means global.""" + return self._storage.list_categories(scope_prefix=path) + + def reset(self, scope: str | None = None) -> None: + """Reset (delete all) memories in scope. None = all.""" + self._storage.reset(scope_prefix=scope) + + async def aextract_memories(self, content: str) -> list[str]: + """Async variant of extract_memories.""" + return self.extract_memories(content) + + async def aremember( + self, + content: str, + scope: str | None = None, + categories: list[str] | None = None, + metadata: dict[str, Any] | None = None, + importance: float | None = None, + source: str | None = None, + private: bool = False, + ) -> MemoryRecord | None: + """Async remember: delegates to sync for now.""" + return self.remember( + content, + scope=scope, + categories=categories, + metadata=metadata, + importance=importance, + source=source, + private=private, + ) + + async def aremember_many( + self, + contents: list[str], + scope: str | None = None, + categories: list[str] | None = None, + metadata: dict[str, Any] | None = None, + importance: float | None = None, + source: str | None = None, + private: bool = False, + agent_role: str | None = None, + ) -> list[MemoryRecord]: + """Async remember_many: delegates to sync for now.""" + return self.remember_many( + contents, + scope=scope, + categories=categories, + metadata=metadata, + importance=importance, + source=source, + private=private, + agent_role=agent_role, + ) + + async def arecall( + self, + query: str, + scope: str | None = None, + categories: list[str] | None = None, + limit: int = 10, + depth: Literal["shallow", "deep"] = "deep", + source: str | None = None, + include_private: bool = False, + ) -> list[MemoryMatch]: + """Async recall: delegates to sync for now.""" + return self.recall( + query, + scope=scope, + categories=categories, + limit=limit, + depth=depth, + source=source, + include_private=include_private, + ) diff --git a/lib/crewai/src/crewai/rag/embeddings/factory.py b/lib/crewai/src/crewai/rag/embeddings/factory.py index 41a9233da..802779320 100644 --- a/lib/crewai/src/crewai/rag/embeddings/factory.py +++ b/lib/crewai/src/crewai/rag/embeddings/factory.py @@ -216,6 +216,10 @@ def build_embedder_from_dict( def build_embedder_from_dict(spec: ONNXProviderSpec) -> ONNXMiniLM_L6_V2: ... +@overload +def build_embedder_from_dict(spec: dict[str, Any]) -> EmbeddingFunction[Any]: ... + + def build_embedder_from_dict(spec): # type: ignore[no-untyped-def] """Build an embedding function instance from a dictionary specification. @@ -341,6 +345,10 @@ def build_embedder(spec: Text2VecProviderSpec) -> Text2VecEmbeddingFunction: ... def build_embedder(spec: ONNXProviderSpec) -> ONNXMiniLM_L6_V2: ... +@overload +def build_embedder(spec: dict[str, Any]) -> EmbeddingFunction[Any]: ... + + def build_embedder(spec): # type: ignore[no-untyped-def] """Build an embedding function from either a provider spec or a provider instance. diff --git a/lib/crewai/src/crewai/task.py b/lib/crewai/src/crewai/task.py index eac42f956..cfcb01799 100644 --- a/lib/crewai/src/crewai/task.py +++ b/lib/crewai/src/crewai/task.py @@ -1,5 +1,6 @@ from __future__ import annotations +import asyncio from concurrent.futures import Future from copy import copy as shallow_copy import datetime @@ -585,16 +586,29 @@ class Task(BaseModel): self._post_agent_execution(agent) - if not self._guardrails and not self._guardrail: + if isinstance(result, BaseModel): + raw = result.model_dump_json() + if self.output_pydantic: + pydantic_output = result + json_output = None + elif self.output_json: + pydantic_output = None + json_output = result.model_dump() + else: + pydantic_output = None + json_output = None + elif not self._guardrails and not self._guardrail: + raw = result pydantic_output, json_output = self._export_output(result) else: + raw = result pydantic_output, json_output = None, None task_output = TaskOutput( name=self.name or self.description, description=self.description, expected_output=self.expected_output, - raw=result, + raw=raw, pydantic=pydantic_output, json_dict=json_output, agent=agent.role, @@ -624,11 +638,15 @@ class Task(BaseModel): self.end_time = datetime.datetime.now() if self.callback: - self.callback(self.output) + cb_result = self.callback(self.output) + if inspect.isawaitable(cb_result): + await cb_result crew = self.agent.crew # type: ignore[union-attr] if crew and crew.task_callback and crew.task_callback != self.callback: - crew.task_callback(self.output) + cb_result = crew.task_callback(self.output) + if inspect.isawaitable(cb_result): + await cb_result if self.output_file: content = ( @@ -682,16 +700,29 @@ class Task(BaseModel): self._post_agent_execution(agent) - if not self._guardrails and not self._guardrail: + if isinstance(result, BaseModel): + raw = result.model_dump_json() + if self.output_pydantic: + pydantic_output = result + json_output = None + elif self.output_json: + pydantic_output = None + json_output = result.model_dump() + else: + pydantic_output = None + json_output = None + elif not self._guardrails and not self._guardrail: + raw = result pydantic_output, json_output = self._export_output(result) else: + raw = result pydantic_output, json_output = None, None task_output = TaskOutput( name=self.name or self.description, description=self.description, expected_output=self.expected_output, - raw=result, + raw=raw, pydantic=pydantic_output, json_dict=json_output, agent=agent.role, @@ -722,11 +753,15 @@ class Task(BaseModel): self.end_time = datetime.datetime.now() if self.callback: - self.callback(self.output) + cb_result = self.callback(self.output) + if inspect.iscoroutine(cb_result): + asyncio.run(cb_result) crew = self.agent.crew # type: ignore[union-attr] if crew and crew.task_callback and crew.task_callback != self.callback: - crew.task_callback(self.output) + cb_result = crew.task_callback(self.output) + if inspect.iscoroutine(cb_result): + asyncio.run(cb_result) if self.output_file: content = ( diff --git a/lib/crewai/src/crewai/telemetry/__init__.py b/lib/crewai/src/crewai/telemetry/__init__.py index 38739d88a..b927aa02e 100644 --- a/lib/crewai/src/crewai/telemetry/__init__.py +++ b/lib/crewai/src/crewai/telemetry/__init__.py @@ -1,5 +1,4 @@ from crewai.telemetry.telemetry import Telemetry - __all__ = ["Telemetry"] diff --git a/lib/crewai/src/crewai/telemetry/telemetry.py b/lib/crewai/src/crewai/telemetry/telemetry.py index 04303fc3d..136a7d7d0 100644 --- a/lib/crewai/src/crewai/telemetry/telemetry.py +++ b/lib/crewai/src/crewai/telemetry/telemetry.py @@ -173,6 +173,12 @@ class Telemetry: self._original_handlers: dict[int, Any] = {} + if threading.current_thread() is not threading.main_thread(): + logger.debug( + "Skipping signal handler registration: not running in main thread" + ) + return + self._register_signal_handler(signal.SIGTERM, SigTermEvent, shutdown=True) self._register_signal_handler(signal.SIGINT, SigIntEvent, shutdown=True) if hasattr(signal, "SIGHUP"): diff --git a/lib/crewai/src/crewai/tools/__init__.py b/lib/crewai/src/crewai/tools/__init__.py index ef698c90a..a2415b1b2 100644 --- a/lib/crewai/src/crewai/tools/__init__.py +++ b/lib/crewai/src/crewai/tools/__init__.py @@ -1,7 +1,6 @@ from crewai.tools.base_tool import BaseTool, EnvVar, tool - __all__ = [ "BaseTool", "EnvVar", diff --git a/lib/crewai/src/crewai/tools/base_tool.py b/lib/crewai/src/crewai/tools/base_tool.py index 8a10cdfa3..07fa61b07 100644 --- a/lib/crewai/src/crewai/tools/base_tool.py +++ b/lib/crewai/src/crewai/tools/base_tool.py @@ -23,7 +23,7 @@ from pydantic import ( ) from typing_extensions import TypeIs -from crewai.tools.structured_tool import CrewStructuredTool +from crewai.tools.structured_tool import CrewStructuredTool, build_schema_hint from crewai.utilities.printer import Printer from crewai.utilities.pydantic_schema_utils import generate_model_description from crewai.utilities.string_utils import sanitize_tool_name @@ -150,14 +150,39 @@ class BaseTool(BaseModel, ABC): super().model_post_init(__context) + def _validate_kwargs(self, kwargs: dict[str, Any]) -> dict[str, Any]: + """Validate keyword arguments against args_schema if present. + + Args: + kwargs: The keyword arguments to validate. + + Returns: + Validated (and possibly coerced) keyword arguments. + + Raises: + ValueError: If validation against args_schema fails. + """ + if self.args_schema is not None and self.args_schema.model_fields: + try: + validated = self.args_schema.model_validate(kwargs) + return validated.model_dump() + except Exception as e: + hint = build_schema_hint(self.args_schema) + raise ValueError( + f"Tool '{self.name}' arguments validation failed: {e}{hint}" + ) from e + return kwargs + def run( self, *args: Any, **kwargs: Any, ) -> Any: + if not args: + kwargs = self._validate_kwargs(kwargs) + result = self._run(*args, **kwargs) - # If _run is async, we safely run it if asyncio.iscoroutine(result): result = asyncio.run(result) @@ -179,6 +204,8 @@ class BaseTool(BaseModel, ABC): Returns: The result of the tool execution. """ + if not args: + kwargs = self._validate_kwargs(kwargs) result = await self._arun(*args, **kwargs) self.current_usage_count += 1 return result @@ -331,6 +358,9 @@ class Tool(BaseTool, Generic[P, R]): Returns: The result of the tool execution. """ + if not args: + kwargs = self._validate_kwargs(kwargs) # type: ignore[assignment] + result = self.func(*args, **kwargs) if asyncio.iscoroutine(result): @@ -361,6 +391,8 @@ class Tool(BaseTool, Generic[P, R]): Returns: The result of the tool execution. """ + if not args: + kwargs = self._validate_kwargs(kwargs) # type: ignore[assignment] result = await self._arun(*args, **kwargs) self.current_usage_count += 1 return result diff --git a/lib/crewai/src/crewai/tools/mcp_native_tool.py b/lib/crewai/src/crewai/tools/mcp_native_tool.py index f25b2f4d7..d14c26a5a 100644 --- a/lib/crewai/src/crewai/tools/mcp_native_tool.py +++ b/lib/crewai/src/crewai/tools/mcp_native_tool.py @@ -27,14 +27,16 @@ class MCPNativeTool(BaseTool): tool_name: str, tool_schema: dict[str, Any], server_name: str, + original_tool_name: str | None = None, ) -> None: """Initialize native MCP tool. Args: mcp_client: MCPClient instance with active session. - tool_name: Original name of the tool on the MCP server. + tool_name: Name of the tool (may be prefixed). tool_schema: Schema information for the tool. server_name: Name of the MCP server for prefixing. + original_tool_name: Original name of the tool on the MCP server. """ # Create tool name with server prefix to avoid conflicts prefixed_name = f"{server_name}_{tool_name}" @@ -57,7 +59,7 @@ class MCPNativeTool(BaseTool): # Set instance attributes after super().__init__ self._mcp_client = mcp_client - self._original_tool_name = tool_name + self._original_tool_name = original_tool_name or tool_name self._server_name = server_name # self._logger = logging.getLogger(__name__) diff --git a/lib/crewai/src/crewai/tools/memory_tools.py b/lib/crewai/src/crewai/tools/memory_tools.py new file mode 100644 index 000000000..9e4df03e9 --- /dev/null +++ b/lib/crewai/src/crewai/tools/memory_tools.py @@ -0,0 +1,131 @@ +"""Memory tools that give agents active recall and remember capabilities.""" + +from __future__ import annotations + +from typing import Any + +from pydantic import BaseModel, Field + +from crewai.tools.base_tool import BaseTool +from crewai.utilities.i18n import get_i18n + + +class RecallMemorySchema(BaseModel): + """Schema for the recall memory tool.""" + + queries: list[str] = Field( + ..., + description=( + "One or more search queries. Pass a single item for a focused search, " + "or multiple items to search for several things at once." + ), + ) + + +class RecallMemoryTool(BaseTool): + """Tool that lets an agent search memory for one or more queries at once.""" + + name: str = "Search memory" + description: str = "" + args_schema: type[BaseModel] = RecallMemorySchema + memory: Any = Field(exclude=True) + + def _run( + self, + queries: list[str] | str, + **kwargs: Any, + ) -> str: + """Search memory for relevant information. + + Args: + queries: One or more search queries (string or list of strings). + + Returns: + Formatted string of matching memories, or a message if none found. + """ + if isinstance(queries, str): + queries = [queries] + + all_lines: list[str] = [] + seen_ids: set[str] = set() + for query in queries: + matches = self.memory.recall(query, limit=20) + for m in matches: + if m.record.id not in seen_ids: + seen_ids.add(m.record.id) + all_lines.append(m.format()) + + if not all_lines: + return "No relevant memories found." + return "Found memories:\n" + "\n".join(all_lines) + + +class RememberSchema(BaseModel): + """Schema for the remember tool.""" + + contents: list[str] = Field( + ..., + description=( + "One or more facts, decisions, or observations to remember. " + "Pass a single item or multiple items at once." + ), + ) + + +class RememberTool(BaseTool): + """Tool that lets an agent save one or more items to memory at once.""" + + name: str = "Save to memory" + description: str = "" + args_schema: type[BaseModel] = RememberSchema + memory: Any = Field(exclude=True) + + def _run(self, contents: list[str] | str, **kwargs: Any) -> str: + """Store one or more items in memory. The system infers scope, categories, and importance. + + Args: + contents: One or more items to remember (string or list of strings). + + Returns: + Confirmation with the number of items saved. + """ + if isinstance(contents, str): + contents = [contents] + if len(contents) == 1: + record = self.memory.remember(contents[0]) + return ( + f"Saved to memory (scope={record.scope}, " + f"importance={record.importance:.1f})." + ) + self.memory.remember_many(contents) + return f"Saving {len(contents)} items to memory in background." + + +def create_memory_tools(memory: Any) -> list[BaseTool]: + """Create Recall and Remember tools for the given memory instance. + + When memory is read-only (``_read_only=True``), only the RecallMemoryTool + is returned — the RememberTool is omitted so agents are never offered a + save capability they cannot use. + + Args: + memory: A Memory, MemoryScope, or MemorySlice instance. + + Returns: + List containing a RecallMemoryTool and, if not read-only, a RememberTool. + """ + i18n = get_i18n() + tools: list[BaseTool] = [ + RecallMemoryTool( + memory=memory, + description=i18n.tools("recall_memory"), + ), + ] + if not getattr(memory, "_read_only", False): + tools.append( + RememberTool( + memory=memory, + description=i18n.tools("save_to_memory"), + ) + ) + return tools diff --git a/lib/crewai/src/crewai/tools/structured_tool.py b/lib/crewai/src/crewai/tools/structured_tool.py index 44f0af2d9..4b95caeb7 100644 --- a/lib/crewai/src/crewai/tools/structured_tool.py +++ b/lib/crewai/src/crewai/tools/structured_tool.py @@ -17,6 +17,27 @@ if TYPE_CHECKING: from crewai.tools.base_tool import BaseTool +def build_schema_hint(args_schema: type[BaseModel]) -> str: + """Build a human-readable hint from a Pydantic model's JSON schema. + + Args: + args_schema: The Pydantic model class to extract schema from. + + Returns: + A formatted string with expected arguments and required fields, + or empty string if schema extraction fails. + """ + try: + schema = args_schema.model_json_schema() + return ( + f"\nExpected arguments: " + f"{json.dumps(schema.get('properties', {}))}" + f"\nRequired: {json.dumps(schema.get('required', []))}" + ) + except Exception: + return "" + + class ToolUsageLimitExceededError(Exception): """Exception raised when a tool has reached its maximum usage limit.""" @@ -208,7 +229,8 @@ class CrewStructuredTool: validated_args = self.args_schema.model_validate(raw_args) return validated_args.model_dump() except Exception as e: - raise ValueError(f"Arguments validation failed: {e}") from e + hint = build_schema_hint(self.args_schema) + raise ValueError(f"Arguments validation failed: {e}{hint}") from e async def ainvoke( self, diff --git a/lib/crewai/src/crewai/translations/en.json b/lib/crewai/src/crewai/translations/en.json index e15f2f5bf..833f6e9e7 100644 --- a/lib/crewai/src/crewai/translations/en.json +++ b/lib/crewai/src/crewai/translations/en.json @@ -7,7 +7,7 @@ "slices": { "observation": "\nObservation:", "task": "\nCurrent Task: {input}\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:", - "memory": "\n\n# Useful context: \n{memory}", + "memory": "\n\n# Memories from past conversations:\n{memory}\n\nIMPORTANT: The memories above are an automatic selection and may be INCOMPLETE. If the task involves counting, listing, or summing items (e.g. 'how many', 'total', 'list all'), you MUST use the Search memory tool with several different queries before answering — do NOT rely solely on the memories shown above. Enumerate each distinct item you find before giving a final count.", "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}", "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```", "no_tools": "", @@ -34,7 +34,11 @@ "lite_agent_response_format": "Format your final answer according to the following OpenAPI schema: {response_format}\n\nIMPORTANT: Preserve the original content exactly as-is. Do NOT rewrite, paraphrase, or modify the meaning of the content. Only structure it to match the schema format.\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.", "knowledge_search_query": "The original query is: {task_prompt}.", "knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions.", - "human_feedback_collapse": "Based on the following human feedback, determine which outcome best matches their intent.\n\nFeedback: {feedback}\n\nPossible outcomes: {outcomes}\n\nRespond with ONLY one of the exact outcome values listed above, nothing else." + "human_feedback_collapse": "Based on the following human feedback, determine which outcome best matches their intent.\n\nFeedback: {feedback}\n\nPossible outcomes: {outcomes}\n\nRespond with ONLY one of the exact outcome values listed above, nothing else.", + "hitl_pre_review_system": "You are reviewing content before a human sees it. Apply the lessons from past human feedback to improve the output. Preserve the original meaning and structure, but incorporate the corrections and preferences indicated by the lessons.", + "hitl_pre_review_user": "Output to review:\n{output}\n\nLessons from past human feedback:\n{lessons}\n\nApply the lessons to improve the output.", + "hitl_distill_system": "You extract generalizable lessons from human feedback on system outputs. A lesson should be a reusable rule or preference that applies to future similar outputs -- not a one-time correction specific to this exact content.\n\nExamples of good lessons:\n- Always include source citations when making factual claims\n- Use bullet points instead of long paragraphs for action items\n- Avoid technical jargon when the audience is non-technical\n\nIf the feedback is just approval (e.g. looks good, approved) or contains no generalizable guidance, return an empty list.", + "hitl_distill_user": "Method: {method_name}\n\nSystem output:\n{output}\n\nHuman feedback:\n{feedback}\n\nExtract generalizable lessons. Return an empty list if none." }, "errors": { "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}", @@ -55,7 +59,19 @@ "name": "Add image to content", "description": "See image to understand its content, you can optionally ask a question about the image", "default_action": "Please provide a detailed description of this image, including all visual elements, context, and any notable details you can observe." - } + }, + "recall_memory": "Search through the team's shared memory for relevant information. Pass one or more queries to search for multiple things at once. Use this when you need to find facts, decisions, preferences, or past results that may have been stored previously. IMPORTANT: For questions that require counting, summing, or listing items across multiple conversations (e.g. 'how many X', 'total Y', 'list all Z'), you MUST search multiple times with different phrasings to ensure you find ALL relevant items before giving a final count or total. Do not rely on a single search — items may be described differently across conversations.", + "save_to_memory": "Store one or more important facts, decisions, observations, or lessons in memory so they can be recalled later by you or other agents. Pass multiple items at once when you have several things worth remembering." + }, + "memory": { + "query_system": "You analyze a query for searching memory.\nGiven the query and available scopes, output:\n1. keywords: Key entities or keywords that can be used to filter by category.\n2. suggested_scopes: Which available scopes are most relevant (empty for all).\n3. complexity: 'simple' or 'complex'.\n4. recall_queries: 1-3 short, targeted search phrases distilled from the query. Each should be a concise phrase optimized for semantic vector search. If the query is already short and focused, return it as-is in a single-item list. For long task descriptions, extract the distinct things worth searching for.\n5. time_filter: If the query references a time period (like 'last week', 'yesterday', 'in January'), return an ISO 8601 date string for the earliest relevant date (e.g. '2026-02-01'). Return null if no time constraint is implied.", + "extract_memories_system": "You extract discrete, reusable memory statements from raw content (e.g. a task description and its result, or a conversation between a user and an assistant).\n\nFor the given content, output a list of memory statements. Each memory must:\n- Be one clear sentence or short statement\n- Be understandable without the original context\n- Capture a decision, fact, outcome, preference, lesson, or observation worth remembering\n- NOT be a vague summary or a restatement of the task description\n- NOT duplicate the same idea in different words\n\nWhen the content is a conversation, pay special attention to facts stated by the user (first-person statements). These personal facts are HIGH PRIORITY and must always be extracted:\n- What the user did, bought, made, visited, attended, or completed\n- Names of people, pets, places, brands, and specific items the user mentions\n- Quantities, durations, dates, and measurements the user states\n- Subordinate clauses and casual asides often contain important personal details (e.g. \"by the way, it took me 4 hours\" or \"my Golden Retriever Max\")\n\nPreserve exact names and numbers — never generalize (e.g. keep \"lavender gin fizz\" not just \"cocktail\", keep \"12 largemouth bass\" not just \"fish caught\", keep \"Golden Retriever\" not just \"dog\").\n\nAdditional extraction rules:\n- Presupposed facts: When the user reveals a fact indirectly in a question (e.g. \"What collar suits a Golden Retriever like Max?\" presupposes Max is a Golden Retriever), extract that fact as a separate memory.\n- Date precision: Always preserve the full date including day-of-month when stated (e.g. \"February 14th\" not just \"February\", \"March 5\" not just \"March\").\n- Life events in passing: When the user mentions a life event (birth, wedding, graduation, move, adoption) while discussing something else, extract the life event as its own memory (e.g. \"my friend David had a baby boy named Jasper\" is a birth fact, even if mentioned while planning to send congratulations).\n\nIf there is nothing worth remembering (e.g. empty result, no decisions or facts), return an empty list.\nOutput a JSON object with a single key \"memories\" whose value is a list of strings.", + "extract_memories_user": "Content:\n{content}\n\nExtract memory statements as described. Return structured output.", + "query_user": "Query: {query}\n\nAvailable scopes: {available_scopes}\n{scope_desc}\n\nReturn the analysis as structured output.", + "save_system": "You analyze content to be stored in a hierarchical memory system.\nGiven the content and the existing scopes and categories, output:\n1. suggested_scope: The best matching existing scope path, or a new path if none fit (use / for root).\n2. categories: A list of categories (reuse existing when relevant, add new ones if needed).\n3. importance: A number from 0.0 to 1.0 indicating how significant this memory is.\n4. extracted_metadata: A JSON object with any entities, dates, or topics you can extract.", + "save_user": "Content to store:\n{content}\n\nExisting scopes: {existing_scopes}\nExisting categories: {existing_categories}\n\nReturn the analysis as structured output.", + "consolidation_system": "You are comparing new content against existing memories to decide how to consolidate them.\n\nFor each existing memory, choose one action:\n- 'keep': The existing memory is still accurate and not redundant with the new content.\n- 'update': The existing memory should be updated with new information. Provide the updated content.\n- 'delete': The existing memory is outdated, superseded, or contradicted by the new content.\n\nAlso decide whether the new content should be inserted as a separate memory:\n- insert_new=true: The new content adds information not fully captured by existing memories (even after updates).\n- insert_new=false: The new content is fully captured by the existing memories (after any updates).\n\nBe conservative: prefer 'keep' when unsure. Only 'update' or 'delete' when there is a clear contradiction, supersession, or redundancy.", + "consolidation_user": "New content to consider storing:\n{new_content}\n\nExisting similar memories:\n{records_summary}\n\nReturn the consolidation plan as structured output." }, "reasoning": { "initial_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are creating a strategic plan for a task that requires your expertise and unique perspective.", diff --git a/lib/crewai/src/crewai/utilities/agent_utils.py b/lib/crewai/src/crewai/utilities/agent_utils.py index 22b498541..e4f3d3fee 100644 --- a/lib/crewai/src/crewai/utilities/agent_utils.py +++ b/lib/crewai/src/crewai/utilities/agent_utils.py @@ -3,6 +3,7 @@ from __future__ import annotations import asyncio from collections.abc import Callable, Sequence import concurrent.futures +import inspect import json import re from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict @@ -138,7 +139,11 @@ def render_text_description_and_args( def convert_tools_to_openai_schema( tools: Sequence[BaseTool | CrewStructuredTool], -) -> tuple[list[dict[str, Any]], dict[str, Callable[..., Any]]]: +) -> tuple[ + list[dict[str, Any]], + dict[str, Callable[..., Any]], + dict[str, BaseTool | CrewStructuredTool], +]: """Convert CrewAI tools to OpenAI function calling format. This function converts CrewAI BaseTool and CrewStructuredTool objects @@ -151,23 +156,21 @@ def convert_tools_to_openai_schema( Returns: Tuple containing: - List of OpenAI-format tool schema dictionaries - - Dict mapping tool names to their callable run() methods - - Example: - >>> tools = [CalculatorTool(), SearchTool()] - >>> schemas, functions = convert_tools_to_openai_schema(tools) - >>> # schemas can be passed to llm.call(tools=schemas) - >>> # functions can be passed to llm.call(available_functions=functions) + - Dict mapping sanitized tool names to their callable run() methods + - Dict mapping sanitized tool names to their original tool objects """ openai_tools: list[dict[str, Any]] = [] available_functions: dict[str, Callable[..., Any]] = {} + tool_name_mapping: dict[str, BaseTool | CrewStructuredTool] = {} for tool in tools: # Get the JSON schema for tool parameters parameters: dict[str, Any] = {} if hasattr(tool, "args_schema") and tool.args_schema is not None: try: - schema_output = generate_model_description(tool.args_schema) + schema_output = generate_model_description( + tool.args_schema, strip_null_types=False + ) parameters = schema_output.get("json_schema", {}).get("schema", {}) # Remove title and description from schema root as they're redundant parameters.pop("title", None) @@ -183,6 +186,14 @@ def convert_tools_to_openai_schema( sanitized_name = sanitize_tool_name(tool.name) + if sanitized_name in available_functions: + counter = 2 + candidate = sanitize_tool_name(f"{sanitized_name}_{counter}") + while candidate in available_functions: + counter += 1 + candidate = sanitize_tool_name(f"{sanitized_name}_{counter}") + sanitized_name = candidate + schema: dict[str, Any] = { "type": "function", "function": { @@ -194,8 +205,9 @@ def convert_tools_to_openai_schema( } openai_tools.append(schema) available_functions[sanitized_name] = tool.run # type: ignore[union-attr] + tool_name_mapping[sanitized_name] = tool - return openai_tools, available_functions + return openai_tools, available_functions, tool_name_mapping def has_reached_max_iterations(iterations: int, max_iterations: int) -> bool: @@ -501,7 +513,9 @@ def handle_agent_action_core( - TODO: Remove messages parameter and its usage. """ if step_callback: - step_callback(tool_result) + cb_result = step_callback(tool_result) + if inspect.iscoroutine(cb_result): + asyncio.run(cb_result) formatted_answer.text += f"\nObservation: {tool_result.result}" formatted_answer.result = tool_result.result @@ -1143,6 +1157,36 @@ def extract_tool_call_info( return None +def parse_tool_call_args( + func_args: dict[str, Any] | str, + func_name: str, + call_id: str, + original_tool: Any = None, +) -> tuple[dict[str, Any], None] | tuple[None, dict[str, Any]]: + """Parse tool call arguments from a JSON string or dict. + + Returns: + ``(args_dict, None)`` on success, or ``(None, error_result)`` on + JSON parse failure where ``error_result`` is a ready-to-return dict + with the same shape as ``_execute_single_native_tool_call`` return values. + """ + if isinstance(func_args, str): + try: + return json.loads(func_args), None + except json.JSONDecodeError as e: + return None, { + "call_id": call_id, + "func_name": func_name, + "result": ( + f"Error: Failed to parse tool arguments as JSON: {e}. " + f"Please provide valid JSON arguments for the '{func_name}' tool." + ), + "from_cache": False, + "original_tool": original_tool, + } + return func_args, None + + def _setup_before_llm_call_hooks( executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None, printer: Printer, diff --git a/lib/crewai/src/crewai/utilities/i18n.py b/lib/crewai/src/crewai/utilities/i18n.py index 104e452a7..0968286e2 100644 --- a/lib/crewai/src/crewai/utilities/i18n.py +++ b/lib/crewai/src/crewai/utilities/i18n.py @@ -86,10 +86,21 @@ class I18N(BaseModel): """ return self.retrieve("tools", tool) + def memory(self, key: str) -> str: + """Retrieve a memory prompt by key. + + Args: + key: The key of the memory prompt to retrieve. + + Returns: + The memory prompt as a string. + """ + return self.retrieve("memory", key) + def retrieve( self, kind: Literal[ - "slices", "errors", "tools", "reasoning", "hierarchical_manager_agent" + "slices", "errors", "tools", "reasoning", "hierarchical_manager_agent", "memory" ], key: str, ) -> str: diff --git a/lib/crewai/src/crewai/utilities/llm_utils.py b/lib/crewai/src/crewai/utilities/llm_utils.py index 129f064d5..55a42968a 100644 --- a/lib/crewai/src/crewai/utilities/llm_utils.py +++ b/lib/crewai/src/crewai/utilities/llm_utils.py @@ -69,7 +69,7 @@ def create_llm( UNACCEPTED_ATTRIBUTES: Final[list[str]] = [ "AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY", - "AWS_REGION_NAME", + "AWS_DEFAULT_REGION", ] @@ -146,7 +146,7 @@ def _llm_via_environment_or_fallback() -> LLM | None: unaccepted_attributes = [ "AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY", - "AWS_REGION_NAME", + "AWS_DEFAULT_REGION", ] set_provider = model_name.partition("/")[0] if "/" in model_name else "openai" diff --git a/lib/crewai/src/crewai/utilities/pydantic_schema_utils.py b/lib/crewai/src/crewai/utilities/pydantic_schema_utils.py index 191f38c35..87d80da81 100644 --- a/lib/crewai/src/crewai/utilities/pydantic_schema_utils.py +++ b/lib/crewai/src/crewai/utilities/pydantic_schema_utils.py @@ -417,7 +417,11 @@ def strip_null_from_types(schema: dict[str, Any]) -> dict[str, Any]: return schema -def generate_model_description(model: type[BaseModel]) -> ModelDescription: +def generate_model_description( + model: type[BaseModel], + *, + strip_null_types: bool = True, +) -> ModelDescription: """Generate JSON schema description of a Pydantic model. This function takes a Pydantic model class and returns its JSON schema, @@ -426,6 +430,9 @@ def generate_model_description(model: type[BaseModel]) -> ModelDescription: Args: model: A Pydantic model class. + strip_null_types: When ``True`` (default), remove ``null`` from + ``anyOf`` / ``type`` arrays. Set to ``False`` to allow sending ``null`` for + optional fields. Returns: A ModelDescription with JSON schema representation of the model. @@ -442,7 +449,9 @@ def generate_model_description(model: type[BaseModel]) -> ModelDescription: json_schema = fix_discriminator_mappings(json_schema) json_schema = convert_oneof_to_anyof(json_schema) json_schema = ensure_all_properties_required(json_schema) - json_schema = strip_null_from_types(json_schema) + + if strip_null_types: + json_schema = strip_null_from_types(json_schema) return { "type": "json_schema", @@ -482,10 +491,66 @@ FORMAT_TYPE_MAP: dict[str, type[Any]] = { } +def build_rich_field_description(prop_schema: dict[str, Any]) -> str: + """Build a comprehensive field description including constraints. + + Embeds format, enum, pattern, min/max, and example constraints into the + description text so that LLMs can understand tool parameter requirements + without inspecting the raw JSON Schema. + + Args: + prop_schema: Property schema with description and constraints. + + Returns: + Enhanced description with format, enum, and other constraints. + """ + parts: list[str] = [] + + description = prop_schema.get("description", "") + if description: + parts.append(description) + + format_type = prop_schema.get("format") + if format_type: + parts.append(f"Format: {format_type}") + + enum_values = prop_schema.get("enum") + if enum_values: + enum_str = ", ".join(repr(v) for v in enum_values) + parts.append(f"Allowed values: [{enum_str}]") + + pattern = prop_schema.get("pattern") + if pattern: + parts.append(f"Pattern: {pattern}") + + minimum = prop_schema.get("minimum") + maximum = prop_schema.get("maximum") + if minimum is not None: + parts.append(f"Minimum: {minimum}") + if maximum is not None: + parts.append(f"Maximum: {maximum}") + + min_length = prop_schema.get("minLength") + max_length = prop_schema.get("maxLength") + if min_length is not None: + parts.append(f"Min length: {min_length}") + if max_length is not None: + parts.append(f"Max length: {max_length}") + + examples = prop_schema.get("examples") + if examples: + examples_str = ", ".join(repr(e) for e in examples[:3]) + parts.append(f"Examples: {examples_str}") + + return ". ".join(parts) if parts else "" + + def create_model_from_schema( # type: ignore[no-any-unimported] json_schema: dict[str, Any], *, root_schema: dict[str, Any] | None = None, + model_name: str | None = None, + enrich_descriptions: bool = False, __config__: ConfigDict | None = None, __base__: type[BaseModel] | None = None, __module__: str = __name__, @@ -503,6 +568,13 @@ def create_model_from_schema( # type: ignore[no-any-unimported] json_schema: A dictionary representing the JSON schema. root_schema: The root schema containing $defs. If not provided, the current schema is treated as the root schema. + model_name: Override for the model name. If not provided, the schema + ``title`` field is used, falling back to ``"DynamicModel"``. + enrich_descriptions: When True, augment field descriptions with + constraint info (format, enum, pattern, min/max, examples) via + :func:`build_rich_field_description`. Useful for LLM-facing tool + schemas where constraints in the description help the model + understand parameter requirements. __config__: Pydantic configuration for the generated model. __base__: Base class for the generated model. Defaults to BaseModel. __module__: Module name for the generated model class. @@ -539,10 +611,14 @@ def create_model_from_schema( # type: ignore[no-any-unimported] if "title" not in json_schema and "title" in (root_schema or {}): json_schema["title"] = (root_schema or {}).get("title") - model_name = json_schema.get("title") or "DynamicModel" + effective_name = model_name or json_schema.get("title") or "DynamicModel" field_definitions = { name: _json_schema_to_pydantic_field( - name, prop, json_schema.get("required", []), effective_root + name, + prop, + json_schema.get("required", []), + effective_root, + enrich_descriptions=enrich_descriptions, ) for name, prop in (json_schema.get("properties", {}) or {}).items() } @@ -550,7 +626,7 @@ def create_model_from_schema( # type: ignore[no-any-unimported] effective_config = __config__ or ConfigDict(extra="forbid") return create_model_base( - model_name, + effective_name, __config__=effective_config, __base__=__base__, __module__=__module__, @@ -565,6 +641,8 @@ def _json_schema_to_pydantic_field( json_schema: dict[str, Any], required: list[str], root_schema: dict[str, Any], + *, + enrich_descriptions: bool = False, ) -> Any: """Convert a JSON schema property to a Pydantic field definition. @@ -573,20 +651,29 @@ def _json_schema_to_pydantic_field( json_schema: The JSON schema for this field. required: List of required field names. root_schema: The root schema for resolving $ref. + enrich_descriptions: When True, embed constraints in the description. Returns: A tuple of (type, Field) for use with create_model. """ - type_ = _json_schema_to_pydantic_type(json_schema, root_schema, name_=name.title()) - description = json_schema.get("description") - examples = json_schema.get("examples") + type_ = _json_schema_to_pydantic_type( + json_schema, root_schema, name_=name.title(), enrich_descriptions=enrich_descriptions + ) is_required = name in required field_params: dict[str, Any] = {} schema_extra: dict[str, Any] = {} - if description: - field_params["description"] = description + if enrich_descriptions: + rich_desc = build_rich_field_description(json_schema) + if rich_desc: + field_params["description"] = rich_desc + else: + description = json_schema.get("description") + if description: + field_params["description"] = description + + examples = json_schema.get("examples") if examples: schema_extra["examples"] = examples @@ -702,6 +789,7 @@ def _json_schema_to_pydantic_type( root_schema: dict[str, Any], *, name_: str | None = None, + enrich_descriptions: bool = False, ) -> Any: """Convert a JSON schema to a Python/Pydantic type. @@ -709,6 +797,7 @@ def _json_schema_to_pydantic_type( json_schema: The JSON schema to convert. root_schema: The root schema for resolving $ref. name_: Optional name for nested models. + enrich_descriptions: Propagated to nested model creation. Returns: A Python type corresponding to the JSON schema. @@ -716,7 +805,9 @@ def _json_schema_to_pydantic_type( ref = json_schema.get("$ref") if ref: ref_schema = _resolve_ref(ref, root_schema) - return _json_schema_to_pydantic_type(ref_schema, root_schema, name_=name_) + return _json_schema_to_pydantic_type( + ref_schema, root_schema, name_=name_, enrich_descriptions=enrich_descriptions + ) enum_values = json_schema.get("enum") if enum_values: @@ -731,7 +822,10 @@ def _json_schema_to_pydantic_type( if any_of_schemas: any_of_types = [ _json_schema_to_pydantic_type( - schema, root_schema, name_=f"{name_ or 'Union'}Option{i}" + schema, + root_schema, + name_=f"{name_ or 'Union'}Option{i}", + enrich_descriptions=enrich_descriptions, ) for i, schema in enumerate(any_of_schemas) ] @@ -741,10 +835,14 @@ def _json_schema_to_pydantic_type( if all_of_schemas: if len(all_of_schemas) == 1: return _json_schema_to_pydantic_type( - all_of_schemas[0], root_schema, name_=name_ + all_of_schemas[0], root_schema, name_=name_, + enrich_descriptions=enrich_descriptions, ) merged = _merge_all_of_schemas(all_of_schemas, root_schema) - return _json_schema_to_pydantic_type(merged, root_schema, name_=name_) + return _json_schema_to_pydantic_type( + merged, root_schema, name_=name_, + enrich_descriptions=enrich_descriptions, + ) type_ = json_schema.get("type") @@ -760,7 +858,8 @@ def _json_schema_to_pydantic_type( items_schema = json_schema.get("items") if items_schema: item_type = _json_schema_to_pydantic_type( - items_schema, root_schema, name_=name_ + items_schema, root_schema, name_=name_, + enrich_descriptions=enrich_descriptions, ) return list[item_type] # type: ignore[valid-type] return list @@ -770,7 +869,10 @@ def _json_schema_to_pydantic_type( json_schema_ = json_schema.copy() if json_schema_.get("title") is None: json_schema_["title"] = name_ or "DynamicModel" - return create_model_from_schema(json_schema_, root_schema=root_schema) + return create_model_from_schema( + json_schema_, root_schema=root_schema, + enrich_descriptions=enrich_descriptions, + ) return dict if type_ == "null": return None diff --git a/lib/crewai/src/crewai/utilities/string_utils.py b/lib/crewai/src/crewai/utilities/string_utils.py index 8834c2e38..98735b3ea 100644 --- a/lib/crewai/src/crewai/utilities/string_utils.py +++ b/lib/crewai/src/crewai/utilities/string_utils.py @@ -2,6 +2,7 @@ # https://github.com/un33k/python-slugify # MIT License +import hashlib import re from typing import Any, Final import unicodedata @@ -40,7 +41,9 @@ def sanitize_tool_name(name: str, max_length: int = _MAX_TOOL_NAME_LENGTH) -> st name = name.strip("_") if len(name) > max_length: - name = name[:max_length].rstrip("_") + name_hash = hashlib.sha256(name.encode()).hexdigest()[:8] + suffix = f"_{name_hash}" + name = name[: max_length - len(suffix)].rstrip("_") + suffix return name diff --git a/lib/crewai/tests/agents/test_agent_executor.py b/lib/crewai/tests/agents/test_agent_executor.py index 8560d9321..ab886ff38 100644 --- a/lib/crewai/tests/agents/test_agent_executor.py +++ b/lib/crewai/tests/agents/test_agent_executor.py @@ -4,6 +4,7 @@ Tests the Flow-based agent executor implementation including state management, flow methods, routing logic, and error handling. """ +import time from unittest.mock import Mock, patch import pytest @@ -122,7 +123,7 @@ class TestAgentExecutor: executor.state.iterations = 10 result = executor.check_max_iterations() - assert result == "force_final_answer" + assert result == "max_iterations_exceeded" def test_route_by_answer_type_action(self, mock_dependencies): """Test routing for AgentAction.""" @@ -372,10 +373,7 @@ class TestFlowInvoke: task.human_input = False crew = Mock() - crew._short_term_memory = None - crew._long_term_memory = None - crew._entity_memory = None - crew._external_memory = None + crew._memory = None agent = Mock() agent.role = "Test" @@ -398,14 +396,10 @@ class TestFlowInvoke: } @patch.object(AgentExecutor, "kickoff") - @patch.object(AgentExecutor, "_create_short_term_memory") - @patch.object(AgentExecutor, "_create_long_term_memory") - @patch.object(AgentExecutor, "_create_external_memory") + @patch.object(AgentExecutor, "_save_to_memory") def test_invoke_success( self, - mock_external_memory, - mock_long_term_memory, - mock_short_term_memory, + mock_save_to_memory, mock_kickoff, mock_dependencies, ): @@ -425,9 +419,7 @@ class TestFlowInvoke: assert result == {"output": "Final result"} mock_kickoff.assert_called_once() - mock_short_term_memory.assert_called_once() - mock_long_term_memory.assert_called_once() - mock_external_memory.assert_called_once() + mock_save_to_memory.assert_called_once() @patch.object(AgentExecutor, "kickoff") def test_invoke_failure_no_agent_finish(self, mock_kickoff, mock_dependencies): @@ -443,14 +435,10 @@ class TestFlowInvoke: executor.invoke(inputs) @patch.object(AgentExecutor, "kickoff") - @patch.object(AgentExecutor, "_create_short_term_memory") - @patch.object(AgentExecutor, "_create_long_term_memory") - @patch.object(AgentExecutor, "_create_external_memory") + @patch.object(AgentExecutor, "_save_to_memory") def test_invoke_with_system_prompt( self, - mock_external_memory, - mock_long_term_memory, - mock_short_term_memory, + mock_save_to_memory, mock_kickoff, mock_dependencies, ): @@ -470,10 +458,181 @@ class TestFlowInvoke: inputs = {"input": "test", "tool_names": "", "tools": ""} result = executor.invoke(inputs) - mock_short_term_memory.assert_called_once() - mock_long_term_memory.assert_called_once() - mock_external_memory.assert_called_once() + mock_save_to_memory.assert_called_once() mock_kickoff.assert_called_once() assert result == {"output": "Done"} assert len(executor.state.messages) >= 2 + + +class TestNativeToolExecution: + """Test native tool execution behavior.""" + + @pytest.fixture + def mock_dependencies(self): + llm = Mock() + llm.supports_stop_words.return_value = True + + task = Mock() + task.name = "Test Task" + task.description = "Test" + task.human_input = False + task.response_model = None + + crew = Mock() + crew._memory = None + crew.verbose = False + crew._train = False + + agent = Mock() + agent.id = "test-agent-id" + agent.role = "Test Agent" + agent.verbose = False + agent.key = "test-key" + + prompt = {"prompt": "Test {input} {tool_names} {tools}"} + + tools_handler = Mock() + tools_handler.cache = None + + return { + "llm": llm, + "task": task, + "crew": crew, + "agent": agent, + "prompt": prompt, + "max_iter": 10, + "tools": [], + "tools_names": "", + "stop_words": [], + "tools_description": "", + "tools_handler": tools_handler, + } + + def test_execute_native_tool_runs_parallel_for_multiple_calls( + self, mock_dependencies + ): + executor = AgentExecutor(**mock_dependencies) + + def slow_one() -> str: + time.sleep(0.2) + return "one" + + def slow_two() -> str: + time.sleep(0.2) + return "two" + + executor._available_functions = {"slow_one": slow_one, "slow_two": slow_two} + executor.state.pending_tool_calls = [ + { + "id": "call_1", + "function": {"name": "slow_one", "arguments": "{}"}, + }, + { + "id": "call_2", + "function": {"name": "slow_two", "arguments": "{}"}, + }, + ] + + started = time.perf_counter() + result = executor.execute_native_tool() + elapsed = time.perf_counter() - started + + assert result == "native_tool_completed" + assert elapsed < 0.5 + tool_messages = [m for m in executor.state.messages if m.get("role") == "tool"] + assert len(tool_messages) == 2 + assert tool_messages[0]["tool_call_id"] == "call_1" + assert tool_messages[1]["tool_call_id"] == "call_2" + + def test_execute_native_tool_falls_back_to_sequential_for_result_as_answer( + self, mock_dependencies + ): + executor = AgentExecutor(**mock_dependencies) + + def slow_one() -> str: + time.sleep(0.2) + return "one" + + def slow_two() -> str: + time.sleep(0.2) + return "two" + + result_tool = Mock() + result_tool.name = "slow_one" + result_tool.result_as_answer = True + result_tool.max_usage_count = None + result_tool.current_usage_count = 0 + + executor.original_tools = [result_tool] + executor._available_functions = {"slow_one": slow_one, "slow_two": slow_two} + executor.state.pending_tool_calls = [ + { + "id": "call_1", + "function": {"name": "slow_one", "arguments": "{}"}, + }, + { + "id": "call_2", + "function": {"name": "slow_two", "arguments": "{}"}, + }, + ] + + started = time.perf_counter() + result = executor.execute_native_tool() + elapsed = time.perf_counter() - started + + assert result == "tool_result_is_final" + assert elapsed >= 0.2 + assert elapsed < 0.8 + assert isinstance(executor.state.current_answer, AgentFinish) + assert executor.state.current_answer.output == "one" + + def test_execute_native_tool_result_as_answer_short_circuits_remaining_calls( + self, mock_dependencies + ): + executor = AgentExecutor(**mock_dependencies) + call_counts = {"slow_one": 0, "slow_two": 0} + + def slow_one() -> str: + call_counts["slow_one"] += 1 + time.sleep(0.2) + return "one" + + def slow_two() -> str: + call_counts["slow_two"] += 1 + time.sleep(0.2) + return "two" + + result_tool = Mock() + result_tool.name = "slow_one" + result_tool.result_as_answer = True + result_tool.max_usage_count = None + result_tool.current_usage_count = 0 + + executor.original_tools = [result_tool] + executor._available_functions = {"slow_one": slow_one, "slow_two": slow_two} + executor.state.pending_tool_calls = [ + { + "id": "call_1", + "function": {"name": "slow_one", "arguments": "{}"}, + }, + { + "id": "call_2", + "function": {"name": "slow_two", "arguments": "{}"}, + }, + ] + + started = time.perf_counter() + result = executor.execute_native_tool() + elapsed = time.perf_counter() - started + + assert result == "tool_result_is_final" + assert isinstance(executor.state.current_answer, AgentFinish) + assert executor.state.current_answer.output == "one" + assert call_counts["slow_one"] == 1 + assert call_counts["slow_two"] == 0 + assert elapsed < 0.5 + + tool_messages = [m for m in executor.state.messages if m.get("role") == "tool"] + assert len(tool_messages) == 1 + assert tool_messages[0]["tool_call_id"] == "call_1" diff --git a/lib/crewai/tests/agents/test_async_agent_executor.py b/lib/crewai/tests/agents/test_async_agent_executor.py index 4dc72ab2a..01297bdcc 100644 --- a/lib/crewai/tests/agents/test_async_agent_executor.py +++ b/lib/crewai/tests/agents/test_async_agent_executor.py @@ -2,7 +2,7 @@ import asyncio from typing import Any -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import AsyncMock, MagicMock, Mock, patch import pytest @@ -95,16 +95,14 @@ class TestAsyncAgentExecutor: ), ): with patch.object(executor, "_show_start_logs"): - with patch.object(executor, "_create_short_term_memory"): - with patch.object(executor, "_create_long_term_memory"): - with patch.object(executor, "_create_external_memory"): - result = await executor.ainvoke( - { - "input": "test input", - "tool_names": "", - "tools": "", - } - ) + with patch.object(executor, "_save_to_memory"): + result = await executor.ainvoke( + { + "input": "test input", + "tool_names": "", + "tools": "", + } + ) assert result == {"output": expected_output} @@ -273,16 +271,14 @@ class TestAsyncAgentExecutor: ): with patch.object(executor, "_show_start_logs"): with patch.object(executor, "_show_logs"): - with patch.object(executor, "_create_short_term_memory"): - with patch.object(executor, "_create_long_term_memory"): - with patch.object(executor, "_create_external_memory"): - return await executor.ainvoke( - { - "input": f"test {executor_id}", - "tool_names": "", - "tools": "", - } - ) + with patch.object(executor, "_save_to_memory"): + return await executor.ainvoke( + { + "input": f"test {executor_id}", + "tool_names": "", + "tools": "", + } + ) results = await asyncio.gather( create_and_run_executor(1), @@ -295,6 +291,46 @@ class TestAsyncAgentExecutor: assert max_concurrent > 1, f"Expected concurrent execution, max concurrent was {max_concurrent}" +class TestInvokeStepCallback: + """Tests for _invoke_step_callback with sync and async callbacks.""" + + def test_invoke_step_callback_with_sync_callback( + self, executor: CrewAgentExecutor + ) -> None: + """Test that a sync step callback is called normally.""" + callback = Mock() + executor.step_callback = callback + answer = AgentFinish(thought="thinking", output="test", text="final") + + executor._invoke_step_callback(answer) + + callback.assert_called_once_with(answer) + + def test_invoke_step_callback_with_async_callback( + self, executor: CrewAgentExecutor + ) -> None: + """Test that an async step callback is awaited via asyncio.run.""" + async_callback = AsyncMock() + executor.step_callback = async_callback + answer = AgentFinish(thought="thinking", output="test", text="final") + + with patch("crewai.agents.crew_agent_executor.asyncio.run") as mock_run: + executor._invoke_step_callback(answer) + + async_callback.assert_called_once_with(answer) + mock_run.assert_called_once() + + def test_invoke_step_callback_with_none( + self, executor: CrewAgentExecutor + ) -> None: + """Test that no error is raised when step_callback is None.""" + executor.step_callback = None + answer = AgentFinish(thought="thinking", output="test", text="final") + + # Should not raise + executor._invoke_step_callback(answer) + + class TestAsyncLLMResponseHelper: """Tests for aget_llm_response helper function.""" diff --git a/lib/crewai/tests/agents/test_lite_agent.py b/lib/crewai/tests/agents/test_lite_agent.py index 6f989a27c..ac03ffc28 100644 --- a/lib/crewai/tests/agents/test_lite_agent.py +++ b/lib/crewai/tests/agents/test_lite_agent.py @@ -16,6 +16,7 @@ import pytest from crewai import LLM, Agent from crewai.flow import Flow, start from crewai.tools import BaseTool +from crewai.types.usage_metrics import UsageMetrics # A simple test tool @@ -658,7 +659,7 @@ def test_agent_kickoff_with_platform_tools(mock_get, mock_post): @patch.dict("os.environ", {"EXA_API_KEY": "test_exa_key"}) -@patch("crewai.agent.Agent._get_external_mcp_tools") +@patch("crewai.agent.Agent.get_mcp_tools") @pytest.mark.vcr() def test_agent_kickoff_with_mcp_tools(mock_get_mcp_tools): """Test that Agent.kickoff() properly integrates MCP tools with LiteAgent""" @@ -690,7 +691,7 @@ def test_agent_kickoff_with_mcp_tools(mock_get_mcp_tools): assert result.raw is not None # Verify MCP tools were retrieved - mock_get_mcp_tools.assert_called_once_with("https://mcp.exa.ai/mcp?api_key=test_exa_key&profile=research") + mock_get_mcp_tools.assert_called_once_with(["https://mcp.exa.ai/mcp?api_key=test_exa_key&profile=research"]) # ============================================================================ @@ -1064,3 +1065,98 @@ def test_lite_agent_verbose_false_suppresses_printer_output(): agent2.kickoff("Say hello") mock_printer.print.assert_not_called() + + +# --- LiteAgent memory integration --- + + +@pytest.mark.filterwarnings("ignore:LiteAgent is deprecated") +def test_lite_agent_memory_none_default(): + """With memory=None (default), _memory is None and no memory is used.""" + mock_llm = Mock(spec=LLM) + mock_llm.call.return_value = "Final Answer: Ok" + mock_llm.stop = [] + mock_llm.get_token_usage_summary.return_value = UsageMetrics( + total_tokens=10, + prompt_tokens=5, + completion_tokens=5, + cached_prompt_tokens=0, + successful_requests=1, + ) + agent = LiteAgent( + role="Test", + goal="Test goal", + backstory="Test backstory", + llm=mock_llm, + memory=None, + verbose=False, + ) + assert agent._memory is None + + +@pytest.mark.filterwarnings("ignore:LiteAgent is deprecated") +def test_lite_agent_memory_true_resolves_to_default_memory(): + """With memory=True, _memory is a Memory instance.""" + from crewai.memory.unified_memory import Memory + + mock_llm = Mock(spec=LLM) + mock_llm.call.return_value = "Final Answer: Ok" + mock_llm.stop = [] + mock_llm.get_token_usage_summary.return_value = UsageMetrics( + total_tokens=10, + prompt_tokens=5, + completion_tokens=5, + cached_prompt_tokens=0, + successful_requests=1, + ) + agent = LiteAgent( + role="Test", + goal="Test goal", + backstory="Test backstory", + llm=mock_llm, + memory=True, + verbose=False, + ) + assert agent._memory is not None + assert isinstance(agent._memory, Memory) + + +@pytest.mark.filterwarnings("ignore:LiteAgent is deprecated") +def test_lite_agent_memory_instance_recall_and_save_called(): + """With a custom memory instance, kickoff calls recall and then extract_memories/remember.""" + mock_llm = Mock(spec=LLM) + mock_llm.call.return_value = "Final Answer: The answer is 42." + mock_llm.stop = [] + mock_llm.supports_stop_words.return_value = False + mock_llm.get_token_usage_summary.return_value = UsageMetrics( + total_tokens=10, + prompt_tokens=5, + completion_tokens=5, + cached_prompt_tokens=0, + successful_requests=1, + ) + mock_memory = Mock() + mock_memory._read_only = False + mock_memory.recall.return_value = [] + mock_memory.extract_memories.return_value = ["Fact one.", "Fact two."] + + agent = LiteAgent( + role="Test", + goal="Test goal", + backstory="Test backstory", + llm=mock_llm, + memory=mock_memory, + verbose=False, + ) + assert agent._memory is mock_memory + + agent.kickoff("What is the answer?") + + mock_memory.recall.assert_called_once() + call_kw = mock_memory.recall.call_args[1] + assert call_kw.get("limit") == 10 + # depth is not passed explicitly; Memory.recall() defaults to "deep" + mock_memory.extract_memories.assert_called_once() + mock_memory.remember_many.assert_called_once_with( + ["Fact one.", "Fact two."], agent_role="Test" + ) diff --git a/lib/crewai/tests/agents/test_native_tool_calling.py b/lib/crewai/tests/agents/test_native_tool_calling.py index fde883df9..73a2c5156 100644 --- a/lib/crewai/tests/agents/test_native_tool_calling.py +++ b/lib/crewai/tests/agents/test_native_tool_calling.py @@ -6,13 +6,20 @@ when the LLM supports it, across multiple providers. from __future__ import annotations +from collections.abc import Generator import os -from unittest.mock import patch +import threading +import time +from collections import Counter +from unittest.mock import Mock, patch import pytest from pydantic import BaseModel, Field from crewai import Agent, Crew, Task +from crewai.events import crewai_event_bus +from crewai.hooks import register_after_tool_call_hook, register_before_tool_call_hook +from crewai.hooks.tool_hooks import ToolCallHookContext from crewai.llm import LLM from crewai.tools.base_tool import BaseTool @@ -64,6 +71,73 @@ class FailingTool(BaseTool): def _run(self) -> str: raise Exception("This tool always fails") + +class LocalSearchInput(BaseModel): + query: str = Field(description="Search query") + + +class ParallelProbe: + """Thread-safe in-memory recorder for tool execution windows.""" + + _lock = threading.Lock() + _windows: list[tuple[str, float, float]] = [] + + @classmethod + def reset(cls) -> None: + with cls._lock: + cls._windows = [] + + @classmethod + def record(cls, tool_name: str, start: float, end: float) -> None: + with cls._lock: + cls._windows.append((tool_name, start, end)) + + @classmethod + def windows(cls) -> list[tuple[str, float, float]]: + with cls._lock: + return list(cls._windows) + + +def _parallel_prompt() -> str: + return ( + "This is a tool-calling compliance test. " + "In your next assistant turn, emit exactly 3 tool calls in the same response (parallel tool calls), in this order: " + "1) parallel_local_search_one(query='latest OpenAI model release notes'), " + "2) parallel_local_search_two(query='latest Anthropic model release notes'), " + "3) parallel_local_search_three(query='latest Gemini model release notes'). " + "Do not call any other tools and do not answer before those 3 tool calls are emitted. " + "After the tool results return, provide a one paragraph summary." + ) + + +def _max_concurrency(windows: list[tuple[str, float, float]]) -> int: + points: list[tuple[float, int]] = [] + for _, start, end in windows: + points.append((start, 1)) + points.append((end, -1)) + points.sort(key=lambda p: (p[0], p[1])) + + current = 0 + maximum = 0 + for _, delta in points: + current += delta + if current > maximum: + maximum = current + return maximum + + +def _assert_tools_overlapped() -> None: + windows = ParallelProbe.windows() + local_windows = [ + w + for w in windows + if w[0].startswith("parallel_local_search_") + ] + + assert len(local_windows) >= 3, f"Expected at least 3 local tool calls, got {len(local_windows)}" + assert _max_concurrency(local_windows) >= 2, "Expected overlapping local tool executions" + + @pytest.fixture def calculator_tool() -> CalculatorTool: """Create a calculator tool for testing.""" @@ -82,6 +156,65 @@ def failing_tool() -> BaseTool: ) + +@pytest.fixture +def parallel_tools() -> list[BaseTool]: + """Create local tools used to verify native parallel execution deterministically.""" + + class ParallelLocalSearchOne(BaseTool): + name: str = "parallel_local_search_one" + description: str = "Local search tool #1 for concurrency testing." + args_schema: type[BaseModel] = LocalSearchInput + + def _run(self, query: str) -> str: + start = time.perf_counter() + time.sleep(1.0) + end = time.perf_counter() + ParallelProbe.record(self.name, start, end) + return f"[one] {query}" + + class ParallelLocalSearchTwo(BaseTool): + name: str = "parallel_local_search_two" + description: str = "Local search tool #2 for concurrency testing." + args_schema: type[BaseModel] = LocalSearchInput + + def _run(self, query: str) -> str: + start = time.perf_counter() + time.sleep(1.0) + end = time.perf_counter() + ParallelProbe.record(self.name, start, end) + return f"[two] {query}" + + class ParallelLocalSearchThree(BaseTool): + name: str = "parallel_local_search_three" + description: str = "Local search tool #3 for concurrency testing." + args_schema: type[BaseModel] = LocalSearchInput + + def _run(self, query: str) -> str: + start = time.perf_counter() + time.sleep(1.0) + end = time.perf_counter() + ParallelProbe.record(self.name, start, end) + return f"[three] {query}" + + return [ + ParallelLocalSearchOne(), + ParallelLocalSearchTwo(), + ParallelLocalSearchThree(), + ] + + +def _attach_parallel_probe_handler() -> None: + @crewai_event_bus.on(ToolUsageFinishedEvent) + def _capture_tool_window(_source, event: ToolUsageFinishedEvent): + if not event.tool_name.startswith("parallel_local_search_"): + return + ParallelProbe.record( + event.tool_name, + event.started_at.timestamp(), + event.finished_at.timestamp(), + ) + # ============================================================================= # OpenAI Provider Tests # ============================================================================= @@ -122,7 +255,7 @@ class TestOpenAINativeToolCalling: self, calculator_tool: CalculatorTool ) -> None: """Test OpenAI agent kickoff with mocked LLM call.""" - llm = LLM(model="gpt-4o-mini") + llm = LLM(model="gpt-5-nano") with patch.object(llm, "call", return_value="The answer is 120.") as mock_call: agent = Agent( @@ -146,6 +279,174 @@ class TestOpenAINativeToolCalling: assert mock_call.called assert result is not None + @pytest.mark.vcr() + @pytest.mark.timeout(180) + def test_openai_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="gpt-5-nano", temperature=1), + verbose=False, + max_iter=3, + ) + task = Task( + description=_parallel_prompt(), + expected_output="A one sentence summary of both tool outputs", + agent=agent, + ) + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + assert result is not None + _assert_tools_overlapped() + + @pytest.mark.vcr() + @pytest.mark.timeout(180) + def test_openai_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="gpt-4o-mini"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + + @pytest.mark.vcr() + @pytest.mark.timeout(180) + def test_openai_parallel_native_tool_calling_tool_hook_parity_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + hook_calls: dict[str, list[dict[str, str]]] = {"before": [], "after": []} + + def before_hook(context: ToolCallHookContext) -> bool | None: + if context.tool_name.startswith("parallel_local_search_"): + hook_calls["before"].append( + { + "tool_name": context.tool_name, + "query": str(context.tool_input.get("query", "")), + } + ) + return None + + def after_hook(context: ToolCallHookContext) -> str | None: + if context.tool_name.startswith("parallel_local_search_"): + hook_calls["after"].append( + { + "tool_name": context.tool_name, + "query": str(context.tool_input.get("query", "")), + } + ) + return None + + register_before_tool_call_hook(before_hook) + register_after_tool_call_hook(after_hook) + + try: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="gpt-5-nano", temperature=1), + verbose=False, + max_iter=3, + ) + task = Task( + description=_parallel_prompt(), + expected_output="A one sentence summary of both tool outputs", + agent=agent, + ) + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + assert result is not None + _assert_tools_overlapped() + + before_names = [call["tool_name"] for call in hook_calls["before"]] + after_names = [call["tool_name"] for call in hook_calls["after"]] + assert len(before_names) >= 3, "Expected before hooks for all parallel calls" + assert Counter(before_names) == Counter(after_names) + assert all(call["query"] for call in hook_calls["before"]) + assert all(call["query"] for call in hook_calls["after"]) + finally: + from crewai.hooks import ( + unregister_after_tool_call_hook, + unregister_before_tool_call_hook, + ) + + unregister_before_tool_call_hook(before_hook) + unregister_after_tool_call_hook(after_hook) + + @pytest.mark.vcr() + @pytest.mark.timeout(180) + def test_openai_parallel_native_tool_calling_tool_hook_parity_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + hook_calls: dict[str, list[dict[str, str]]] = {"before": [], "after": []} + + def before_hook(context: ToolCallHookContext) -> bool | None: + if context.tool_name.startswith("parallel_local_search_"): + hook_calls["before"].append( + { + "tool_name": context.tool_name, + "query": str(context.tool_input.get("query", "")), + } + ) + return None + + def after_hook(context: ToolCallHookContext) -> str | None: + if context.tool_name.startswith("parallel_local_search_"): + hook_calls["after"].append( + { + "tool_name": context.tool_name, + "query": str(context.tool_input.get("query", "")), + } + ) + return None + + register_before_tool_call_hook(before_hook) + register_after_tool_call_hook(after_hook) + + try: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="gpt-5-nano", temperature=1), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + + assert result is not None + _assert_tools_overlapped() + + before_names = [call["tool_name"] for call in hook_calls["before"]] + after_names = [call["tool_name"] for call in hook_calls["after"]] + assert len(before_names) >= 3, "Expected before hooks for all parallel calls" + assert Counter(before_names) == Counter(after_names) + assert all(call["query"] for call in hook_calls["before"]) + assert all(call["query"] for call in hook_calls["after"]) + finally: + from crewai.hooks import ( + unregister_after_tool_call_hook, + unregister_before_tool_call_hook, + ) + + unregister_before_tool_call_hook(before_hook) + unregister_after_tool_call_hook(after_hook) + # ============================================================================= # Anthropic Provider Tests @@ -217,6 +518,46 @@ class TestAnthropicNativeToolCalling: assert mock_call.called assert result is not None + @pytest.mark.vcr() + def test_anthropic_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="anthropic/claude-sonnet-4-6"), + verbose=False, + max_iter=3, + ) + task = Task( + description=_parallel_prompt(), + expected_output="A one sentence summary of both tool outputs", + agent=agent, + ) + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + assert result is not None + _assert_tools_overlapped() + + @pytest.mark.vcr() + def test_anthropic_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="anthropic/claude-sonnet-4-6"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + # ============================================================================= # Google/Gemini Provider Tests @@ -247,7 +588,7 @@ class TestGeminiNativeToolCalling: goal="Help users with mathematical calculations", backstory="You are a helpful math assistant.", tools=[calculator_tool], - llm=LLM(model="gemini/gemini-2.0-flash-exp"), + llm=LLM(model="gemini/gemini-2.5-flash"), ) task = Task( @@ -266,7 +607,7 @@ class TestGeminiNativeToolCalling: self, calculator_tool: CalculatorTool ) -> None: """Test Gemini agent kickoff with mocked LLM call.""" - llm = LLM(model="gemini/gemini-2.0-flash-001") + llm = LLM(model="gemini/gemini-2.5-flash") with patch.object(llm, "call", return_value="The answer is 120.") as mock_call: agent = Agent( @@ -290,6 +631,46 @@ class TestGeminiNativeToolCalling: assert mock_call.called assert result is not None + @pytest.mark.vcr() + def test_gemini_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="gemini/gemini-2.5-flash"), + verbose=False, + max_iter=3, + ) + task = Task( + description=_parallel_prompt(), + expected_output="A one sentence summary of both tool outputs", + agent=agent, + ) + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + assert result is not None + _assert_tools_overlapped() + + @pytest.mark.vcr() + def test_gemini_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="gemini/gemini-2.5-flash"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + # ============================================================================= # Azure Provider Tests @@ -324,7 +705,7 @@ class TestAzureNativeToolCalling: goal="Help users with mathematical calculations", backstory="You are a helpful math assistant.", tools=[calculator_tool], - llm=LLM(model="azure/gpt-4o-mini"), + llm=LLM(model="azure/gpt-5-nano"), verbose=False, max_iter=3, ) @@ -347,7 +728,7 @@ class TestAzureNativeToolCalling: ) -> None: """Test Azure agent kickoff with mocked LLM call.""" llm = LLM( - model="azure/gpt-4o-mini", + model="azure/gpt-5-nano", api_key="test-key", base_url="https://test.openai.azure.com", ) @@ -374,6 +755,46 @@ class TestAzureNativeToolCalling: assert mock_call.called assert result is not None + @pytest.mark.vcr() + def test_azure_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="azure/gpt-5-nano"), + verbose=False, + max_iter=3, + ) + task = Task( + description=_parallel_prompt(), + expected_output="A one sentence summary of both tool outputs", + agent=agent, + ) + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + assert result is not None + _assert_tools_overlapped() + + @pytest.mark.vcr() + def test_azure_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="azure/gpt-5-nano"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + # ============================================================================= # Bedrock Provider Tests @@ -384,18 +805,30 @@ class TestBedrockNativeToolCalling: """Tests for native tool calling with AWS Bedrock models.""" @pytest.fixture(autouse=True) - def mock_aws_env(self): - """Mock AWS environment variables for tests.""" - env_vars = { - "AWS_ACCESS_KEY_ID": "test-key", - "AWS_SECRET_ACCESS_KEY": "test-secret", - "AWS_REGION": "us-east-1", - } - if "AWS_ACCESS_KEY_ID" not in os.environ: - with patch.dict(os.environ, env_vars): - yield - else: - yield + def validate_bedrock_credentials_for_live_recording(self): + """Run Bedrock tests only when explicitly enabled.""" + run_live_bedrock = os.getenv("RUN_BEDROCK_LIVE_TESTS", "false").lower() == "true" + + if not run_live_bedrock: + pytest.skip( + "Skipping Bedrock tests by default. " + "Set RUN_BEDROCK_LIVE_TESTS=true with valid AWS credentials to enable." + ) + + access_key = os.getenv("AWS_ACCESS_KEY_ID", "") + secret_key = os.getenv("AWS_SECRET_ACCESS_KEY", "") + if ( + not access_key + or not secret_key + or access_key.startswith(("fake-", "test-")) + or secret_key.startswith(("fake-", "test-")) + ): + pytest.skip( + "Skipping Bedrock tests: valid AWS credentials are required when " + "RUN_BEDROCK_LIVE_TESTS=true." + ) + + yield @pytest.mark.vcr() def test_bedrock_agent_kickoff_with_tools_mocked( @@ -427,6 +860,46 @@ class TestBedrockNativeToolCalling: assert result.raw is not None assert "120" in str(result.raw) + @pytest.mark.vcr() + def test_bedrock_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0"), + verbose=False, + max_iter=3, + ) + task = Task( + description=_parallel_prompt(), + expected_output="A one sentence summary of both tool outputs", + agent=agent, + ) + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + assert result is not None + _assert_tools_overlapped() + + @pytest.mark.vcr() + def test_bedrock_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + agent = Agent( + role="Parallel Tool Agent", + goal="Use both tools exactly as instructed", + backstory="You follow tool instructions precisely.", + tools=parallel_tools, + llm=LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + # ============================================================================= # Cross-Provider Native Tool Calling Behavior Tests @@ -439,7 +912,7 @@ class TestNativeToolCallingBehavior: def test_supports_function_calling_check(self) -> None: """Test that supports_function_calling() is properly checked.""" # OpenAI should support function calling - openai_llm = LLM(model="gpt-4o-mini") + openai_llm = LLM(model="gpt-5-nano") assert hasattr(openai_llm, "supports_function_calling") assert openai_llm.supports_function_calling() is True @@ -475,7 +948,7 @@ class TestNativeToolCallingTokenUsage: goal="Perform calculations efficiently", backstory="You calculate things.", tools=[calculator_tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=3, ) @@ -519,7 +992,7 @@ def test_native_tool_calling_error_handling(failing_tool: FailingTool): goal="Perform calculations efficiently", backstory="You calculate things.", tools=[failing_tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=3, ) @@ -578,7 +1051,7 @@ class TestMaxUsageCountWithNativeToolCalling: goal="Call the counting tool multiple times", backstory="You are an agent that counts things.", tools=[tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=5, ) @@ -606,7 +1079,7 @@ class TestMaxUsageCountWithNativeToolCalling: goal="Use the counting tool as many times as requested", backstory="You are an agent that counts things. You must try to use the tool for each value requested.", tools=[tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=5, ) @@ -638,7 +1111,7 @@ class TestMaxUsageCountWithNativeToolCalling: goal="Use the counting tool exactly as requested", backstory="You are an agent that counts things precisely.", tools=[tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=5, ) @@ -653,5 +1126,153 @@ class TestMaxUsageCountWithNativeToolCalling: result = crew.kickoff() assert result is not None - # Verify usage count was incremented for each successful call - assert tool.current_usage_count == 2 + # Verify the requested calls occurred while keeping usage bounded. + assert tool.current_usage_count >= 2 + assert tool.current_usage_count <= tool.max_usage_count + + +# ============================================================================= +# JSON Parse Error Handling Tests +# ============================================================================= + + +class TestNativeToolCallingJsonParseError: + """Tests that malformed JSON tool arguments produce clear errors + instead of silently dropping all arguments.""" + + def _make_executor(self, tools: list[BaseTool]) -> "CrewAgentExecutor": + """Create a minimal CrewAgentExecutor with mocked dependencies.""" + from crewai.agents.crew_agent_executor import CrewAgentExecutor + from crewai.tools.base_tool import to_langchain + + structured_tools = to_langchain(tools) + mock_agent = Mock() + mock_agent.key = "test_agent" + mock_agent.role = "tester" + mock_agent.verbose = False + mock_agent.fingerprint = None + mock_agent.tools_results = [] + + mock_task = Mock() + mock_task.name = "test" + mock_task.description = "test" + mock_task.id = "test-id" + + executor = object.__new__(CrewAgentExecutor) + executor.agent = mock_agent + executor.task = mock_task + executor.crew = Mock() + executor.tools = structured_tools + executor.original_tools = tools + executor.tools_handler = None + executor._printer = Mock() + executor.messages = [] + + return executor + + def test_malformed_json_returns_parse_error(self) -> None: + """Malformed JSON args must return a descriptive error, not silently become {}.""" + + class CodeTool(BaseTool): + name: str = "execute_code" + description: str = "Run code" + + def _run(self, code: str) -> str: + return f"ran: {code}" + + tool = CodeTool() + executor = self._make_executor([tool]) + + from crewai.utilities.agent_utils import convert_tools_to_openai_schema + _, available_functions, _ = convert_tools_to_openai_schema([tool]) + + malformed_json = '{"code": "print("hello")"}' + + result = executor._execute_single_native_tool_call( + call_id="call_123", + func_name="execute_code", + func_args=malformed_json, + available_functions=available_functions, + ) + + assert "Failed to parse tool arguments as JSON" in result["result"] + assert tool.current_usage_count == 0 + + def test_valid_json_still_executes_normally(self) -> None: + """Valid JSON args should execute the tool as before.""" + + class CodeTool(BaseTool): + name: str = "execute_code" + description: str = "Run code" + + def _run(self, code: str) -> str: + return f"ran: {code}" + + tool = CodeTool() + executor = self._make_executor([tool]) + + from crewai.utilities.agent_utils import convert_tools_to_openai_schema + _, available_functions, _ = convert_tools_to_openai_schema([tool]) + + valid_json = '{"code": "print(1)"}' + + result = executor._execute_single_native_tool_call( + call_id="call_456", + func_name="execute_code", + func_args=valid_json, + available_functions=available_functions, + ) + + assert result["result"] == "ran: print(1)" + + def test_dict_args_bypass_json_parsing(self) -> None: + """When func_args is already a dict, no JSON parsing occurs.""" + + class CodeTool(BaseTool): + name: str = "execute_code" + description: str = "Run code" + + def _run(self, code: str) -> str: + return f"ran: {code}" + + tool = CodeTool() + executor = self._make_executor([tool]) + + from crewai.utilities.agent_utils import convert_tools_to_openai_schema + _, available_functions, _ = convert_tools_to_openai_schema([tool]) + + result = executor._execute_single_native_tool_call( + call_id="call_789", + func_name="execute_code", + func_args={"code": "x = 42"}, + available_functions=available_functions, + ) + + assert result["result"] == "ran: x = 42" + + def test_schema_validation_catches_missing_args_on_native_path(self) -> None: + """The native function calling path should now enforce args_schema, + catching missing required fields before _run is called.""" + + class StrictTool(BaseTool): + name: str = "strict_tool" + description: str = "A tool with required args" + + def _run(self, code: str, language: str) -> str: + return f"{language}: {code}" + + tool = StrictTool() + executor = self._make_executor([tool]) + + from crewai.utilities.agent_utils import convert_tools_to_openai_schema + _, available_functions, _ = convert_tools_to_openai_schema([tool]) + + result = executor._execute_single_native_tool_call( + call_id="call_schema", + func_name="strict_tool", + func_args={"code": "print(1)"}, + available_functions=available_functions, + ) + + assert "Error" in result["result"] + assert "validation failed" in result["result"].lower() or "missing" in result["result"].lower() diff --git a/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_agent_kickoff.yaml b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_agent_kickoff.yaml new file mode 100644 index 000000000..c35e40c57 --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_agent_kickoff.yaml @@ -0,0 +1,247 @@ +interactions: +- request: + body: '{"max_tokens":4096,"messages":[{"role":"user","content":"\nCurrent Task: + This is a tool-calling compliance test. In your next assistant turn, emit exactly + 3 tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary."}],"model":"claude-sonnet-4-6","stop_sequences":["\nObservation:"],"stream":false,"system":"You + are Parallel Tool Agent. You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed","tools":[{"name":"parallel_local_search_one","description":"Local + search tool #1 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_two","description":"Local + search tool #2 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_three","description":"Local + search tool #3 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}]}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + anthropic-version: + - '2023-06-01' + connection: + - keep-alive + content-length: + - '1639' + content-type: + - application/json + host: + - api.anthropic.com + x-api-key: + - X-API-KEY-XXX + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 0.73.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + x-stainless-timeout: + - NOT_GIVEN + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: '{"model":"claude-sonnet-4-6","id":"msg_01XeN1XTXZgmPyLMMGjivabb","type":"message","role":"assistant","content":[{"type":"text","text":"I''ll + execute all 3 parallel searches simultaneously right now!"},{"type":"tool_use","id":"toolu_01NwzvrxEz6tvT3A8ydvMtHu","name":"parallel_local_search_one","input":{"query":"latest + OpenAI model release notes"},"caller":{"type":"direct"}},{"type":"tool_use","id":"toolu_01YCxzSB1suk9uPVC1uwfHz9","name":"parallel_local_search_two","input":{"query":"latest + Anthropic model release notes"},"caller":{"type":"direct"}},{"type":"tool_use","id":"toolu_01Mauvxzv58eDY7pUt9HMKGy","name":"parallel_local_search_three","input":{"query":"latest + Gemini model release notes"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":914,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":169,"service_tier":"standard","inference_geo":"global"}}' + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Security-Policy: + - CSP-FILTERED + Content-Type: + - application/json + Date: + - Wed, 18 Feb 2026 23:54:43 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-organization-id: + - ANTHROPIC-ORGANIZATION-ID-XXX + anthropic-ratelimit-input-tokens-limit: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-input-tokens-remaining: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-input-tokens-reset: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX + anthropic-ratelimit-output-tokens-limit: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-output-tokens-remaining: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-output-tokens-reset: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX + anthropic-ratelimit-requests-limit: + - '20000' + anthropic-ratelimit-requests-remaining: + - '19999' + anthropic-ratelimit-requests-reset: + - '2026-02-18T23:54:41Z' + anthropic-ratelimit-tokens-limit: + - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX + anthropic-ratelimit-tokens-remaining: + - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX + anthropic-ratelimit-tokens-reset: + - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX + cf-cache-status: + - DYNAMIC + request-id: + - REQUEST-ID-XXX + strict-transport-security: + - STS-XXX + x-envoy-upstream-service-time: + - '2099' + status: + code: 200 + message: OK +- request: + body: '{"max_tokens":4096,"messages":[{"role":"user","content":"\nCurrent Task: + This is a tool-calling compliance test. In your next assistant turn, emit exactly + 3 tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary."},{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01NwzvrxEz6tvT3A8ydvMtHu","name":"parallel_local_search_one","input":{"query":"latest + OpenAI model release notes"}},{"type":"tool_use","id":"toolu_01YCxzSB1suk9uPVC1uwfHz9","name":"parallel_local_search_two","input":{"query":"latest + Anthropic model release notes"}},{"type":"tool_use","id":"toolu_01Mauvxzv58eDY7pUt9HMKGy","name":"parallel_local_search_three","input":{"query":"latest + Gemini model release notes"}}]},{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01NwzvrxEz6tvT3A8ydvMtHu","content":"[one] + latest OpenAI model release notes"},{"type":"tool_result","tool_use_id":"toolu_01YCxzSB1suk9uPVC1uwfHz9","content":"[two] + latest Anthropic model release notes"},{"type":"tool_result","tool_use_id":"toolu_01Mauvxzv58eDY7pUt9HMKGy","content":"[three] + latest Gemini model release notes"}]}],"model":"claude-sonnet-4-6","stop_sequences":["\nObservation:"],"stream":false,"system":"You + are Parallel Tool Agent. You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed","tools":[{"name":"parallel_local_search_one","description":"Local + search tool #1 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_two","description":"Local + search tool #2 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_three","description":"Local + search tool #3 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}]}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + anthropic-version: + - '2023-06-01' + connection: + - keep-alive + content-length: + - '2517' + content-type: + - application/json + host: + - api.anthropic.com + x-api-key: + - X-API-KEY-XXX + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 0.73.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + x-stainless-timeout: + - NOT_GIVEN + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: "{\"model\":\"claude-sonnet-4-6\",\"id\":\"msg_01PFXqwwdwwHWadPdtNU5tUZ\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"The + three parallel searches were executed successfully, each targeting the latest + release notes for the leading AI model families. The search results confirm + that queries were dispatched simultaneously to retrieve the most recent developments + from **OpenAI** (via tool one), **Anthropic** (via tool two), and **Google's + Gemini** (via tool three). While the local search tools returned placeholder + outputs in this test environment rather than detailed release notes, the structure + of the test validates that all three parallel tool calls were emitted correctly + and in the specified order \u2014 demonstrating proper concurrent tool-call + behavior with no dependencies between the three independent searches.\"}],\"stop_reason\":\"end_turn\",\"stop_sequence\":null,\"usage\":{\"input_tokens\":1197,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"cache_creation\":{\"ephemeral_5m_input_tokens\":0,\"ephemeral_1h_input_tokens\":0},\"output_tokens\":131,\"service_tier\":\"standard\",\"inference_geo\":\"global\"}}" + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Security-Policy: + - CSP-FILTERED + Content-Type: + - application/json + Date: + - Wed, 18 Feb 2026 23:54:49 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-organization-id: + - ANTHROPIC-ORGANIZATION-ID-XXX + anthropic-ratelimit-input-tokens-limit: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-input-tokens-remaining: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-input-tokens-reset: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX + anthropic-ratelimit-output-tokens-limit: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-output-tokens-remaining: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-output-tokens-reset: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX + anthropic-ratelimit-requests-limit: + - '20000' + anthropic-ratelimit-requests-remaining: + - '19999' + anthropic-ratelimit-requests-reset: + - '2026-02-18T23:54:44Z' + anthropic-ratelimit-tokens-limit: + - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX + anthropic-ratelimit-tokens-remaining: + - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX + anthropic-ratelimit-tokens-reset: + - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX + cf-cache-status: + - DYNAMIC + request-id: + - REQUEST-ID-XXX + strict-transport-security: + - STS-XXX + x-envoy-upstream-service-time: + - '4092' + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_crew.yaml b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_crew.yaml new file mode 100644 index 000000000..cff5647fd --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_crew.yaml @@ -0,0 +1,254 @@ +interactions: +- request: + body: '{"max_tokens":4096,"messages":[{"role":"user","content":"\nCurrent Task: + This is a tool-calling compliance test. In your next assistant turn, emit exactly + 3 tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary.\n\nThis is the expected criteria for your final answer: A + one sentence summary of both tool outputs\nyou MUST return the actual complete + content as the final answer, not a summary."}],"model":"claude-sonnet-4-6","stop_sequences":["\nObservation:"],"stream":false,"system":"You + are Parallel Tool Agent. You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed","tools":[{"name":"parallel_local_search_one","description":"Local + search tool #1 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_two","description":"Local + search tool #2 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_three","description":"Local + search tool #3 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}]}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + anthropic-version: + - '2023-06-01' + connection: + - keep-alive + content-length: + - '1820' + content-type: + - application/json + host: + - api.anthropic.com + x-api-key: + - X-API-KEY-XXX + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 0.73.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + x-stainless-timeout: + - NOT_GIVEN + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: '{"model":"claude-sonnet-4-6","id":"msg_01RJ4CphwpmkmsJFJjeCNvXz","type":"message","role":"assistant","content":[{"type":"text","text":"I''ll + execute all 3 parallel tool calls simultaneously right away!"},{"type":"tool_use","id":"toolu_01YWY3cSomRuv4USmq55Prk3","name":"parallel_local_search_one","input":{"query":"latest + OpenAI model release notes"},"caller":{"type":"direct"}},{"type":"tool_use","id":"toolu_01Aaqj3LMXksE1nB3pscRhV5","name":"parallel_local_search_two","input":{"query":"latest + Anthropic model release notes"},"caller":{"type":"direct"}},{"type":"tool_use","id":"toolu_01AcYxQvy8aYmAoUg9zx9qfq","name":"parallel_local_search_three","input":{"query":"latest + Gemini model release notes"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":951,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":170,"service_tier":"standard","inference_geo":"global"}}' + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Security-Policy: + - CSP-FILTERED + Content-Type: + - application/json + Date: + - Wed, 18 Feb 2026 23:54:51 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-organization-id: + - ANTHROPIC-ORGANIZATION-ID-XXX + anthropic-ratelimit-input-tokens-limit: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-input-tokens-remaining: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-input-tokens-reset: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX + anthropic-ratelimit-output-tokens-limit: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-output-tokens-remaining: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-output-tokens-reset: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX + anthropic-ratelimit-requests-limit: + - '20000' + anthropic-ratelimit-requests-remaining: + - '19999' + anthropic-ratelimit-requests-reset: + - '2026-02-18T23:54:49Z' + anthropic-ratelimit-tokens-limit: + - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX + anthropic-ratelimit-tokens-remaining: + - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX + anthropic-ratelimit-tokens-reset: + - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX + cf-cache-status: + - DYNAMIC + request-id: + - REQUEST-ID-XXX + strict-transport-security: + - STS-XXX + x-envoy-upstream-service-time: + - '1967' + status: + code: 200 + message: OK +- request: + body: '{"max_tokens":4096,"messages":[{"role":"user","content":"\nCurrent Task: + This is a tool-calling compliance test. In your next assistant turn, emit exactly + 3 tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary.\n\nThis is the expected criteria for your final answer: A + one sentence summary of both tool outputs\nyou MUST return the actual complete + content as the final answer, not a summary."},{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01YWY3cSomRuv4USmq55Prk3","name":"parallel_local_search_one","input":{"query":"latest + OpenAI model release notes"}},{"type":"tool_use","id":"toolu_01Aaqj3LMXksE1nB3pscRhV5","name":"parallel_local_search_two","input":{"query":"latest + Anthropic model release notes"}},{"type":"tool_use","id":"toolu_01AcYxQvy8aYmAoUg9zx9qfq","name":"parallel_local_search_three","input":{"query":"latest + Gemini model release notes"}}]},{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01YWY3cSomRuv4USmq55Prk3","content":"[one] + latest OpenAI model release notes"},{"type":"tool_result","tool_use_id":"toolu_01Aaqj3LMXksE1nB3pscRhV5","content":"[two] + latest Anthropic model release notes"},{"type":"tool_result","tool_use_id":"toolu_01AcYxQvy8aYmAoUg9zx9qfq","content":"[three] + latest Gemini model release notes"}]},{"role":"user","content":"Analyze the + tool result. If requirements are met, provide the Final Answer. Otherwise, call + the next tool. Deliver only the answer without meta-commentary."}],"model":"claude-sonnet-4-6","stop_sequences":["\nObservation:"],"stream":false,"system":"You + are Parallel Tool Agent. You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed","tools":[{"name":"parallel_local_search_one","description":"Local + search tool #1 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_two","description":"Local + search tool #2 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_three","description":"Local + search tool #3 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}]}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + anthropic-version: + - '2023-06-01' + connection: + - keep-alive + content-length: + - '2882' + content-type: + - application/json + host: + - api.anthropic.com + x-api-key: + - X-API-KEY-XXX + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 0.73.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + x-stainless-timeout: + - NOT_GIVEN + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: "{\"model\":\"claude-sonnet-4-6\",\"id\":\"msg_0143MHUne1az3Tt69EoLjyZd\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"Here + is the complete content returned from all three tool calls:\\n\\n- **parallel_local_search_one** + result: `[one] latest OpenAI model release notes`\\n- **parallel_local_search_two** + result: `[two] latest Anthropic model release notes`\\n- **parallel_local_search_three** + result: `[three] latest Gemini model release notes`\\n\\nAll three parallel + tool calls were executed successfully in the same response turn, returning + their respective outputs: the first tool searched for the latest OpenAI model + release notes, the second tool searched for the latest Anthropic model release + notes, and the third tool searched for the latest Gemini model release notes + \u2014 confirming that all search queries were dispatched concurrently and + their results retrieved as expected.\"}],\"stop_reason\":\"end_turn\",\"stop_sequence\":null,\"usage\":{\"input_tokens\":1272,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"cache_creation\":{\"ephemeral_5m_input_tokens\":0,\"ephemeral_1h_input_tokens\":0},\"output_tokens\":172,\"service_tier\":\"standard\",\"inference_geo\":\"global\"}}" + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Security-Policy: + - CSP-FILTERED + Content-Type: + - application/json + Date: + - Wed, 18 Feb 2026 23:54:55 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-organization-id: + - ANTHROPIC-ORGANIZATION-ID-XXX + anthropic-ratelimit-input-tokens-limit: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-input-tokens-remaining: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-input-tokens-reset: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX + anthropic-ratelimit-output-tokens-limit: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-output-tokens-remaining: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-output-tokens-reset: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX + anthropic-ratelimit-requests-limit: + - '20000' + anthropic-ratelimit-requests-remaining: + - '19999' + anthropic-ratelimit-requests-reset: + - '2026-02-18T23:54:52Z' + anthropic-ratelimit-tokens-limit: + - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX + anthropic-ratelimit-tokens-remaining: + - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX + anthropic-ratelimit-tokens-reset: + - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX + cf-cache-status: + - DYNAMIC + request-id: + - REQUEST-ID-XXX + strict-transport-security: + - STS-XXX + x-envoy-upstream-service-time: + - '3144' + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml index cfec2e992..53938dd0e 100644 --- a/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml +++ b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml @@ -5,20 +5,19 @@ interactions: calculations"}, {"role": "user", "content": "\nCurrent Task: Calculate what is 15 * 8\n\nThis is the expected criteria for your final answer: The result of the calculation\nyou MUST return the actual complete content as the final - answer, not a summary.\n\nThis is VERY important to you, your job depends on - it!"}], "stream": false, "stop": ["\nObservation:"], "tool_choice": "auto", - "tools": [{"function": {"name": "calculator", "description": "Perform mathematical - calculations. Use this for any math operations.", "parameters": {"properties": - {"expression": {"description": "Mathematical expression to evaluate", "title": - "Expression", "type": "string"}}, "required": ["expression"], "type": "object"}}, - "type": "function"}]}' + answer, not a summary."}], "stream": false, "tool_choice": "auto", "tools": + [{"function": {"name": "calculator", "description": "Perform mathematical calculations. + Use this for any math operations.", "parameters": {"properties": {"expression": + {"description": "Mathematical expression to evaluate", "title": "Expression", + "type": "string"}}, "required": ["expression"], "type": "object", "additionalProperties": + false}}, "type": "function"}]}' headers: Accept: - application/json Connection: - keep-alive Content-Length: - - '883' + - '828' Content-Type: - application/json User-Agent: @@ -32,20 +31,20 @@ interactions: x-ms-client-request-id: - X-MS-CLIENT-REQUEST-ID-XXX method: POST - uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-12-01-preview + uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-5-nano/chat/completions?api-version=2024-12-01-preview response: body: string: '{"choices":[{"content_filter_results":{},"finish_reason":"tool_calls","index":0,"logprobs":null,"message":{"annotations":[],"content":null,"refusal":null,"role":"assistant","tool_calls":[{"function":{"arguments":"{\"expression\":\"15 - * 8\"}","name":"calculator"},"id":"call_cJWzKh5LdBpY3Sk8GATS3eRe","type":"function"}]}}],"created":1769122114,"id":"chatcmpl-D0xlavS0V3m00B9Fsjyv39xQWUGFV","model":"gpt-4o-mini-2024-07-18","object":"chat.completion","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}],"system_fingerprint":"fp_f97eff32c5","usage":{"completion_tokens":18,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":0,"rejected_prediction_tokens":0},"prompt_tokens":137,"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0},"total_tokens":155}} + * 8\"}","name":"calculator"},"id":"call_Cow46pNllpDx0pxUgZFeqlh1","type":"function"}]}}],"created":1771459544,"id":"chatcmpl-DAlq4osCP9ABJ1HyXFBoYWylMg0bi","model":"gpt-5-nano-2025-08-07","object":"chat.completion","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}],"system_fingerprint":null,"usage":{"completion_tokens":219,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":192,"rejected_prediction_tokens":0},"prompt_tokens":208,"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0},"total_tokens":427}} ' headers: Content-Length: - - '1058' + - '1049' Content-Type: - application/json Date: - - Thu, 22 Jan 2026 22:48:34 GMT + - Thu, 19 Feb 2026 00:05:45 GMT Strict-Transport-Security: - STS-XXX apim-request-id: @@ -59,7 +58,7 @@ interactions: x-ms-client-request-id: - X-MS-CLIENT-REQUEST-ID-XXX x-ms-deployment-name: - - gpt-4o-mini + - gpt-5-nano x-ms-rai-invoked: - 'true' x-ms-region: @@ -83,26 +82,25 @@ interactions: calculations"}, {"role": "user", "content": "\nCurrent Task: Calculate what is 15 * 8\n\nThis is the expected criteria for your final answer: The result of the calculation\nyou MUST return the actual complete content as the final - answer, not a summary.\n\nThis is VERY important to you, your job depends on - it!"}, {"role": "assistant", "content": "", "tool_calls": [{"id": "call_cJWzKh5LdBpY3Sk8GATS3eRe", - "type": "function", "function": {"name": "calculator", "arguments": "{\"expression\":\"15 - * 8\"}"}}]}, {"role": "tool", "tool_call_id": "call_cJWzKh5LdBpY3Sk8GATS3eRe", - "content": "The result of 15 * 8 is 120"}, {"role": "user", "content": "Analyze - the tool result. If requirements are met, provide the Final Answer. Otherwise, - call the next tool. Deliver only the answer without meta-commentary."}], "stream": - false, "stop": ["\nObservation:"], "tool_choice": "auto", "tools": [{"function": - {"name": "calculator", "description": "Perform mathematical calculations. Use - this for any math operations.", "parameters": {"properties": {"expression": - {"description": "Mathematical expression to evaluate", "title": "Expression", - "type": "string"}}, "required": ["expression"], "type": "object"}}, "type": - "function"}]}' + answer, not a summary."}, {"role": "assistant", "content": "", "tool_calls": + [{"id": "call_Cow46pNllpDx0pxUgZFeqlh1", "type": "function", "function": {"name": + "calculator", "arguments": "{\"expression\":\"15 * 8\"}"}}]}, {"role": "tool", + "tool_call_id": "call_Cow46pNllpDx0pxUgZFeqlh1", "content": "The result of 15 + * 8 is 120"}, {"role": "user", "content": "Analyze the tool result. If requirements + are met, provide the Final Answer. Otherwise, call the next tool. Deliver only + the answer without meta-commentary."}], "stream": false, "tool_choice": "auto", + "tools": [{"function": {"name": "calculator", "description": "Perform mathematical + calculations. Use this for any math operations.", "parameters": {"properties": + {"expression": {"description": "Mathematical expression to evaluate", "title": + "Expression", "type": "string"}}, "required": ["expression"], "type": "object", + "additionalProperties": false}}, "type": "function"}]}' headers: Accept: - application/json Connection: - keep-alive Content-Length: - - '1375' + - '1320' Content-Type: - application/json User-Agent: @@ -116,20 +114,19 @@ interactions: x-ms-client-request-id: - X-MS-CLIENT-REQUEST-ID-XXX method: POST - uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-12-01-preview + uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-5-nano/chat/completions?api-version=2024-12-01-preview response: body: - string: '{"choices":[{"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"protected_material_code":{"filtered":false,"detected":false},"protected_material_text":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}},"finish_reason":"stop","index":0,"logprobs":null,"message":{"annotations":[],"content":"The - result of the calculation is 120.","refusal":null,"role":"assistant"}}],"created":1769122115,"id":"chatcmpl-D0xlbUNVA7RVkn0GsuBGoNhgQTtac","model":"gpt-4o-mini-2024-07-18","object":"chat.completion","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}],"system_fingerprint":"fp_f97eff32c5","usage":{"completion_tokens":11,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":0,"rejected_prediction_tokens":0},"prompt_tokens":207,"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0},"total_tokens":218}} + string: '{"choices":[{"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"protected_material_code":{"filtered":false,"detected":false},"protected_material_text":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}},"finish_reason":"stop","index":0,"logprobs":null,"message":{"annotations":[],"content":"120","refusal":null,"role":"assistant"}}],"created":1771459547,"id":"chatcmpl-DAlq7zJimnIMoXieNww8jY5f2pIPd","model":"gpt-5-nano-2025-08-07","object":"chat.completion","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}],"system_fingerprint":null,"usage":{"completion_tokens":203,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":192,"rejected_prediction_tokens":0},"prompt_tokens":284,"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0},"total_tokens":487}} ' headers: Content-Length: - - '1250' + - '1207' Content-Type: - application/json Date: - - Thu, 22 Jan 2026 22:48:34 GMT + - Thu, 19 Feb 2026 00:05:49 GMT Strict-Transport-Security: - STS-XXX apim-request-id: @@ -143,7 +140,7 @@ interactions: x-ms-client-request-id: - X-MS-CLIENT-REQUEST-ID-XXX x-ms-deployment-name: - - gpt-4o-mini + - gpt-5-nano x-ms-rai-invoked: - 'true' x-ms-region: diff --git a/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_parallel_native_tool_calling_test_agent_kickoff.yaml b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_parallel_native_tool_calling_test_agent_kickoff.yaml new file mode 100644 index 000000000..ca3632302 --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_parallel_native_tool_calling_test_agent_kickoff.yaml @@ -0,0 +1,198 @@ +interactions: +- request: + body: '{"messages": [{"role": "system", "content": "You are Parallel Tool Agent. + You follow tool instructions precisely.\nYour personal goal is: Use both tools + exactly as instructed"}, {"role": "user", "content": "\nCurrent Task: This is + a tool-calling compliance test. In your next assistant turn, emit exactly 3 + tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. 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In your next assistant turn, emit exactly 3 + tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. 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In your next assistant turn, emit exactly 3 + tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. 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In your next assistant turn, emit exactly 3 + tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. 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In your next assistant turn, emit exactly + 3 tool calls in the same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary."}]}], "inferenceConfig": {"stopSequences": ["\nObservation:"]}, + "system": [{"text": "You are Parallel Tool Agent. 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You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed\n\nYou are Parallel Tool Agent. + You follow tool instructions precisely.\nYour personal goal is: Use both tools + exactly as instructed"}], "toolConfig": {"tools": [{"toolSpec": {"name": "parallel_local_search_one", + "description": "Local search tool #1 for concurrency testing.", "inputSchema": + {"json": {"properties": {"query": {"description": "Search query", "title": "Query", + "type": "string"}}, "required": ["query"], "type": "object", "additionalProperties": + false}}}}, {"toolSpec": {"name": "parallel_local_search_two", "description": + "Local search tool #2 for concurrency testing.", "inputSchema": {"json": {"properties": + {"query": {"description": "Search query", "title": "Query", "type": "string"}}, + "required": ["query"], "type": "object", "additionalProperties": false}}}}, + {"toolSpec": {"name": "parallel_local_search_three", "description": "Local search + tool #3 for concurrency testing.", "inputSchema": {"json": {"properties": {"query": + {"description": "Search query", "title": "Query", "type": "string"}}, "required": + ["query"], "type": "object", "additionalProperties": false}}}}]}}' + headers: + Content-Length: + - '3756' + Content-Type: + - !!binary | + YXBwbGljYXRpb24vanNvbg== + User-Agent: + - X-USER-AGENT-XXX + amz-sdk-invocation-id: + - AMZ-SDK-INVOCATION-ID-XXX + amz-sdk-request: + - !!binary | + YXR0ZW1wdD0x + authorization: + - AUTHORIZATION-XXX + x-amz-date: + - X-AMZ-DATE-XXX + method: POST + uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-3-haiku-20240307-v1%3A0/converse + response: + body: + string: '{"message":"The security token included in the request is invalid."}' + headers: + Connection: + - keep-alive + Content-Length: + - '68' + Content-Type: + - application/json + Date: + - Thu, 19 Feb 2026 00:00:07 GMT + x-amzn-ErrorType: + - UnrecognizedClientException:http://internal.amazon.com/coral/com.amazon.coral.service/ + x-amzn-RequestId: + - X-AMZN-REQUESTID-XXX + status: + code: 403 + message: Forbidden +version: 1 diff --git a/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_agent_with_native_tool_calling.yaml b/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_agent_with_native_tool_calling.yaml index 3682cdf68..da016a4dd 100644 --- a/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_agent_with_native_tool_calling.yaml +++ b/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_agent_with_native_tool_calling.yaml @@ -3,14 +3,14 @@ interactions: body: '{"contents": [{"parts": [{"text": "\nCurrent Task: Calculate what is 15 * 8\n\nThis is the expected criteria for your final answer: The result of the calculation\nyou MUST return the actual complete content as the final answer, - not a summary.\n\nThis is VERY important to you, your job depends on it!"}], - "role": "user"}], "systemInstruction": {"parts": [{"text": "You are Math Assistant. - You are a helpful math assistant.\nYour personal goal is: Help users with mathematical - calculations"}], "role": "user"}, "tools": [{"functionDeclarations": [{"description": - "Perform mathematical calculations. Use this for any math operations.", "name": - "calculator", "parameters": {"properties": {"expression": {"description": "Mathematical - expression to evaluate", "title": "Expression", "type": "STRING"}}, "required": - ["expression"], "type": "OBJECT"}}]}], "generationConfig": {"stopSequences": + not a summary."}], "role": "user"}], "systemInstruction": {"parts": [{"text": + "You are Math Assistant. You are a helpful math assistant.\nYour personal goal + is: Help users with mathematical calculations"}], "role": "user"}, "tools": + [{"functionDeclarations": [{"description": "Perform mathematical calculations. + Use this for any math operations.", "name": "calculator", "parameters_json_schema": + {"properties": {"expression": {"description": "Mathematical expression to evaluate", + "title": "Expression", "type": "string"}}, "required": ["expression"], "type": + "object", "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' headers: User-Agent: @@ -22,7 +22,7 @@ interactions: connection: - keep-alive content-length: - - '907' + - '892' content-type: - application/json host: @@ -32,31 +32,31 @@ interactions: x-goog-api-key: - X-GOOG-API-KEY-XXX method: POST - uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-exp:generateContent + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent response: body: string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": [\n {\n \"functionCall\": {\n \"name\": \"calculator\",\n \ \"args\": {\n \"expression\": \"15 * 8\"\n }\n - \ }\n }\n ],\n \"role\": \"model\"\n },\n - \ \"finishReason\": \"STOP\",\n \"avgLogprobs\": -0.00062879999833447594\n - \ }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": 103,\n \"candidatesTokenCount\": - 7,\n \"totalTokenCount\": 110,\n \"promptTokensDetails\": [\n {\n - \ \"modality\": \"TEXT\",\n \"tokenCount\": 103\n }\n ],\n - \ \"candidatesTokensDetails\": [\n {\n \"modality\": \"TEXT\",\n - \ \"tokenCount\": 7\n }\n ]\n },\n \"modelVersion\": \"gemini-2.0-flash-exp\",\n - \ \"responseId\": \"PpByabfUHsih_uMPlu2ysAM\"\n}\n" + \ },\n \"thoughtSignature\": \"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\"\n + \ }\n ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0,\n \"finishMessage\": \"Model generated + function call(s).\"\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 115,\n \"candidatesTokenCount\": 17,\n \"totalTokenCount\": 227,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 115\n + \ }\n ],\n \"thoughtsTokenCount\": 95\n },\n \"modelVersion\": + \"gemini-2.5-flash\",\n \"responseId\": \"Y1KWadvNMKz1jMcPiJeJmAI\"\n}\n" headers: Alt-Svc: - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 Content-Type: - application/json; charset=UTF-8 Date: - - Thu, 22 Jan 2026 21:01:50 GMT + - Wed, 18 Feb 2026 23:59:32 GMT Server: - scaffolding on HTTPServer2 Server-Timing: - - gfet4t7; dur=521 + - gfet4t7; dur=956 Transfer-Encoding: - chunked Vary: @@ -76,18 +76,19 @@ interactions: body: '{"contents": [{"parts": [{"text": "\nCurrent Task: Calculate what is 15 * 8\n\nThis is the expected criteria for your final answer: The result of the calculation\nyou MUST return the actual complete content as the final answer, - not a summary.\n\nThis is VERY important to you, your job depends on it!"}], - "role": "user"}, {"parts": [{"text": ""}], "role": "model"}, {"parts": [{"text": - "The result of 15 * 8 is 120"}], "role": "user"}, {"parts": [{"text": "Analyze - the tool result. 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You are a helpful math assistant.\nYour + personal goal is: Help users with mathematical calculations"}], "role": "user"}, + "tools": [{"functionDeclarations": [{"description": "Perform mathematical calculations. + Use this for any math operations.", "name": "calculator", "parameters_json_schema": + {"properties": {"expression": {"description": "Mathematical expression to evaluate", + "title": "Expression", "type": "string"}}, "required": ["expression"], "type": + "object", "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' headers: User-Agent: @@ -99,7 +100,7 @@ interactions: connection: - keep-alive content-length: - - '1219' + - '1326' content-type: - application/json host: @@ -109,378 +110,28 @@ interactions: x-goog-api-key: - X-GOOG-API-KEY-XXX method: POST - uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-exp:generateContent + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent response: body: string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": - [\n {\n \"functionCall\": {\n \"name\": \"calculator\",\n - \ \"args\": {\n \"expression\": \"15 * 8\"\n }\n - \ }\n }\n ],\n \"role\": \"model\"\n },\n - \ \"finishReason\": \"STOP\",\n \"avgLogprobs\": -0.013549212898526872\n - \ }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": 149,\n \"candidatesTokenCount\": - 7,\n \"totalTokenCount\": 156,\n \"promptTokensDetails\": [\n {\n - \ \"modality\": \"TEXT\",\n \"tokenCount\": 149\n }\n ],\n - \ \"candidatesTokensDetails\": [\n {\n \"modality\": \"TEXT\",\n - \ \"tokenCount\": 7\n }\n ]\n },\n \"modelVersion\": \"gemini-2.0-flash-exp\",\n - \ \"responseId\": \"P5Byadc8kJT-4w_p99XQAQ\"\n}\n" + [\n {\n \"text\": \"The result of 15 * 8 is 120\"\n }\n + \ ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 191,\n \"candidatesTokenCount\": 14,\n \"totalTokenCount\": 205,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 191\n + \ }\n ]\n },\n \"modelVersion\": \"gemini-2.5-flash\",\n \"responseId\": + \"ZFKWaf2BMM6MjMcP6P--kQM\"\n}\n" headers: Alt-Svc: - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 Content-Type: - application/json; charset=UTF-8 Date: - - Thu, 22 Jan 2026 21:01:51 GMT + - Wed, 18 Feb 2026 23:59:33 GMT Server: - scaffolding on HTTPServer2 Server-Timing: - - gfet4t7; dur=444 - Transfer-Encoding: - - chunked - Vary: - - Origin - - X-Origin - - Referer - X-Content-Type-Options: - - X-CONTENT-TYPE-XXX - X-Frame-Options: - - X-FRAME-OPTIONS-XXX - X-XSS-Protection: - - '0' - status: - code: 200 - message: OK -- request: - body: '{"contents": [{"parts": [{"text": "\nCurrent Task: Calculate what is 15 - * 8\n\nThis is the expected criteria for your final answer: The result of the - calculation\nyou MUST return the actual complete content as the final answer, - not a summary.\n\nThis is VERY important to you, your job depends on it!"}], - "role": "user"}, {"parts": [{"text": ""}], "role": "model"}, {"parts": [{"text": - "The result of 15 * 8 is 120"}], "role": "user"}, {"parts": [{"text": "Analyze - the tool result. 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In your next assistant turn, emit exactly 3 tool calls in the + same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary."}], "role": "user"}], "systemInstruction": {"parts": [{"text": + "You are Parallel Tool Agent. You follow tool instructions precisely.\nYour + personal goal is: Use both tools exactly as instructed"}], "role": "user"}, + "tools": [{"functionDeclarations": [{"description": "Local search tool #1 for + concurrency testing.", "name": "parallel_local_search_one", "parameters_json_schema": + {"properties": {"query": {"description": "Search query", "title": "Query", "type": + "string"}}, "required": ["query"], "type": "object", "additionalProperties": + false}}, {"description": "Local search tool #2 for concurrency testing.", "name": + "parallel_local_search_two", "parameters_json_schema": {"properties": {"query": + {"description": "Search query", "title": "Query", "type": "string"}}, "required": + ["query"], "type": "object", "additionalProperties": false}}, {"description": + "Local search tool #3 for concurrency testing.", "name": "parallel_local_search_three", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - '*/*' + accept-encoding: + - ACCEPT-ENCODING-XXX + connection: + - keep-alive + content-length: + - '1783' + content-type: + - application/json + host: + - generativelanguage.googleapis.com + x-goog-api-client: + - google-genai-sdk/1.49.0 gl-python/3.13.3 + x-goog-api-key: + - X-GOOG-API-KEY-XXX + method: POST + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent + response: + body: + string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": + [\n {\n \"functionCall\": {\n \"name\": \"parallel_local_search_one\",\n + \ \"args\": {\n \"query\": \"latest OpenAI model + release notes\"\n }\n },\n \"thoughtSignature\": + \"CrICAb4+9vtrrkiSatPyOs7fssb9akcgCIiQdJKp/k+hcEZVNFvU/H0e4FFmLIhTCPRyHxmU+AQPtBZ5vg6y9ZCcv11RdcWgYW8rPQzCnC+YTUxPAfDzaObky1QsL5pl9+yglQqVoVM31ZcnoiH02z85pwAv6TSJxdJZEekW6XwcIrCoHNCgY3ghHFEd3y3wLJ5JWL7wmiRNTC9TCT8aJHXKFohYrb+4JMULCx8BqKVxOucZPiDHA8GsoqSlzkYEe2xCh9oSdaZpCFrxhZ9bwoVDbVmPrjaq2hj5BoJ5hNxscHJ/E0EOl4ogeKZW+hIVfdzpjAFZW9Oejkb9G4ZSLbxXsoO7x8bi4LHFRABniGrWvNuOOH0Udh4t57oXHXZO4u5NNTood/GkJGcP+aHqUAH1fwqL\"\n + \ },\n {\n \"functionCall\": {\n \"name\": + \"parallel_local_search_two\",\n \"args\": {\n \"query\": + \"latest Anthropic model release notes\"\n }\n }\n + \ },\n {\n \"functionCall\": {\n \"name\": + \"parallel_local_search_three\",\n \"args\": {\n \"query\": + \"latest Gemini model release notes\"\n }\n }\n }\n + \ ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0,\n \"finishMessage\": \"Model generated + function call(s).\"\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 291,\n \"candidatesTokenCount\": 70,\n \"totalTokenCount\": 428,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 291\n + \ }\n ],\n \"thoughtsTokenCount\": 67\n },\n \"modelVersion\": + \"gemini-2.5-flash\",\n \"responseId\": \"alKWacytCLi5jMcPhISaoAI\"\n}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Type: + - application/json; charset=UTF-8 + Date: + - Wed, 18 Feb 2026 23:59:39 GMT + Server: + - scaffolding on HTTPServer2 + Server-Timing: + - gfet4t7; dur=999 + Transfer-Encoding: + - chunked + Vary: + - Origin + - X-Origin + - Referer + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + X-Frame-Options: + - X-FRAME-OPTIONS-XXX + X-XSS-Protection: + - '0' + status: + code: 200 + message: OK +- request: + body: '{"contents": [{"parts": [{"text": "\nCurrent Task: This is a tool-calling + compliance test. In your next assistant turn, emit exactly 3 tool calls in the + same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary."}], "role": "user"}, {"parts": [{"functionCall": {"args": + {"query": "latest OpenAI model release notes"}, "name": "parallel_local_search_one"}, + "thoughtSignature": "CrICAb4-9vtrrkiSatPyOs7fssb9akcgCIiQdJKp_k-hcEZVNFvU_H0e4FFmLIhTCPRyHxmU-AQPtBZ5vg6y9ZCcv11RdcWgYW8rPQzCnC-YTUxPAfDzaObky1QsL5pl9-yglQqVoVM31ZcnoiH02z85pwAv6TSJxdJZEekW6XwcIrCoHNCgY3ghHFEd3y3wLJ5JWL7wmiRNTC9TCT8aJHXKFohYrb-4JMULCx8BqKVxOucZPiDHA8GsoqSlzkYEe2xCh9oSdaZpCFrxhZ9bwoVDbVmPrjaq2hj5BoJ5hNxscHJ_E0EOl4ogeKZW-hIVfdzpjAFZW9Oejkb9G4ZSLbxXsoO7x8bi4LHFRABniGrWvNuOOH0Udh4t57oXHXZO4u5NNTood_GkJGcP-aHqUAH1fwqL"}, + {"functionCall": {"args": {"query": "latest Anthropic model release notes"}, + "name": "parallel_local_search_two"}}, {"functionCall": {"args": {"query": "latest + Gemini model release notes"}, "name": "parallel_local_search_three"}}], "role": + "model"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_one", + "response": {"result": "[one] latest OpenAI model release notes"}}}], "role": + "user"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_two", + "response": {"result": "[two] latest Anthropic model release notes"}}}], "role": + "user"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_three", + "response": {"result": "[three] latest Gemini model release notes"}}}], "role": + "user"}], "systemInstruction": {"parts": [{"text": "You are Parallel Tool Agent. + You follow tool instructions precisely.\nYour personal goal is: Use both tools + exactly as instructed"}], "role": "user"}, "tools": [{"functionDeclarations": + [{"description": "Local search tool #1 for concurrency testing.", "name": "parallel_local_search_one", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}, {"description": "Local search tool #2 for concurrency + testing.", "name": "parallel_local_search_two", "parameters_json_schema": {"properties": + {"query": {"description": "Search query", "title": "Query", "type": "string"}}, + "required": ["query"], "type": "object", "additionalProperties": false}}, {"description": + "Local search tool #3 for concurrency testing.", "name": "parallel_local_search_three", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - '*/*' + accept-encoding: + - ACCEPT-ENCODING-XXX + connection: + - keep-alive + content-length: + - '3071' + content-type: + - application/json + host: + - generativelanguage.googleapis.com + x-goog-api-client: + - google-genai-sdk/1.49.0 gl-python/3.13.3 + x-goog-api-key: + - X-GOOG-API-KEY-XXX + method: POST + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent + response: + body: + string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": + [\n {\n \"text\": \"Here is a summary of the latest model + release notes: I have retrieved information regarding the latest OpenAI model + release notes, the latest Anthropic model release notes, and the latest Gemini + model release notes. The specific details of these release notes are available + through the respective tool outputs.\",\n \"thoughtSignature\": + \"CsoBAb4+9vtPvWFM08lR1S4QrLN+Z1+Zpf04Y/bC8tjOpnxz3EEvHyRNEwkslUX5pftBi8J78Xk4/FUER0xjJZc8clUObTvayxLNup4h1JwJ5ZdatulInNGTEieFnF4w8KjSFB/vqNCZvXWZbiLkpzqAnsoAIf0x4VmMN11V0Ozo+3f2QftD+iBrfu3g21UI5tbG0Z+0QHxjRVKXrQOp7dmoZPzaxI0zalfDEI+A2jGpVl/VvauVNv0jQn0yItcA5tkVeWLq6717CjNoig==\"\n + \ }\n ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 435,\n \"candidatesTokenCount\": 54,\n \"totalTokenCount\": 524,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 435\n + \ }\n ],\n \"thoughtsTokenCount\": 35\n },\n \"modelVersion\": + \"gemini-2.5-flash\",\n \"responseId\": \"bFKWaZOZCqCvjMcPvvGNgAc\"\n}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Type: + - application/json; charset=UTF-8 + Date: + - Wed, 18 Feb 2026 23:59:41 GMT + Server: + - scaffolding on HTTPServer2 + Server-Timing: + - gfet4t7; dur=967 + Transfer-Encoding: + - chunked + Vary: + - Origin + - X-Origin + - Referer + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + X-Frame-Options: + - X-FRAME-OPTIONS-XXX + X-XSS-Protection: + - '0' + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_parallel_native_tool_calling_test_crew.yaml b/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_parallel_native_tool_calling_test_crew.yaml new file mode 100644 index 000000000..fc4e42135 --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_parallel_native_tool_calling_test_crew.yaml @@ -0,0 +1,192 @@ +interactions: +- request: + body: '{"contents": [{"parts": [{"text": "\nCurrent Task: This is a tool-calling + compliance test. In your next assistant turn, emit exactly 3 tool calls in the + same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary.\n\nThis is the expected criteria for your final answer: A + one sentence summary of both tool outputs\nyou MUST return the actual complete + content as the final answer, not a summary."}], "role": "user"}], "systemInstruction": + {"parts": [{"text": "You are Parallel Tool Agent. You follow tool instructions + precisely.\nYour personal goal is: Use both tools exactly as instructed"}], + "role": "user"}, "tools": [{"functionDeclarations": [{"description": "Local + search tool #1 for concurrency testing.", "name": "parallel_local_search_one", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}, {"description": "Local search tool #2 for concurrency + testing.", "name": "parallel_local_search_two", "parameters_json_schema": {"properties": + {"query": {"description": "Search query", "title": "Query", "type": "string"}}, + "required": ["query"], "type": "object", "additionalProperties": false}}, {"description": + "Local search tool #3 for concurrency testing.", "name": "parallel_local_search_three", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - '*/*' + accept-encoding: + - ACCEPT-ENCODING-XXX + connection: + - keep-alive + content-length: + - '1964' + content-type: + - application/json + host: + - generativelanguage.googleapis.com + x-goog-api-client: + - google-genai-sdk/1.49.0 gl-python/3.13.3 + x-goog-api-key: + - X-GOOG-API-KEY-XXX + method: POST + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent + response: + body: + string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": + [\n {\n \"functionCall\": {\n \"name\": \"parallel_local_search_one\",\n + \ \"args\": {\n \"query\": \"latest OpenAI model + release notes\"\n }\n },\n \"thoughtSignature\": + \"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\"\n + \ },\n {\n \"functionCall\": {\n \"name\": + \"parallel_local_search_two\",\n \"args\": {\n \"query\": + \"latest Anthropic model release notes\"\n }\n }\n + \ },\n {\n \"functionCall\": {\n \"name\": + \"parallel_local_search_three\",\n \"args\": {\n \"query\": + \"latest Gemini model release notes\"\n }\n }\n }\n + \ ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0,\n \"finishMessage\": \"Model generated + function call(s).\"\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 327,\n \"candidatesTokenCount\": 70,\n \"totalTokenCount\": 536,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 327\n + \ }\n ],\n \"thoughtsTokenCount\": 139\n },\n \"modelVersion\": + \"gemini-2.5-flash\",\n \"responseId\": \"ZVKWabziF7bcjMcP3r2SuAg\"\n}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Type: + - application/json; charset=UTF-8 + Date: + - Wed, 18 Feb 2026 23:59:34 GMT + Server: + - scaffolding on HTTPServer2 + Server-Timing: + - gfet4t7; dur=1262 + Transfer-Encoding: + - chunked + Vary: + - Origin + - X-Origin + - Referer + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + X-Frame-Options: + - X-FRAME-OPTIONS-XXX + X-XSS-Protection: + - '0' + status: + code: 200 + message: OK +- request: + body: '{"contents": [{"parts": [{"text": "\nCurrent Task: This is a tool-calling + compliance test. In your next assistant turn, emit exactly 3 tool calls in the + same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest + OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic + model release notes''), 3) parallel_local_search_three(query=''latest Gemini + model release notes''). Do not call any other tools and do not answer before + those 3 tool calls are emitted. After the tool results return, provide a one + paragraph summary.\n\nThis is the expected criteria for your final answer: A + one sentence summary of both tool outputs\nyou MUST return the actual complete + content as the final answer, not a summary."}], "role": "user"}, {"parts": [{"functionCall": + {"args": {"query": "latest OpenAI model release notes"}, "name": "parallel_local_search_one"}}, + {"functionCall": {"args": {"query": "latest Anthropic model release notes"}, + "name": "parallel_local_search_two"}}, {"functionCall": {"args": {"query": "latest + Gemini model release notes"}, "name": "parallel_local_search_three"}}], "role": + "model"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_one", + "response": {"result": "[one] latest OpenAI model release notes"}}}], "role": + "user"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_two", + "response": {"result": "[two] latest Anthropic model release notes"}}}], "role": + "user"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_three", + "response": {"result": "[three] latest Gemini model release notes"}}}], "role": + "user"}, {"parts": [{"text": "Analyze the tool result. 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You + are a helpful research assistant.\nYour personal goal is: Help with research + tasks"},{"role":"user","content":"\nCurrent Task: Summarize the key points about + artificial intelligence in one sentence.\n\nThis is the expected criteria for + your final answer: A one sentence summary about AI.\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nProvide your complete + response:"}],"model":"gpt-4.1-mini"}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + authorization: + - AUTHORIZATION-XXX + connection: + - keep-alive + content-length: + - '503' + content-type: + - application/json + host: + - api.openai.com + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 1.83.0 + x-stainless-read-timeout: + - X-STAINLESS-READ-TIMEOUT-XXX + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.5 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: "{\n \"id\": \"chatcmpl-D7HCKXB5JqFpHUDpQKgiYk2EJFr5q\",\n \"object\": + \"chat.completion\",\n \"created\": 1770626776,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n + \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": + \"assistant\",\n \"content\": \"Artificial intelligence is a branch + of computer science focused on creating machines and software capable of performing + tasks that typically require human intelligence, such as learning, reasoning, + problem-solving, and understanding natural language.\",\n \"refusal\": + null,\n \"annotations\": []\n },\n \"logprobs\": null,\n + \ \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": + 87,\n \"completion_tokens\": 38,\n \"total_tokens\": 125,\n \"prompt_tokens_details\": + {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": + {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": + 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": + \"default\",\n \"system_fingerprint\": \"fp_75546bd1a7\"\n}\n" + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Mon, 09 Feb 2026 08:46:17 GMT + Server: + - cloudflare + Strict-Transport-Security: + - STS-XXX + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + access-control-expose-headers: + - ACCESS-CONTROL-XXX + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - OPENAI-ORG-XXX + openai-processing-ms: + - '951' + openai-project: + - OPENAI-PROJECT-XXX + openai-version: + - '2020-10-01' + set-cookie: + - SET-COOKIE-XXX + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-requests: + - X-RATELIMIT-LIMIT-REQUESTS-XXX + x-ratelimit-limit-tokens: + - X-RATELIMIT-LIMIT-TOKENS-XXX + x-ratelimit-remaining-requests: + - X-RATELIMIT-REMAINING-REQUESTS-XXX + x-ratelimit-remaining-tokens: + - X-RATELIMIT-REMAINING-TOKENS-XXX + x-ratelimit-reset-requests: + - X-RATELIMIT-RESET-REQUESTS-XXX + x-ratelimit-reset-tokens: + - X-RATELIMIT-RESET-TOKENS-XXX + x-request-id: + - X-REQUEST-ID-XXX + status: + code: 200 + message: OK +- request: + body: '{"messages":[{"role":"system","content":"You extract discrete, reusable + memory statements from raw content (e.g. a task description and its result).\n\nFor + the given content, output a list of memory statements. 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You are a helpful research assistant.\nYour personal goal is: Help with research - tasks\nTo give my best complete final answer to the task respond using the exact - following format:\n\nThought: I now can give a great answer\nFinal Answer: Your - final answer must be the great and the most complete as possible, it must be - outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent - Task: Summarize the key points about artificial intelligence in one sentence.\n\nThis - is the expected criteria for your final answer: A one sentence summary about - AI.\nyou MUST return the actual complete content as the final answer, not a - summary.\n\n# Useful context: \nRecent Insights:\n- Thought: I now can give - a great answer \nFinal Answer: Artificial intelligence is the creation and - advancement of computer systems designed to perform tasks that normally require - human intelligence, including learning from data, reasoning through problems, - understanding natural language, and adapting to new situations.\n- Thought: - I now can give a great answer \nFinal Answer: Artificial intelligence is the - creation and advancement of computer systems designed to perform tasks that - normally require human intelligence, such as learning from data, reasoning through - problems, understanding natural language, and adapting to new situations.\n- - Thought: I now can give a great answer\nFinal Answer: Artificial intelligence - is the development of computer systems capable of performing tasks that typically - require human intelligence, such as learning, reasoning, problem-solving, and - understanding language.\nEntities:\n- Artificial Intelligence(Concept): The - creation and advancement of computer systems designed to perform tasks that - normally require human intelligence.\n- Artificial intelligence(Concept): The - creation and advancement of computer systems designed to perform tasks that - normally require human intelligence, such as learning from data, reasoning through - problems, understanding natural language, and adapting to new situations.\n- - Artificial intelligence(Concept): The creation and advancement of computer systems - designed to perform tasks that normally require human intelligence, including - learning from data, reasoning through problems, understanding natural language, - and adapting to new situations.\n- Artificial intelligence(Concept): The creation - and advancement of computer systems designed to perform tasks that normally - require human intelligence, including learning from data, reasoning through - problems, understanding natural language, and adapting to new situations.\n- - Artificial Intelligence(Concept): The creation and advancement of computer systems - designed to perform tasks that normally require human intelligence, including - learning from data, reasoning through problems, understanding natural language, - and adapting to new situations.\n\nBegin! This is VERY important to you, use - the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}' + tasks"},{"role":"user","content":"\nCurrent Task: Summarize the key points about + artificial intelligence in one sentence.\n\nThis is the expected criteria for + your final answer: A one sentence summary about AI.\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nProvide your complete + response:"}],"model":"gpt-4.1-mini"}' headers: User-Agent: - X-USER-AGENT-XXX @@ -4361,7 +9050,7 @@ interactions: connection: - keep-alive content-length: - - '3124' + - '503' content-type: - application/json host: @@ -4383,26 +9072,25 @@ interactions: x-stainless-runtime: - CPython x-stainless-runtime-version: - - 3.13.3 + - 3.13.5 method: POST uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-D2MmyAcb5PoNx5o58RqLqnu7mpG7s\",\n \"object\": - \"chat.completion\",\n \"created\": 1769456628,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n + string: "{\n \"id\": \"chatcmpl-D7HVonYql8FxF7eOt8NHeJfeK13Gi\",\n \"object\": + \"chat.completion\",\n \"created\": 1770627984,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": - 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- X-FRAME-OPTIONS-XXX - X-XSS-Protection: - - '0' - status: - code: 200 - message: OK -- request: - body: '{"messages":[{"role":"system","content":"Convert all responses into valid - JSON output."},{"role":"user","content":"Assess the quality of the task completed - based on the description, expected output, and actual results.\n\nTask Description:\nSummarize - the key points about artificial intelligence in one sentence.\n\nExpected Output:\nA - one sentence summary about AI.\n\nActual Output:\nThought: I now can give a - great answer \nFinal Answer: Artificial intelligence is the creation and advancement - of computer systems designed to perform tasks that normally require human intelligence, - including learning from data, reasoning through problems, understanding natural - language, and adapting to new situations.\n\nPlease provide:\n- Bullet points - suggestions to improve future similar tasks\n- A score from 0 to 10 evaluating - on completion, quality, and overall performance- Entities extracted from the - task output, if any, their type, description, and relationships"}],"model":"gpt-4.1-mini","response_format":{"type":"json_schema","json_schema":{"schema":{"$defs":{"Entity":{"properties":{"name":{"description":"The - name of the entity.","title":"Name","type":"string"},"type":{"description":"The - type of the entity.","title":"Type","type":"string"},"description":{"description":"Description - of the entity.","title":"Description","type":"string"},"relationships":{"description":"Relationships - of the entity.","items":{"type":"string"},"title":"Relationships","type":"array"}},"required":["name","type","description","relationships"],"title":"Entity","type":"object","additionalProperties":false}},"properties":{"suggestions":{"description":"Suggestions - to improve future similar tasks.","items":{"type":"string"},"title":"Suggestions","type":"array"},"quality":{"description":"A - score from 0 to 10 evaluating on completion, quality, and overall performance, - all taking into account the task description, expected output, and the result - of the task.","title":"Quality","type":"number"},"entities":{"description":"Entities - extracted from the task output.","items":{"$ref":"#/$defs/Entity"},"title":"Entities","type":"array"}},"required":["suggestions","quality","entities"],"title":"TaskEvaluation","type":"object","additionalProperties":false},"name":"TaskEvaluation","strict":true}},"stream":false}' + body: '{"messages":[{"role":"system","content":"You extract discrete, reusable + memory statements from raw content (e.g. a task description and its result).\n\nFor + the given content, output a list of memory statements. Each memory must:\n- + Be one clear sentence or short statement\n- Be understandable without the original + context\n- Capture a decision, fact, outcome, preference, lesson, or observation + worth remembering\n- NOT be a vague summary or a restatement of the task description\n- + NOT duplicate the same idea in different words\n\nIf there is nothing worth + remembering (e.g. empty result, no decisions or facts), return an empty list.\nOutput + a JSON object with a single key \"memories\" whose value is a list of strings."},{"role":"user","content":"Content:\nTask: + Summarize the key points about artificial intelligence in one sentence.\nAgent: + Research Assistant\nExpected result: A one sentence summary about AI.\nResult: + Artificial intelligence is the development of computer systems capable of performing + tasks that typically require human intelligence, such as learning, reasoning, + problem-solving, and understanding natural language.\n\nExtract memory statements + as described. Return structured output."}],"model":"gpt-4o-mini","response_format":{"type":"json_schema","json_schema":{"schema":{"description":"LLM + output for extracting discrete memories from raw content.","properties":{"memories":{"description":"List + of discrete, self-contained memory statements extracted from the content.","items":{"type":"string"},"title":"Memories","type":"array"}},"title":"ExtractedMemories","type":"object","additionalProperties":false,"required":["memories"]},"name":"ExtractedMemories","strict":true}},"stream":false}' headers: User-Agent: - X-USER-AGENT-XXX @@ -5572,7 +9173,137 @@ interactions: connection: - keep-alive content-length: - - '2296' + - '1720' + content-type: + - application/json + host: + - api.openai.com + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-helper-method: + - beta.chat.completions.parse + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 1.83.0 + x-stainless-read-timeout: + - X-STAINLESS-READ-TIMEOUT-XXX + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.5 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: "{\n \"id\": \"chatcmpl-D7HVpAQMwQ24zZkweU4tAUuEzvQF2\",\n \"object\": + \"chat.completion\",\n \"created\": 1770627985,\n \"model\": \"gpt-4o-mini-2024-07-18\",\n + \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": + \"assistant\",\n \"content\": \"{\\\"memories\\\":[\\\"Artificial intelligence + involves developing computer systems that can perform tasks requiring human + intelligence.\\\"]}\",\n \"refusal\": null,\n \"annotations\": + []\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n + \ }\n ],\n \"usage\": {\n \"prompt_tokens\": 305,\n \"completion_tokens\": + 20,\n \"total_tokens\": 325,\n \"prompt_tokens_details\": {\n \"cached_tokens\": + 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": + {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": + 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": + \"default\",\n \"system_fingerprint\": \"fp_f4ae844694\"\n}\n" + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Mon, 09 Feb 2026 09:06:26 GMT + Server: + - cloudflare + Strict-Transport-Security: + - STS-XXX + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + access-control-expose-headers: + - ACCESS-CONTROL-XXX + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - OPENAI-ORG-XXX + openai-processing-ms: + - '586' + openai-project: + - OPENAI-PROJECT-XXX + openai-version: + - '2020-10-01' + set-cookie: + - SET-COOKIE-XXX + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-requests: + - X-RATELIMIT-LIMIT-REQUESTS-XXX + x-ratelimit-limit-tokens: + - X-RATELIMIT-LIMIT-TOKENS-XXX + x-ratelimit-remaining-requests: + - X-RATELIMIT-REMAINING-REQUESTS-XXX + x-ratelimit-remaining-tokens: + - X-RATELIMIT-REMAINING-TOKENS-XXX + x-ratelimit-reset-requests: + - X-RATELIMIT-RESET-REQUESTS-XXX + x-ratelimit-reset-tokens: + - X-RATELIMIT-RESET-TOKENS-XXX + x-request-id: + - X-REQUEST-ID-XXX + status: + code: 200 + message: OK +- request: + body: '{"messages":[{"role":"system","content":"You analyze content to be stored + in a hierarchical memory system.\nGiven the content and the existing scopes + and categories, output:\n1. suggested_scope: The best matching existing scope + path, or a new path if none fit (use / for root).\n2. categories: A list of + categories (reuse existing when relevant, add new ones if needed).\n3. importance: + A number from 0.0 to 1.0 indicating how significant this memory is.\n4. extracted_metadata: + A JSON object with any entities, dates, or topics you can extract."},{"role":"user","content":"Content + to 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Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each - idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI and AI agents Task Output: - **The Rise of AI Agents in Remote Work** \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator** \nThe boundaries - of human creativity are being challenged as AI becomes a key player in the creative process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI** \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms - to tailor lessons based on individual learning styles and paces. An article addressing this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making** \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. - By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier** \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying - on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.", "agent_id": "ddff2e62-4b2d-474d-a519-2acef5744e2b", "agent_role": "Task Execution Evaluator", "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You are Task Execution Evaluator. Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed\nYour personal goal is: Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.\nTo give my best complete - final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent Task: Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI - and AI agents Task Output: - **The Rise of AI Agents in Remote Work** \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator** \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process. - An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI** \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this - topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making** \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore - how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier** \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, - allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.\n\nThis is the expected criteria for your final answer: Evaluation Score from 1 to 10 based on the performance of the agents on the tasks\nyou MUST return the actual complete content as the final answer, not a summary.\nEnsure your final answer contains only the content in the following format: {\n \"quality\": float\n}\n\nEnsure the final output does not include any code block markers like ```json or ```python.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "1d66949b-ab73-4070-a772-77fbdcda1823", - "timestamp": "2025-10-21T14:21:26.580077+00:00", "type": "llm_call_completed", "event_data": {"timestamp": "2025-10-21T14:21:26.580077+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, "task_id": "73f03e42-b56a-4e3b-8015-2377ba04393b", "task_name": "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI and AI - agents Task Output: - **The Rise of AI Agents in Remote Work** \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator** \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process. An example - can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI** \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this topic could - synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making** \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies - are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier** \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing - for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.", "agent_id": "ddff2e62-4b2d-474d-a519-2acef5744e2b", "agent_role": "Task Execution Evaluator", "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": "You are Task Execution Evaluator. Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed\nYour personal goal is: Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your - final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent Task: Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI and AI agents Task Output: - **The Rise of AI Agents in Remote Work** \nAs remote work continues to crystallize in the corporate - environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator** \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, - and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI** \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories - and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making** \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an - equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier** \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the - potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.\n\nThis is the expected criteria for your final answer: Evaluation Score from 1 to 10 based on the performance of the agents on the tasks\nyou MUST return the actual complete content as the final answer, not a summary.\nEnsure your final answer contains only the content in the following format: {\n \"quality\": float\n}\n\nEnsure the final output does not include any code block markers like ```json or ```python.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}], "response": "I now can give a great answer \nFinal Answer: {\n \"quality\": 9.5\n}", "call_type": "", "model": "gpt-4o-mini"}}, {"event_id": "4bd6a925-d033-4703-85ad-762ace24440d", "timestamp": "2025-10-21T14:21:26.580236+00:00", "type": "agent_execution_completed", - "event_data": {"agent_role": "Task Execution Evaluator", "agent_goal": "Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.", "agent_backstory": "Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed"}}, {"event_id": "85a0dad3-6767-4613-8565-d4c1f23921b4", "timestamp": "2025-10-21T14:21:26.581604+00:00", "type": "task_completed", "event_data": {"task_description": "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea - that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI and AI agents Task Output: - **The Rise of AI Agents in Remote Work** \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke - critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator** \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through - AI** \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making** \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. - This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier** \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers - would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.", "task_name": "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article - about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI and AI agents Task Output: - **The Rise of AI Agents in Remote Work** \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the - evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator** \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI** \nIn the realm of - education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making** \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could - pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier** \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced - understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.", "task_id": "73f03e42-b56a-4e3b-8015-2377ba04393b", "output_raw": "{\n \"quality\": 9.5\n}", "output_format": "OutputFormat.PYDANTIC", "agent_role": "Task Execution Evaluator"}}, {"event_id": "09e44c6e-89d4-48b4-a9d5-035bf00f288d", "timestamp": "2025-10-21T14:21:26.581917+00:00", "type": "task_completed", "event_data": {"task_description": "Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each - idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.", "task_name": "Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.", "task_id": "b6c0fa7b-c537-48d9-9456-914fd6dbc421", "output_raw": "- **The Rise of AI Agents in Remote Work** \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity - and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator** \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity - itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI** \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making** \nAs organizations increasingly rely on AI for decision-making\u2014be - it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier** \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed - with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. 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By exploring advances + in reinforcement learning, natural language understanding, and multi-agent + collaboration, readers would gain insight into how modern AI agents operate + with increasing autonomy. The piece could include cutting-edge research examples + and practical applications, illustrating how this evolution unlocks new possibilities + across industries such as healthcare, logistics, and customer service, truly + showcasing the expanding potential and impact of AI agents in everyday life.\\n\\n- + **Designing Trustworthy AI Agents: Balancing Transparency and Performance** + \ \\n Focusing on the critical issue of trust, this article would explore + the tension between creating AI agents that offer high performance and those + designed to be transparent and explainable to users. It would delve into techniques + like explainable AI (XAI), confidence scoring, and user-centric design principles + that foster trust and accountability. 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Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed\nYour personal goal is: Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent Task: Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating - on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. 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Meyer \n - An accessible and detailed account covering the entire war, including social and political impacts.\n\n4. \"The Sleepwalkers: How Europe Went to War in 1914\" by Christopher Clark \n - Explores the complex political landscape and decisions that led to the outbreak of the war.\n\n5. \"The Pity of War: Explaining World War I\" by Niall Ferguson \n - Offers a controversial interpretation of the war’s causes and outcomes.\n\n6. \"World War I: A Very Short Introduction\" by Michael Howard \n - A brief but thorough overview of WWI, including its military and political aspects.\n\n7. \"Goodbye to All That\" by Robert Graves \n - A personal memoir of a British officer’s experiences - during the war, highlighting the human side.\n\n8. \"Storm of Steel\" by Ernst Jünger \n - A German soldier’s firsthand account of combat on the Western Front.\n\n9. \"The War That Ended Peace: The Road to 1914\" by Margaret MacMillan \n - Focuses on the lead-up to WWI and the political tensions that culminated in the conflict.\n\n10. \"The First World War: An Illustrated History\" by Hew Strachan \n - Combines detailed research with rich illustrations to provide a comprehensive history of the war.\n\nThese books provide a range of perspectives, from military history and political analysis to personal memoirs and social impact, offering comprehensive information about the First World War.\n\n Guardrail:\n Ensure the authors are from Italy\n\n Your task:\n - Confirm if the Task result complies with the guardrail.\n - If not, provide clear feedback explaining what is wrong (e.g., by how much it violates the rule, or what specific part fails).\n - - Focus only on identifying issues — do not propose corrections.\n - If the Task result complies with the guardrail, saying that is valid\n "}],"model":"gpt-4.1-mini"}' + body: "{\"messages\":[{\"role\":\"system\",\"content\":\"You are Guardrail Agent. + You are a expert at validating the output of a task. By providing effective + feedback if the output is not valid.\\nYour personal goal is: Validate the output + of the task\"},{\"role\":\"user\",\"content\":\"\\nCurrent Task: \\n Ensure + the following task result complies with the given guardrail.\\n\\n Task + result:\\n Here is a list of available books on the First World War:\\n\\n1. + \\\"The Guns of August\\\" by Barbara W. Tuchman\\n2. \\\"A World Undone: The + Story of the Great War, 1914 to 1918\\\" by G.J. Meyer\\n3. \\\"The First World + War\\\" by John Keegan\\n4. \\\"The Sleepwalkers: How Europe Went to War in + 1914\\\" by Christopher Clark\\n5. \\\"To End All Wars: A Story of Loyalty and + Rebellion, 1914-1918\\\" by Adam Hochschild\\n6. \\\"The Pity of War\\\" by + Niall Ferguson\\n7. \\\"Catastrophe 1914: Europe Goes to War\\\" by Max Hastings\\n8. + \\\"World War I: The Definitive Visual History\\\" by R.G. Grant\\n9. \\\"The + War That Ended Peace: The Road to 1914\\\" by Margaret MacMillan\\n10. \\\"Paris + 1919: Six Months That Changed the World\\\" by Margaret MacMillan\\n\\nThese + books cover various aspects of the First World War, including its causes, key + battles, political consequences, and personal experiences.\\n\\n Guardrail:\\n + \ Ensure the authors are from Italy\\n\\n Your task:\\n - + Confirm if the Task result complies with the guardrail.\\n - If not, + provide clear feedback explaining what is wrong (e.g., by how much it violates + the rule, or what specific part fails).\\n - Focus only on identifying + issues \u2014 do not propose corrections.\\n - If the Task result complies + with the guardrail, saying that is valid\\n \\n\\nProvide your complete + response:\"}],\"model\":\"gpt-4.1-mini\",\"response_format\":{\"type\":\"json_schema\",\"json_schema\":{\"schema\":{\"properties\":{\"valid\":{\"description\":\"Whether + the task output complies with the guardrail\",\"title\":\"Valid\",\"type\":\"boolean\"},\"feedback\":{\"anyOf\":[{\"type\":\"string\"},{\"type\":\"null\"}],\"description\":\"A + feedback about the task output if it is not valid\",\"title\":\"Feedback\"}},\"required\":[\"valid\",\"feedback\"],\"title\":\"LLMGuardrailResult\",\"type\":\"object\",\"additionalProperties\":false},\"name\":\"LLMGuardrailResult\",\"strict\":true}},\"stream\":false}" headers: + User-Agent: + - X-USER-AGENT-XXX accept: - application/json accept-encoding: - - gzip, deflate, zstd + - ACCEPT-ENCODING-XXX + authorization: + - AUTHORIZATION-XXX connection: - keep-alive content-length: - - '4210' + - '2271' content-type: - application/json cookie: - - __cf_bm=REDACTED; _cfuvid=REDACTED + - COOKIE-XXX host: - api.openai.com - user-agent: - - OpenAI/Python 1.109.1 x-stainless-arch: - - arm64 - x-stainless-async: - - 'false' - x-stainless-lang: - - python - x-stainless-os: - - MacOS - x-stainless-package-version: - - 1.109.1 - x-stainless-read-timeout: - - '600' - x-stainless-retry-count: - - '0' - x-stainless-runtime: - - CPython - x-stainless-runtime-version: - - 3.12.9 - method: POST - uri: https://api.openai.com/v1/chat/completions - response: - body: - string: "{\n \"id\": \"chatcmpl-CYgCSORvqXRvHk9WrMZInBh6xp6DI\",\n \"object\": \"chat.completion\",\n \"created\": 1762381404,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"{\\n \\\"valid\\\": false,\\n \\\"feedback\\\": \\\"None of the listed authors are from Italy; all authors mentioned are from various other countries such as the United States, United Kingdom, Germany, and Canada. 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The guardrail requires that the authors + be from Italy, but all the authors mentioned, such as Barbara W. Tuchman, + G.J. Meyer, John Keegan, Christopher Clark, Adam Hochschild, Niall Ferguson, + Max Hastings, R.G. Grant, and Margaret MacMillan, are not Italian. 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Test Backstory\nYour personal goal is: Test Goal\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: Gather information about available books on the First World War\n\nThis is the expected criteria for your final answer: A list of available books on the First World War\nyou MUST return the actual complete content as the final answer, not a summary.\n\nThis is the context you''re working with:\n### Previous attempt failed validation: The output indicates that none of the authors mentioned are from Italy, while the guardrail requires authors to be from Italy. Therefore, the output does not comply with the guardrail.\n\n\n### Previous - result:\nList of available books on the First World War:\n\n1. \"The Guns of August\" by Barbara W. Tuchman \n - A detailed narrative of the first month of World War I, focusing on the political and military events that set the stage for the conflict.\n\n2. \"The First World War\" by John Keegan \n - A comprehensive analysis of the causes, battles, and consequences of WWI by one of the foremost military historians.\n\n3. \"A World Undone: The Story of the Great War, 1914 to 1918\" by G.J. 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This is VERY - important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}' + body: '{"messages":[{"role":"system","content":"You are Test Agent. Test Backstory\nYour + personal goal is: Test Goal"},{"role":"user","content":"\nCurrent Task: Gather + information about available books on the First World War\n\nThis is the expected + criteria for your final answer: A list of available books on the First World + War\nyou MUST return the actual complete content as the final answer, not a + summary.\n\nProvide your complete response:"},{"role":"assistant","content":"Here + is a list of available books on the First World War:\n\n1. \"The Guns of August\" + by Barbara W. Tuchman\n2. \"A World Undone: The Story of the Great War, 1914 + to 1918\" by G.J. Meyer\n3. \"The First World War\" by John Keegan\n4. \"The + Sleepwalkers: How Europe Went to War in 1914\" by Christopher Clark\n5. \"To + End All Wars: A Story of Loyalty and Rebellion, 1914-1918\" by Adam Hochschild\n6. + \"The Pity of War\" by Niall Ferguson\n7. \"Catastrophe 1914: Europe Goes to + War\" by Max Hastings\n8. \"World War I: The Definitive Visual History\" by + R.G. Grant\n9. \"The War That Ended Peace: The Road to 1914\" by Margaret MacMillan\n10. + \"Paris 1919: Six Months That Changed the World\" by Margaret MacMillan\n\nThese + books cover various aspects of the First World War, including its causes, key + battles, political consequences, and personal experiences."},{"role":"system","content":"You + are Test Agent. Test Backstory\nYour personal goal is: Test Goal"},{"role":"user","content":"\nCurrent + Task: Gather information about available books on the First World War\n\nThis + is the expected criteria for your final answer: A list of available books on + the First World War\nyou MUST return the actual complete content as the final + answer, not a summary.\n\nThis is the context you''re working with:\n### Previous + attempt failed validation: None of the authors listed are from Italy. The guardrail + requires that the authors be from Italy, but all the authors mentioned, such + as Barbara W. Tuchman, G.J. Meyer, John Keegan, Christopher Clark, Adam Hochschild, + Niall Ferguson, Max Hastings, R.G. Grant, and Margaret MacMillan, are not Italian. + Therefore, the task result does not comply with the guardrail.\n\n\n### Previous + result:\nHere is a list of available books on the First World War:\n\n1. \"The + Guns of August\" by Barbara W. Tuchman\n2. \"A World Undone: The Story of the + Great War, 1914 to 1918\" by G.J. Meyer\n3. \"The First World War\" by John + Keegan\n4. \"The Sleepwalkers: How Europe Went to War in 1914\" by Christopher + Clark\n5. \"To End All Wars: A Story of Loyalty and Rebellion, 1914-1918\" by + Adam Hochschild\n6. \"The Pity of War\" by Niall Ferguson\n7. \"Catastrophe + 1914: Europe Goes to War\" by Max Hastings\n8. \"World War I: The Definitive + Visual History\" by R.G. Grant\n9. \"The War That Ended Peace: The Road to 1914\" + by Margaret MacMillan\n10. \"Paris 1919: Six Months That Changed the World\" + by Margaret MacMillan\n\nThese books cover various aspects of the First World + War, including its causes, key battles, political consequences, and personal + experiences.\n\n\nTry again, making sure to address the validation error.\n\nProvide + your complete response:"}],"model":"gpt-4.1-mini"}' headers: + User-Agent: + - X-USER-AGENT-XXX accept: - application/json accept-encoding: - - gzip, deflate, zstd + - ACCEPT-ENCODING-XXX + authorization: + - AUTHORIZATION-XXX connection: - keep-alive content-length: - - '3139' + - '3185' content-type: - application/json cookie: - - __cf_bm=REDACTED; _cfuvid=REDACTED + - COOKIE-XXX host: - api.openai.com - user-agent: - - OpenAI/Python 1.109.1 x-stainless-arch: - - arm64 + - X-STAINLESS-ARCH-XXX x-stainless-async: - 'false' x-stainless-lang: - python x-stainless-os: - - MacOS + - X-STAINLESS-OS-XXX x-stainless-package-version: - - 1.109.1 + - 1.83.0 x-stainless-read-timeout: - - '600' + - X-STAINLESS-READ-TIMEOUT-XXX x-stainless-retry-count: - '0' x-stainless-runtime: - CPython x-stainless-runtime-version: - - 3.12.9 + - 3.13.12 method: POST uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-CYgCUFGx7P3dG6mkKENsAdMSjbDP2\",\n \"object\": \"chat.completion\",\n \"created\": 1762381406,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"Thought: I now can give a great answer\\nFinal Answer: List of available books on the First World War by Italian authors or published in Italy:\\n\\n1. \\\"La Grande Guerra\\\" by Alessandro Barbero \\n - A comprehensive history of the First World War with a focus on Italy’s involvement and the impact on Italian society and politics.\\n\\n2. \\\"La guerra bianca: Come gli Italiani hanno combattuto la Prima guerra mondiale\\\" by Alessandro Barbero \\n - Explores the harsh conditions of mountain warfare endured by Italian soldiers, with vivid descriptions of battles and daily life on the Alpine front.\\n\\n3. \\\"La Prima Guerra Mondiale\\\" by Paolo Mieli \\n - Provides a broad overview\ - \ of the war, including Italy's role and the broader European context, combining historical facts with cultural insights.\\n\\n4. \\\"1915-1918: La guerra italiana sul fronte interno\\\" by Giovanni De Luna \\n - Focuses on the social and political effects of the war within Italy, examining public opinion, propaganda, and the home front.\\n\\n5. \\\"Il Giorno della Vittoria: 4 Novembre 1918\\\" by Paolo Gaspari \\n - Detailed analysis of Italy’s final victories and the aftermath of the war, including the political and social changes that followed.\\n\\n6. \\\"La Guerra prima della Grande Guerra\\\" by Alessandro Barbero \\n - Discusses the international tensions preceding the First World War with an Italian perspective on diplomatic and military developments.\\n\\n7. \\\"La guerra dimenticata: Il fronte orientale, 1915-1917\\\" by Luca Micheletti \\n - Covers Italy’s campaigns on the Eastern Front, less frequently discussed in general histories of the war, providing new\ - \ insights into Italy’s military efforts.\\n\\n8. \\\"Tornare a casa. 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includeSubDomains; preload + - STS-XXX Transfer-Encoding: - chunked X-Content-Type-Options: - - nosniff + - X-CONTENT-TYPE-XXX access-control-expose-headers: - - X-Request-ID + - ACCESS-CONTROL-XXX alt-svc: - h3=":443"; ma=86400 cf-cache-status: - DYNAMIC openai-organization: - - user-hortuttj2f3qtmxyik2zxf4q + - OPENAI-ORG-XXX openai-processing-ms: - - '395' - openai-project: - - proj_fL4UBWR1CMpAAdgzaSKqsVvA - openai-version: - - '2020-10-01' - x-envoy-upstream-service-time: - '420' - x-openai-proxy-wasm: - - v0.1 - x-ratelimit-limit-requests: - - '500' - x-ratelimit-limit-tokens: - - '200000' - x-ratelimit-remaining-requests: - - '499' - x-ratelimit-remaining-tokens: - - '198710' - x-ratelimit-reset-requests: - - 120ms - x-ratelimit-reset-tokens: - - 386ms - x-request-id: - - req_REDACTED - status: - code: 200 - message: OK -- request: - body: '{"messages":[{"role":"system","content":"Ensure your final answer strictly adheres to the following OpenAPI schema: {\n \"type\": \"json_schema\",\n \"json_schema\": {\n \"name\": \"LLMGuardrailResult\",\n \"strict\": true,\n \"schema\": {\n \"properties\": {\n \"valid\": {\n \"description\": \"Whether the task output complies with the guardrail\",\n \"title\": \"Valid\",\n \"type\": \"boolean\"\n },\n \"feedback\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"A feedback about the task output if it is not valid\",\n \"title\": \"Feedback\"\n }\n },\n \"required\": [\n \"valid\",\n \"feedback\"\n ],\n \"title\": \"LLMGuardrailResult\",\n \"type\": \"object\",\n \"additionalProperties\": - false\n }\n }\n}\n\nDo not include the OpenAPI schema in the final output. 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Test Backstory\nYour personal goal is: Test Goal\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: Test task\n\nThis is the expected criteria for your final answer: Output\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}' + body: '{"messages":[{"role":"system","content":"You are Test Agent. Test Backstory\nYour + personal goal is: Test Goal"},{"role":"user","content":"\nCurrent Task: Gather + information about available books on the First World War\n\nThis is the expected + criteria for your final answer: A list of available books on the First World + War\nyou MUST return the actual complete content as the final answer, not a + summary.\n\nProvide your complete response:"},{"role":"assistant","content":"Here + is a list of available books on the First World War:\n\n1. \"The Guns of August\" + by Barbara W. Tuchman\n2. \"A World Undone: The Story of the Great War, 1914 + to 1918\" by G.J. Meyer\n3. \"The First World War\" by John Keegan\n4. \"The + Sleepwalkers: How Europe Went to War in 1914\" by Christopher Clark\n5. \"To + End All Wars: A Story of Loyalty and Rebellion, 1914-1918\" by Adam Hochschild\n6. + \"The Pity of War\" by Niall Ferguson\n7. \"Catastrophe 1914: Europe Goes to + War\" by Max Hastings\n8. \"World War I: The Definitive Visual History\" by + R.G. Grant\n9. \"The War That Ended Peace: The Road to 1914\" by Margaret MacMillan\n10. + \"Paris 1919: Six Months That Changed the World\" by Margaret MacMillan\n\nThese + books cover various aspects of the First World War, including its causes, key + battles, political consequences, and personal experiences."},{"role":"system","content":"You + are Test Agent. Test Backstory\nYour personal goal is: Test Goal"},{"role":"user","content":"\nCurrent + Task: Gather information about available books on the First World War\n\nThis + is the expected criteria for your final answer: A list of available books on + the First World War\nyou MUST return the actual complete content as the final + answer, not a summary.\n\nThis is the context you''re working with:\n### Previous + attempt failed validation: None of the authors listed are from Italy. The guardrail + requires that the authors be from Italy, but all the authors mentioned, such + as Barbara W. Tuchman, G.J. Meyer, John Keegan, Christopher Clark, Adam Hochschild, + Niall Ferguson, Max Hastings, R.G. Grant, and Margaret MacMillan, are not Italian. + Therefore, the task result does not comply with the guardrail.\n\n\n### Previous + result:\nHere is a list of available books on the First World War:\n\n1. \"The + Guns of August\" by Barbara W. Tuchman\n2. \"A World Undone: The Story of the + Great War, 1914 to 1918\" by G.J. Meyer\n3. \"The First World War\" by John + Keegan\n4. \"The Sleepwalkers: How Europe Went to War in 1914\" by Christopher + Clark\n5. \"To End All Wars: A Story of Loyalty and Rebellion, 1914-1918\" by + Adam Hochschild\n6. \"The Pity of War\" by Niall Ferguson\n7. \"Catastrophe + 1914: Europe Goes to War\" by Max Hastings\n8. \"World War I: The Definitive + Visual History\" by R.G. Grant\n9. \"The War That Ended Peace: The Road to 1914\" + by Margaret MacMillan\n10. \"Paris 1919: Six Months That Changed the World\" + by Margaret MacMillan\n\nThese books cover various aspects of the First World + War, including its causes, key battles, political consequences, and personal + experiences.\n\n\nTry again, making sure to address the validation error.\n\nProvide + your complete response:"},{"role":"assistant","content":"Here is a list of available + books on the First World War by Italian authors:\n\n1. \"La Grande Guerra\" + by Alessandro Barbero \n2. \"Il grande nulla: La prima guerra mondiale\" by + Emilio Gentile \n3. \"La guerra italiana 1915-1918\" by Paolo Piccione \n4. + \"La prima guerra mondiale\" by Andrea di Robilant \n5. \"1914-1918: La guerra + e la memoria\" edited by Paolo G. Minuto \n\nThese books provide various perspectives + on the First World War from Italian historians and authors."},{"role":"system","content":"You + are Test Agent. Test Backstory\nYour personal goal is: Test Goal"},{"role":"user","content":"\nCurrent + Task: Test task\n\nThis is the expected criteria for your final answer: Output\nyou + MUST return the actual complete content as the final answer, not a summary.\n\nProvide + your complete response:"}],"model":"gpt-4.1-mini"}' headers: + User-Agent: + - X-USER-AGENT-XXX accept: - application/json accept-encoding: - - gzip, deflate, zstd + - ACCEPT-ENCODING-XXX + authorization: + - AUTHORIZATION-XXX connection: - keep-alive content-length: - - '770' + - '4036' content-type: - application/json cookie: - - __cf_bm=REDACTED; _cfuvid=REDACTED + - COOKIE-XXX host: - api.openai.com - user-agent: - - OpenAI/Python 1.109.1 x-stainless-arch: - - arm64 + - X-STAINLESS-ARCH-XXX x-stainless-async: - 'false' x-stainless-lang: - python x-stainless-os: - - MacOS + - X-STAINLESS-OS-XXX x-stainless-package-version: - - 1.109.1 + - 1.83.0 x-stainless-read-timeout: - - '600' + - X-STAINLESS-READ-TIMEOUT-XXX x-stainless-retry-count: - '0' x-stainless-runtime: - CPython x-stainless-runtime-version: - - 3.12.9 + - 3.13.12 method: POST uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-CYgCdUkawcuGKhJQYVfLlwbbDwljX\",\n \"object\": \"chat.completion\",\n \"created\": 1762381415,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"Thought: I now can give a great answer\\nFinal Answer: Output\",\n \"refusal\": null,\n \"annotations\": []\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": 153,\n \"completion_tokens\": 14,\n \"total_tokens\": 167,\n \"prompt_tokens_details\": {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": \"default\",\n \"system_fingerprint\": \"fp_4c2851f862\"\n}\n" + string: "{\n \"id\": \"chatcmpl-DDGAMOb5FXfsZwEhCqnUP11VAcuqF\",\n \"object\": + \"chat.completion\",\n \"created\": 1772052778,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n + \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": + \"assistant\",\n \"content\": \"Output\",\n \"refusal\": null,\n + \ \"annotations\": []\n },\n \"logprobs\": null,\n \"finish_reason\": + \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": 948,\n \"completion_tokens\": + 1,\n \"total_tokens\": 949,\n \"prompt_tokens_details\": {\n \"cached_tokens\": + 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": + {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": + 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": + \"default\",\n \"system_fingerprint\": \"fp_a391f2cee0\"\n}\n" headers: CF-RAY: - - REDACTED-RAY + - CF-RAY-XXX Connection: - keep-alive Content-Type: - application/json Date: - - Wed, 05 Nov 2025 22:23:36 GMT + - Wed, 25 Feb 2026 20:52:59 GMT Server: - cloudflare Strict-Transport-Security: - - max-age=31536000; includeSubDomains; preload + - STS-XXX Transfer-Encoding: - chunked X-Content-Type-Options: - - nosniff + - X-CONTENT-TYPE-XXX access-control-expose-headers: - - X-Request-ID + - ACCESS-CONTROL-XXX alt-svc: - h3=":443"; ma=86400 cf-cache-status: - DYNAMIC openai-organization: - - user-hortuttj2f3qtmxyik2zxf4q + - OPENAI-ORG-XXX openai-processing-ms: - - '441' + - '347' openai-project: - - proj_fL4UBWR1CMpAAdgzaSKqsVvA + - OPENAI-PROJECT-XXX openai-version: - '2020-10-01' - x-envoy-upstream-service-time: - - '462' x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: - - '500' + - X-RATELIMIT-LIMIT-REQUESTS-XXX x-ratelimit-limit-tokens: - - '200000' + - X-RATELIMIT-LIMIT-TOKENS-XXX x-ratelimit-remaining-requests: - - '499' + - X-RATELIMIT-REMAINING-REQUESTS-XXX x-ratelimit-remaining-tokens: - - '199832' + - X-RATELIMIT-REMAINING-TOKENS-XXX x-ratelimit-reset-requests: - - 120ms + - X-RATELIMIT-RESET-REQUESTS-XXX x-ratelimit-reset-tokens: - - 50ms + - X-RATELIMIT-RESET-TOKENS-XXX x-request-id: - - req_REDACTED + - X-REQUEST-ID-XXX status: code: 200 message: OK diff --git a/lib/crewai/tests/cassettes/test_guardrail_when_an_error_occurs.yaml b/lib/crewai/tests/cassettes/test_guardrail_when_an_error_occurs.yaml index 1ac34dfe5..af0bc5aaf 100644 --- a/lib/crewai/tests/cassettes/test_guardrail_when_an_error_occurs.yaml +++ b/lib/crewai/tests/cassettes/test_guardrail_when_an_error_occurs.yaml @@ -1,189 +1,121 @@ interactions: - request: - body: '{"messages": [{"role": "system", "content": "You are Test Agent. 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You are a expert at validating the output of a task. By providing effective feedback if the output is not valid.\nYour personal goal is: Validate the output of the task\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {\n \"valid\": bool,\n \"feedback\": str | None\n}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python."}, {"role": "user", "content": "\n Ensure the following task result complies with the given guardrail.\n\n Task result:\n Here is a comprehensive list of available books on - the First World War:\n\n1. **\"The Sleepwalkers: How Europe Went to War in 1914\" by Christopher Clark** \n This book delves into the complex factors that led to the outbreak of the war, offering insights into the political and social dynamics of early 20th century Europe.\n\n2. **\"A World Undone: The Story of the Great War, 1914 to 1918\" by G.J. 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It''s one + of the most fundamental concepts in math and is a building block for all other + math skills. Teaching addition to a 6-year-old involves using simple numbers + and relatable examples that help them visualize and understand the concept of + adding together.\n\n**Angle:**\nTo make the concept of addition fun and engaging, + we can use everyday objects that a child is familiar with, such as toys, fruits, + or drawing items. Incorporating visuals and interactive elements will keep their + attention and help reinforce the idea of combining numbers.\n\n**Examples:**\n\n1. + **Using Objects:**\n - **Scenario:** Let\u2019s say you have 2 apples and + your friend gives you 3 more apples.\n - **Visual**: Arrange the apples in + front of the child.\n - **Question:** \"How many apples do you have now?\"\n - + **Calculation:** 2 apples (your apples) + 3 apples (friend''s apples) = 5 apples. \n - + **Conclusion:** \"You now have 5 apples!\"\n\n2. **Drawing Pictures:**\n - + **Scenario:** Draw 4 stars on one side of the paper and 2 stars on the other + side.\n - **Activity:** Ask the child to count the stars in the first group + and then the second group.\n - **Question:** \"If we put them together, how + many stars do we have?\"\n - **Calculation:** 4 stars + 2 stars = 6 stars. \n - + **Conclusion:** \"You drew 6 stars all together!\"\n\n3. **Story Problems:**\n - + **Scenario:** \"You have 5 toy cars, and you buy 3 more from the store. How + many cars do you have?\"\n - **Interaction:** Create a fun story around the + toy cars (perhaps the cars are going on an adventure).\n - **Calculation:** + 5 toy cars + 3 toy cars = 8 toy cars. \n - **Conclusion:** \"You now have + a total of 8 toy cars for your adventure!\"\n\n4. **Games:**\n - **Activity:** + Play a simple game where you roll a pair of dice. 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It''s one of the most fundamental concepts in math + and is a building block for all other math skills. Teaching addition to a 6-year-old + involves using simple numbers and relatable examples that help them visualize + and understand the concept of adding together.\n\n**Angle:**\nTo make the concept + of addition fun and engaging, we can use everyday objects that a child is familiar + with, such as toys, fruits, or drawing items. Incorporating visuals and interactive + elements will keep their attention and help reinforce the idea of combining + numbers.\n\n**Examples:**\n\n1. **Using Objects:**\n - **Scenario:** Let\u2019s + say you have 2 apples and your friend gives you 3 more apples.\n - **Visual**: + Arrange the apples in front of the child.\n - **Question:** \"How many apples + do you have now?\"\n - **Calculation:** 2 apples (your apples) + 3 apples + (friend''s apples) = 5 apples. \n - **Conclusion:** \"You now have 5 apples!\"\n\n2. + **Drawing Pictures:**\n - **Scenario:** Draw 4 stars on one side of the paper + and 2 stars on the other side.\n - **Activity:** Ask the child to count the + stars in the first group and then the second group.\n - **Question:** \"If + we put them together, how many stars do we have?\"\n - **Calculation:** 4 + stars + 2 stars = 6 stars. \n - **Conclusion:** \"You drew 6 stars all together!\"\n\n3. + **Story Problems:**\n - **Scenario:** \"You have 5 toy cars, and you buy 3 + more from the store. 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Incorporating visuals and interactive elements will keep their + attention and help reinforce the idea of combining numbers.\n\n**Examples:**\n\n1. + **Using Objects:**\n - **Scenario:** Let\u2019s say you have 2 apples and + your friend gives you 3 more apples.\n - **Visual**: Arrange the apples in + front of the child.\n - **Question:** \"How many apples do you have now?\"\n - + **Calculation:** 2 apples (your apples) + 3 apples (friend''s apples) = 5 apples. \n - + **Conclusion:** \"You now have 5 apples!\"\n\n2. **Drawing Pictures:**\n - + **Scenario:** Draw 4 stars on one side of the paper and 2 stars on the other + side.\n - **Activity:** Ask the child to count the stars in the first group + and then the second group.\n - **Question:** \"If we put them together, how + many stars do we have?\"\n - **Calculation:** 4 stars + 2 stars = 6 stars. \n - + **Conclusion:** \"You drew 6 stars all together!\"\n\n3. **Story Problems:**\n - + **Scenario:** \"You have 5 toy cars, and you buy 3 more from the store. 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It''s one of the most fundamental concepts in math + and is a building block for all other math skills. Teaching addition to a 6-year-old + involves using simple numbers and relatable examples that help them visualize + and understand the concept of adding together.\n\n**Angle:**\nTo make the concept + of addition fun and engaging, we can use everyday objects that a child is familiar + with, such as toys, fruits, or drawing items. Incorporating visuals and interactive + elements will keep their attention and help reinforce the idea of combining + numbers.\n\n**Examples:**\n\n1. **Using Objects:**\n - **Scenario:** Let’s + say you have 2 apples and your friend gives you 3 more apples.\n - **Visual**: + Arrange the apples in front of the child.\n - **Question:** \"How many apples + do you have now?\"\n - **Calculation:** 2 apples (your apples) + 3 apples + (friend''s apples) = 5 apples. \n - **Conclusion:** \"You now have 5 apples!\"\n\n2. + **Drawing Pictures:**\n - **Scenario:** Draw 4 stars on one side of the paper + and 2 stars on the other side.\n - **Activity:** Ask the child to count the + stars in the first group and then the second group.\n - **Question:** \"If + we put them together, how many stars do we have?\"\n - **Calculation:** 4 + stars + 2 stars = 6 stars. \n - **Conclusion:** \"You drew 6 stars all together!\"\n\n3. + **Story Problems:**\n - **Scenario:** \"You have 5 toy cars, and you buy 3 + more from the store. How many cars do you have?\"\n - **Interaction:** Create + a fun story around the toy cars (perhaps the cars are going on an adventure).\n - + **Calculation:** 5 toy cars + 3 toy cars = 8 toy cars. \n - **Conclusion:** + \"You now have a total of 8 toy cars for your adventure!\"\n\n4. **Games:**\n - + **Activity:** Play a simple game where you roll a pair of dice. 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x-ratelimit-limit-tokens: + - '10000000' + x-ratelimit-remaining-requests: + - '9999' + x-ratelimit-remaining-tokens: + - '9999963' + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_457f533e12f84f9ab20f97d4416ea060 + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/cassettes/test_output_json_dict_hierarchical.yaml b/lib/crewai/tests/cassettes/test_output_json_dict_hierarchical.yaml index 59fc8a1e9..472fc68e7 100644 --- a/lib/crewai/tests/cassettes/test_output_json_dict_hierarchical.yaml +++ b/lib/crewai/tests/cassettes/test_output_json_dict_hierarchical.yaml @@ -426,4 +426,121 @@ interactions: status: code: 200 message: OK +- request: + body: '{"messages":[{"role":"user","content":"Thought: I now can give a great + answer\nFinal Answer: I would assign a score of 4 to the title \"The impact + of AI in the future of work.\" The title is very relevant and timely, as artificial + intelligence is a major transformative force affecting the labor market and + employment trends. It is clear and concise, effectively highlighting the focus + on AI''s influence on the future of work. However, while it is engaging and + implies substantial potential impact, it could be slightly more specific or + dynamic to reach an excellent level. 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includeSubDomains; preload + - STS-XXX Transfer-Encoding: - chunked X-Content-Type-Options: - - nosniff + - X-CONTENT-TYPE-XXX access-control-expose-headers: - - X-Request-ID + - ACCESS-CONTROL-XXX alt-svc: - h3=":443"; ma=86400 cf-cache-status: - DYNAMIC openai-organization: - - user-hortuttj2f3qtmxyik2zxf4q + - OPENAI-ORG-XXX openai-processing-ms: - - '983' + - '309' openai-project: - - proj_fL4UBWR1CMpAAdgzaSKqsVvA + - OPENAI-PROJECT-XXX openai-version: - '2020-10-01' - x-envoy-upstream-service-time: - - '1002' x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: - - '500' + - X-RATELIMIT-LIMIT-REQUESTS-XXX x-ratelimit-limit-tokens: - - '200000' + - X-RATELIMIT-LIMIT-TOKENS-XXX x-ratelimit-remaining-requests: - - '499' + - X-RATELIMIT-REMAINING-REQUESTS-XXX x-ratelimit-remaining-tokens: - - '199659' + - X-RATELIMIT-REMAINING-TOKENS-XXX x-ratelimit-reset-requests: - - 120ms + - X-RATELIMIT-RESET-REQUESTS-XXX x-ratelimit-reset-tokens: - - 102ms + - X-RATELIMIT-RESET-TOKENS-XXX x-request-id: - - req_REDACTED + - X-REQUEST-ID-XXX status: code: 200 message: OK diff --git a/lib/crewai/tests/cassettes/test_output_pydantic_hierarchical.yaml b/lib/crewai/tests/cassettes/test_output_pydantic_hierarchical.yaml index f48000904..7ee93fe76 100644 --- a/lib/crewai/tests/cassettes/test_output_pydantic_hierarchical.yaml +++ b/lib/crewai/tests/cassettes/test_output_pydantic_hierarchical.yaml @@ -10,28 +10,29 @@ interactions: Task: Give me an integer score between 1-5 for the following title: ''The impact of AI in the future of work''\n\nThis is the expected criteria for your final answer: The score of the title.\nyou MUST return the actual complete content - as the final answer, not a summary.\nEnsure your final answer strictly adheres - to the following OpenAPI schema: {\n \"properties\": {\n \"score\": {\n \"title\": + as the final answer, not a summary.\nFormat your final answer according to the + following OpenAPI schema: {\n \"properties\": {\n \"score\": {\n \"title\": \"Score\",\n \"type\": \"integer\"\n }\n },\n \"required\": [\n \"score\"\n ],\n \"title\": - \"ScoreOutput\",\n \"type\": \"object\",\n \"additionalProperties\": false\n}\n\nDo + \"ScoreOutput\",\n \"type\": \"object\",\n \"additionalProperties\": false\n}\n\nIMPORTANT: + Preserve the original content exactly as-is. Do NOT rewrite, paraphrase, or + modify the meaning of the content. Only structure it to match the schema format.\n\nDo not include the OpenAPI schema in the final output. Ensure the final output - does not include any code block markers like ```json or ```python.\n\nThis is - VERY important to you, your job depends on it!"}],"model":"gpt-4o","tool_choice":"auto","tools":[{"type":"function","function":{"name":"Delegate_work_to_coworker","description":"Delegate + does not include any code block markers like ```json or ```python."}],"model":"gpt-4o","response_format":{"type":"json_schema","json_schema":{"schema":{"properties":{"score":{"title":"Score","type":"integer"}},"required":["score"],"title":"ScoreOutput","type":"object","additionalProperties":false},"name":"ScoreOutput","strict":true}},"stream":false,"tool_choice":"auto","tools":[{"type":"function","function":{"name":"delegate_work_to_coworker","description":"Delegate a specific task to one of the following coworkers: Scorer\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely - everything you know, don''t reference things but instead explain them.","parameters":{"properties":{"task":{"description":"The + everything you know, don''t reference things but instead explain them.","strict":true,"parameters":{"properties":{"task":{"description":"The task to delegate","title":"Task","type":"string"},"context":{"description":"The context for the task","title":"Context","type":"string"},"coworker":{"description":"The - role/name of the coworker to delegate to","title":"Coworker","type":"string"}},"required":["task","context","coworker"],"type":"object"}}},{"type":"function","function":{"name":"Ask_question_to_coworker","description":"Ask + role/name of the coworker to delegate to","title":"Coworker","type":"string"}},"required":["task","context","coworker"],"type":"object","additionalProperties":false}}},{"type":"function","function":{"name":"ask_question_to_coworker","description":"Ask a specific question to one of the following coworkers: Scorer\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolutely everything you know, don''t reference things but instead - explain them.","parameters":{"properties":{"question":{"description":"The question - to ask","title":"Question","type":"string"},"context":{"description":"The context - for the question","title":"Context","type":"string"},"coworker":{"description":"The - role/name of the coworker to ask","title":"Coworker","type":"string"}},"required":["question","context","coworker"],"type":"object"}}}]}' + explain them.","strict":true,"parameters":{"properties":{"question":{"description":"The + question to ask","title":"Question","type":"string"},"context":{"description":"The + context for the question","title":"Context","type":"string"},"coworker":{"description":"The + role/name of the coworker to ask","title":"Coworker","type":"string"}},"required":["question","context","coworker"],"type":"object","additionalProperties":false}}}]}' headers: User-Agent: - X-USER-AGENT-XXX @@ -44,7 +45,7 @@ interactions: connection: - keep-alive content-length: - - '2959' + - '3415' content-type: - application/json host: @@ -53,6 +54,8 @@ interactions: - X-STAINLESS-ARCH-XXX x-stainless-async: - 'false' + x-stainless-helper-method: + - beta.chat.completions.parse x-stainless-lang: - python x-stainless-os: @@ -66,31 +69,33 @@ interactions: x-stainless-runtime: - CPython x-stainless-runtime-version: - - 3.13.3 + - 3.13.12 method: POST uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-D0u1dSPVqe5art2HXWibsPOp3SOti\",\n \"object\": - \"chat.completion\",\n \"created\": 1769107733,\n \"model\": \"gpt-4o-2024-08-06\",\n + string: "{\n \"id\": \"chatcmpl-DDG9wKD6IRmnAwBS1tw4NMVccsPnZ\",\n \"object\": + \"chat.completion\",\n \"created\": 1772052752,\n \"model\": \"gpt-4o-2024-08-06\",\n \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": null,\n \"tool_calls\": [\n {\n - \ \"id\": \"call_AEHe6pv1NqguBRA5q9CHVSn3\",\n \"type\": - \"function\",\n \"function\": {\n \"name\": \"Delegate_work_to_coworker\",\n - \ \"arguments\": \"{\\\"task\\\":\\\"Provide an integer score - between 1-5 for the title 'The impact of AI in the future of work'. The score - should reflect how engaging, relevant, and thought-provoking the title is.\\\",\\\"context\\\":\\\"You - need to evaluate how well the title 'The impact of AI in the future of work' - meets the criteria of being engaging, relevant, and thought-provoking in the - context of emerging technologies and their implications on future work environments.\\\",\\\"coworker\\\":\\\"Scorer\\\"}\"\n - \ }\n }\n ],\n \"refusal\": null,\n \"annotations\": - []\n },\n \"logprobs\": null,\n \"finish_reason\": \"tool_calls\"\n - \ }\n ],\n \"usage\": {\n \"prompt_tokens\": 562,\n \"completion_tokens\": - 111,\n \"total_tokens\": 673,\n \"prompt_tokens_details\": {\n \"cached_tokens\": + \ \"id\": \"call_VzfUuCi89kzEC9gJgiMCz5B2\",\n \"type\": + \"function\",\n \"function\": {\n \"name\": \"delegate_work_to_coworker\",\n + \ \"arguments\": \"{\\\"task\\\":\\\"Evaluate the title 'The impact + of AI in the future of work' and give an integer score between 1-5 based on + how compelling or effective the title is.\\\",\\\"context\\\":\\\"You are + asked to evaluate a title 'The impact of AI in the future of work' and provide + an integer score between 1-5. The criteria for evaluation include how informative, + engaging, relevant, and clear the title is. Additionally, consider how the + title may attract the intended audience's interest and its potential impact + on readers.\\\",\\\"coworker\\\":\\\"Scorer\\\"}\"\n }\n }\n + \ ],\n \"refusal\": null,\n \"annotations\": []\n },\n + \ \"logprobs\": null,\n \"finish_reason\": \"tool_calls\"\n }\n + \ ],\n \"usage\": {\n \"prompt_tokens\": 613,\n \"completion_tokens\": + 127,\n \"total_tokens\": 740,\n \"prompt_tokens_details\": {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": - \"default\",\n \"system_fingerprint\": \"fp_deacdd5f6f\"\n}\n" + \"default\",\n \"system_fingerprint\": \"fp_64dfa806c7\"\n}\n" headers: CF-RAY: - CF-RAY-XXX @@ -99,11 +104,9 @@ interactions: Content-Type: - application/json Date: - - Thu, 22 Jan 2026 18:48:56 GMT + - Wed, 25 Feb 2026 20:52:34 GMT Server: - cloudflare - Set-Cookie: - - SET-COOKIE-XXX Strict-Transport-Security: - STS-XXX Transfer-Encoding: @@ -119,146 +122,13 @@ interactions: openai-organization: - OPENAI-ORG-XXX openai-processing-ms: - - '3849' + - '2259' openai-project: - OPENAI-PROJECT-XXX openai-version: - '2020-10-01' - x-envoy-upstream-service-time: - - '3973' - x-openai-proxy-wasm: - - v0.1 - x-ratelimit-limit-requests: - - X-RATELIMIT-LIMIT-REQUESTS-XXX - x-ratelimit-limit-tokens: - - X-RATELIMIT-LIMIT-TOKENS-XXX - x-ratelimit-remaining-requests: - - X-RATELIMIT-REMAINING-REQUESTS-XXX - x-ratelimit-remaining-tokens: - - X-RATELIMIT-REMAINING-TOKENS-XXX - x-ratelimit-reset-requests: - - X-RATELIMIT-RESET-REQUESTS-XXX - x-ratelimit-reset-tokens: - - X-RATELIMIT-RESET-TOKENS-XXX - x-request-id: - - X-REQUEST-ID-XXX - status: - code: 200 - message: OK -- request: - body: '{"messages":[{"role":"system","content":"You are Scorer. You''re an expert - scorer, specialized in scoring titles.\nYour personal goal is: Score the title\nTo - give my best complete final answer to the task respond using the exact following - format:\n\nThought: I now can give a great answer\nFinal Answer: Your final - answer must be the great and the most complete as possible, it must be outcome - described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent - Task: Provide an integer score between 1-5 for the title ''The impact of AI - in the future of work''. The score should reflect how engaging, relevant, and - thought-provoking the title is.\n\nThis is the expected criteria for your final - answer: Your best answer to your coworker asking you this, accounting for the - context shared.\nyou MUST return the actual complete content as the final answer, - not a summary.\n\nThis is the context you''re working with:\nYou need to evaluate - how well the title ''The impact of AI in the future of work'' meets the criteria - of being engaging, relevant, and thought-provoking in the context of emerging - technologies and their implications on future work environments.\n\nBegin! This - is VERY important to you, use the tools available and give your best Final Answer, - your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}' - headers: - User-Agent: - - X-USER-AGENT-XXX - accept: - - application/json - accept-encoding: - - ACCEPT-ENCODING-XXX - authorization: - - AUTHORIZATION-XXX - connection: - - keep-alive - content-length: - - '1348' - content-type: - - application/json - host: - - api.openai.com - x-stainless-arch: - - X-STAINLESS-ARCH-XXX - x-stainless-async: - - 'false' - x-stainless-lang: - - python - x-stainless-os: - - X-STAINLESS-OS-XXX - x-stainless-package-version: - - 1.83.0 - x-stainless-read-timeout: - - X-STAINLESS-READ-TIMEOUT-XXX - x-stainless-retry-count: - - '0' - x-stainless-runtime: - - CPython - x-stainless-runtime-version: - - 3.13.3 - method: POST - uri: https://api.openai.com/v1/chat/completions - response: - body: - string: "{\n \"id\": \"chatcmpl-D0u1hKGQrrJVYOcW1tAlQMgAjcaDX\",\n \"object\": - \"chat.completion\",\n \"created\": 1769107737,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n - \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": - \"assistant\",\n \"content\": \"Thought: I now can give a great answer\\nFinal - Answer: The title 'The impact of AI in the future of work' is highly relevant - given the current and growing significance of artificial intelligence in transforming - work environments across industries. It is engaging because AI's influence - on future employment is a topic of widespread interest and concern, prompting - readers to explore its implications. Furthermore, it is thought-provoking - as it invites consideration of both the opportunities and challenges AI presents - for the workforce, including changes in job roles, skills, and economic structures. - However, the title could be more captivating or specific to heighten curiosity - and emphasize particular aspects of AI's impact. Overall, it effectively meets - the criteria but could be slightly enhanced for maximum engagement. Considering - all factors, I would score it a 4 out of 5.\",\n \"refusal\": null,\n - \ \"annotations\": []\n },\n \"logprobs\": null,\n \"finish_reason\": - \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": 264,\n \"completion_tokens\": - 160,\n \"total_tokens\": 424,\n \"prompt_tokens_details\": {\n \"cached_tokens\": - 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": - {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": - 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": - \"default\",\n \"system_fingerprint\": \"fp_376a7ccef1\"\n}\n" - headers: - CF-RAY: - - CF-RAY-XXX - Connection: - - keep-alive - Content-Type: - - application/json - Date: - - Thu, 22 Jan 2026 18:49:00 GMT - Server: - - cloudflare - Set-Cookie: + set-cookie: - SET-COOKIE-XXX - Strict-Transport-Security: - - STS-XXX - Transfer-Encoding: - - chunked - X-Content-Type-Options: - - X-CONTENT-TYPE-XXX - access-control-expose-headers: - - ACCESS-CONTROL-XXX - alt-svc: - - h3=":443"; ma=86400 - cf-cache-status: - - DYNAMIC - openai-organization: - - OPENAI-ORG-XXX - openai-processing-ms: - - '3273' - openai-project: - - OPENAI-PROJECT-XXX - openai-version: - - '2020-10-01' - x-envoy-upstream-service-time: - - '3299' x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -289,47 +159,29 @@ interactions: Task: Give me an integer score between 1-5 for the following title: ''The impact of AI in the future of work''\n\nThis is the expected criteria for your final answer: The score of the title.\nyou MUST return the actual complete content - as the final answer, not a summary.\nEnsure your final answer strictly adheres - to the following OpenAPI schema: {\n \"properties\": {\n \"score\": {\n \"title\": + as the final answer, not a summary.\nFormat your final answer according to the + following OpenAPI schema: {\n \"properties\": {\n \"score\": {\n \"title\": \"Score\",\n \"type\": \"integer\"\n }\n },\n \"required\": [\n \"score\"\n ],\n \"title\": - \"ScoreOutput\",\n \"type\": \"object\",\n \"additionalProperties\": false\n}\n\nDo + \"ScoreOutput\",\n \"type\": \"object\",\n \"additionalProperties\": false\n}\n\nIMPORTANT: + Preserve the original content exactly as-is. Do NOT rewrite, paraphrase, or + modify the meaning of the content. Only structure it to match the schema format.\n\nDo not include the OpenAPI schema in the final output. Ensure the final output - does not include any code block markers like ```json or ```python.\n\nThis is - VERY important to you, your job depends on it!"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_AEHe6pv1NqguBRA5q9CHVSn3","type":"function","function":{"name":"Delegate_work_to_coworker","arguments":"{\"task\":\"Provide - an integer score between 1-5 for the title ''The impact of AI in the future - of work''. The score should reflect how engaging, relevant, and thought-provoking - the title is.\",\"context\":\"You need to evaluate how well the title ''The - impact of AI in the future of work'' meets the criteria of being engaging, relevant, - and thought-provoking in the context of emerging technologies and their implications - on future work environments.\",\"coworker\":\"Scorer\"}"}}]},{"role":"tool","tool_call_id":"call_AEHe6pv1NqguBRA5q9CHVSn3","content":"The - title ''The impact of AI in the future of work'' is highly relevant given the - current and growing significance of artificial intelligence in transforming - work environments across industries. It is engaging because AI''s influence - on future employment is a topic of widespread interest and concern, prompting - readers to explore its implications. Furthermore, it is thought-provoking as - it invites consideration of both the opportunities and challenges AI presents - for the workforce, including changes in job roles, skills, and economic structures. - However, the title could be more captivating or specific to heighten curiosity - and emphasize particular aspects of AI''s impact. Overall, it effectively meets - the criteria but could be slightly enhanced for maximum engagement. Considering - all factors, I would score it a 4 out of 5."},{"role":"user","content":"Analyze - the tool result. If requirements are met, provide the Final Answer. Otherwise, - call the next tool. Deliver only the answer without meta-commentary."}],"model":"gpt-4o","tool_choice":"auto","tools":[{"type":"function","function":{"name":"Delegate_work_to_coworker","description":"Delegate + does not include any code block markers like ```json or ```python."}],"model":"gpt-4o","tool_choice":"auto","tools":[{"type":"function","function":{"name":"delegate_work_to_coworker","description":"Delegate a specific task to one of the following coworkers: Scorer\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely - everything you know, don''t reference things but instead explain them.","parameters":{"properties":{"task":{"description":"The + everything you know, don''t reference things but instead explain them.","strict":true,"parameters":{"properties":{"task":{"description":"The task to delegate","title":"Task","type":"string"},"context":{"description":"The context for the task","title":"Context","type":"string"},"coworker":{"description":"The - role/name of the coworker to delegate to","title":"Coworker","type":"string"}},"required":["task","context","coworker"],"type":"object"}}},{"type":"function","function":{"name":"Ask_question_to_coworker","description":"Ask + role/name of the coworker to delegate to","title":"Coworker","type":"string"}},"required":["task","context","coworker"],"type":"object","additionalProperties":false}}},{"type":"function","function":{"name":"ask_question_to_coworker","description":"Ask a specific question to one of the following coworkers: Scorer\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolutely everything you know, don''t reference things but instead - explain them.","parameters":{"properties":{"question":{"description":"The question - to ask","title":"Question","type":"string"},"context":{"description":"The context - for the question","title":"Context","type":"string"},"coworker":{"description":"The - role/name of the coworker to ask","title":"Coworker","type":"string"}},"required":["question","context","coworker"],"type":"object"}}}]}' + explain them.","strict":true,"parameters":{"properties":{"question":{"description":"The + question to ask","title":"Question","type":"string"},"context":{"description":"The + context for the question","title":"Context","type":"string"},"coworker":{"description":"The + role/name of the coworker to ask","title":"Coworker","type":"string"}},"required":["question","context","coworker"],"type":"object","additionalProperties":false}}}]}' headers: User-Agent: - X-USER-AGENT-XXX @@ -342,7 +194,7 @@ interactions: connection: - keep-alive content-length: - - '4694' + - '3151' content-type: - application/json cookie: @@ -366,22 +218,31 @@ interactions: x-stainless-runtime: - CPython x-stainless-runtime-version: - - 3.13.3 + - 3.13.12 method: POST uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-D0u1kZrAEdxxk1GHhh8iEvvddrv5C\",\n \"object\": - \"chat.completion\",\n \"created\": 1769107740,\n \"model\": \"gpt-4o-2024-08-06\",\n + string: "{\n \"id\": \"chatcmpl-DDG9zJ5ZtuBIJLBxuTBqV4pYyaAf3\",\n \"object\": + \"chat.completion\",\n \"created\": 1772052755,\n \"model\": \"gpt-4o-2024-08-06\",\n \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": - \"assistant\",\n \"content\": \"{\\\"score\\\":4}\",\n \"refusal\": - null,\n \"annotations\": []\n },\n \"logprobs\": null,\n - \ \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": - 868,\n \"completion_tokens\": 6,\n \"total_tokens\": 874,\n \"prompt_tokens_details\": - {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": + \"assistant\",\n \"content\": null,\n \"tool_calls\": [\n {\n + \ \"id\": \"call_IdyahKEb4Ez9fWTlL0SWNU97\",\n \"type\": + \"function\",\n \"function\": {\n \"name\": \"ask_question_to_coworker\",\n + \ \"arguments\": \"{\\\"question\\\":\\\"What score would you + give between 1-5 to the following title: 'The impact of AI in the future of + work' and why?\\\",\\\"context\\\":\\\"Your task is to evaluate the title + based on its ability to intrigue, its clarity, and relevance. 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You are a expert at validating the output of a task. By providing effective feedback if the output is not valid.\\nYour personal goal is: Validate the output - of the task\\nTo give my best complete final answer to the task respond using - the exact following format:\\n\\nThought: I now can give a great answer\\nFinal - Answer: Your final answer must be the great and the most complete as possible, - it must be outcome described.\\n\\nI MUST use these formats, my job depends - on it!\"},{\"role\":\"user\",\"content\":\"\\nCurrent Task: \\n Ensure + of the task\"},{\"role\":\"user\",\"content\":\"\\nCurrent Task: \\n Ensure the following task result complies with the given guardrail.\\n\\n Task result:\\n \\n Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy @@ -17,8 +13,9 @@ interactions: what is wrong (e.g., by how much it violates the rule, or what specific part fails).\\n - Focus only on identifying issues \u2014 do not propose corrections.\\n \ - If the Task result complies with the guardrail, saying that is valid\\n - \ \\n\\nBegin! 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It contains 22 words, which exceeds the limit of 10 words."}],"model":"gpt-4o","response_format":{"type":"json_schema","json_schema":{"schema":{"properties":{"valid":{"description":"Whether - the task output complies with the guardrail","title":"Valid","type":"boolean"},"feedback":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"A - feedback about the task output if it is not valid","title":"Feedback"}},"required":["valid","feedback"],"title":"LLMGuardrailResult","type":"object","additionalProperties":false},"name":"LLMGuardrailResult","strict":true}},"stream":false}' - headers: - User-Agent: - - X-USER-AGENT-XXX - accept: - - application/json - accept-encoding: - - ACCEPT-ENCODING-XXX - authorization: - - AUTHORIZATION-XXX - connection: - - keep-alive - content-length: - - '1835' - content-type: - - application/json - cookie: - - COOKIE-XXX - host: - - api.openai.com - x-stainless-arch: - - X-STAINLESS-ARCH-XXX - x-stainless-async: - - 'false' x-stainless-helper-method: - beta.chat.completions.parse x-stainless-lang: @@ -191,23 +52,24 @@ interactions: x-stainless-runtime: - CPython x-stainless-runtime-version: - - 3.13.3 + - 3.13.12 method: POST uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-Cy7yJiPCk4fXuogyT5e8XeGRLCSf8\",\n \"object\": - \"chat.completion\",\n \"created\": 1768446359,\n \"model\": \"gpt-4o-2024-08-06\",\n + string: "{\n \"id\": \"chatcmpl-DDGANa7LCEtvfCZsEly4mNksTjCX3\",\n \"object\": + \"chat.completion\",\n \"created\": 1772052779,\n \"model\": \"gpt-4o-2024-08-06\",\n \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"{\\\"valid\\\":false,\\\"feedback\\\":\\\"The - task output exceeds the word limit of 10 words by containing 22 words.\\\"}\",\n - \ \"refusal\": null,\n \"annotations\": []\n },\n \"logprobs\": - null,\n \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": - 363,\n \"completion_tokens\": 25,\n \"total_tokens\": 388,\n \"prompt_tokens_details\": - {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": + task result contains more than 10 words. Specifically, it has 20 words, which + exceeds the guardrail limit by 10 words.\\\"}\",\n \"refusal\": null,\n + \ \"annotations\": []\n },\n \"logprobs\": null,\n \"finish_reason\": + \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": 290,\n \"completion_tokens\": + 37,\n \"total_tokens\": 327,\n \"prompt_tokens_details\": {\n \"cached_tokens\": + 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": - \"default\",\n \"system_fingerprint\": \"fp_a0e9480a2f\"\n}\n" + \"default\",\n \"system_fingerprint\": \"fp_64dfa806c7\"\n}\n" headers: CF-RAY: - CF-RAY-XXX @@ -216,7 +78,7 @@ interactions: Content-Type: - application/json Date: - - Thu, 15 Jan 2026 03:05:59 GMT + - Wed, 25 Feb 2026 20:53:00 GMT Server: - cloudflare Strict-Transport-Security: @@ -231,18 +93,16 @@ interactions: - h3=":443"; ma=86400 cf-cache-status: - DYNAMIC - content-length: - - '913' openai-organization: - OPENAI-ORG-XXX openai-processing-ms: - - '488' + - '1108' openai-project: - OPENAI-PROJECT-XXX openai-version: - '2020-10-01' - x-envoy-upstream-service-time: - - '507' + set-cookie: + - SET-COOKIE-XXX x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -266,11 +126,7 @@ interactions: body: "{\"messages\":[{\"role\":\"system\",\"content\":\"You are Guardrail Agent. You are a expert at validating the output of a task. By providing effective feedback if the output is not valid.\\nYour personal goal is: Validate the output - of the task\\nTo give my best complete final answer to the task respond using - the exact following format:\\n\\nThought: I now can give a great answer\\nFinal - Answer: Your final answer must be the great and the most complete as possible, - it must be outcome described.\\n\\nI MUST use these formats, my job depends - on it!\"},{\"role\":\"user\",\"content\":\"\\nCurrent Task: \\n Ensure + of the task\"},{\"role\":\"user\",\"content\":\"\\nCurrent Task: \\n Ensure the following task result complies with the given guardrail.\\n\\n Task result:\\n \\n Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy @@ -280,8 +136,9 @@ interactions: explaining what is wrong (e.g., by how much it violates the rule, or what specific part fails).\\n - Focus only on identifying issues \u2014 do not propose corrections.\\n - If the Task result complies with the guardrail, saying - that is valid\\n \\n\\nBegin! This is VERY important to you, use the - tools available and give your best Final Answer, your job depends on it!\\n\\nThought:\"}],\"model\":\"gpt-4o\"}" + that is valid\\n \\n\\nProvide your complete response:\"}],\"model\":\"gpt-4o\",\"response_format\":{\"type\":\"json_schema\",\"json_schema\":{\"schema\":{\"properties\":{\"valid\":{\"description\":\"Whether + the task output complies with the guardrail\",\"title\":\"Valid\",\"type\":\"boolean\"},\"feedback\":{\"anyOf\":[{\"type\":\"string\"},{\"type\":\"null\"}],\"description\":\"A + feedback about the task output if it is not valid\",\"title\":\"Feedback\"}},\"required\":[\"valid\",\"feedback\"],\"title\":\"LLMGuardrailResult\",\"type\":\"object\",\"additionalProperties\":false},\"name\":\"LLMGuardrailResult\",\"strict\":true}},\"stream\":false}" headers: User-Agent: - X-USER-AGENT-XXX @@ -294,7 +151,7 @@ interactions: connection: - keep-alive content-length: - - '1468' + - '1568' content-type: - application/json host: @@ -303,144 +160,6 @@ interactions: - X-STAINLESS-ARCH-XXX x-stainless-async: - 'false' - x-stainless-lang: - - python - x-stainless-os: - - X-STAINLESS-OS-XXX - x-stainless-package-version: - - 1.83.0 - x-stainless-read-timeout: - - X-STAINLESS-READ-TIMEOUT-XXX - x-stainless-retry-count: - - '0' - x-stainless-runtime: - - CPython - x-stainless-runtime-version: - - 3.13.3 - method: POST - uri: https://api.openai.com/v1/chat/completions - response: - body: - string: "{\n \"id\": \"chatcmpl-Cy7yKa0rmi2YoTLpyXt9hjeLt2rTI\",\n \"object\": - \"chat.completion\",\n \"created\": 1768446360,\n \"model\": \"gpt-4o-2024-08-06\",\n - \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": - \"assistant\",\n \"content\": \"First, I'll count the number of words - in the Task result to ensure it complies with the guardrail. \\n\\nThe Task - result is: \\\"Lorem Ipsum is simply dummy text of the printing and typesetting - industry. 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Please try again.", style="bold red" ) - @patch("requests.post") + @patch("crewai.cli.authentication.main.httpx.post") def test_poll_for_token_error(self, mock_post): """Test the method to poll for token (error path).""" # Setup mock to return error @@ -338,5 +338,5 @@ class TestAuthenticationCommand: device_code_data = {"device_code": "test_device_code", "interval": 1} - with pytest.raises(requests.HTTPError): + with pytest.raises(httpx.HTTPError): self.auth_command._poll_for_token(device_code_data) diff --git a/lib/crewai/tests/cli/deploy/test_deploy_main.py b/lib/crewai/tests/cli/deploy/test_deploy_main.py index f33dfbbd5..4b818cc58 100644 --- a/lib/crewai/tests/cli/deploy/test_deploy_main.py +++ b/lib/crewai/tests/cli/deploy/test_deploy_main.py @@ -4,10 +4,11 @@ from io import StringIO from unittest.mock import MagicMock, Mock, patch import pytest -import requests +import json + +import httpx from crewai.cli.deploy.main import DeployCommand from crewai.cli.utils import parse_toml -from requests.exceptions import JSONDecodeError class TestDeployCommand(unittest.TestCase): @@ -37,18 +38,18 @@ class TestDeployCommand(unittest.TestCase): DeployCommand() def test_validate_response_successful_response(self): - mock_response = Mock(spec=requests.Response) + mock_response = Mock(spec=httpx.Response) mock_response.json.return_value = {"message": "Success"} mock_response.status_code = 200 - mock_response.ok = True + mock_response.is_success = True with patch("sys.stdout", new=StringIO()) as fake_out: self.deploy_command._validate_response(mock_response) assert fake_out.getvalue() == "" def test_validate_response_json_decode_error(self): - mock_response = Mock(spec=requests.Response) - mock_response.json.side_effect = JSONDecodeError("Decode error", "", 0) + mock_response = Mock(spec=httpx.Response) + mock_response.json.side_effect = json.JSONDecodeError("Decode error", "", 0) mock_response.status_code = 500 mock_response.content = b"Invalid JSON" @@ -64,13 +65,13 @@ class TestDeployCommand(unittest.TestCase): assert "Response:\nInvalid JSON" in output def test_validate_response_422_error(self): - mock_response = Mock(spec=requests.Response) + mock_response = Mock(spec=httpx.Response) mock_response.json.return_value = { "field1": ["Error message 1"], "field2": ["Error message 2"], } mock_response.status_code = 422 - mock_response.ok = False + mock_response.is_success = False with patch("sys.stdout", new=StringIO()) as fake_out: with pytest.raises(SystemExit): @@ -84,10 +85,10 @@ class TestDeployCommand(unittest.TestCase): assert "Field2 Error message 2" in output def test_validate_response_other_error(self): - mock_response = Mock(spec=requests.Response) + mock_response = Mock(spec=httpx.Response) mock_response.json.return_value = {"error": "Something went wrong"} mock_response.status_code = 500 - mock_response.ok = False + mock_response.is_success = False with patch("sys.stdout", new=StringIO()) as fake_out: with pytest.raises(SystemExit): diff --git a/lib/crewai/tests/cli/enterprise/test_main.py b/lib/crewai/tests/cli/enterprise/test_main.py index e6be4e006..8a225dc41 100644 --- a/lib/crewai/tests/cli/enterprise/test_main.py +++ b/lib/crewai/tests/cli/enterprise/test_main.py @@ -3,8 +3,9 @@ import unittest from pathlib import Path from unittest.mock import Mock, patch -import requests -from requests.exceptions import JSONDecodeError +import json + +import httpx from crewai.cli.enterprise.main import EnterpriseConfigureCommand from crewai.cli.settings.main import SettingsCommand @@ -25,7 +26,7 @@ class TestEnterpriseConfigureCommand(unittest.TestCase): def tearDown(self): shutil.rmtree(self.test_dir) - @patch('crewai.cli.enterprise.main.requests.get') + @patch('crewai.cli.enterprise.main.httpx.get') @patch('crewai.cli.enterprise.main.get_crewai_version') def test_successful_configuration(self, mock_get_version, mock_requests_get): mock_get_version.return_value = "1.0.0" @@ -73,19 +74,23 @@ class TestEnterpriseConfigureCommand(unittest.TestCase): self.assertEqual(call_args[0], key) self.assertEqual(call_args[1], value) - @patch('crewai.cli.enterprise.main.requests.get') + @patch('crewai.cli.enterprise.main.httpx.get') @patch('crewai.cli.enterprise.main.get_crewai_version') def test_http_error_handling(self, mock_get_version, mock_requests_get): mock_get_version.return_value = "1.0.0" mock_response = Mock() - mock_response.raise_for_status.side_effect = requests.HTTPError("404 Not Found") + mock_response.raise_for_status.side_effect = httpx.HTTPStatusError( + "404 Not Found", + request=httpx.Request("GET", "http://test"), + response=httpx.Response(404), + ) mock_requests_get.return_value = mock_response with self.assertRaises(SystemExit): self.enterprise_command.configure("https://enterprise.example.com") - @patch('crewai.cli.enterprise.main.requests.get') + @patch('crewai.cli.enterprise.main.httpx.get') @patch('crewai.cli.enterprise.main.get_crewai_version') def test_invalid_json_response(self, mock_get_version, mock_requests_get): mock_get_version.return_value = "1.0.0" @@ -93,13 +98,13 @@ class TestEnterpriseConfigureCommand(unittest.TestCase): mock_response = Mock() mock_response.status_code = 200 mock_response.raise_for_status.return_value = None - mock_response.json.side_effect = JSONDecodeError("Invalid JSON", "", 0) + mock_response.json.side_effect = json.JSONDecodeError("Invalid JSON", "", 0) mock_requests_get.return_value = mock_response with self.assertRaises(SystemExit): self.enterprise_command.configure("https://enterprise.example.com") - @patch('crewai.cli.enterprise.main.requests.get') + @patch('crewai.cli.enterprise.main.httpx.get') @patch('crewai.cli.enterprise.main.get_crewai_version') def test_missing_required_fields(self, mock_get_version, mock_requests_get): mock_get_version.return_value = "1.0.0" @@ -115,7 +120,7 @@ class TestEnterpriseConfigureCommand(unittest.TestCase): with self.assertRaises(SystemExit): self.enterprise_command.configure("https://enterprise.example.com") - @patch('crewai.cli.enterprise.main.requests.get') + @patch('crewai.cli.enterprise.main.httpx.get') @patch('crewai.cli.enterprise.main.get_crewai_version') def test_settings_update_error(self, mock_get_version, mock_requests_get): mock_get_version.return_value = "1.0.0" diff --git a/lib/crewai/tests/cli/organization/test_main.py b/lib/crewai/tests/cli/organization/test_main.py index c0620fe33..0db790cbb 100644 --- a/lib/crewai/tests/cli/organization/test_main.py +++ b/lib/crewai/tests/cli/organization/test_main.py @@ -3,7 +3,7 @@ from unittest.mock import MagicMock, patch, call import pytest from click.testing import CliRunner -import requests +import httpx from crewai.cli.organization.main import OrganizationCommand from crewai.cli.cli import org_list, switch, current @@ -115,7 +115,7 @@ class TestOrganizationCommand(unittest.TestCase): def test_list_organizations_api_error(self, mock_console): self.org_command.plus_api_client = MagicMock() self.org_command.plus_api_client.get_organizations.side_effect = ( - requests.exceptions.RequestException("API Error") + httpx.HTTPError("API Error") ) with pytest.raises(SystemExit): @@ -201,8 +201,10 @@ class TestOrganizationCommand(unittest.TestCase): @patch("crewai.cli.organization.main.console") def test_list_organizations_unauthorized(self, mock_console): mock_response = MagicMock() - mock_http_error = requests.exceptions.HTTPError( - "401 Client Error: Unauthorized", response=MagicMock(status_code=401) + mock_http_error = httpx.HTTPStatusError( + "401 Client Error: Unauthorized", + request=httpx.Request("GET", "http://test"), + response=httpx.Response(401), ) mock_response.raise_for_status.side_effect = mock_http_error @@ -219,8 +221,10 @@ class TestOrganizationCommand(unittest.TestCase): @patch("crewai.cli.organization.main.console") def test_switch_organization_unauthorized(self, mock_console): mock_response = MagicMock() - mock_http_error = requests.exceptions.HTTPError( - "401 Client Error: Unauthorized", response=MagicMock(status_code=401) + mock_http_error = httpx.HTTPStatusError( + "401 Client Error: Unauthorized", + request=httpx.Request("GET", "http://test"), + response=httpx.Response(401), ) mock_response.raise_for_status.side_effect = mock_http_error diff --git a/lib/crewai/tests/cli/test_cli.py b/lib/crewai/tests/cli/test_cli.py index 4f4141269..ed74a6036 100644 --- a/lib/crewai/tests/cli/test_cli.py +++ b/lib/crewai/tests/cli/test_cli.py @@ -66,7 +66,9 @@ def mock_crew(): def mock_get_crews(mock_crew): with mock.patch( "crewai.cli.reset_memories_command.get_crews", return_value=[mock_crew] - ) as mock_get_crew: + ) as mock_get_crew, mock.patch( + "crewai.cli.reset_memories_command.get_flows", return_value=[] + ): yield mock_get_crew @@ -85,39 +87,41 @@ def test_reset_all_memories(mock_get_crews, runner): assert call_count == 1, "reset_memories should have been called once" -def test_reset_short_term_memories(mock_get_crews, runner): - result = runner.invoke(reset_memories, ["-s"]) +def test_reset_memory(mock_get_crews, runner): + result = runner.invoke(reset_memories, ["-m"]) call_count = 0 for crew in mock_get_crews.return_value: - crew.reset_memories.assert_called_once_with(command_type="short") + crew.reset_memories.assert_called_once_with(command_type="memory") assert ( - f"[Crew ({crew.name})] Short term memory has been reset." in result.output + f"[Crew ({crew.name})] Memory has been reset." in result.output ) call_count += 1 assert call_count == 1, "reset_memories should have been called once" -def test_reset_entity_memories(mock_get_crews, runner): +def test_reset_short_flag_deprecated_maps_to_memory(mock_get_crews, runner): + result = runner.invoke(reset_memories, ["-s"]) + assert "deprecated" in result.output.lower() + for crew in mock_get_crews.return_value: + crew.reset_memories.assert_called_once_with(command_type="memory") + assert f"[Crew ({crew.name})] Memory has been reset." in result.output + + +def test_reset_entity_flag_deprecated_maps_to_memory(mock_get_crews, runner): result = runner.invoke(reset_memories, ["-e"]) - call_count = 0 + assert "deprecated" in result.output.lower() for crew in mock_get_crews.return_value: - crew.reset_memories.assert_called_once_with(command_type="entity") - assert f"[Crew ({crew.name})] Entity memory has been reset." in result.output - call_count += 1 - - assert call_count == 1, "reset_memories should have been called once" + crew.reset_memories.assert_called_once_with(command_type="memory") + assert f"[Crew ({crew.name})] Memory has been reset." in result.output -def test_reset_long_term_memories(mock_get_crews, runner): +def test_reset_long_flag_deprecated_maps_to_memory(mock_get_crews, runner): result = runner.invoke(reset_memories, ["-l"]) - call_count = 0 + assert "deprecated" in result.output.lower() for crew in mock_get_crews.return_value: - crew.reset_memories.assert_called_once_with(command_type="long") - assert f"[Crew ({crew.name})] Long term memory has been reset." in result.output - call_count += 1 - - assert call_count == 1, "reset_memories should have been called once" + crew.reset_memories.assert_called_once_with(command_type="memory") + assert f"[Crew ({crew.name})] Memory has been reset." in result.output def test_reset_kickoff_outputs(mock_get_crews, runner): @@ -134,17 +138,14 @@ def test_reset_kickoff_outputs(mock_get_crews, runner): assert call_count == 1, "reset_memories should have been called once" -def test_reset_multiple_memory_flags(mock_get_crews, runner): +def test_reset_multiple_legacy_flags_collapsed_to_single_memory_reset(mock_get_crews, runner): result = runner.invoke(reset_memories, ["-s", "-l"]) + # Both legacy flags collapse to a single --memory reset + assert "deprecated" in result.output.lower() call_count = 0 for crew in mock_get_crews.return_value: - crew.reset_memories.assert_has_calls( - [mock.call(command_type="long"), mock.call(command_type="short")] - ) - assert ( - f"[Crew ({crew.name})] Long term memory has been reset.\n" - f"[Crew ({crew.name})] Short term memory has been reset.\n" in result.output - ) + crew.reset_memories.assert_called_once_with(command_type="memory") + assert f"[Crew ({crew.name})] Memory has been reset." in result.output call_count += 1 assert call_count == 1, "reset_memories should have been called once" @@ -194,6 +195,79 @@ def test_reset_memory_from_many_crews(mock_get_crews, runner): assert call_count == 2, "reset_memories should have been called twice" +@pytest.fixture +def mock_flow(): + _mock = mock.Mock() + _mock.name = "TestFlow" + _mock.memory = mock.Mock() + _mock.memory.reset = mock.Mock() + return _mock + + +@pytest.fixture +def mock_get_flows(mock_flow): + with mock.patch( + "crewai.cli.reset_memories_command.get_flows", return_value=[mock_flow] + ) as mock_get_flow, mock.patch( + "crewai.cli.reset_memories_command.get_crews", return_value=[] + ): + yield mock_get_flow + + +def test_reset_flow_memory(mock_get_flows, mock_flow, runner): + result = runner.invoke(reset_memories, ["-m"]) + mock_flow.memory.reset.assert_called_once() + assert "[Flow (TestFlow)] Memory has been reset." in result.output + + +def test_reset_flow_all_memories(mock_get_flows, mock_flow, runner): + result = runner.invoke(reset_memories, ["-a"]) + mock_flow.memory.reset.assert_called_once() + assert "[Flow (TestFlow)] Reset memories command has been completed." in result.output + + +def test_reset_flow_knowledge_no_effect(mock_get_flows, mock_flow, runner): + result = runner.invoke(reset_memories, ["--knowledge"]) + mock_flow.memory.reset.assert_not_called() + assert "[Flow (TestFlow)]" not in result.output + + +def test_reset_no_crew_or_flow_found(runner): + with mock.patch( + "crewai.cli.reset_memories_command.get_crews", return_value=[] + ), mock.patch( + "crewai.cli.reset_memories_command.get_flows", return_value=[] + ): + result = runner.invoke(reset_memories, ["-m"]) + assert "No crew or flow found." in result.output + + +def test_reset_crew_and_flow_memory(mock_crew, mock_flow, runner): + with mock.patch( + "crewai.cli.reset_memories_command.get_crews", return_value=[mock_crew] + ), mock.patch( + "crewai.cli.reset_memories_command.get_flows", return_value=[mock_flow] + ): + result = runner.invoke(reset_memories, ["-m"]) + mock_crew.reset_memories.assert_called_once_with(command_type="memory") + mock_flow.memory.reset.assert_called_once() + assert f"[Crew ({mock_crew.name})] Memory has been reset." in result.output + assert "[Flow (TestFlow)] Memory has been reset." in result.output + + +def test_reset_flow_memory_none(runner): + mock_flow = mock.Mock() + mock_flow.name = "NoMemFlow" + mock_flow.memory = None + with mock.patch( + "crewai.cli.reset_memories_command.get_crews", return_value=[] + ), mock.patch( + "crewai.cli.reset_memories_command.get_flows", return_value=[mock_flow] + ): + result = runner.invoke(reset_memories, ["-m"]) + assert "[Flow (NoMemFlow)] Memory has been reset." in result.output + + def test_reset_no_memory_flags(runner): result = runner.invoke( reset_memories, diff --git a/lib/crewai/tests/cli/test_plus_api.py b/lib/crewai/tests/cli/test_plus_api.py index 70eff917e..95a322e21 100644 --- a/lib/crewai/tests/cli/test_plus_api.py +++ b/lib/crewai/tests/cli/test_plus_api.py @@ -28,14 +28,26 @@ class TestPlusAPI(unittest.TestCase): response = self.api.login_to_tool_repository() mock_make_request.assert_called_once_with( - "POST", "/crewai_plus/api/v1/tools/login" + "POST", "/crewai_plus/api/v1/tools/login", json={} + ) + self.assertEqual(response, mock_response) + + @patch("crewai.cli.plus_api.PlusAPI._make_request") + def test_login_to_tool_repository_with_user_identifier(self, mock_make_request): + mock_response = MagicMock() + mock_make_request.return_value = mock_response + + response = self.api.login_to_tool_repository(user_identifier="test-hash-123") + + mock_make_request.assert_called_once_with( + "POST", "/crewai_plus/api/v1/tools/login", json={"user_identifier": "test-hash-123"} ) self.assertEqual(response, mock_response) def assert_request_with_org_id( - self, mock_make_request, method: str, endpoint: str, **kwargs + self, mock_client_instance, method: str, endpoint: str, **kwargs ): - mock_make_request.assert_called_once_with( + mock_client_instance.request.assert_called_once_with( method, f"{os.getenv('CREWAI_PLUS_URL')}{endpoint}", headers={ @@ -49,24 +61,25 @@ class TestPlusAPI(unittest.TestCase): ) @patch("crewai.cli.plus_api.Settings") - @patch("requests.Session.request") + @patch("crewai.cli.plus_api.httpx.Client") def test_login_to_tool_repository_with_org_uuid( - self, mock_make_request, mock_settings_class + self, mock_client_class, mock_settings_class ): mock_settings = MagicMock() mock_settings.org_uuid = self.org_uuid mock_settings.enterprise_base_url = os.getenv('CREWAI_PLUS_URL') mock_settings_class.return_value = mock_settings - # re-initialize Client self.api = PlusAPI(self.api_key) + mock_client_instance = MagicMock() mock_response = MagicMock() - mock_make_request.return_value = mock_response + mock_client_instance.request.return_value = mock_response + mock_client_class.return_value.__enter__.return_value = mock_client_instance response = self.api.login_to_tool_repository() self.assert_request_with_org_id( - mock_make_request, "POST", "/crewai_plus/api/v1/tools/login" + mock_client_instance, "POST", "/crewai_plus/api/v1/tools/login", json={} ) self.assertEqual(response, mock_response) @@ -82,23 +95,23 @@ class TestPlusAPI(unittest.TestCase): self.assertEqual(response, mock_response) @patch("crewai.cli.plus_api.Settings") - @patch("requests.Session.request") - def test_get_tool_with_org_uuid(self, mock_make_request, mock_settings_class): + @patch("crewai.cli.plus_api.httpx.Client") + def test_get_tool_with_org_uuid(self, mock_client_class, mock_settings_class): mock_settings = MagicMock() mock_settings.org_uuid = self.org_uuid mock_settings.enterprise_base_url = os.getenv('CREWAI_PLUS_URL') mock_settings_class.return_value = mock_settings - # re-initialize Client self.api = PlusAPI(self.api_key) - # Set up mock response + mock_client_instance = MagicMock() mock_response = MagicMock() - mock_make_request.return_value = mock_response + mock_client_instance.request.return_value = mock_response + mock_client_class.return_value.__enter__.return_value = mock_client_instance response = self.api.get_tool("test_tool_handle") self.assert_request_with_org_id( - mock_make_request, "GET", "/crewai_plus/api/v1/tools/test_tool_handle" + mock_client_instance, "GET", "/crewai_plus/api/v1/tools/test_tool_handle" ) self.assertEqual(response, mock_response) @@ -130,18 +143,18 @@ class TestPlusAPI(unittest.TestCase): self.assertEqual(response, mock_response) @patch("crewai.cli.plus_api.Settings") - @patch("requests.Session.request") - def test_publish_tool_with_org_uuid(self, mock_make_request, mock_settings_class): + @patch("crewai.cli.plus_api.httpx.Client") + def test_publish_tool_with_org_uuid(self, mock_client_class, mock_settings_class): mock_settings = MagicMock() mock_settings.org_uuid = self.org_uuid mock_settings.enterprise_base_url = os.getenv('CREWAI_PLUS_URL') mock_settings_class.return_value = mock_settings - # re-initialize Client self.api = PlusAPI(self.api_key) - # Set up mock response + mock_client_instance = MagicMock() mock_response = MagicMock() - mock_make_request.return_value = mock_response + mock_client_instance.request.return_value = mock_response + mock_client_class.return_value.__enter__.return_value = mock_client_instance handle = "test_tool_handle" public = True @@ -153,7 +166,6 @@ class TestPlusAPI(unittest.TestCase): handle, public, version, description, encoded_file ) - # Expected params including organization_uuid expected_params = { "handle": handle, "public": public, @@ -164,7 +176,7 @@ class TestPlusAPI(unittest.TestCase): } self.assert_request_with_org_id( - mock_make_request, "POST", "/crewai_plus/api/v1/tools", json=expected_params + mock_client_instance, "POST", "/crewai_plus/api/v1/tools", json=expected_params ) self.assertEqual(response, mock_response) @@ -195,20 +207,19 @@ class TestPlusAPI(unittest.TestCase): ) self.assertEqual(response, mock_response) - @patch("crewai.cli.plus_api.requests.Session") - def test_make_request(self, mock_session): + @patch("crewai.cli.plus_api.httpx.Client") + def test_make_request(self, mock_client_class): + mock_client_instance = MagicMock() mock_response = MagicMock() - - mock_session_instance = mock_session.return_value - mock_session_instance.request.return_value = mock_response + mock_client_instance.request.return_value = mock_response + mock_client_class.return_value.__enter__.return_value = mock_client_instance response = self.api._make_request("GET", "test_endpoint") - mock_session.assert_called_once() - mock_session_instance.request.assert_called_once_with( + mock_client_class.assert_called_once_with(trust_env=False, verify=True) + mock_client_instance.request.assert_called_once_with( "GET", f"{self.api.base_url}/test_endpoint", headers=self.api.headers ) - mock_session_instance.trust_env = False self.assertEqual(response, mock_response) @patch("crewai.cli.plus_api.PlusAPI._make_request") diff --git a/lib/crewai/tests/cli/tools/test_main.py b/lib/crewai/tests/cli/tools/test_main.py index 71acea76d..6661011d3 100644 --- a/lib/crewai/tests/cli/tools/test_main.py +++ b/lib/crewai/tests/cli/tools/test_main.py @@ -351,7 +351,7 @@ def test_publish_api_error( mock_response = MagicMock() mock_response.status_code = 500 mock_response.json.return_value = {"error": "Internal Server Error"} - mock_response.ok = False + mock_response.is_success = False mock_publish.return_value = mock_response with raises(SystemExit): diff --git a/lib/crewai/tests/cli/triggers/test_main.py b/lib/crewai/tests/cli/triggers/test_main.py index 93d24568d..641abc7cf 100644 --- a/lib/crewai/tests/cli/triggers/test_main.py +++ b/lib/crewai/tests/cli/triggers/test_main.py @@ -3,7 +3,7 @@ import subprocess import unittest from unittest.mock import Mock, patch -import requests +import httpx from crewai.cli.triggers.main import TriggersCommand @@ -21,7 +21,7 @@ class TestTriggersCommand(unittest.TestCase): @patch("crewai.cli.triggers.main.console.print") def test_list_triggers_success(self, mock_console_print): - mock_response = Mock(spec=requests.Response) + mock_response = Mock(spec=httpx.Response) mock_response.status_code = 200 mock_response.ok = True mock_response.json.return_value = { @@ -50,7 +50,7 @@ class TestTriggersCommand(unittest.TestCase): @patch("crewai.cli.triggers.main.console.print") def test_list_triggers_no_apps(self, mock_console_print): - mock_response = Mock(spec=requests.Response) + mock_response = Mock(spec=httpx.Response) mock_response.status_code = 200 mock_response.ok = True mock_response.json.return_value = {"apps": []} @@ -81,7 +81,7 @@ class TestTriggersCommand(unittest.TestCase): @patch("crewai.cli.triggers.main.console.print") @patch.object(TriggersCommand, "_run_crew_with_payload") def test_execute_with_trigger_success(self, mock_run_crew, mock_console_print): - mock_response = Mock(spec=requests.Response) + mock_response = Mock(spec=httpx.Response) mock_response.status_code = 200 mock_response.ok = True mock_response.json.return_value = { @@ -99,7 +99,7 @@ class TestTriggersCommand(unittest.TestCase): @patch("crewai.cli.triggers.main.console.print") def test_execute_with_trigger_not_found(self, mock_console_print): - mock_response = Mock(spec=requests.Response) + mock_response = Mock(spec=httpx.Response) mock_response.status_code = 404 mock_response.json.return_value = {"error": "Trigger not found"} self.mock_client.get_trigger_payload.return_value = mock_response @@ -159,7 +159,7 @@ class TestTriggersCommand(unittest.TestCase): @patch("crewai.cli.triggers.main.console.print") def test_execute_with_trigger_with_default_error_message(self, mock_console_print): - mock_response = Mock(spec=requests.Response) + mock_response = Mock(spec=httpx.Response) mock_response.status_code = 404 mock_response.json.return_value = {} self.mock_client.get_trigger_payload.return_value = mock_response diff --git a/lib/crewai/tests/llms/bedrock/test_bedrock.py b/lib/crewai/tests/llms/bedrock/test_bedrock.py index efe3191e7..531e4d967 100644 --- a/lib/crewai/tests/llms/bedrock/test_bedrock.py +++ b/lib/crewai/tests/llms/bedrock/test_bedrock.py @@ -437,17 +437,36 @@ def test_bedrock_aws_credentials_configuration(): """ Test that AWS credentials configuration works properly """ + aws_access_key_id = "test-access-key" + aws_secret_access_key = "test-secret-key" + aws_region_name = "us-east-1" + + # Test with environment variables with patch.dict(os.environ, { - "AWS_ACCESS_KEY_ID": "test-access-key", - "AWS_SECRET_ACCESS_KEY": "test-secret-key", - "AWS_DEFAULT_REGION": "us-east-1" + "AWS_ACCESS_KEY_ID": aws_access_key_id, + "AWS_SECRET_ACCESS_KEY": aws_secret_access_key, + "AWS_DEFAULT_REGION": aws_region_name }): llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") from crewai.llms.providers.bedrock.completion import BedrockCompletion assert isinstance(llm, BedrockCompletion) - assert llm.region_name == "us-east-1" + assert llm.region_name == aws_region_name + assert llm.aws_access_key_id == aws_access_key_id + assert llm.aws_secret_access_key == aws_secret_access_key + + # Test with litellm environment variables + with patch.dict(os.environ, { + "AWS_ACCESS_KEY_ID": aws_access_key_id, + "AWS_SECRET_ACCESS_KEY": aws_secret_access_key, + "AWS_REGION_NAME": aws_region_name + }): + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + from crewai.llms.providers.bedrock.completion import BedrockCompletion + assert isinstance(llm, BedrockCompletion) + assert llm.region_name == aws_region_name # Test with explicit credentials llm_explicit = LLM( diff --git a/lib/crewai/tests/llms/google/test_google.py b/lib/crewai/tests/llms/google/test_google.py index 3f86388d5..6f475ef49 100644 --- a/lib/crewai/tests/llms/google/test_google.py +++ b/lib/crewai/tests/llms/google/test_google.py @@ -957,6 +957,47 @@ def test_gemini_agent_kickoff_structured_output_with_tools(): +@pytest.mark.vcr() +def test_gemini_crew_structured_output_with_tools(): + """ + Test that a crew with Gemini can use both tools and output_pydantic on a task. + """ + from pydantic import BaseModel, Field + from crewai.tools import tool + + class CalculationResult(BaseModel): + operation: str = Field(description="The mathematical operation performed") + result: int = Field(description="The result of the calculation") + explanation: str = Field(description="Brief explanation of the calculation") + + @tool + def add_numbers(a: int, b: int) -> int: + """Add two numbers together and return the sum.""" + return a + b + + agent = Agent( + role="Calculator", + goal="Perform calculations using available tools", + backstory="You are a calculator assistant that uses tools to compute results.", + llm=LLM(model="google/gemini-2.0-flash-001"), + tools=[add_numbers], + ) + + task = Task( + description="Calculate 15 + 27 using your add_numbers tool. Report the result.", + expected_output="A structured calculation result", + output_pydantic=CalculationResult, + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + assert result.pydantic is not None, "Expected pydantic output but got None" + assert isinstance(result.pydantic, CalculationResult) + assert result.pydantic.result == 42, f"Expected 42 but got {result.pydantic.result}" + + def test_gemini_stop_words_not_applied_to_structured_output(): """ Test that stop words are NOT applied when response_model is provided. diff --git a/lib/crewai/tests/mcp/test_amp_mcp.py b/lib/crewai/tests/mcp/test_amp_mcp.py new file mode 100644 index 000000000..3c4001f3c --- /dev/null +++ b/lib/crewai/tests/mcp/test_amp_mcp.py @@ -0,0 +1,373 @@ +"""Tests for AMP MCP config fetching and tool resolution.""" + +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest +from crewai.agent.core import Agent +from crewai.mcp.config import MCPServerHTTP, MCPServerSSE +from crewai.mcp.tool_resolver import MCPToolResolver +from crewai.tools.base_tool import BaseTool + + +@pytest.fixture +def agent(): + return Agent( + role="Test Agent", + goal="Test goal", + backstory="Test backstory", + ) + + +@pytest.fixture +def resolver(agent): + return MCPToolResolver(agent=agent, logger=agent._logger) + + +@pytest.fixture +def mock_tool_definitions(): + return [ + { + "name": "search", + "description": "Search tool", + "inputSchema": { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"], + }, + }, + { + "name": "create_page", + "description": "Create a page", + "inputSchema": {}, + }, + ] + + +class TestBuildMCPConfigFromDict: + def test_builds_http_config(self): + config_dict = { + "type": "http", + "url": "https://mcp.example.com/api", + "headers": {"Authorization": "Bearer token123"}, + "streamable": True, + "cache_tools_list": False, + } + + result = MCPToolResolver._build_mcp_config_from_dict(config_dict) + + assert isinstance(result, MCPServerHTTP) + assert result.url == "https://mcp.example.com/api" + assert result.headers == {"Authorization": "Bearer token123"} + assert result.streamable is True + assert result.cache_tools_list is False + + def test_builds_sse_config(self): + config_dict = { + "type": "sse", + "url": "https://mcp.example.com/sse", + "headers": {"Authorization": "Bearer token123"}, + "cache_tools_list": True, + } + + result = MCPToolResolver._build_mcp_config_from_dict(config_dict) + + assert isinstance(result, MCPServerSSE) + assert result.url == "https://mcp.example.com/sse" + assert result.headers == {"Authorization": "Bearer token123"} + assert result.cache_tools_list is True + + def test_defaults_to_http(self): + config_dict = { + "url": "https://mcp.example.com/api", + } + + result = MCPToolResolver._build_mcp_config_from_dict(config_dict) + + assert isinstance(result, MCPServerHTTP) + assert result.streamable is True + + def test_http_defaults(self): + config_dict = { + "type": "http", + "url": "https://mcp.example.com/api", + } + + result = MCPToolResolver._build_mcp_config_from_dict(config_dict) + + assert result.headers is None + assert result.streamable is True + assert result.cache_tools_list is False + + +class TestFetchAmpMCPConfigs: + @patch("crewai.cli.plus_api.PlusAPI") + @patch("crewai_tools.tools.crewai_platform_tools.misc.get_platform_integration_token", return_value="test-api-key") + def test_fetches_configs_successfully(self, mock_get_token, mock_plus_api_class, resolver): + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "configs": { + "notion": { + "type": "sse", + "url": "https://mcp.notion.so/sse", + "headers": {"Authorization": "Bearer notion-token"}, + }, + "github": { + "type": "http", + "url": "https://mcp.github.com/api", + "headers": {"Authorization": "Bearer gh-token"}, + }, + }, + } + mock_plus_api = MagicMock() + mock_plus_api.get_mcp_configs.return_value = mock_response + mock_plus_api_class.return_value = mock_plus_api + + result = resolver._fetch_amp_mcp_configs(["notion", "github"]) + + assert "notion" in result + assert "github" in result + assert result["notion"]["url"] == "https://mcp.notion.so/sse" + mock_plus_api_class.assert_called_once_with(api_key="test-api-key") + mock_plus_api.get_mcp_configs.assert_called_once_with(["notion", "github"]) + + @patch("crewai.cli.plus_api.PlusAPI") + @patch("crewai_tools.tools.crewai_platform_tools.misc.get_platform_integration_token", return_value="test-api-key") + def test_omits_missing_slugs(self, mock_get_token, mock_plus_api_class, resolver): + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "configs": {"notion": {"type": "sse", "url": "https://mcp.notion.so/sse"}}, + } + mock_plus_api = MagicMock() + mock_plus_api.get_mcp_configs.return_value = mock_response + mock_plus_api_class.return_value = mock_plus_api + + result = resolver._fetch_amp_mcp_configs(["notion", "missing-server"]) + + assert "notion" in result + assert "missing-server" not in result + + @patch("crewai.cli.plus_api.PlusAPI") + @patch("crewai_tools.tools.crewai_platform_tools.misc.get_platform_integration_token", return_value="test-api-key") + def test_returns_empty_on_http_error(self, mock_get_token, mock_plus_api_class, resolver): + mock_response = MagicMock() + mock_response.status_code = 500 + mock_plus_api = MagicMock() + mock_plus_api.get_mcp_configs.return_value = mock_response + mock_plus_api_class.return_value = mock_plus_api + + result = resolver._fetch_amp_mcp_configs(["notion"]) + + assert result == {} + + @patch("crewai.cli.plus_api.PlusAPI") + @patch("crewai_tools.tools.crewai_platform_tools.misc.get_platform_integration_token", return_value="test-api-key") + def test_returns_empty_on_network_error(self, mock_get_token, mock_plus_api_class, resolver): + import httpx + + mock_plus_api = MagicMock() + mock_plus_api.get_mcp_configs.side_effect = httpx.ConnectError("Connection refused") + mock_plus_api_class.return_value = mock_plus_api + + result = resolver._fetch_amp_mcp_configs(["notion"]) + + assert result == {} + + @patch("crewai_tools.tools.crewai_platform_tools.misc.get_platform_integration_token", side_effect=Exception("No token")) + def test_returns_empty_when_no_token(self, mock_get_token, resolver): + result = resolver._fetch_amp_mcp_configs(["notion"]) + + assert result == {} + + +class TestParseAmpRef: + def test_bare_slug(self): + slug, tool = MCPToolResolver._parse_amp_ref("notion") + assert slug == "notion" + assert tool is None + + def test_bare_slug_with_tool(self): + slug, tool = MCPToolResolver._parse_amp_ref("notion#search") + assert slug == "notion" + assert tool == "search" + + def test_bare_slug_with_empty_tool(self): + slug, tool = MCPToolResolver._parse_amp_ref("notion#") + assert slug == "notion" + assert tool is None + + def test_legacy_prefix_slug(self): + slug, tool = MCPToolResolver._parse_amp_ref("crewai-amp:notion") + assert slug == "notion" + assert tool is None + + def test_legacy_prefix_with_tool(self): + slug, tool = MCPToolResolver._parse_amp_ref("crewai-amp:notion#search") + assert slug == "notion" + assert tool == "search" + + +class TestGetMCPToolsAmpIntegration: + @patch("crewai.mcp.tool_resolver.MCPClient") + @patch.object(MCPToolResolver, "_fetch_amp_mcp_configs") + def test_single_request_for_multiple_amp_refs( + self, mock_fetch, mock_client_class, agent, mock_tool_definitions + ): + mock_fetch.return_value = { + "notion": { + "type": "sse", + "url": "https://mcp.notion.so/sse", + "headers": {"Authorization": "Bearer token"}, + }, + "github": { + "type": "http", + "url": "https://mcp.github.com/api", + "headers": {"Authorization": "Bearer gh-token"}, + "streamable": True, + }, + } + + mock_client = AsyncMock() + mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) + mock_client.connected = False + mock_client.connect = AsyncMock() + mock_client.disconnect = AsyncMock() + mock_client_class.return_value = mock_client + + tools = agent.get_mcp_tools(["notion", "github"]) + + mock_fetch.assert_called_once_with(["notion", "github"]) + assert len(tools) == 4 # 2 tools per server + + @patch("crewai.mcp.tool_resolver.MCPClient") + @patch.object(MCPToolResolver, "_fetch_amp_mcp_configs") + def test_tool_filter_with_hash_syntax( + self, mock_fetch, mock_client_class, agent, mock_tool_definitions + ): + mock_fetch.return_value = { + "notion": { + "type": "sse", + "url": "https://mcp.notion.so/sse", + "headers": {"Authorization": "Bearer token"}, + }, + } + + mock_client = AsyncMock() + mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) + mock_client.connected = False + mock_client.connect = AsyncMock() + mock_client.disconnect = AsyncMock() + mock_client_class.return_value = mock_client + + tools = agent.get_mcp_tools(["notion#search"]) + + mock_fetch.assert_called_once_with(["notion"]) + assert len(tools) == 1 + assert tools[0].name == "mcp_notion_so_sse_search" + + @patch("crewai.mcp.tool_resolver.MCPClient") + @patch.object(MCPToolResolver, "_fetch_amp_mcp_configs") + def test_deduplicates_slugs( + self, mock_fetch, mock_client_class, agent, mock_tool_definitions + ): + mock_fetch.return_value = { + "notion": { + "type": "sse", + "url": "https://mcp.notion.so/sse", + "headers": {"Authorization": "Bearer token"}, + }, + } + + mock_client = AsyncMock() + mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) + mock_client.connected = False + mock_client.connect = AsyncMock() + mock_client.disconnect = AsyncMock() + mock_client_class.return_value = mock_client + + tools = agent.get_mcp_tools(["notion#search", "notion#create_page"]) + + mock_fetch.assert_called_once_with(["notion"]) + assert len(tools) == 2 + + @patch.object(MCPToolResolver, "_fetch_amp_mcp_configs") + def test_skips_missing_configs_gracefully(self, mock_fetch, agent): + mock_fetch.return_value = {} + + tools = agent.get_mcp_tools(["missing-server"]) + + assert tools == [] + + @patch("crewai.mcp.tool_resolver.MCPClient") + @patch.object(MCPToolResolver, "_fetch_amp_mcp_configs") + def test_legacy_crewai_amp_prefix_still_works( + self, mock_fetch, mock_client_class, agent, mock_tool_definitions + ): + mock_fetch.return_value = { + "notion": { + "type": "sse", + "url": "https://mcp.notion.so/sse", + "headers": {"Authorization": "Bearer token"}, + }, + } + + mock_client = AsyncMock() + mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) + mock_client.connected = False + mock_client.connect = AsyncMock() + mock_client.disconnect = AsyncMock() + mock_client_class.return_value = mock_client + + tools = agent.get_mcp_tools(["crewai-amp:notion"]) + + mock_fetch.assert_called_once_with(["notion"]) + assert len(tools) == 2 + + @patch("crewai.mcp.tool_resolver.MCPClient") + @patch.object(MCPToolResolver, "_fetch_amp_mcp_configs") + @patch.object(MCPToolResolver, "_resolve_external") + def test_non_amp_items_unaffected( + self, + mock_external, + mock_fetch, + mock_client_class, + agent, + mock_tool_definitions, + ): + mock_fetch.return_value = { + "notion": { + "type": "sse", + "url": "https://mcp.notion.so/sse", + }, + } + + mock_client = AsyncMock() + mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) + mock_client.connected = False + mock_client.connect = AsyncMock() + mock_client.disconnect = AsyncMock() + mock_client_class.return_value = mock_client + + mock_external_tool = MagicMock(spec=BaseTool) + mock_external.return_value = [mock_external_tool] + + http_config = MCPServerHTTP( + url="https://other.mcp.com/api", + headers={"Authorization": "Bearer other"}, + ) + + tools = agent.get_mcp_tools( + [ + "notion", + "https://external.mcp.com/api", + http_config, + ] + ) + + mock_fetch.assert_called_once_with(["notion"]) + mock_external.assert_called_once_with("https://external.mcp.com/api") + # 2 from notion + 1 from external + 2 from http_config + assert len(tools) == 5 diff --git a/lib/crewai/tests/mcp/test_mcp_config.py b/lib/crewai/tests/mcp/test_mcp_config.py index e55a7d504..24fc59769 100644 --- a/lib/crewai/tests/mcp/test_mcp_config.py +++ b/lib/crewai/tests/mcp/test_mcp_config.py @@ -1,5 +1,5 @@ import asyncio -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import AsyncMock, patch import pytest from crewai.agent.core import Agent @@ -46,7 +46,7 @@ def test_agent_with_stdio_mcp_config(mock_tool_definitions): ) - with patch("crewai.agent.core.MCPClient") as mock_client_class: + with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class: mock_client = AsyncMock() mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) mock_client.connected = False # Will trigger connect @@ -82,7 +82,7 @@ def test_agent_with_http_mcp_config(mock_tool_definitions): mcps=[http_config], ) - with patch("crewai.agent.core.MCPClient") as mock_client_class: + with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class: mock_client = AsyncMock() mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) mock_client.connected = False # Will trigger connect @@ -117,7 +117,7 @@ def test_agent_with_sse_mcp_config(mock_tool_definitions): mcps=[sse_config], ) - with patch("crewai.agent.core.MCPClient") as mock_client_class: + with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class: mock_client = AsyncMock() mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) mock_client.connected = False @@ -141,7 +141,7 @@ def test_mcp_tool_execution_in_sync_context(mock_tool_definitions): """Test MCPNativeTool execution in synchronous context (normal crew execution).""" http_config = MCPServerHTTP(url="https://api.example.com/mcp") - with patch("crewai.agent.core.MCPClient") as mock_client_class: + with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class: mock_client = AsyncMock() mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) mock_client.connected = False @@ -173,7 +173,7 @@ async def test_mcp_tool_execution_in_async_context(mock_tool_definitions): """Test MCPNativeTool execution in async context (e.g., from a Flow).""" http_config = MCPServerHTTP(url="https://api.example.com/mcp") - with patch("crewai.agent.core.MCPClient") as mock_client_class: + with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class: mock_client = AsyncMock() mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions) mock_client.connected = False diff --git a/lib/crewai/tests/memory/test_async_memory.py b/lib/crewai/tests/memory/test_async_memory.py deleted file mode 100644 index 15c4c33eb..000000000 --- a/lib/crewai/tests/memory/test_async_memory.py +++ /dev/null @@ -1,496 +0,0 @@ -"""Tests for async memory operations.""" - -import threading -from collections import defaultdict -from unittest.mock import ANY, AsyncMock, MagicMock, patch - -import pytest - -from crewai.agent import Agent -from crewai.crew import Crew -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemoryQueryCompletedEvent, - MemoryQueryStartedEvent, - MemorySaveCompletedEvent, - MemorySaveStartedEvent, -) -from crewai.memory.contextual.contextual_memory import ContextualMemory -from crewai.memory.entity.entity_memory import EntityMemory -from crewai.memory.entity.entity_memory_item import EntityMemoryItem -from crewai.memory.external.external_memory import ExternalMemory -from crewai.memory.long_term.long_term_memory import LongTermMemory -from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem -from crewai.memory.short_term.short_term_memory import ShortTermMemory -from crewai.task import Task - - -@pytest.fixture -def mock_agent(): - """Fixture to create a mock agent.""" - return Agent( - role="Researcher", - goal="Search relevant data and provide results", - backstory="You are a researcher at a leading tech think tank.", - tools=[], - verbose=True, - ) - - -@pytest.fixture -def mock_task(mock_agent): - """Fixture to create a mock task.""" - return Task( - description="Perform a search on specific topics.", - expected_output="A list of relevant URLs based on the search query.", - agent=mock_agent, - ) - - -@pytest.fixture -def short_term_memory(mock_agent, mock_task): - """Fixture to create a ShortTermMemory instance.""" - return ShortTermMemory(crew=Crew(agents=[mock_agent], tasks=[mock_task])) - - -@pytest.fixture -def long_term_memory(tmp_path): - """Fixture to create a LongTermMemory instance.""" - db_path = str(tmp_path / "test_ltm.db") - return LongTermMemory(path=db_path) - - -@pytest.fixture -def entity_memory(tmp_path, mock_agent, mock_task): - """Fixture to create an EntityMemory instance.""" - return EntityMemory( - crew=Crew(agents=[mock_agent], tasks=[mock_task]), - path=str(tmp_path / "test_entities"), - ) - - -class TestAsyncShortTermMemory: - """Tests for async ShortTermMemory operations.""" - - @pytest.mark.asyncio - async def test_asave_emits_events(self, short_term_memory): - """Test that asave emits the correct events.""" - events: dict[str, list] = defaultdict(list) - condition = threading.Condition() - - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - with condition: - events["MemorySaveStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - with condition: - events["MemorySaveCompletedEvent"].append(event) - condition.notify() - - await short_term_memory.asave( - value="async test value", - metadata={"task": "async_test_task"}, - ) - - with condition: - success = condition.wait_for( - lambda: len(events["MemorySaveStartedEvent"]) >= 1 - and len(events["MemorySaveCompletedEvent"]) >= 1, - timeout=5, - ) - assert success, "Timeout waiting for async save events" - - assert len(events["MemorySaveStartedEvent"]) >= 1 - assert len(events["MemorySaveCompletedEvent"]) >= 1 - assert events["MemorySaveStartedEvent"][-1].value == "async test value" - assert events["MemorySaveStartedEvent"][-1].source_type == "short_term_memory" - - @pytest.mark.asyncio - async def test_asearch_emits_events(self, short_term_memory): - """Test that asearch emits the correct events.""" - events: dict[str, list] = defaultdict(list) - search_started = threading.Event() - search_completed = threading.Event() - - with patch.object(short_term_memory.storage, "asearch", new_callable=AsyncMock, return_value=[]): - - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - events["MemoryQueryStartedEvent"].append(event) - search_started.set() - - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - events["MemoryQueryCompletedEvent"].append(event) - search_completed.set() - - await short_term_memory.asearch( - query="async test query", - limit=3, - score_threshold=0.35, - ) - - assert search_started.wait(timeout=2), "Timeout waiting for search started event" - assert search_completed.wait(timeout=2), "Timeout waiting for search completed event" - - assert len(events["MemoryQueryStartedEvent"]) >= 1 - assert len(events["MemoryQueryCompletedEvent"]) >= 1 - assert events["MemoryQueryStartedEvent"][-1].query == "async test query" - assert events["MemoryQueryStartedEvent"][-1].source_type == "short_term_memory" - - -class TestAsyncLongTermMemory: - """Tests for async LongTermMemory operations.""" - - @pytest.mark.asyncio - async def test_asave_emits_events(self, long_term_memory): - """Test that asave emits the correct events.""" - events: dict[str, list] = defaultdict(list) - condition = threading.Condition() - - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - with condition: - events["MemorySaveStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - with condition: - events["MemorySaveCompletedEvent"].append(event) - condition.notify() - - item = LongTermMemoryItem( - task="async test task", - agent="test_agent", - expected_output="test output", - datetime="2024-01-01T00:00:00", - quality=0.9, - metadata={"task": "async test task", "quality": 0.9}, - ) - - await long_term_memory.asave(item) - - with condition: - success = condition.wait_for( - lambda: len(events["MemorySaveStartedEvent"]) >= 1 - and len(events["MemorySaveCompletedEvent"]) >= 1, - timeout=5, - ) - assert success, "Timeout waiting for async save events" - - assert len(events["MemorySaveStartedEvent"]) >= 1 - assert len(events["MemorySaveCompletedEvent"]) >= 1 - assert events["MemorySaveStartedEvent"][-1].source_type == "long_term_memory" - - @pytest.mark.asyncio - async def test_asearch_emits_events(self, long_term_memory): - """Test that asearch emits the correct events.""" - events: dict[str, list] = defaultdict(list) - search_started = threading.Event() - search_completed = threading.Event() - - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - events["MemoryQueryStartedEvent"].append(event) - search_started.set() - - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - events["MemoryQueryCompletedEvent"].append(event) - search_completed.set() - - await long_term_memory.asearch(task="async test task", latest_n=3) - - assert search_started.wait(timeout=2), "Timeout waiting for search started event" - assert search_completed.wait(timeout=2), "Timeout waiting for search completed event" - - assert len(events["MemoryQueryStartedEvent"]) >= 1 - assert len(events["MemoryQueryCompletedEvent"]) >= 1 - assert events["MemoryQueryStartedEvent"][-1].source_type == "long_term_memory" - - @pytest.mark.asyncio - async def test_asave_and_asearch_integration(self, long_term_memory): - """Test that asave followed by asearch works correctly.""" - item = LongTermMemoryItem( - task="integration test task", - agent="test_agent", - expected_output="test output", - datetime="2024-01-01T00:00:00", - quality=0.9, - metadata={"task": "integration test task", "quality": 0.9}, - ) - - await long_term_memory.asave(item) - results = await long_term_memory.asearch(task="integration test task", latest_n=1) - - assert results is not None - assert len(results) == 1 - assert results[0]["metadata"]["agent"] == "test_agent" - - -class TestAsyncEntityMemory: - """Tests for async EntityMemory operations.""" - - @pytest.mark.asyncio - async def test_asave_single_item_emits_events(self, entity_memory): - """Test that asave with a single item emits the correct events.""" - events: dict[str, list] = defaultdict(list) - condition = threading.Condition() - - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - with condition: - events["MemorySaveStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - with condition: - events["MemorySaveCompletedEvent"].append(event) - condition.notify() - - item = EntityMemoryItem( - name="TestEntity", - type="Person", - description="A test entity for async operations", - relationships="Related to other test entities", - ) - - await entity_memory.asave(item) - - with condition: - success = condition.wait_for( - lambda: len(events["MemorySaveStartedEvent"]) >= 1 - and len(events["MemorySaveCompletedEvent"]) >= 1, - timeout=5, - ) - assert success, "Timeout waiting for async save events" - - assert len(events["MemorySaveStartedEvent"]) >= 1 - assert len(events["MemorySaveCompletedEvent"]) >= 1 - assert events["MemorySaveStartedEvent"][-1].source_type == "entity_memory" - - @pytest.mark.asyncio - async def test_asearch_emits_events(self, entity_memory): - """Test that asearch emits the correct events.""" - events: dict[str, list] = defaultdict(list) - search_started = threading.Event() - search_completed = threading.Event() - - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - events["MemoryQueryStartedEvent"].append(event) - search_started.set() - - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - events["MemoryQueryCompletedEvent"].append(event) - search_completed.set() - - await entity_memory.asearch(query="TestEntity", limit=5, score_threshold=0.6) - - assert search_started.wait(timeout=2), "Timeout waiting for search started event" - assert search_completed.wait(timeout=2), "Timeout waiting for search completed event" - - assert len(events["MemoryQueryStartedEvent"]) >= 1 - assert len(events["MemoryQueryCompletedEvent"]) >= 1 - assert events["MemoryQueryStartedEvent"][-1].source_type == "entity_memory" - - -class TestAsyncContextualMemory: - """Tests for async ContextualMemory operations.""" - - @pytest.mark.asyncio - async def test_abuild_context_for_task_with_empty_query(self, mock_task): - """Test that abuild_context_for_task returns empty string for empty query.""" - mock_task.description = "" - contextual_memory = ContextualMemory( - stm=None, - ltm=None, - em=None, - exm=None, - ) - - result = await contextual_memory.abuild_context_for_task(mock_task, "") - assert result == "" - - @pytest.mark.asyncio - async def test_abuild_context_for_task_with_none_memories(self, mock_task): - """Test that abuild_context_for_task handles None memory sources.""" - contextual_memory = ContextualMemory( - stm=None, - ltm=None, - em=None, - exm=None, - ) - - result = await contextual_memory.abuild_context_for_task(mock_task, "some context") - assert result == "" - - @pytest.mark.asyncio - async def test_abuild_context_for_task_aggregates_results(self, mock_agent, mock_task): - """Test that abuild_context_for_task aggregates results from all memory sources.""" - mock_stm = MagicMock(spec=ShortTermMemory) - mock_stm.asearch = AsyncMock(return_value=[{"content": "STM insight"}]) - - mock_ltm = MagicMock(spec=LongTermMemory) - mock_ltm.asearch = AsyncMock( - return_value=[{"metadata": {"suggestions": ["LTM suggestion"]}}] - ) - - mock_em = MagicMock(spec=EntityMemory) - mock_em.asearch = AsyncMock(return_value=[{"content": "Entity info"}]) - - mock_exm = MagicMock(spec=ExternalMemory) - mock_exm.asearch = AsyncMock(return_value=[{"content": "External memory"}]) - - contextual_memory = ContextualMemory( - stm=mock_stm, - ltm=mock_ltm, - em=mock_em, - exm=mock_exm, - agent=mock_agent, - task=mock_task, - ) - - result = await contextual_memory.abuild_context_for_task(mock_task, "additional context") - - assert "Recent Insights:" in result - assert "STM insight" in result - assert "Historical Data:" in result - assert "LTM suggestion" in result - assert "Entities:" in result - assert "Entity info" in result - assert "External memories:" in result - assert "External memory" in result - - @pytest.mark.asyncio - async def test_afetch_stm_context_returns_formatted_results(self, mock_agent, mock_task): - """Test that _afetch_stm_context returns properly formatted results.""" - mock_stm = MagicMock(spec=ShortTermMemory) - mock_stm.asearch = AsyncMock( - return_value=[ - {"content": "First insight"}, - {"content": "Second insight"}, - ] - ) - - contextual_memory = ContextualMemory( - stm=mock_stm, - ltm=None, - em=None, - exm=None, - ) - - result = await contextual_memory._afetch_stm_context("test query") - - assert "Recent Insights:" in result - assert "- First insight" in result - assert "- Second insight" in result - - @pytest.mark.asyncio - async def test_afetch_ltm_context_returns_formatted_results(self, mock_agent, mock_task): - """Test that _afetch_ltm_context returns properly formatted results.""" - mock_ltm = MagicMock(spec=LongTermMemory) - mock_ltm.asearch = AsyncMock( - return_value=[ - {"metadata": {"suggestions": ["Suggestion 1", "Suggestion 2"]}}, - ] - ) - - contextual_memory = ContextualMemory( - stm=None, - ltm=mock_ltm, - em=None, - exm=None, - ) - - result = await contextual_memory._afetch_ltm_context("test task") - - assert "Historical Data:" in result - assert "- Suggestion 1" in result - assert "- Suggestion 2" in result - - @pytest.mark.asyncio - async def test_afetch_entity_context_returns_formatted_results(self, mock_agent, mock_task): - """Test that _afetch_entity_context returns properly formatted results.""" - mock_em = MagicMock(spec=EntityMemory) - mock_em.asearch = AsyncMock( - return_value=[ - {"content": "Entity A details"}, - {"content": "Entity B details"}, - ] - ) - - contextual_memory = ContextualMemory( - stm=None, - ltm=None, - em=mock_em, - exm=None, - ) - - result = await contextual_memory._afetch_entity_context("test query") - - assert "Entities:" in result - assert "- Entity A details" in result - assert "- Entity B details" in result - - @pytest.mark.asyncio - async def test_afetch_external_context_returns_formatted_results(self): - """Test that _afetch_external_context returns properly formatted results.""" - mock_exm = MagicMock(spec=ExternalMemory) - mock_exm.asearch = AsyncMock( - return_value=[ - {"content": "External data 1"}, - {"content": "External data 2"}, - ] - ) - - contextual_memory = ContextualMemory( - stm=None, - ltm=None, - em=None, - exm=mock_exm, - ) - - result = await contextual_memory._afetch_external_context("test query") - - assert "External memories:" in result - assert "- External data 1" in result - assert "- External data 2" in result - - @pytest.mark.asyncio - async def test_afetch_methods_return_empty_for_empty_results(self): - """Test that async fetch methods return empty string for no results.""" - mock_stm = MagicMock(spec=ShortTermMemory) - mock_stm.asearch = AsyncMock(return_value=[]) - - mock_ltm = MagicMock(spec=LongTermMemory) - mock_ltm.asearch = AsyncMock(return_value=[]) - - mock_em = MagicMock(spec=EntityMemory) - mock_em.asearch = AsyncMock(return_value=[]) - - mock_exm = MagicMock(spec=ExternalMemory) - mock_exm.asearch = AsyncMock(return_value=[]) - - contextual_memory = ContextualMemory( - stm=mock_stm, - ltm=mock_ltm, - em=mock_em, - exm=mock_exm, - ) - - stm_result = await contextual_memory._afetch_stm_context("query") - ltm_result = await contextual_memory._afetch_ltm_context("task") - em_result = await contextual_memory._afetch_entity_context("query") - exm_result = await contextual_memory._afetch_external_context("query") - - assert stm_result == "" - assert ltm_result is None - assert em_result == "" - assert exm_result == "" \ No newline at end of file diff --git a/lib/crewai/tests/memory/test_external_memory.py b/lib/crewai/tests/memory/test_external_memory.py deleted file mode 100644 index 1872bc0af..000000000 --- a/lib/crewai/tests/memory/test_external_memory.py +++ /dev/null @@ -1,422 +0,0 @@ -import threading -from collections import defaultdict -from unittest.mock import ANY, MagicMock, patch - -import pytest -from mem0.memory.main import Memory - -from crewai.agent import Agent -from crewai.crew import Crew, Process -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemoryQueryCompletedEvent, - MemoryQueryStartedEvent, - MemorySaveCompletedEvent, - MemorySaveStartedEvent, -) -from crewai.memory.external.external_memory import ExternalMemory -from crewai.memory.external.external_memory_item import ExternalMemoryItem -from crewai.memory.storage.interface import Storage -from crewai.task import Task - - -@pytest.fixture(autouse=True) -def cleanup_event_handlers(): - """Cleanup event handlers before and after each test""" - # Cleanup before test - with crewai_event_bus._rwlock.w_locked(): - crewai_event_bus._sync_handlers = {} - crewai_event_bus._async_handlers = {} - crewai_event_bus._handler_dependencies = {} - crewai_event_bus._execution_plan_cache = {} - - yield - - # Cleanup after test - with crewai_event_bus._rwlock.w_locked(): - crewai_event_bus._sync_handlers = {} - crewai_event_bus._async_handlers = {} - crewai_event_bus._handler_dependencies = {} - crewai_event_bus._execution_plan_cache = {} - - -@pytest.fixture -def mock_mem0_memory(): - mock_memory = MagicMock(spec=Memory) - return mock_memory - - -@pytest.fixture -def patch_configure_mem0(mock_mem0_memory): - with patch( - "crewai.memory.external.external_memory.ExternalMemory._configure_mem0", - return_value=mock_mem0_memory, - ) as mocked: - yield mocked - - -@pytest.fixture -def external_memory_with_mocked_config(patch_configure_mem0): - embedder_config = {"provider": "mem0"} - external_memory = ExternalMemory(embedder_config=embedder_config) - return external_memory - - -@pytest.fixture -def crew_with_external_memory(external_memory_with_mocked_config, patch_configure_mem0): - agent = Agent( - role="Researcher", - goal="Search relevant data and provide results", - backstory="You are a researcher at a leading tech think tank.", - tools=[], - verbose=True, - ) - - task = Task( - description="Perform a search on specific topics.", - expected_output="A list of relevant URLs based on the search query.", - agent=agent, - ) - - crew = Crew( - agents=[agent], - tasks=[task], - verbose=True, - process=Process.sequential, - memory=True, - external_memory=external_memory_with_mocked_config, - ) - - return crew - - -@pytest.fixture -def crew_with_external_memory_without_memory_flag( - external_memory_with_mocked_config, patch_configure_mem0 -): - agent = Agent( - role="Researcher", - goal="Search relevant data and provide results", - backstory="You are a researcher at a leading tech think tank.", - tools=[], - verbose=True, - ) - - task = Task( - description="Perform a search on specific topics.", - expected_output="A list of relevant URLs based on the search query.", - agent=agent, - ) - - crew = Crew( - agents=[agent], - tasks=[task], - verbose=True, - process=Process.sequential, - external_memory=external_memory_with_mocked_config, - ) - - return crew - - -def test_external_memory_initialization(external_memory_with_mocked_config): - assert external_memory_with_mocked_config is not None - assert isinstance(external_memory_with_mocked_config, ExternalMemory) - - -def test_external_memory_save(external_memory_with_mocked_config): - memory_item = ExternalMemoryItem( - value="test value", metadata={"task": "test_task"}, agent="test_agent" - ) - - with patch.object(ExternalMemory, "save") as mock_save: - external_memory_with_mocked_config.save( - value=memory_item.value, - metadata=memory_item.metadata, - agent=memory_item.agent, - ) - - mock_save.assert_called_once_with( - value=memory_item.value, - metadata=memory_item.metadata, - agent=memory_item.agent, - ) - - -def test_external_memory_reset(external_memory_with_mocked_config): - with patch( - "crewai.memory.external.external_memory.ExternalMemory.reset" - ) as mock_reset: - external_memory_with_mocked_config.reset() - mock_reset.assert_called_once() - - -def test_external_memory_supported_storages(): - supported_storages = ExternalMemory.external_supported_storages() - assert "mem0" in supported_storages - assert callable(supported_storages["mem0"]) - - -def test_external_memory_create_storage_invalid_provider(): - embedder_config = {"provider": "invalid_provider", "config": {}} - - with pytest.raises(ValueError, match="Provider invalid_provider not supported"): - ExternalMemory.create_storage(None, embedder_config) - - -def test_external_memory_create_storage_missing_provider(): - embedder_config = {"config": {}} - - with pytest.raises( - ValueError, match="embedder_config must include a 'provider' key" - ): - ExternalMemory.create_storage(None, embedder_config) - - -def test_external_memory_create_storage_missing_config(): - with pytest.raises(ValueError, match="embedder_config is required"): - ExternalMemory.create_storage(None, None) - - -def test_crew_with_external_memory_initialization(crew_with_external_memory): - assert crew_with_external_memory._external_memory is not None - assert isinstance(crew_with_external_memory._external_memory, ExternalMemory) - assert crew_with_external_memory._external_memory.crew == crew_with_external_memory - - -@pytest.mark.parametrize("mem_type", ["external", "all"]) -def test_crew_external_memory_reset(mem_type, crew_with_external_memory): - with patch( - "crewai.memory.external.external_memory.ExternalMemory.reset" - ) as mock_reset: - crew_with_external_memory.reset_memories(mem_type) - mock_reset.assert_called_once() - - -@pytest.mark.parametrize("mem_method", ["search", "save"]) -@pytest.mark.vcr() -def test_crew_external_memory_save_with_memory_flag( - mem_method, crew_with_external_memory -): - with patch( - f"crewai.memory.external.external_memory.ExternalMemory.{mem_method}" - ) as mock_method: - crew_with_external_memory.kickoff() - assert mock_method.call_count > 0 - - -@pytest.mark.parametrize("mem_method", ["search", "save"]) -@pytest.mark.vcr() -def test_crew_external_memory_save_using_crew_without_memory_flag( - mem_method, crew_with_external_memory_without_memory_flag -): - with patch( - f"crewai.memory.external.external_memory.ExternalMemory.{mem_method}" - ) as mock_method: - crew_with_external_memory_without_memory_flag.kickoff() - assert mock_method.call_count > 0 - - -@pytest.fixture -def custom_storage(): - class CustomStorage(Storage): - def __init__(self): - self.memories = [] - - def save(self, value, metadata=None, agent=None): - self.memories.append({"value": value, "metadata": metadata, "agent": agent}) - - def search(self, query, limit=10, score_threshold=0.5): - return self.memories - - def reset(self): - self.memories = [] - - custom_storage = CustomStorage() - return custom_storage - - -def test_external_memory_custom_storage(custom_storage, crew_with_external_memory): - external_memory = ExternalMemory(storage=custom_storage) - - # by ensuring the crew is set, we can test that the storage is used - external_memory.set_crew(crew_with_external_memory) - - test_value = "test value" - test_metadata = {"source": "test"} - external_memory.save(value=test_value, metadata=test_metadata) - - results = external_memory.search("test") - assert len(results) == 1 - assert results[0]["value"] == test_value - assert results[0]["metadata"] == test_metadata - - external_memory.reset() - results = external_memory.search("test") - assert len(results) == 0 - - -def test_external_memory_search_events( - custom_storage, external_memory_with_mocked_config -): - events: dict[str, list] = defaultdict(list) - condition = threading.Condition() - - external_memory_with_mocked_config.storage = custom_storage - - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - with condition: - events["MemoryQueryStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - with condition: - events["MemoryQueryCompletedEvent"].append(event) - condition.notify() - - external_memory_with_mocked_config.search( - query="test value", - limit=3, - score_threshold=0.35, - ) - - with condition: - success = condition.wait_for( - lambda: len(events["MemoryQueryStartedEvent"]) >= 1 - and len(events["MemoryQueryCompletedEvent"]) >= 1, - timeout=10, - ) - assert success, "Timeout waiting for search events" - assert len(events["MemoryQueryStartedEvent"]) == 1 - assert len(events["MemoryQueryCompletedEvent"]) == 1 - - assert dict(events["MemoryQueryStartedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_query_started", - "source_fingerprint": None, - "source_type": "external_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "query": "test value", - "limit": 3, - "score_threshold": 0.35, - } - - assert dict(events["MemoryQueryCompletedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_query_completed", - "source_fingerprint": None, - "source_type": "external_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": ANY, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "query": "test value", - "results": [], - "limit": 3, - "score_threshold": 0.35, - "query_time_ms": ANY, - } - - -def test_external_memory_save_events( - custom_storage, external_memory_with_mocked_config -): - events: dict[str, list] = defaultdict(list) - condition = threading.Condition() - - external_memory_with_mocked_config.storage = custom_storage - - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - with condition: - events["MemorySaveStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - with condition: - events["MemorySaveCompletedEvent"].append(event) - condition.notify() - - external_memory_with_mocked_config.save( - value="saving value", - metadata={"task": "test_task"}, - ) - - with condition: - success = condition.wait_for( - lambda: len(events["MemorySaveStartedEvent"]) >= 1 - and len(events["MemorySaveCompletedEvent"]) >= 1, - timeout=10, - ) - assert success, "Timeout waiting for save events" - assert len(events["MemorySaveStartedEvent"]) == 1 - assert len(events["MemorySaveCompletedEvent"]) == 1 - - assert dict(events["MemorySaveStartedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_save_started", - "source_fingerprint": None, - "source_type": "external_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "value": "saving value", - "metadata": {"task": "test_task"}, - } - - assert dict(events["MemorySaveCompletedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_save_completed", - "source_fingerprint": None, - "source_type": "external_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": ANY, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "value": "saving value", - "metadata": {"task": "test_task"}, - "save_time_ms": ANY, - } diff --git a/lib/crewai/tests/memory/test_long_term_memory.py b/lib/crewai/tests/memory/test_long_term_memory.py deleted file mode 100644 index 500fab169..000000000 --- a/lib/crewai/tests/memory/test_long_term_memory.py +++ /dev/null @@ -1,207 +0,0 @@ -import threading -from collections import defaultdict -from unittest.mock import ANY - -import pytest - -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemoryQueryCompletedEvent, - MemoryQueryStartedEvent, - MemorySaveCompletedEvent, - MemorySaveStartedEvent, -) -from crewai.memory.long_term.long_term_memory import LongTermMemory -from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem - - -@pytest.fixture -def long_term_memory(): - """Fixture to create a LongTermMemory instance""" - return LongTermMemory() - - -def test_long_term_memory_save_events(long_term_memory): - events = defaultdict(list) - condition = threading.Condition() - - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - with condition: - events["MemorySaveStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - with condition: - events["MemorySaveCompletedEvent"].append(event) - condition.notify() - - memory = LongTermMemoryItem( - agent="test_agent", - task="test_task", - expected_output="test_output", - datetime="test_datetime", - quality=0.5, - metadata={"task": "test_task", "quality": 0.5}, - ) - long_term_memory.save(memory) - - with condition: - success = condition.wait_for( - lambda: len(events["MemorySaveStartedEvent"]) >= 1 - and len(events["MemorySaveCompletedEvent"]) >= 1, - timeout=5, - ) - assert success, "Timeout waiting for save events" - assert len(events["MemorySaveStartedEvent"]) == 1 - assert len(events["MemorySaveCompletedEvent"]) == 1 - assert len(events["MemorySaveFailedEvent"]) == 0 - - assert dict(events["MemorySaveStartedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_save_started", - "source_fingerprint": None, - "source_type": "long_term_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": "test_agent", - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "value": "test_task", - "metadata": {"task": "test_task", "quality": 0.5}, - } - assert dict(events["MemorySaveCompletedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_save_completed", - "source_fingerprint": None, - "source_type": "long_term_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": "test_agent", - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "value": "test_task", - "metadata": { - "task": "test_task", - "quality": 0.5, - "agent": "test_agent", - "expected_output": "test_output", - }, - "save_time_ms": ANY, - } - - -def test_long_term_memory_search_events(long_term_memory): - events = defaultdict(list) - condition = threading.Condition() - - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - with condition: - events["MemoryQueryStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - with condition: - events["MemoryQueryCompletedEvent"].append(event) - condition.notify() - - test_query = "test query" - - long_term_memory.search(test_query, latest_n=5) - - with condition: - success = condition.wait_for( - lambda: len(events["MemoryQueryStartedEvent"]) >= 1 - and len(events["MemoryQueryCompletedEvent"]) >= 1, - timeout=5, - ) - assert success, "Timeout waiting for search events" - assert len(events["MemoryQueryStartedEvent"]) == 1 - assert len(events["MemoryQueryCompletedEvent"]) == 1 - assert len(events["MemoryQueryFailedEvent"]) == 0 - - assert dict(events["MemoryQueryStartedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_query_started", - "source_fingerprint": None, - "source_type": "long_term_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "query": "test query", - "limit": 5, - "score_threshold": None, - } - - assert dict(events["MemoryQueryCompletedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_query_completed", - "source_fingerprint": None, - "source_type": "long_term_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": ANY, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "query": "test query", - "results": None, - "limit": 5, - "score_threshold": None, - "query_time_ms": ANY, - } - - -def test_save_and_search(long_term_memory): - memory = LongTermMemoryItem( - agent="test_agent", - task="test_task", - expected_output="test_output", - datetime="test_datetime", - quality=0.5, - metadata={"task": "test_task", "quality": 0.5}, - ) - long_term_memory.save(memory) - find = long_term_memory.search("test_task", latest_n=5)[0] - assert find["score"] == 0.5 - assert find["datetime"] == "test_datetime" - assert find["metadata"]["agent"] == "test_agent" - assert find["metadata"]["quality"] == 0.5 - assert find["metadata"]["task"] == "test_task" - assert find["metadata"]["expected_output"] == "test_output" diff --git a/lib/crewai/tests/memory/test_short_term_memory.py b/lib/crewai/tests/memory/test_short_term_memory.py deleted file mode 100644 index 5e74b688d..000000000 --- a/lib/crewai/tests/memory/test_short_term_memory.py +++ /dev/null @@ -1,231 +0,0 @@ -import threading -from collections import defaultdict -from unittest.mock import ANY, patch - -import pytest -from crewai.agent import Agent -from crewai.crew import Crew -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemoryQueryCompletedEvent, - MemoryQueryStartedEvent, - MemorySaveCompletedEvent, - MemorySaveStartedEvent, -) -from crewai.memory.short_term.short_term_memory import ShortTermMemory -from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem -from crewai.task import Task - - -@pytest.fixture -def short_term_memory(): - """Fixture to create a ShortTermMemory instance""" - agent = Agent( - role="Researcher", - goal="Search relevant data and provide results", - backstory="You are a researcher at a leading tech think tank.", - tools=[], - verbose=True, - ) - - task = Task( - description="Perform a search on specific topics.", - expected_output="A list of relevant URLs based on the search query.", - agent=agent, - ) - return ShortTermMemory(crew=Crew(agents=[agent], tasks=[task])) - - -def test_short_term_memory_search_events(short_term_memory): - events = defaultdict(list) - search_started = threading.Event() - search_completed = threading.Event() - - with patch.object(short_term_memory.storage, "search", return_value=[]): - - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - events["MemoryQueryStartedEvent"].append(event) - search_started.set() - - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - events["MemoryQueryCompletedEvent"].append(event) - search_completed.set() - - short_term_memory.search( - query="test value", - limit=3, - score_threshold=0.35, - ) - - assert search_started.wait(timeout=2), ( - "Timeout waiting for search started event" - ) - assert search_completed.wait(timeout=2), ( - "Timeout waiting for search completed event" - ) - - assert len(events["MemoryQueryStartedEvent"]) == 1 - assert len(events["MemoryQueryCompletedEvent"]) == 1 - - assert dict(events["MemoryQueryStartedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_query_started", - "source_fingerprint": None, - "source_type": "short_term_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "query": "test value", - "limit": 3, - "score_threshold": 0.35, - } - - assert dict(events["MemoryQueryCompletedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_query_completed", - "source_fingerprint": None, - "source_type": "short_term_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "query": "test value", - "results": [], - "limit": 3, - "score_threshold": 0.35, - "query_time_ms": ANY, - } - - -def test_short_term_memory_save_events(short_term_memory): - events: dict[str, list] = defaultdict(list) - condition = threading.Condition() - - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - with condition: - events["MemorySaveStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - with condition: - events["MemorySaveCompletedEvent"].append(event) - condition.notify() - - short_term_memory.save( - value="test value", - metadata={"task": "test_task"}, - ) - - with condition: - success = condition.wait_for( - lambda: len(events["MemorySaveStartedEvent"]) >= 1 - and len(events["MemorySaveCompletedEvent"]) >= 1, - timeout=5, - ) - assert success, "Timeout waiting for save events" - - assert len(events["MemorySaveStartedEvent"]) == 1 - assert len(events["MemorySaveCompletedEvent"]) == 1 - - assert dict(events["MemorySaveStartedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_save_started", - "source_fingerprint": None, - "source_type": "short_term_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "value": "test value", - "metadata": {"task": "test_task"}, - } - - assert dict(events["MemorySaveCompletedEvent"][0]) == { - "timestamp": ANY, - "type": "memory_save_completed", - "source_fingerprint": None, - "source_type": "short_term_memory", - "fingerprint_metadata": None, - "task_id": None, - "task_name": None, - "from_task": None, - "from_agent": None, - "agent_role": None, - "agent_id": None, - "event_id": ANY, - "parent_event_id": None, - "previous_event_id": ANY, - "triggered_by_event_id": None, - "started_event_id": ANY, - "emission_sequence": ANY, - "value": "test value", - "metadata": {"task": "test_task"}, - "save_time_ms": ANY, - } - - -def test_save_and_search(short_term_memory): - memory = ShortTermMemoryItem( - data="""test value test value test value test value test value test value - test value test value test value test value test value test value - test value test value test value test value test value test value""", - agent="test_agent", - metadata={"task": "test_task"}, - ) - - with patch.object(ShortTermMemory, "save") as mock_save: - short_term_memory.save( - value=memory.data, - metadata=memory.metadata, - agent=memory.agent, - ) - - mock_save.assert_called_once_with( - value=memory.data, - metadata=memory.metadata, - agent=memory.agent, - ) - - expected_result = [ - { - "content": memory.data, - "metadata": {"agent": "test_agent"}, - "score": 0.95, - } - ] - with patch.object(ShortTermMemory, "search", return_value=expected_result): - find = short_term_memory.search("test value", score_threshold=0.01)[0] - assert find["content"] == memory.data, "Data value mismatch." - assert find["metadata"]["agent"] == "test_agent", "Agent value mismatch." diff --git a/lib/crewai/tests/memory/test_unified_memory.py b/lib/crewai/tests/memory/test_unified_memory.py new file mode 100644 index 000000000..26e2a1929 --- /dev/null +++ b/lib/crewai/tests/memory/test_unified_memory.py @@ -0,0 +1,1001 @@ +"""Tests for unified memory: types, storage, Memory, MemoryScope, MemorySlice, Flow integration.""" + +from __future__ import annotations + +from datetime import datetime, timedelta +from pathlib import Path +from unittest.mock import MagicMock + +import pytest + +from crewai.utilities.printer import Printer +from crewai.memory.types import ( + MemoryConfig, + MemoryMatch, + MemoryRecord, + ScopeInfo, + compute_composite_score, +) + + +# --- Types --- + + +def test_memory_record_defaults() -> None: + r = MemoryRecord(content="hello") + assert r.content == "hello" + assert r.scope == "/" + assert r.categories == [] + assert r.importance == 0.5 + assert r.embedding is None + assert r.id is not None + assert isinstance(r.created_at, datetime) + + +def test_memory_match() -> None: + r = MemoryRecord(content="x", scope="/a") + m = MemoryMatch(record=r, score=0.9, match_reasons=["semantic"]) + assert m.record.content == "x" + assert m.score == 0.9 + assert m.match_reasons == ["semantic"] + + +def test_scope_info() -> None: + i = ScopeInfo(path="/", record_count=5, categories=["c1"], child_scopes=["/a"]) + assert i.path == "/" + assert i.record_count == 5 + assert i.categories == ["c1"] + assert i.child_scopes == ["/a"] + + +def test_memory_config() -> None: + c = MemoryConfig() + assert c.recency_weight == 0.3 + assert c.semantic_weight == 0.5 + assert c.importance_weight == 0.2 + + +# --- LanceDB storage --- + + +@pytest.fixture +def lancedb_path(tmp_path: Path) -> Path: + return tmp_path / "mem" + + +def test_lancedb_save_search(lancedb_path: Path) -> None: + from crewai.memory.storage.lancedb_storage import LanceDBStorage + + storage = LanceDBStorage(path=str(lancedb_path), vector_dim=4) + r = MemoryRecord( + content="test content", + scope="/foo", + categories=["cat1"], + importance=0.8, + embedding=[0.1, 0.2, 0.3, 0.4], + ) + storage.save([r]) + results = storage.search( + [0.1, 0.2, 0.3, 0.4], + scope_prefix="/foo", + limit=5, + ) + assert len(results) == 1 + rec, score = results[0] + assert rec.content == "test content" + assert rec.scope == "/foo" + assert score >= 0.0 + + +def test_lancedb_delete_count(lancedb_path: Path) -> None: + from crewai.memory.storage.lancedb_storage import LanceDBStorage + + storage = LanceDBStorage(path=str(lancedb_path), vector_dim=4) + r = MemoryRecord(content="x", scope="/", embedding=[0.0] * 4) + storage.save([r]) + assert storage.count() == 1 + n = storage.delete(scope_prefix="/") + assert n >= 1 + assert storage.count() == 0 + + +def test_lancedb_list_scopes_get_scope_info(lancedb_path: Path) -> None: + from crewai.memory.storage.lancedb_storage import LanceDBStorage + + storage = LanceDBStorage(path=str(lancedb_path), vector_dim=4) + storage.save([ + MemoryRecord(content="a", scope="/", embedding=[0.0] * 4), + MemoryRecord(content="b", scope="/team", embedding=[0.0] * 4), + ]) + scopes = storage.list_scopes("/") + assert "/team" in scopes # list_scopes returns children, not root itself + info = storage.get_scope_info("/") + assert info.record_count >= 1 + assert info.path == "/" + + +# --- Memory class (with mock embedder, no LLM for explicit remember) --- + + +@pytest.fixture +def mock_embedder() -> MagicMock: + """Embedder mock that returns one embedding per input text (batch-aware).""" + m = MagicMock() + m.side_effect = lambda texts: [[0.1] * 1536 for _ in texts] + return m + + +@pytest.fixture +def memory_with_storage(tmp_path: Path, mock_embedder: MagicMock) -> None: + import os + os.environ.pop("OPENAI_API_KEY", None) + + +def test_memory_remember_recall_shallow(tmp_path: Path, mock_embedder: MagicMock) -> None: + from crewai.memory.unified_memory import Memory + + m = Memory( + storage=str(tmp_path / "db"), + llm=MagicMock(), + embedder=mock_embedder, + ) + # Explicit scope/categories/importance so no LLM analysis + r = m.remember( + "We decided to use Python.", + scope="/project", + categories=["decision"], + importance=0.7, + ) + assert r.content == "We decided to use Python." + assert r.scope == "/project" + + matches = m.recall("Python decision", scope="/project", limit=5, depth="shallow") + assert len(matches) >= 1 + assert "Python" in matches[0].record.content or "python" in matches[0].record.content.lower() + + +def test_memory_forget(tmp_path: Path, mock_embedder: MagicMock) -> None: + from crewai.memory.unified_memory import Memory + + m = Memory(storage=str(tmp_path / "db2"), llm=MagicMock(), embedder=mock_embedder) + m.remember("To forget", scope="/x", categories=[], importance=0.5, metadata={}) + assert m._storage.count("/x") >= 1 + n = m.forget(scope="/x") + assert n >= 1 + assert m._storage.count("/x") == 0 + + +def test_memory_scope_slice(tmp_path: Path, mock_embedder: MagicMock) -> None: + from crewai.memory.unified_memory import Memory + + mem = Memory(storage=str(tmp_path / "db3"), llm=MagicMock(), embedder=mock_embedder) + sc = mem.scope("/agent/1") + assert sc._root in ("/agent/1", "/agent/1/") + sl = mem.slice(["/a", "/b"], read_only=True) + assert sl._read_only is True + assert "/a" in sl._scopes and "/b" in sl._scopes + + +def test_memory_list_scopes_info_tree(tmp_path: Path, mock_embedder: MagicMock) -> None: + from crewai.memory.unified_memory import Memory + + m = Memory(storage=str(tmp_path / "db4"), llm=MagicMock(), embedder=mock_embedder) + m.remember("Root", scope="/", categories=[], importance=0.5, metadata={}) + m.remember("Team note", scope="/team", categories=[], importance=0.5, metadata={}) + scopes = m.list_scopes("/") + assert "/team" in scopes # list_scopes returns children, not root itself + info = m.info("/") + assert info.record_count >= 1 + tree = m.tree("/", max_depth=2) + assert "/" in tree or "0 records" in tree or "1 records" in tree + + +# --- MemoryScope --- + + +def test_memory_scope_remember_recall(tmp_path: Path, mock_embedder: MagicMock) -> None: + from crewai.memory.unified_memory import Memory + from crewai.memory.memory_scope import MemoryScope + + mem = Memory(storage=str(tmp_path / "db5"), llm=MagicMock(), embedder=mock_embedder) + scope = MemoryScope(mem, "/crew/1") + scope.remember("Scoped note", scope="/", categories=[], importance=0.5, metadata={}) + results = scope.recall("note", limit=5, depth="shallow") + assert len(results) >= 1 + + +# --- MemorySlice recall (read-only) --- + + +def test_memory_slice_recall(tmp_path: Path, mock_embedder: MagicMock) -> None: + from crewai.memory.unified_memory import Memory + from crewai.memory.memory_scope import MemorySlice + + mem = Memory(storage=str(tmp_path / "db6"), llm=MagicMock(), embedder=mock_embedder) + mem.remember("In scope A", scope="/a", categories=[], importance=0.5, metadata={}) + sl = MemorySlice(mem, ["/a"], read_only=True) + matches = sl.recall("scope", limit=5, depth="shallow") + assert isinstance(matches, list) + + +def test_memory_slice_remember_is_noop_when_read_only(tmp_path: Path, mock_embedder: MagicMock) -> None: + from crewai.memory.unified_memory import Memory + from crewai.memory.memory_scope import MemorySlice + + mem = Memory(storage=str(tmp_path / "db7"), llm=MagicMock(), embedder=mock_embedder) + sl = MemorySlice(mem, ["/a"], read_only=True) + result = sl.remember("x", scope="/a") + assert result is None + assert mem.list_records() == [] + + +# --- Flow memory --- + + +def test_flow_has_default_memory() -> None: + """Flow auto-creates a Memory instance when none is provided.""" + from crewai.flow.flow import Flow + from crewai.memory.unified_memory import Memory + + class DefaultFlow(Flow): + pass + + f = DefaultFlow() + assert f.memory is not None + assert isinstance(f.memory, Memory) + + +def test_flow_recall_remember_raise_when_memory_explicitly_none() -> None: + """Flow raises ValueError when memory is explicitly set to None.""" + from crewai.flow.flow import Flow + + class NoMemoryFlow(Flow): + memory = None + + f = NoMemoryFlow() + # Explicitly set to None after __init__ auto-creates + f.memory = None + with pytest.raises(ValueError, match="No memory configured"): + f.recall("query") + with pytest.raises(ValueError, match="No memory configured"): + f.remember("content") + + +def test_flow_recall_remember_with_memory(tmp_path: Path, mock_embedder: MagicMock) -> None: + from crewai.flow.flow import Flow + from crewai.memory.unified_memory import Memory + + mem = Memory(storage=str(tmp_path / "flow_db"), llm=MagicMock(), embedder=mock_embedder) + + class FlowWithMemory(Flow): + memory = mem + + f = FlowWithMemory() + f.remember("Flow remembered this", scope="/flow", categories=[], importance=0.6, metadata={}) + results = f.recall("remembered", limit=5, depth="shallow") + assert len(results) >= 1 + + +# --- extract_memories --- + + +def test_memory_extract_memories_returns_list_from_llm(tmp_path: Path) -> None: + """Memory.extract_memories() delegates to LLM and returns list of strings.""" + from crewai.memory.analyze import ExtractedMemories + from crewai.memory.unified_memory import Memory + + mock_llm = MagicMock() + mock_llm.supports_function_calling.return_value = True + mock_llm.call.return_value = ExtractedMemories( + memories=["We use Python for the backend.", "API rate limit is 100/min."] + ) + + mem = Memory( + storage=str(tmp_path / "extract_db"), + llm=mock_llm, + embedder=MagicMock(return_value=[[0.1] * 1536]), + ) + result = mem.extract_memories("Task: Build API. Result: We used Python and set rate limit 100/min.") + assert result == ["We use Python for the backend.", "API rate limit is 100/min."] + mock_llm.call.assert_called_once() + call_kw = mock_llm.call.call_args[1] + assert call_kw.get("response_model") == ExtractedMemories + + +def test_memory_extract_memories_empty_content_returns_empty_list(tmp_path: Path) -> None: + """Memory.extract_memories() with empty/whitespace content returns [] without calling LLM.""" + from crewai.memory.unified_memory import Memory + + mock_llm = MagicMock() + mem = Memory(storage=str(tmp_path / "empty_db"), llm=mock_llm, embedder=MagicMock()) + assert mem.extract_memories("") == [] + assert mem.extract_memories(" \n ") == [] + mock_llm.call.assert_not_called() + + +def test_executor_save_to_memory_calls_extract_then_remember_per_item() -> None: + """_save_to_memory calls memory.extract_memories(raw) then memory.remember(m) for each.""" + from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin + from crewai.agents.parser import AgentFinish + + mock_memory = MagicMock() + mock_memory._read_only = False + mock_memory.extract_memories.return_value = ["Fact A.", "Fact B."] + + mock_agent = MagicMock() + mock_agent.memory = mock_memory + mock_agent._logger = MagicMock() + mock_agent.role = "Researcher" + + mock_task = MagicMock() + mock_task.description = "Do research" + mock_task.expected_output = "A report" + + class MinimalExecutor(CrewAgentExecutorMixin): + crew = None + agent = mock_agent + task = mock_task + iterations = 0 + max_iter = 1 + messages = [] + _i18n = MagicMock() + _printer = Printer() + + executor = MinimalExecutor() + executor._save_to_memory( + AgentFinish(thought="", output="We found X and Y.", text="We found X and Y.") + ) + + raw_expected = "Task: Do research\nAgent: Researcher\nExpected result: A report\nResult: We found X and Y." + mock_memory.extract_memories.assert_called_once_with(raw_expected) + mock_memory.remember_many.assert_called_once() + saved_contents = mock_memory.remember_many.call_args.args[0] + assert saved_contents == ["Fact A.", "Fact B."] + + +def test_executor_save_to_memory_skips_delegation_output() -> None: + """_save_to_memory does nothing when output contains delegate action.""" + from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin + from crewai.agents.parser import AgentFinish + from crewai.utilities.string_utils import sanitize_tool_name + + mock_memory = MagicMock() + mock_memory._read_only = False + mock_agent = MagicMock() + mock_agent.memory = mock_memory + mock_agent._logger = MagicMock() + mock_task = MagicMock(description="Task", expected_output="Out") + + class MinimalExecutor(CrewAgentExecutorMixin): + crew = None + agent = mock_agent + task = mock_task + iterations = 0 + max_iter = 1 + messages = [] + _i18n = MagicMock() + _printer = Printer() + + delegate_text = f"Action: {sanitize_tool_name('Delegate work to coworker')}" + full_text = delegate_text + " rest" + executor = MinimalExecutor() + executor._save_to_memory( + AgentFinish(thought="", output=full_text, text=full_text) + ) + + mock_memory.extract_memories.assert_not_called() + mock_memory.remember.assert_not_called() + + +def test_memory_scope_extract_memories_delegates() -> None: + """MemoryScope.extract_memories delegates to underlying Memory.""" + from crewai.memory.memory_scope import MemoryScope + + mock_memory = MagicMock() + mock_memory.extract_memories.return_value = ["Scoped fact."] + scope = MemoryScope(mock_memory, "/agent/1") + result = scope.extract_memories("Some content") + mock_memory.extract_memories.assert_called_once_with("Some content") + assert result == ["Scoped fact."] + + +def test_memory_slice_extract_memories_delegates() -> None: + """MemorySlice.extract_memories delegates to underlying Memory.""" + from crewai.memory.memory_scope import MemorySlice + + mock_memory = MagicMock() + mock_memory.extract_memories.return_value = ["Sliced fact."] + sl = MemorySlice(mock_memory, ["/a", "/b"], read_only=True) + result = sl.extract_memories("Some content") + mock_memory.extract_memories.assert_called_once_with("Some content") + assert result == ["Sliced fact."] + + +def test_flow_extract_memories_raises_when_memory_explicitly_none() -> None: + """Flow.extract_memories raises ValueError when memory is explicitly set to None.""" + from crewai.flow.flow import Flow + + f = Flow() + f.memory = None + with pytest.raises(ValueError, match="No memory configured"): + f.extract_memories("some content") + + +def test_flow_extract_memories_delegates_when_memory_present() -> None: + """Flow.extract_memories delegates to flow memory and returns list.""" + from crewai.flow.flow import Flow + + mock_memory = MagicMock() + mock_memory.extract_memories.return_value = ["Flow fact 1.", "Flow fact 2."] + + class FlowWithMemory(Flow): + memory = mock_memory + + f = FlowWithMemory() + result = f.extract_memories("content here") + mock_memory.extract_memories.assert_called_once_with("content here") + assert result == ["Flow fact 1.", "Flow fact 2."] + + +# --- Composite scoring --- + + +def test_composite_score_brand_new_memory() -> None: + """Brand-new memory has decay ~ 1.0; composite = 0.5*0.8 + 0.3*1.0 + 0.2*0.7 = 0.84.""" + config = MemoryConfig() + record = MemoryRecord( + content="test", + scope="/", + importance=0.7, + created_at=datetime.utcnow(), + ) + score, reasons = compute_composite_score(record, 0.8, config) + assert 0.82 <= score <= 0.86 + assert "semantic" in reasons + assert "recency" in reasons + assert "importance" in reasons + + +def test_composite_score_old_memory_decayed() -> None: + """Memory 60 days old (2 half-lives) has decay = 0.25; composite ~ 0.575.""" + config = MemoryConfig(recency_half_life_days=30) + old_date = datetime.utcnow() - timedelta(days=60) + record = MemoryRecord( + content="old", + scope="/", + importance=0.5, + created_at=old_date, + ) + score, reasons = compute_composite_score(record, 0.8, config) + assert 0.55 <= score <= 0.60 + assert "semantic" in reasons + assert "recency" not in reasons # decay 0.25 is not > 0.5 + + +def test_composite_score_reranks_results( + tmp_path: Path, mock_embedder: MagicMock +) -> None: + """Same semantic score: high-importance recent memory ranks first.""" + from crewai.memory.unified_memory import Memory + + # Use same dim as default LanceDB (1536) so storage does not overwrite embedding + emb = [0.1] * 1536 + mem = Memory( + storage=str(tmp_path / "rerank_db"), + llm=MagicMock(), + embedder=MagicMock(return_value=[emb]), + ) + # Save both records directly to storage (bypass encoding flow) + # to test composite scoring in isolation without consolidation merging them. + record_high = MemoryRecord( + content="Important decision", + scope="/", + categories=[], + importance=1.0, + embedding=emb, + ) + mem._storage.save([record_high]) + old = datetime.utcnow() - timedelta(days=90) + record_low = MemoryRecord( + content="Old trivial note", + scope="/", + importance=0.1, + created_at=old, + embedding=emb, + ) + mem._storage.save([record_low]) + + matches = mem.recall("decision", scope="/", limit=5, depth="shallow") + assert len(matches) >= 2 + # Top result should be the high-importance recent one (stored via remember) + assert "Important" in matches[0].record.content or "important" in matches[0].record.content.lower() + + +def test_composite_score_match_reasons_populated() -> None: + """match_reasons includes recency for fresh, importance for high-importance; omits for old/low.""" + config = MemoryConfig() + fresh_high = MemoryRecord( + content="x", + importance=0.9, + created_at=datetime.utcnow(), + ) + score1, reasons1 = compute_composite_score(fresh_high, 0.5, config) + assert "semantic" in reasons1 + assert "recency" in reasons1 + assert "importance" in reasons1 + + old_low = MemoryRecord( + content="y", + importance=0.1, + created_at=datetime.utcnow() - timedelta(days=60), + ) + score2, reasons2 = compute_composite_score(old_low, 0.5, config) + assert "semantic" in reasons2 + assert "recency" not in reasons2 + assert "importance" not in reasons2 + + +def test_composite_score_custom_config() -> None: + """Zero recency/importance weights => composite equals semantic score.""" + config = MemoryConfig( + recency_weight=0.0, + semantic_weight=1.0, + importance_weight=0.0, + ) + record = MemoryRecord( + content="any", + importance=0.9, + created_at=datetime.utcnow(), + ) + score, reasons = compute_composite_score(record, 0.73, config) + assert score == pytest.approx(0.73, rel=1e-5) + assert "semantic" in reasons + + +# --- LLM fallback --- + + +def test_analyze_for_save_llm_failure_returns_defaults() -> None: + """When LLM raises, analyze_for_save returns safe defaults.""" + from crewai.memory.analyze import MemoryAnalysis, analyze_for_save + + llm = MagicMock() + llm.supports_function_calling.return_value = False + llm.call.side_effect = RuntimeError("API rate limit") + result = analyze_for_save( + "some content", + existing_scopes=["/", "/project"], + existing_categories=["cat1"], + llm=llm, + ) + assert isinstance(result, MemoryAnalysis) + assert result.suggested_scope == "/" + assert result.categories == [] + assert result.importance == 0.5 + assert result.extracted_metadata.entities == [] + assert result.extracted_metadata.dates == [] + assert result.extracted_metadata.topics == [] + + +def test_extract_memories_llm_failure_returns_raw() -> None: + """When LLM raises, extract_memories_from_content returns [content].""" + from crewai.memory.analyze import extract_memories_from_content + + llm = MagicMock() + llm.call.side_effect = RuntimeError("Network error") + content = "Task result: We chose PostgreSQL." + result = extract_memories_from_content(content, llm) + assert result == [content] + + +def test_analyze_query_llm_failure_returns_defaults() -> None: + """When LLM raises, analyze_query returns safe defaults with available scopes.""" + from crewai.memory.analyze import QueryAnalysis, analyze_query + + llm = MagicMock() + llm.call.side_effect = RuntimeError("Timeout") + result = analyze_query( + "what did we decide?", + available_scopes=["/", "/project", "/team", "/company", "/other", "/extra"], + scope_info=None, + llm=llm, + ) + assert isinstance(result, QueryAnalysis) + assert result.keywords == [] + assert result.complexity == "simple" + assert result.suggested_scopes == ["/", "/project", "/team", "/company", "/other"] + + +def test_remember_survives_llm_failure( + tmp_path: Path, mock_embedder: MagicMock +) -> None: + """When the LLM raises during parallel_analyze, remember() still saves with defaults.""" + from crewai.memory.unified_memory import Memory + + llm = MagicMock() + llm.call.side_effect = RuntimeError("LLM unavailable") + mem = Memory( + storage=str(tmp_path / "fallback_db"), + llm=llm, + embedder=mock_embedder, + ) + record = mem.remember("We decided to use PostgreSQL.") + assert record.content == "We decided to use PostgreSQL." + assert record.scope == "/" + assert record.categories == [] + assert record.importance == 0.5 + assert record.id is not None + assert mem._storage.count() == 1 + + +# --- Agent.kickoff() memory integration --- + + +def test_agent_kickoff_memory_recall_and_save(tmp_path: Path, mock_embedder: MagicMock) -> None: + """Agent.kickoff() with memory should recall before execution and save after.""" + from unittest.mock import Mock, patch + + from crewai.agent.core import Agent + from crewai.llm import LLM + from crewai.memory.unified_memory import Memory + from crewai.types.usage_metrics import UsageMetrics + + # Create a real memory with mock embedder + mem = Memory( + storage=str(tmp_path / "agent_kickoff_db"), + llm=MagicMock(), + embedder=mock_embedder, + ) + + # Pre-populate a memory record + mem.remember("The team uses PostgreSQL.", scope="/", categories=["database"], importance=0.8) + + # Create mock LLM for the agent + mock_llm = Mock(spec=LLM) + mock_llm.call.return_value = "Final Answer: PostgreSQL is the database." + mock_llm.stop = [] + mock_llm.supports_stop_words.return_value = False + mock_llm.supports_function_calling.return_value = False + mock_llm.get_token_usage_summary.return_value = UsageMetrics( + total_tokens=10, prompt_tokens=5, completion_tokens=5, + cached_prompt_tokens=0, successful_requests=1, + ) + + agent = Agent( + role="Tester", + goal="Test memory integration", + backstory="You test things.", + llm=mock_llm, + memory=mem, + verbose=False, + ) + + # Mock recall to verify it's called, but return real results + with patch.object(mem, "recall", wraps=mem.recall) as recall_mock, \ + patch.object(mem, "extract_memories", return_value=["PostgreSQL is used."]) as extract_mock, \ + patch.object(mem, "remember_many", wraps=mem.remember_many) as remember_many_mock: + result = agent.kickoff("What database do we use?") + + assert result is not None + assert result.raw is not None + + # Verify recall was called (passive memory injection) + recall_mock.assert_called_once() + + # Verify extract_memories and remember_many were called (passive batch save) + extract_mock.assert_called_once() + raw_content = extract_mock.call_args.args[0] + assert "Input:" in raw_content + assert "Agent:" in raw_content + assert "Result:" in raw_content + + # remember_many was called with the extracted memories + remember_many_mock.assert_called_once() + saved_contents = remember_many_mock.call_args.args[0] + assert "PostgreSQL is used." in saved_contents + + +# --- Batch EncodingFlow tests --- + + +def test_batch_embed_single_call(tmp_path: Path) -> None: + """remember_many with 3 items should call the embedder exactly once with all 3 texts.""" + from crewai.memory.unified_memory import Memory + + embedder = MagicMock() + embedder.side_effect = lambda texts: [[0.1] * 1536 for _ in texts] + + llm = MagicMock() + llm.supports_function_calling.return_value = False + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=embedder) + + mem.remember_many( + ["Fact A.", "Fact B.", "Fact C."], + scope="/test", + categories=["test"], + importance=0.5, + ) + mem.drain_writes() # wait for background save + # The embedder should have been called exactly once with all 3 texts + embedder.assert_called_once() + texts_arg = embedder.call_args.args[0] + assert len(texts_arg) == 3 + assert texts_arg == ["Fact A.", "Fact B.", "Fact C."] + + +def test_intra_batch_dedup_drops_near_identical(tmp_path: Path) -> None: + """remember_many with 3 identical strings should store only 1 record.""" + from crewai.memory.unified_memory import Memory + + embedder = MagicMock() + # All identical embeddings -> cosine similarity = 1.0 + embedder.side_effect = lambda texts: [[0.5] * 1536 for _ in texts] + + llm = MagicMock() + llm.supports_function_calling.return_value = False + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=embedder) + + mem.remember_many( + [ + "CrewAI ensures reliable operation.", + "CrewAI ensures reliable operation.", + "CrewAI ensures reliable operation.", + ], + scope="/test", + categories=["reliability"], + importance=0.7, + ) + mem.drain_writes() # wait for background save + assert mem._storage.count() == 1 + + +def test_intra_batch_dedup_keeps_merely_similar(tmp_path: Path) -> None: + """remember_many with distinct items should keep all of them.""" + from crewai.memory.unified_memory import Memory + import math + + # Return different embeddings for different texts + call_count = 0 + + def varying_embedder(texts: list[str]) -> list[list[float]]: + nonlocal call_count + result = [] + for i, _ in enumerate(texts): + # Create orthogonal-ish embeddings so similarity is low + emb = [0.0] * 1536 + idx = (call_count + i) % 1536 + emb[idx] = 1.0 + result.append(emb) + call_count += len(texts) + return result + + embedder = MagicMock(side_effect=varying_embedder) + llm = MagicMock() + llm.supports_function_calling.return_value = False + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=embedder) + + mem.remember_many( + ["CrewAI handles complex tasks.", "Python is the best language."], + scope="/test", + categories=["tech"], + importance=0.6, + ) + mem.drain_writes() # wait for background save + assert mem._storage.count() == 2 + + +def test_batch_consolidation_deduplicates_against_storage( + tmp_path: Path, +) -> None: + """Pre-insert a record, then remember_many with same + new content.""" + from crewai.memory.unified_memory import Memory + from crewai.memory.analyze import ConsolidationPlan + + emb = [0.1] * 1536 + embedder = MagicMock() + embedder.side_effect = lambda texts: [emb for _ in texts] + + llm = MagicMock() + llm.supports_function_calling.return_value = True + # After intra-batch dedup (identical embeddings), only 1 item survives. + # That item hits parallel_analyze which calls analyze_for_consolidation. + # The single-item call returns a ConsolidationPlan directly. + llm.call.return_value = ConsolidationPlan( + actions=[], insert_new=False, insert_reason="duplicate" + ) + + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=embedder) + + # Pre-insert + from crewai.memory.types import MemoryRecord + + mem._storage.save([ + MemoryRecord(content="CrewAI is great.", scope="/test", importance=0.7, embedding=emb), + ]) + assert mem._storage.count() == 1 + + # remember_many with the same content + a new one (all identical embeddings) + mem.remember_many( + ["CrewAI is great.", "CrewAI is wonderful."], + scope="/test", + categories=["review"], + importance=0.7, + ) + mem.drain_writes() # wait for background save + # Intra-batch dedup fires: same embedding = 1.0 >= 0.98, so item 1 is dropped. + # The remaining item finds the pre-existing record (similarity 1.0 >= 0.85). + # LLM says don't insert -> no new records. Total stays at 1. + assert mem._storage.count() == 1 + + +def test_parallel_find_similar_runs_all_searches(tmp_path: Path) -> None: + """remember_many with 3 distinct items should run 3 storage searches.""" + from unittest.mock import patch + from crewai.memory.unified_memory import Memory + + call_count = 0 + + def distinct_embedder(texts: list[str]) -> list[list[float]]: + """Return unique embeddings per text so dedup doesn't drop them.""" + nonlocal call_count + result = [] + for i, _ in enumerate(texts): + emb = [0.0] * 1536 + emb[(call_count + i) % 1536] = 1.0 + result.append(emb) + call_count += len(texts) + return result + + embedder = MagicMock(side_effect=distinct_embedder) + llm = MagicMock() + llm.supports_function_calling.return_value = False + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=embedder) + + with patch.object(mem._storage, "search", wraps=mem._storage.search) as search_mock: + mem.remember_many( + ["Alpha fact.", "Beta fact.", "Gamma fact."], + scope="/test", + categories=["test"], + importance=0.5, + ) + mem.drain_writes() # wait for background save + # All 3 items should trigger a storage search + assert search_mock.call_count == 3 + + +def test_single_remember_uses_batch_flow(tmp_path: Path, mock_embedder: MagicMock) -> None: + """Single remember() should work through the batch flow (batch of 1).""" + from crewai.memory.unified_memory import Memory + + llm = MagicMock() + llm.supports_function_calling.return_value = False + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=mock_embedder) + + record = mem.remember( + "Single fact.", + scope="/project", + categories=["decision"], + importance=0.8, + ) + assert record is not None + assert record.content == "Single fact." + assert record.scope == "/project" + assert record.importance == 0.8 + assert mem._storage.count() == 1 + + +def test_parallel_analyze_runs_concurrent_calls(tmp_path: Path) -> None: + """remember_many with 3 items needing LLM should make 3 concurrent LLM calls.""" + from unittest.mock import call + from crewai.memory.unified_memory import Memory + from crewai.memory.analyze import MemoryAnalysis, ExtractedMetadata + + call_count = 0 + + def distinct_embedder(texts: list[str]) -> list[list[float]]: + """Return unique embeddings per text so dedup doesn't drop them.""" + nonlocal call_count + result = [] + for i, _ in enumerate(texts): + emb = [0.0] * 1536 + emb[(call_count + i) % 1536] = 1.0 + result.append(emb) + call_count += len(texts) + return result + + embedder = MagicMock(side_effect=distinct_embedder) + llm = MagicMock() + llm.supports_function_calling.return_value = True + # Return a valid MemoryAnalysis for field resolution calls + llm.call.return_value = MemoryAnalysis( + suggested_scope="/inferred", + categories=["auto"], + importance=0.6, + extracted_metadata=ExtractedMetadata(), + ) + + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=embedder) + + # No scope/categories/importance -> all 3 need field resolution (Group C) + mem.remember_many(["Fact A.", "Fact B.", "Fact C."]) + mem.drain_writes() # wait for background save + # Each item triggers one analyze_for_save call -> 3 parallel LLM calls + assert llm.call.call_count == 3 + assert mem._storage.count() == 3 + + +# --- Non-blocking save tests --- + + +def test_remember_many_returns_immediately(tmp_path: Path) -> None: + """remember_many() should return an empty list immediately (non-blocking).""" + from crewai.memory.unified_memory import Memory + + call_count = 0 + + def distinct_embedder(texts: list[str]) -> list[list[float]]: + nonlocal call_count + result = [] + for i, _ in enumerate(texts): + emb = [0.0] * 1536 + emb[(call_count + i) % 1536] = 1.0 + result.append(emb) + call_count += len(texts) + return result + + embedder = MagicMock(side_effect=distinct_embedder) + llm = MagicMock() + llm.supports_function_calling.return_value = False + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=embedder) + + result = mem.remember_many( + ["Fact A.", "Fact B."], + scope="/test", + categories=["test"], + importance=0.5, + ) + # Returns immediately with empty list (save is in background) + assert result == [] + # After draining, records should exist + mem.drain_writes() + assert mem._storage.count() == 2 + + +def test_recall_drains_pending_writes(tmp_path: Path, mock_embedder: MagicMock) -> None: + """recall() should automatically wait for pending background saves.""" + from crewai.memory.unified_memory import Memory + + llm = MagicMock() + llm.supports_function_calling.return_value = False + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=mock_embedder) + + # Submit a background save + mem.remember_many( + ["Python is great."], + scope="/test", + categories=["lang"], + importance=0.7, + ) + # Recall should drain the pending save first, then find the record + matches = mem.recall("Python", scope="/test", limit=5, depth="shallow") + assert len(matches) >= 1 + assert "Python" in matches[0].record.content + + +def test_close_drains_and_shuts_down(tmp_path: Path, mock_embedder: MagicMock) -> None: + """close() should drain pending saves and shut down the pool.""" + from crewai.memory.unified_memory import Memory + + llm = MagicMock() + llm.supports_function_calling.return_value = False + mem = Memory(storage=str(tmp_path / "db"), llm=llm, embedder=mock_embedder) + + mem.remember_many( + ["Important fact."], + scope="/test", + categories=["test"], + importance=0.9, + ) + mem.close() + # After close, records should be persisted + assert mem._storage.count() == 1 diff --git a/lib/crewai/tests/rag/embeddings/test_google_vertex_memory_integration.py b/lib/crewai/tests/rag/embeddings/test_google_vertex_memory_integration.py index d6fa9e5ee..149320adf 100644 --- a/lib/crewai/tests/rag/embeddings/test_google_vertex_memory_integration.py +++ b/lib/crewai/tests/rag/embeddings/test_google_vertex_memory_integration.py @@ -1,37 +1,35 @@ """Integration tests for Google Vertex embeddings with Crew memory. These tests make real API calls and use VCR to record/replay responses. +The memory save path (extract_memories + remember) requires LLM and embedding +API calls that are difficult to capture in VCR cassettes (GCP metadata auth, +embedding endpoints). We mock those paths and verify the crew pipeline works +end-to-end while testing memory storage separately with a fake embedder. """ import os -import threading -from collections import defaultdict from unittest.mock import patch import pytest from crewai import Agent, Crew, Task -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemorySaveCompletedEvent, - MemorySaveStartedEvent, -) +from crewai.memory.unified_memory import Memory @pytest.fixture(autouse=True) def setup_vertex_ai_env(): """Set up environment for Vertex AI tests. - + Sets GOOGLE_GENAI_USE_VERTEXAI=true to ensure the SDK uses the Vertex AI backend (aiplatform.googleapis.com) which matches the VCR cassettes. Also mocks GOOGLE_API_KEY if not already set. """ env_updates = {"GOOGLE_GENAI_USE_VERTEXAI": "true"} - - # Add a mock API key if none exists + + # Add a mock API key if "GOOGLE_API_KEY" not in os.environ and "GEMINI_API_KEY" not in os.environ: env_updates["GOOGLE_API_KEY"] = "test-key" - + with patch.dict(os.environ, env_updates): yield @@ -42,7 +40,8 @@ def google_vertex_embedder_config(): return { "provider": "google-vertex", "config": { - "api_key": os.getenv("GOOGLE_API_KEY", "test-key"), + "project_id": os.getenv("GOOGLE_CLOUD_PROJECT", "gen-lang-client-0393486657"), + "location": "us-central1", "model_name": "gemini-embedding-001", }, } @@ -69,51 +68,67 @@ def simple_task(simple_agent): ) +def _fake_embedder(texts: list[str]) -> list[list[float]]: + """Return deterministic fake embeddings for testing storage without real API calls.""" + return [[0.1] * 1536 for _ in texts] + + @pytest.mark.vcr() -@pytest.mark.timeout(120) # Longer timeout for VCR recording +@pytest.mark.timeout(120) def test_crew_memory_with_google_vertex_embedder( google_vertex_embedder_config, simple_agent, simple_task ) -> None: - """Test that Crew with memory=True works with google-vertex embedder and memory is used.""" - # Track memory events - events: dict[str, list] = defaultdict(list) - condition = threading.Condition() + """Test that Crew with google-vertex embedder runs and that memory storage works. - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - with condition: - events["MemorySaveStartedEvent"].append(event) - condition.notify() + The crew kickoff uses VCR-recorded LLM responses. The memory save path + (extract_memories + remember) is mocked during kickoff because it requires + embedding/auth API calls not in the cassette. After kickoff we verify + memory storage works by calling remember() directly with a fake embedder. + """ + from crewai.rag.embeddings.factory import build_embedder - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - with condition: - events["MemorySaveCompletedEvent"].append(event) - condition.notify() + embedder = build_embedder(google_vertex_embedder_config) + memory = Memory(embedder=embedder) crew = Crew( agents=[simple_agent], tasks=[simple_task], - memory=True, - embedder=google_vertex_embedder_config, - verbose=False, + memory=memory, + verbose=True, ) - result = crew.kickoff() + assert crew._memory is memory + + # Mock _save_to_memory during kickoff so it doesn't make embedding API calls + # that VCR can't replay (GCP metadata auth, embedding endpoints). + with patch( + "crewai.agents.agent_builder.base_agent_executor_mixin.CrewAgentExecutorMixin._save_to_memory" + ): + result = crew.kickoff() assert result is not None assert result.raw is not None assert len(result.raw) > 0 - with condition: - success = condition.wait_for( - lambda: len(events["MemorySaveCompletedEvent"]) >= 1, - timeout=10, - ) + # Now verify the memory storage path works by calling remember() directly + # with a fake embedder that doesn't need real API calls. + memory._embedder_instance = _fake_embedder - assert success, "Timeout waiting for memory save events - memory may not be working" - assert len(events["MemorySaveStartedEvent"]) >= 1, "No memory save started events" - assert len(events["MemorySaveCompletedEvent"]) >= 1, "Memory save completed events" + # Pass all fields explicitly to skip LLM analysis in the encoding flow. + record = memory.remember( + content=f"AI summary: {result.raw[:100]}", + scope="/test", + categories=["ai", "summary"], + importance=0.7, + ) + assert record is not None + assert record.scope == "/test" + + info = memory.info("/") + assert info.record_count > 0, ( + f"Expected memories to be saved after manual remember(), " + f"but found {info.record_count} records" + ) @pytest.mark.vcr() @@ -124,21 +139,7 @@ def test_crew_memory_with_google_vertex_project_id(simple_agent, simple_task) -> if not project_id: pytest.skip("GOOGLE_CLOUD_PROJECT environment variable not set") - # Track memory events - events: dict[str, list] = defaultdict(list) - condition = threading.Condition() - - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - with condition: - events["MemorySaveStartedEvent"].append(event) - condition.notify() - - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - with condition: - events["MemorySaveCompletedEvent"].append(event) - condition.notify() + from crewai.rag.embeddings.factory import build_embedder embedder_config = { "provider": "google-vertex", @@ -149,28 +150,22 @@ def test_crew_memory_with_google_vertex_project_id(simple_agent, simple_task) -> }, } + embedder = build_embedder(embedder_config) + memory = Memory(embedder=embedder) + crew = Crew( agents=[simple_agent], tasks=[simple_task], - memory=True, - embedder=embedder_config, + memory=memory, verbose=False, ) - result = crew.kickoff() + assert crew._memory is memory + + with patch( + "crewai.agents.agent_builder.base_agent_executor_mixin.CrewAgentExecutorMixin._save_to_memory" + ): + result = crew.kickoff() - # Verify basic result assert result is not None assert result.raw is not None - - # Wait for memory save events - with condition: - success = condition.wait_for( - lambda: len(events["MemorySaveCompletedEvent"]) >= 1, - timeout=10, - ) - - # Verify memory was actually used - assert success, "Timeout waiting for memory save events - memory may not be working" - assert len(events["MemorySaveStartedEvent"]) >= 1, "No memory save started events" - assert len(events["MemorySaveCompletedEvent"]) >= 1, "No memory save completed events" diff --git a/lib/crewai/tests/rag/test_error_handling.py b/lib/crewai/tests/rag/test_error_handling.py index 1bbab292c..fab568e14 100644 --- a/lib/crewai/tests/rag/test_error_handling.py +++ b/lib/crewai/tests/rag/test_error_handling.py @@ -6,7 +6,6 @@ import pytest from crewai.knowledge.storage.knowledge_storage import ( # type: ignore[import-untyped] KnowledgeStorage, ) -from crewai.memory.storage.rag_storage import RAGStorage # type: ignore[import-untyped] @patch("crewai.knowledge.storage.knowledge_storage.get_rag_client") @@ -67,31 +66,6 @@ def test_knowledge_storage_invalid_embedding_config(mock_get_client: MagicMock) ) -@patch("crewai.memory.storage.rag_storage.get_rag_client") -def test_memory_rag_storage_client_failure(mock_get_client: MagicMock) -> None: - """Test RAGStorage handles RAG client failures in memory operations.""" - mock_client = MagicMock() - mock_get_client.return_value = mock_client - mock_client.search.side_effect = RuntimeError("ChromaDB server error") - - storage = RAGStorage("short_term", crew=None) - - results = storage.search("test query") - assert results == [] - - -@patch("crewai.memory.storage.rag_storage.get_rag_client") -def test_memory_rag_storage_save_failure(mock_get_client: MagicMock) -> None: - """Test RAGStorage handles save operation failures.""" - mock_client = MagicMock() - mock_get_client.return_value = mock_client - mock_client.add_documents.side_effect = Exception("Failed to add documents") - - storage = RAGStorage("long_term", crew=None) - - storage.save("test memory", {"key": "value"}) - - @patch("crewai.knowledge.storage.knowledge_storage.get_rag_client") def test_knowledge_storage_reset_readonly_database(mock_get_client: MagicMock) -> None: """Test KnowledgeStorage reset handles readonly database errors.""" @@ -120,21 +94,6 @@ def test_knowledge_storage_reset_collection_does_not_exist( storage.reset() -@patch("crewai.memory.storage.rag_storage.get_rag_client") -def test_memory_storage_reset_failure_propagation(mock_get_client: MagicMock) -> None: - """Test RAGStorage reset propagates unexpected errors.""" - mock_client = MagicMock() - mock_get_client.return_value = mock_client - mock_client.delete_collection.side_effect = Exception("Unexpected database error") - - storage = RAGStorage("entities", crew=None) - - with pytest.raises( - Exception, match="An error occurred while resetting the entities memory" - ): - storage.reset() - - @patch("crewai.knowledge.storage.knowledge_storage.get_rag_client") def test_knowledge_storage_malformed_search_results(mock_get_client: MagicMock) -> None: """Test KnowledgeStorage handles malformed search results.""" @@ -181,20 +140,6 @@ def test_knowledge_storage_network_interruption(mock_get_client: MagicMock) -> N assert second_attempt[0]["content"] == "recovered result" -@patch("crewai.memory.storage.rag_storage.get_rag_client") -def test_memory_storage_collection_creation_failure(mock_get_client: MagicMock) -> None: - """Test RAGStorage handles collection creation failures.""" - mock_client = MagicMock() - mock_get_client.return_value = mock_client - mock_client.get_or_create_collection.side_effect = Exception( - "Failed to create collection" - ) - - storage = RAGStorage("user_memory", crew=None) - - storage.save("test data", {"metadata": "test"}) - - @patch("crewai.knowledge.storage.knowledge_storage.get_rag_client") def test_knowledge_storage_embedding_dimension_mismatch_detailed( mock_get_client: MagicMock, diff --git a/lib/crewai/tests/rag/test_rag_storage_path.py b/lib/crewai/tests/rag/test_rag_storage_path.py deleted file mode 100644 index 925680094..000000000 --- a/lib/crewai/tests/rag/test_rag_storage_path.py +++ /dev/null @@ -1,82 +0,0 @@ -"""Tests for RAGStorage custom path functionality.""" - -from unittest.mock import MagicMock, patch - -from crewai.memory.storage.rag_storage import RAGStorage - - -@patch("crewai.memory.storage.rag_storage.create_client") -@patch("crewai.memory.storage.rag_storage.build_embedder") -def test_rag_storage_custom_path( - mock_build_embedder: MagicMock, - mock_create_client: MagicMock, -) -> None: - """Test RAGStorage uses custom path when provided.""" - mock_build_embedder.return_value = MagicMock(return_value=[[0.1, 0.2, 0.3]]) - mock_create_client.return_value = MagicMock() - - custom_path = "/custom/memory/path" - embedder_config = {"provider": "openai", "config": {"model": "text-embedding-3-small"}} - - RAGStorage( - type="short_term", - crew=None, - path=custom_path, - embedder_config=embedder_config, - ) - - mock_create_client.assert_called_once() - config_arg = mock_create_client.call_args[0][0] - assert config_arg.settings.persist_directory == custom_path - - -@patch("crewai.memory.storage.rag_storage.create_client") -@patch("crewai.memory.storage.rag_storage.build_embedder") -def test_rag_storage_default_path_when_none( - mock_build_embedder: MagicMock, - mock_create_client: MagicMock, -) -> None: - """Test RAGStorage uses default path when no custom path is provided.""" - mock_build_embedder.return_value = MagicMock(return_value=[[0.1, 0.2, 0.3]]) - mock_create_client.return_value = MagicMock() - - embedder_config = {"provider": "openai", "config": {"model": "text-embedding-3-small"}} - - storage = RAGStorage( - type="short_term", - crew=None, - path=None, - embedder_config=embedder_config, - ) - - mock_create_client.assert_called_once() - assert storage.path is None - - -@patch("crewai.memory.storage.rag_storage.create_client") -@patch("crewai.memory.storage.rag_storage.build_embedder") -def test_rag_storage_custom_path_with_batch_size( - mock_build_embedder: MagicMock, - mock_create_client: MagicMock, -) -> None: - """Test RAGStorage uses custom path with batch_size in config.""" - mock_build_embedder.return_value = MagicMock(return_value=[[0.1, 0.2, 0.3]]) - mock_create_client.return_value = MagicMock() - - custom_path = "/custom/batch/path" - embedder_config = { - "provider": "openai", - "config": {"model": "text-embedding-3-small", "batch_size": 100}, - } - - RAGStorage( - type="long_term", - crew=None, - path=custom_path, - embedder_config=embedder_config, - ) - - mock_create_client.assert_called_once() - config_arg = mock_create_client.call_args[0][0] - assert config_arg.settings.persist_directory == custom_path - assert config_arg.batch_size == 100 \ No newline at end of file diff --git a/lib/crewai/tests/storage/test_mem0_storage.py b/lib/crewai/tests/storage/test_mem0_storage.py deleted file mode 100644 index f219f0b45..000000000 --- a/lib/crewai/tests/storage/test_mem0_storage.py +++ /dev/null @@ -1,504 +0,0 @@ -from unittest.mock import MagicMock, patch - -import pytest -from crewai.memory.storage.mem0_storage import Mem0Storage -from mem0 import Memory, MemoryClient - - -# Define the class (if not already defined) -class MockCrew: - def __init__(self): - self.agents = [MagicMock(role="Test Agent")] - - -# Test data constants -SYSTEM_CONTENT = ( - "You are Friendly chatbot assistant. You are a kind and " - "knowledgeable chatbot assistant. You excel at understanding user needs, " - "providing helpful responses, and maintaining engaging conversations. " - "You remember previous interactions to provide a personalized experience.\n" - "Your personal goal is: Engage in useful and interesting conversations " - "with users while remembering context.\n" - "To give my best complete final answer to the task respond using the exact " - "following format:\n\n" - "Thought: I now can give a great answer\n" - "Final Answer: Your final answer must be the great and the most complete " - "as possible, it must be outcome described.\n\n" - "I MUST use these formats, my job depends on it!" -) - -USER_CONTENT = ( - "\nCurrent Task: Respond to user conversation. User message: " - "What do you know about me?\n\n" - "This is the expected criteria for your final answer: Contextually " - "appropriate, helpful, and friendly response.\n" - "you MUST return the actual complete content as the final answer, " - "not a summary.\n\n" - "# Useful context: \nExternal memories:\n" - "- User is from India\n" - "- User is interested in the solar system\n" - "- User name is Vidit Ostwal\n" - "- User is interested in French cuisine\n\n" - "Begin! This is VERY important to you, use the tools available and give " - "your best Final Answer, your job depends on it!\n\n" - "Thought:" -) - -ASSISTANT_CONTENT = ( - "I now can give a great answer \n" - "Final Answer: Hi Vidit! From our previous conversations, I know you're " - "from India and have a great interest in the solar system. It's fascinating " - "to explore the wonders of space, isn't it? Also, I remember you have a " - "passion for French cuisine, which has so many delightful dishes to explore. " - "If there's anything specific you'd like to discuss or learn about—whether " - "it's about the solar system or some great French recipes—feel free to let " - "me know! I'm here to help." -) - -TEST_DESCRIPTION = ( - "Respond to user conversation. User message: What do you know about me?" -) - -# Extracted content (after processing by _get_user_message and _get_assistant_message) -EXTRACTED_USER_CONTENT = "What do you know about me?" -EXTRACTED_ASSISTANT_CONTENT = ( - "Hi Vidit! From our previous conversations, I know you're " - "from India and have a great interest in the solar system. It's fascinating " - "to explore the wonders of space, isn't it? Also, I remember you have a " - "passion for French cuisine, which has so many delightful dishes to explore. " - "If there's anything specific you'd like to discuss or learn about—whether " - "it's about the solar system or some great French recipes—feel free to let " - "me know! I'm here to help." -) - - -@pytest.fixture -def mock_mem0_memory(): - """Fixture to create a mock Memory instance""" - return MagicMock(spec=Memory) - - -@pytest.fixture -def mem0_storage_with_mocked_config(mock_mem0_memory): - """Fixture to create a Mem0Storage instance with mocked dependencies""" - - # Patch the Memory class to return our mock - with patch( - "mem0.Memory.from_config", return_value=mock_mem0_memory - ) as mock_from_config: - config = { - "vector_store": { - "provider": "mock_vector_store", - "config": {"host": "localhost", "port": 6333}, - }, - "llm": { - "provider": "mock_llm", - "config": {"api_key": "mock-api-key", "model": "mock-model"}, - }, - "embedder": { - "provider": "mock_embedder", - "config": {"api_key": "mock-api-key", "model": "mock-model"}, - }, - "graph_store": { - "provider": "mock_graph_store", - "config": { - "url": "mock-url", - "username": "mock-user", - "password": "mock-password", - }, - }, - "history_db_path": "/mock/path", - "version": "test-version", - "custom_fact_extraction_prompt": "mock prompt 1", - "custom_update_memory_prompt": "mock prompt 2", - } - - # Parameters like run_id, includes, and excludes doesn't matter in Memory OSS - crew = MockCrew() - - embedder_config = { - "user_id": "test_user", - "local_mem0_config": config, - "run_id": "my_run_id", - "includes": "include1", - "excludes": "exclude1", - "infer": True, - } - - mem0_storage = Mem0Storage(type="short_term", crew=crew, config=embedder_config) - return mem0_storage, mock_from_config, config - - -def test_mem0_storage_initialization(mem0_storage_with_mocked_config, mock_mem0_memory): - """Test that Mem0Storage initializes correctly with the mocked config""" - mem0_storage, mock_from_config, config = mem0_storage_with_mocked_config - assert mem0_storage.memory_type == "short_term" - assert mem0_storage.memory is mock_mem0_memory - mock_from_config.assert_called_once_with(config) - - -@pytest.fixture -def mock_mem0_memory_client(): - """Fixture to create a mock MemoryClient instance""" - return MagicMock(spec=MemoryClient) - - -@pytest.fixture -def mem0_storage_with_memory_client_using_config_from_crew(mock_mem0_memory_client): - """Fixture to create a Mem0Storage instance with mocked dependencies""" - - # We need to patch the MemoryClient before it's instantiated - with patch.object(MemoryClient, "__new__", return_value=mock_mem0_memory_client): - crew = MockCrew() - - embedder_config = { - "user_id": "test_user", - "api_key": "ABCDEFGH", - "org_id": "my_org_id", - "project_id": "my_project_id", - "run_id": "my_run_id", - "includes": "include1", - "excludes": "exclude1", - "infer": True, - } - - return Mem0Storage(type="short_term", crew=crew, config=embedder_config) - - -@pytest.fixture -def mem0_storage_with_memory_client_using_explictly_config( - mock_mem0_memory_client, mock_mem0_memory -): - """Fixture to create a Mem0Storage instance with mocked dependencies""" - - # We need to patch both MemoryClient and Memory to prevent actual initialization - with ( - patch.object(MemoryClient, "__new__", return_value=mock_mem0_memory_client), - patch.object(Memory, "__new__", return_value=mock_mem0_memory), - ): - crew = MockCrew() - new_config = {"provider": "mem0", "config": {"api_key": "new-api-key"}} - - return Mem0Storage(type="short_term", crew=crew, config=new_config) - - -def test_mem0_storage_with_memory_client_initialization( - mem0_storage_with_memory_client_using_config_from_crew, mock_mem0_memory_client -): - """Test Mem0Storage initialization with MemoryClient""" - assert ( - mem0_storage_with_memory_client_using_config_from_crew.memory_type - == "short_term" - ) - assert ( - mem0_storage_with_memory_client_using_config_from_crew.memory - is mock_mem0_memory_client - ) - - -def test_mem0_storage_with_explict_config( - mem0_storage_with_memory_client_using_explictly_config, -): - expected_config = {"provider": "mem0", "config": {"api_key": "new-api-key"}} - assert ( - mem0_storage_with_memory_client_using_explictly_config.config == expected_config - ) - - -def test_mem0_storage_updates_project_with_custom_categories(mock_mem0_memory_client): - mock_mem0_memory_client.update_project = MagicMock() - - new_categories = [ - { - "lifestyle_management_concerns": ( - "Tracks daily routines, habits, hobbies and interests " - "including cooking, time management and work-life balance" - ) - }, - ] - - crew = MockCrew() - - config = { - "user_id": "test_user", - "api_key": "ABCDEFGH", - "org_id": "my_org_id", - "project_id": "my_project_id", - "custom_categories": new_categories, - } - - with patch.object(MemoryClient, "__new__", return_value=mock_mem0_memory_client): - _ = Mem0Storage(type="short_term", crew=crew, config=config) - - mock_mem0_memory_client.update_project.assert_called_once_with( - custom_categories=new_categories - ) - - -def test_save_method_with_memory_oss(mem0_storage_with_mocked_config): - """Test save method for different memory types""" - mem0_storage, _, _ = mem0_storage_with_mocked_config - mem0_storage.memory.add = MagicMock() - - # Test short_term memory type (already set in fixture) - test_value = "This is a test memory" - test_metadata = { - "description": TEST_DESCRIPTION, - "messages": [ - {"role": "system", "content": SYSTEM_CONTENT}, - {"role": "user", "content": USER_CONTENT}, - {"role": "assistant", "content": ASSISTANT_CONTENT}, - ], - "agent": "Friendly chatbot assistant", - } - - mem0_storage.save(test_value, test_metadata) - - mem0_storage.memory.add.assert_called_once_with( - [ - {"role": "user", "content": EXTRACTED_USER_CONTENT}, - { - "role": "assistant", - "content": EXTRACTED_ASSISTANT_CONTENT, - }, - ], - infer=True, - metadata={ - "type": "short_term", - "description": TEST_DESCRIPTION, - "agent": "Friendly chatbot assistant", - }, - run_id="my_run_id", - user_id="test_user", - agent_id="Test_Agent", - ) - - -def test_save_method_with_multiple_agents(mem0_storage_with_mocked_config): - mem0_storage, _, _ = mem0_storage_with_mocked_config - mem0_storage.crew.agents = [ - MagicMock(role="Test Agent"), - MagicMock(role="Test Agent 2"), - MagicMock(role="Test Agent 3"), - ] - mem0_storage.memory.add = MagicMock() - - test_value = "This is a test memory" - test_metadata = { - "description": TEST_DESCRIPTION, - "messages": [ - {"role": "system", "content": SYSTEM_CONTENT}, - {"role": "user", "content": USER_CONTENT}, - {"role": "assistant", "content": ASSISTANT_CONTENT}, - ], - "agent": "Friendly chatbot assistant", - } - - mem0_storage.save(test_value, test_metadata) - - mem0_storage.memory.add.assert_called_once_with( - [ - {"role": "user", "content": EXTRACTED_USER_CONTENT}, - { - "role": "assistant", - "content": EXTRACTED_ASSISTANT_CONTENT, - }, - ], - infer=True, - metadata={ - "type": "short_term", - "description": TEST_DESCRIPTION, - "agent": "Friendly chatbot assistant", - }, - run_id="my_run_id", - user_id="test_user", - agent_id="Test_Agent_Test_Agent_2_Test_Agent_3", - ) - - -def test_save_method_with_memory_client( - mem0_storage_with_memory_client_using_config_from_crew, -): - """Test save method for different memory types""" - mem0_storage = mem0_storage_with_memory_client_using_config_from_crew - mem0_storage.memory.add = MagicMock() - - # Test short_term memory type (already set in fixture) - test_value = "This is a test memory" - test_metadata = { - "description": TEST_DESCRIPTION, - "messages": [ - {"role": "system", "content": SYSTEM_CONTENT}, - {"role": "user", "content": USER_CONTENT}, - {"role": "assistant", "content": ASSISTANT_CONTENT}, - ], - "agent": "Friendly chatbot assistant", - } - - mem0_storage.save(test_value, test_metadata) - - mem0_storage.memory.add.assert_called_once_with( - [ - {"role": "user", "content": EXTRACTED_USER_CONTENT}, - { - "role": "assistant", - "content": EXTRACTED_ASSISTANT_CONTENT, - }, - ], - infer=True, - metadata={ - "type": "short_term", - "description": TEST_DESCRIPTION, - "agent": "Friendly chatbot assistant", - }, - version="v2", - run_id="my_run_id", - includes="include1", - excludes="exclude1", - output_format="v1.1", - user_id="test_user", - agent_id="Test_Agent", - ) - - -def test_search_method_with_memory_oss(mem0_storage_with_mocked_config): - """Test search method for different memory types""" - mem0_storage, _, _ = mem0_storage_with_mocked_config - mock_results = { - "results": [ - {"score": 0.9, "memory": "Result 1"}, - {"score": 0.4, "memory": "Result 2"}, - ] - } - mem0_storage.memory.search = MagicMock(return_value=mock_results) - - results = mem0_storage.search("test query", limit=5, score_threshold=0.5) - - mem0_storage.memory.search.assert_called_once_with( - query="test query", - limit=5, - user_id="test_user", - filters={"AND": [{"run_id": "my_run_id"}]}, - threshold=0.5, - ) - - assert len(results) == 2 - assert results[0]["content"] == "Result 1" - - -def test_search_method_with_memory_client( - mem0_storage_with_memory_client_using_config_from_crew, -): - """Test search method for different memory types""" - mem0_storage = mem0_storage_with_memory_client_using_config_from_crew - mock_results = { - "results": [ - {"score": 0.9, "memory": "Result 1"}, - {"score": 0.4, "memory": "Result 2"}, - ] - } - mem0_storage.memory.search = MagicMock(return_value=mock_results) - - results = mem0_storage.search("test query", limit=5, score_threshold=0.5) - - mem0_storage.memory.search.assert_called_once_with( - query="test query", - limit=5, - metadata={"type": "short_term"}, - user_id="test_user", - version="v2", - run_id="my_run_id", - output_format="v1.1", - filters={"AND": [{"run_id": "my_run_id"}]}, - threshold=0.5, - ) - - assert len(results) == 2 - assert results[0]["content"] == "Result 1" - - -def test_mem0_storage_default_infer_value(mock_mem0_memory_client): - """Test that Mem0Storage sets infer=True by default for short_term memory.""" - with patch.object(MemoryClient, "__new__", return_value=mock_mem0_memory_client): - crew = MockCrew() - - config = {"user_id": "test_user", "api_key": "ABCDEFGH"} - - mem0_storage = Mem0Storage(type="short_term", crew=crew, config=config) - assert mem0_storage.infer is True - - -def test_save_memory_using_agent_entity(mock_mem0_memory_client): - config = { - "agent_id": "agent-123", - } - - mock_memory = MagicMock(spec=Memory) - with patch.object(Memory, "__new__", return_value=mock_memory): - mem0_storage = Mem0Storage(type="external", config=config) - mem0_storage.save("test memory", {"key": "value"}) - mem0_storage.memory.add.assert_called_once_with( - [{"role": "assistant", "content": "test memory"}], - infer=True, - metadata={"type": "external", "key": "value"}, - agent_id="agent-123", - ) - - -def test_search_method_with_agent_entity(): - config = { - "agent_id": "agent-123", - } - - mock_memory = MagicMock(spec=Memory) - mock_results = { - "results": [ - {"score": 0.9, "memory": "Result 1"}, - {"score": 0.4, "memory": "Result 2"}, - ] - } - - with patch.object(Memory, "__new__", return_value=mock_memory): - mem0_storage = Mem0Storage(type="external", config=config) - - mem0_storage.memory.search = MagicMock(return_value=mock_results) - results = mem0_storage.search("test query", limit=5, score_threshold=0.5) - - mem0_storage.memory.search.assert_called_once_with( - query="test query", - limit=5, - filters={"AND": [{"agent_id": "agent-123"}]}, - threshold=0.5, - ) - - assert len(results) == 2 - assert results[0]["content"] == "Result 1" - - -def test_search_method_with_agent_id_and_user_id(): - mock_memory = MagicMock(spec=Memory) - mock_results = { - "results": [ - {"score": 0.9, "memory": "Result 1"}, - {"score": 0.4, "memory": "Result 2"}, - ] - } - - with patch.object(Memory, "__new__", return_value=mock_memory): - mem0_storage = Mem0Storage( - type="external", config={"agent_id": "agent-123", "user_id": "user-123"} - ) - - mem0_storage.memory.search = MagicMock(return_value=mock_results) - results = mem0_storage.search("test query", limit=5, score_threshold=0.5) - - mem0_storage.memory.search.assert_called_once_with( - query="test query", - limit=5, - user_id="user-123", - filters={"OR": [{"user_id": "user-123"}, {"agent_id": "agent-123"}]}, - threshold=0.5, - ) - - assert len(results) == 2 - assert results[0]["content"] == "Result 1" diff --git a/lib/crewai/tests/telemetry/test_telemetry.py b/lib/crewai/tests/telemetry/test_telemetry.py index 8f7f5fc70..d0564982d 100644 --- a/lib/crewai/tests/telemetry/test_telemetry.py +++ b/lib/crewai/tests/telemetry/test_telemetry.py @@ -121,3 +121,41 @@ def test_telemetry_singleton_pattern(): thread.join() assert all(instance is telemetry1 for instance in instances) + + +def test_no_signal_handler_traceback_in_non_main_thread(): + """Signal handler registration should be silently skipped in non-main threads. + + Regression test for https://github.com/crewAIInc/crewAI/issues/4289 + """ + errors: list[Exception] = [] + mock_holder: dict = {} + + def init_in_thread(): + try: + Telemetry._instance = None + with ( + patch.dict( + os.environ, + {"CREWAI_DISABLE_TELEMETRY": "false", "OTEL_SDK_DISABLED": "false"}, + ), + patch("crewai.telemetry.telemetry.TracerProvider"), + patch("signal.signal") as mock_signal, + patch("crewai.telemetry.telemetry.logger") as mock_logger, + ): + Telemetry() + mock_holder["signal"] = mock_signal + mock_holder["logger"] = mock_logger + except Exception as exc: + errors.append(exc) + + thread = threading.Thread(target=init_in_thread) + thread.start() + thread.join() + + assert not errors, f"Unexpected error: {errors}" + assert mock_holder, "Thread did not execute" + mock_holder["signal"].assert_not_called() + mock_holder["logger"].debug.assert_any_call( + "Skipping signal handler registration: not running in main thread" + ) diff --git a/lib/crewai/tests/test_crew.py b/lib/crewai/tests/test_crew.py index d2eeb531d..64d122a7c 100644 --- a/lib/crewai/tests/test_crew.py +++ b/lib/crewai/tests/test_crew.py @@ -36,10 +36,7 @@ from crewai.flow import Flow, start from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource from crewai.llm import LLM -from crewai.memory.contextual.contextual_memory import ContextualMemory -from crewai.memory.external.external_memory import ExternalMemory -from crewai.memory.long_term.long_term_memory import LongTermMemory -from crewai.memory.short_term.short_term_memory import ShortTermMemory + from crewai.process import Process from crewai.project import CrewBase, agent, before_kickoff, crew, task from crewai.task import Task @@ -2425,7 +2422,8 @@ def test_multiple_conditional_tasks(researcher, writer): @pytest.mark.vcr() -def test_using_contextual_memory(): +def test_using_memory(): + """With memory=True, crew has _memory and kickoff runs successfully.""" math_researcher = Agent( role="Researcher", goal="You research about math.", @@ -2445,11 +2443,8 @@ def test_using_contextual_memory(): memory=True, ) - with patch.object( - ContextualMemory, "build_context_for_task", return_value="" - ) as contextual_mem: - crew.kickoff() - contextual_mem.assert_called_once() + crew.kickoff() + assert crew._memory is not None @pytest.mark.vcr() @@ -2527,30 +2522,29 @@ def test_memory_events_are_emitted(): crew.kickoff() with condition: + # Wait for retrieval events (always fire) and optionally save events. + # Save events depend on extract_memories + remember LLM calls which + # may not be in VCR cassettes; retrieval events are reliable. success = condition.wait_for( lambda: ( - len(events["MemorySaveStartedEvent"]) >= 3 - and len(events["MemorySaveCompletedEvent"]) >= 3 - and len(events["MemoryQueryStartedEvent"]) >= 3 - and len(events["MemoryQueryCompletedEvent"]) >= 3 + len(events["MemoryRetrievalStartedEvent"]) >= 1 and len(events["MemoryRetrievalCompletedEvent"]) >= 1 + and len(events["MemoryQueryStartedEvent"]) >= 1 + and len(events["MemoryQueryCompletedEvent"]) >= 1 ), - timeout=10, + timeout=30, ) assert success, f"Timeout waiting for memory events. Got: {dict(events)}" - assert len(events["MemorySaveStartedEvent"]) == 3 - assert len(events["MemorySaveCompletedEvent"]) == 3 - assert len(events["MemorySaveFailedEvent"]) == 0 - assert len(events["MemoryQueryStartedEvent"]) == 3 - assert len(events["MemoryQueryCompletedEvent"]) == 3 - assert len(events["MemoryQueryFailedEvent"]) == 0 - assert len(events["MemoryRetrievalStartedEvent"]) == 1 - assert len(events["MemoryRetrievalCompletedEvent"]) == 1 + assert len(events["MemoryRetrievalStartedEvent"]) >= 1 + assert len(events["MemoryRetrievalCompletedEvent"]) >= 1 + assert len(events["MemoryQueryStartedEvent"]) >= 1 + assert len(events["MemoryQueryCompletedEvent"]) >= 1 @pytest.mark.vcr() -def test_using_contextual_memory_with_long_term_memory(): +def test_using_memory_with_remember(): + """With memory=True, crew uses unified memory and kickoff runs successfully.""" math_researcher = Agent( role="Researcher", goal="You research about math.", @@ -2567,19 +2561,16 @@ def test_using_contextual_memory_with_long_term_memory(): crew = Crew( agents=[math_researcher], tasks=[task1], - long_term_memory=LongTermMemory(), + memory=True, ) - with patch.object( - ContextualMemory, "build_context_for_task", return_value="" - ) as contextual_mem: - crew.kickoff() - contextual_mem.assert_called_once() - assert crew.memory is False + crew.kickoff() + assert crew._memory is not None @pytest.mark.vcr() -def test_warning_long_term_memory_without_entity_memory(): +def test_memory_enabled_creates_unified_memory(): + """With unified memory, memory=True creates _memory and kickoff runs.""" math_researcher = Agent( role="Researcher", goal="You research about math.", @@ -2597,55 +2588,16 @@ def test_warning_long_term_memory_without_entity_memory(): crew = Crew( agents=[math_researcher], tasks=[task1], - long_term_memory=LongTermMemory(), + memory=True, ) - with ( - patch("crewai.utilities.printer.Printer.print") as mock_print, - patch( - "crewai.memory.long_term.long_term_memory.LongTermMemory.save" - ) as save_memory, - ): - crew.kickoff() - mock_print.assert_called_with( - content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.", - color="bold_yellow", - ) - save_memory.assert_not_called() + crew.kickoff() + assert crew._memory is not None @pytest.mark.vcr() -def test_long_term_memory_with_memory_flag(): - math_researcher = Agent( - role="Researcher", - goal="You research about math.", - backstory="You're an expert in research and you love to learn new things.", - allow_delegation=False, - ) - - task1 = Task( - description="Research a topic to teach a kid aged 6 about math.", - expected_output="A topic, explanation, angle, and examples.", - agent=math_researcher, - ) - - with ( - patch("crewai.utilities.printer.Printer.print") as mock_print, - patch("crewai.memory.long_term.long_term_memory.LongTermMemory.save") as save_memory, - ): - crew = Crew( - agents=[math_researcher], - tasks=[task1], - memory=True, - long_term_memory=LongTermMemory(), - ) - crew.kickoff() - mock_print.assert_not_called() - save_memory.assert_called_once() - - -@pytest.mark.vcr() -def test_using_contextual_memory_with_short_term_memory(): +def test_memory_remember_called_after_task(): + """With memory=True, extract_memories is called with raw content and remember is called per extracted item.""" math_researcher = Agent( role="Researcher", goal="You research about math.", @@ -2662,19 +2614,58 @@ def test_using_contextual_memory_with_short_term_memory(): crew = Crew( agents=[math_researcher], tasks=[task1], - short_term_memory=ShortTermMemory(), + memory=True, ) with patch.object( - ContextualMemory, "build_context_for_task", return_value="" - ) as contextual_mem: + crew._memory, "extract_memories", wraps=crew._memory.extract_memories + ) as extract_mock, patch.object( + crew._memory, "remember", wraps=crew._memory.remember + ) as remember_mock: crew.kickoff() - contextual_mem.assert_called_once() - assert crew.memory is False + + # extract_memories should be called with the raw content blob + extract_mock.assert_called() + raw = extract_mock.call_args.args[0] + assert "Task:" in raw + assert "Agent:" in raw or "Researcher" in raw + + # remember should be called once per extracted memory (may be 0 if LLM returned none) + if remember_mock.called: + for call in remember_mock.call_args_list: + content = call.args[0] if call.args else call.kwargs.get("content", "") + assert isinstance(content, str) and len(content) > 0 @pytest.mark.vcr() -def test_disabled_memory_using_contextual_memory(): +def test_using_memory_recall_and_save(): + """With memory=True, crew uses unified memory for recall and save.""" + math_researcher = Agent( + role="Researcher", + goal="You research about math.", + backstory="You're an expert in research and you love to learn new things.", + allow_delegation=False, + ) + + task1 = Task( + description="Research a topic to teach a kid aged 6 about math.", + expected_output="A topic, explanation, angle, and examples.", + agent=math_researcher, + ) + + crew = Crew( + agents=[math_researcher], + tasks=[task1], + memory=True, + ) + + crew.kickoff() + assert crew._memory is not None + + +@pytest.mark.vcr() +def test_disabled_memory(): + """With memory=False, crew has no _memory and kickoff runs without memory.""" math_researcher = Agent( role="Researcher", goal="You research about math.", @@ -2694,11 +2685,8 @@ def test_disabled_memory_using_contextual_memory(): memory=False, ) - with patch.object( - ContextualMemory, "build_context_for_task", return_value="" - ) as contextual_mem: - crew.kickoff() - contextual_mem.assert_not_called() + crew.kickoff() + assert getattr(crew, "_memory", None) is None @pytest.mark.vcr() @@ -4446,68 +4434,21 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy(): def test_crew_copy_with_memory(): - """Test that copying a crew with memory enabled does not raise validation errors and copies memory correctly.""" + """Test that copying a crew with memory enabled does not raise and shares the same memory instance.""" agent = Agent(role="Test Agent", goal="Test Goal", backstory="Test Backstory") task = Task(description="Test Task", expected_output="Test Output", agent=agent) crew = Crew(agents=[agent], tasks=[task], memory=True) - original_short_term_id = ( - id(crew._short_term_memory) if crew._short_term_memory else None - ) - original_long_term_id = ( - id(crew._long_term_memory) if crew._long_term_memory else None - ) - original_entity_id = id(crew._entity_memory) if crew._entity_memory else None - original_external_id = id(crew._external_memory) if crew._external_memory else None + assert crew._memory is not None, "Crew with memory=True should have _memory" try: crew_copy = crew.copy() - assert hasattr(crew_copy, "_short_term_memory"), ( - "Copied crew should have _short_term_memory" + assert hasattr(crew_copy, "_memory"), "Copied crew should have _memory" + assert crew_copy._memory is not None, "Copied _memory should not be None" + assert crew_copy._memory is crew._memory, ( + "Copy passes memory=self._memory so clone shares the same memory" ) - assert crew_copy._short_term_memory is not None, ( - "Copied _short_term_memory should not be None" - ) - assert id(crew_copy._short_term_memory) != original_short_term_id, ( - "Copied _short_term_memory should be a new object" - ) - - assert hasattr(crew_copy, "_long_term_memory"), ( - "Copied crew should have _long_term_memory" - ) - assert crew_copy._long_term_memory is not None, ( - "Copied _long_term_memory should not be None" - ) - assert id(crew_copy._long_term_memory) != original_long_term_id, ( - "Copied _long_term_memory should be a new object" - ) - - assert hasattr(crew_copy, "_entity_memory"), ( - "Copied crew should have _entity_memory" - ) - assert crew_copy._entity_memory is not None, ( - "Copied _entity_memory should not be None" - ) - assert id(crew_copy._entity_memory) != original_entity_id, ( - "Copied _entity_memory should be a new object" - ) - - if original_external_id: - assert hasattr(crew_copy, "_external_memory"), ( - "Copied crew should have _external_memory" - ) - assert crew_copy._external_memory is not None, ( - "Copied _external_memory should not be None" - ) - assert id(crew_copy._external_memory) != original_external_id, ( - "Copied _external_memory should be a new object" - ) - else: - assert ( - not hasattr(crew_copy, "_external_memory") - or crew_copy._external_memory is None - ), "Copied _external_memory should be None if not originally present" except pydantic_core.ValidationError as e: if "Input should be an instance of" in str(e) and ("Memory" in str(e)): @@ -4515,7 +4456,7 @@ def test_crew_copy_with_memory(): f"Copying with memory raised Pydantic ValidationError, likely due to incorrect memory copy: {e}" ) else: - raise e # Re-raise other validation errors + raise e except Exception as e: pytest.fail(f"Copying crew raised an unexpected exception: {e}") @@ -4807,9 +4748,8 @@ def test_default_crew_name(researcher, writer): @pytest.mark.vcr() -def test_ensure_exchanged_messages_are_propagated_to_external_memory(): - external_memory = ExternalMemory(storage=MagicMock()) - +def test_memory_remember_receives_task_content(): + """With memory=True, extract_memories receives raw content with task, agent, expected output, and result.""" math_researcher = Agent( role="Researcher", goal="You research about math.", @@ -4826,33 +4766,30 @@ def test_ensure_exchanged_messages_are_propagated_to_external_memory(): crew = Crew( agents=[math_researcher], tasks=[task1], - external_memory=external_memory, + memory=True, ) - with patch.object( - ExternalMemory, "save", return_value=None - ) as external_memory_save: + with ( + # Mock extract_memories to return fake memories and capture the raw input. + # No wraps= needed -- the test only checks what args it receives, not the output. + patch.object( + crew._memory, "extract_memories", return_value=["Fake memory."] + ) as extract_mock, + # Mock recall to avoid LLM calls for query analysis (not in cassette). + patch.object(crew._memory, "recall", return_value=[]), + # Mock remember_many to prevent the background save from triggering + # LLM calls (field resolution) that aren't in the cassette. + patch.object(crew._memory, "remember_many", return_value=[]), + ): crew.kickoff() - external_memory_save.assert_called_once() + extract_mock.assert_called() + raw = extract_mock.call_args.args[0] - call_args = external_memory_save.call_args - - assert "value" in call_args.kwargs or len(call_args.args) > 0 - assert "metadata" in call_args.kwargs or len(call_args.args) > 1 - - if "metadata" in call_args.kwargs: - metadata = call_args.kwargs["metadata"] - else: - metadata = call_args.args[1] - - assert "description" in metadata - assert "messages" in metadata - assert isinstance(metadata["messages"], list) - assert len(metadata["messages"]) >= 2 - - messages = metadata["messages"] - assert messages[0]["role"] == "system" - assert "Researcher" in messages[0]["content"] - assert messages[1]["role"] == "user" - assert "Research a topic to teach a kid aged 6 about math" in messages[1]["content"] + # The raw content passed to extract_memories should contain the task context + assert "Task:" in raw + assert "Research" in raw or "topic" in raw + assert "Agent:" in raw + assert "Researcher" in raw + assert "Expected result:" in raw + assert "Result:" in raw diff --git a/lib/crewai/tests/test_flow.py b/lib/crewai/tests/test_flow.py index 2040e9e5b..ccb08cb0a 100644 --- a/lib/crewai/tests/test_flow.py +++ b/lib/crewai/tests/test_flow.py @@ -1647,3 +1647,249 @@ class TestFlowAkickoff: assert execution_order == ["begin", "route", "path_a"] assert result == "path_a_result" + + +def test_cyclic_flow_or_listeners_fire_every_iteration(): + """Test that or_() listeners reset between cycle iterations through a router. + + Regression test for a bug where _fired_or_listeners was not cleared when + cycles loop through a router/listener instead of a @start method, causing + or_() listeners to permanently suppress after the first iteration. + + Pattern: router classifies → routes to ONE of several handlers → or_() + merge downstream → cycle back. Only one handler fires per iteration, but + the or_() merge must still fire every time. + """ + execution_order = [] + + class CyclicOrFlow(Flow): + iteration = 0 + max_iterations = 3 + + @start() + def begin(self): + execution_order.append("begin") + + @router(or_(begin, "loop_back")) + def route(self): + self.iteration += 1 + execution_order.append(f"route_{self.iteration}") + if self.iteration <= self.max_iterations: + # Alternate between handlers on each iteration + return "type_a" if self.iteration % 2 == 1 else "type_b" + return "done" + + @listen("type_a") + def handler_a(self): + execution_order.append(f"handler_a_{self.iteration}") + + @listen("type_b") + def handler_b(self): + execution_order.append(f"handler_b_{self.iteration}") + + # This or_() listener must fire on EVERY iteration, not just the first + @listen(or_(handler_a, handler_b)) + def merge(self): + execution_order.append(f"merge_{self.iteration}") + + @listen(merge) + def loop_back(self): + execution_order.append(f"loop_back_{self.iteration}") + + flow = CyclicOrFlow() + flow.kickoff() + + # merge must have fired once per iteration (3 times total) + merge_events = [e for e in execution_order if e.startswith("merge_")] + assert len(merge_events) == 3, ( + f"or_() listener 'merge' should fire every iteration, " + f"got {len(merge_events)} fires: {execution_order}" + ) + + # loop_back must have also fired every iteration + loop_back_events = [e for e in execution_order if e.startswith("loop_back_")] + assert len(loop_back_events) == 3, ( + f"'loop_back' should fire every iteration, " + f"got {len(loop_back_events)} fires: {execution_order}" + ) + + # Verify alternating handlers + handler_a_events = [e for e in execution_order if e.startswith("handler_a_")] + handler_b_events = [e for e in execution_order if e.startswith("handler_b_")] + assert len(handler_a_events) == 2 # iterations 1 and 3 + assert len(handler_b_events) == 1 # iteration 2 + + +def test_cyclic_flow_multiple_or_listeners_fire_every_iteration(): + """Test that multiple or_() listeners all reset between cycle iterations. + + Mirrors a real-world pattern: a router classifies messages, handlers process + them, then both a 'send' step (or_ on handlers) and a 'store' step (or_ on + router outputs) must fire on every loop iteration. + """ + execution_order = [] + + class MultiOrCyclicFlow(Flow): + iteration = 0 + max_iterations = 3 + + @start() + def begin(self): + execution_order.append("begin") + + @router(or_(begin, "capture")) + def classify(self): + self.iteration += 1 + execution_order.append(f"classify_{self.iteration}") + if self.iteration <= self.max_iterations: + return "type_a" + return "exit" + + @listen("type_a") + def handle_type_a(self): + execution_order.append(f"handle_a_{self.iteration}") + + # or_() listener on router output strings — must fire every iteration + @listen(or_("type_a", "type_b", "type_c")) + def store(self): + execution_order.append(f"store_{self.iteration}") + + # or_() listener on handler methods — must fire every iteration + @listen(or_(handle_type_a,)) + def send(self): + execution_order.append(f"send_{self.iteration}") + + @listen("send") + def capture(self): + execution_order.append(f"capture_{self.iteration}") + + flow = MultiOrCyclicFlow() + flow.kickoff() + + for method in ["store", "send", "capture"]: + events = [e for e in execution_order if e.startswith(f"{method}_")] + assert len(events) == 3, ( + f"'{method}' should fire every iteration, " + f"got {len(events)} fires: {execution_order}" + ) + + +def test_cyclic_flow_works_with_persist_and_id_input(): + """Cyclic router flows must complete all iterations when persistence is + enabled and 'id' is passed in inputs. + + Regression test: passing ``inputs={"id": ...}`` with a persistence backend + previously caused ``_is_execution_resuming`` to be set even though + ``_completed_methods`` was empty. The flag was never cleared during + execution, so on the second cycle iteration the resumption path in + ``_execute_single_listener`` short-circuited the router with ``(None, None)`` + and the flow silently terminated after a single iteration. + """ + from uuid import uuid4 + + from crewai.flow.persistence import SQLiteFlowPersistence + + execution_order: list[str] = [] + + class PersistCyclicFlow(Flow): + iteration: int = 0 + max_iterations: int = 3 + + @start() + def begin(self): + execution_order.append("begin") + + @router(or_(begin, "capture")) + def classify(self): + self.iteration += 1 + execution_order.append(f"classify_{self.iteration}") + if self.iteration <= self.max_iterations: + return "type_a" + return "exit" + + @listen("type_a") + def handle(self): + execution_order.append(f"handle_{self.iteration}") + + @listen(or_(handle,)) + def send(self): + execution_order.append(f"send_{self.iteration}") + + @listen("send") + def capture(self): + execution_order.append(f"capture_{self.iteration}") + + @listen("exit") + def finish(self): + execution_order.append("finish") + + persistence = SQLiteFlowPersistence() + flow = PersistCyclicFlow(persistence=persistence) + flow.kickoff(inputs={"id": str(uuid4())}) + + assert "finish" in execution_order, ( + f"Flow should have reached 'finish', got: {execution_order}" + ) + # The router fires max_iterations+1 times (3 cycles + the final "exit") + classify_events = [e for e in execution_order if e.startswith("classify_")] + assert len(classify_events) == 4, ( + f"'classify' should fire 4 times (3 cycles + exit), " + f"got {len(classify_events)}: {execution_order}" + ) + # The other methods fire once per "type_a" cycle + for method in ["handle", "send", "capture"]: + events = [e for e in execution_order if e.startswith(f"{method}_")] + assert len(events) == 3, ( + f"'{method}' should fire 3 times, " + f"got {len(events)}: {execution_order}" + ) + + +@pytest.mark.timeout(5) +def test_self_listening_method_does_not_loop(): + """A method whose @listen label matches its own name must not loop forever. + + Without the guard, 'process' re-triggers itself on every completion, + running indefinitely (timeout → FAIL). The fix caps method calls + and raises RecursionError (PASS). + """ + + class SelfListenFlow(Flow): + @start() + def begin(self): + return "process" + + @router(begin) + def route(self): + return "process" + + @listen("process") + def process(self): + pass + + flow = SelfListenFlow() + with pytest.raises(RecursionError, match="infinite loop"): + flow.kickoff() + + +def test_or_condition_self_listen_fires_once(): + """or_() with a self-referencing label only fires once due to or_() guard.""" + call_count = 0 + + class OrSelfListenFlow(Flow): + @start() + def begin(self): + return "process" + + @router(begin) + def route(self): + return "process" + + @listen(or_("other_trigger", "process")) + def process(self): + nonlocal call_count + call_count += 1 + + flow = OrSelfListenFlow() + flow.kickoff() + assert call_count == 1 diff --git a/lib/crewai/tests/test_flow_ask.py b/lib/crewai/tests/test_flow_ask.py new file mode 100644 index 000000000..d198e261c --- /dev/null +++ b/lib/crewai/tests/test_flow_ask.py @@ -0,0 +1,1152 @@ +"""Tests for Flow.ask() user input method. + +This module tests the ask() method on Flow, including basic usage, +timeout behavior, provider resolution, event emission, auto-checkpoint +durability, input history tracking, and integration with flow machinery. +""" + +from __future__ import annotations + +import time +from datetime import datetime +from typing import Any +from unittest.mock import MagicMock, patch + +from crewai.flow import Flow, flow_config, listen, start +from crewai.flow.async_feedback.providers import ConsoleProvider +from crewai.flow.flow import FlowState +from crewai.flow.input_provider import InputProvider, InputResponse + + +# ── Test helpers ───────────────────────────────────────────────── + + +class MockInputProvider: + """Mock input provider that returns pre-configured responses.""" + + def __init__(self, responses: list[str | None]) -> None: + self.responses = responses + self._call_count = 0 + self.messages: list[str] = [] + self.received_metadata: list[dict[str, Any] | None] = [] + + def request_input( + self, message: str, flow: Flow[Any], metadata: dict[str, Any] | None = None + ) -> str | None: + self.messages.append(message) + self.received_metadata.append(metadata) + if self._call_count >= len(self.responses): + return None + response = self.responses[self._call_count] + self._call_count += 1 + return response + + +class SlowMockProvider: + """Mock provider that delays before returning, for timeout tests.""" + + def __init__(self, delay: float, response: str = "delayed") -> None: + self.delay = delay + self.response = response + + def request_input( + self, message: str, flow: Flow[Any], metadata: dict[str, Any] | None = None + ) -> str | None: + time.sleep(self.delay) + return self.response + + +# ── Basic Functionality ────────────────────────────────────────── + + +class TestAskBasic: + """Tests for basic ask() functionality.""" + + def test_ask_returns_user_input(self) -> None: + """ask() returns the string from the input provider.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["hello"]) + + @start() + def my_method(self): + return self.ask("Say something:") + + flow = TestFlow() + result = flow.kickoff() + assert result == "hello" + + def test_ask_in_async_method(self) -> None: + """ask() works inside an async flow method.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["async hello"]) + + @start() + async def my_method(self): + return self.ask("Say something:") + + flow = TestFlow() + result = flow.kickoff() + assert result == "async hello" + + def test_ask_in_start_method(self) -> None: + """ask() works inside a @start() method, flow completes normally.""" + execution_log: list[str] = [] + + class TestFlow(Flow): + input_provider = MockInputProvider(["AI"]) + + @start() + def gather(self): + topic = self.ask("Topic?") + execution_log.append(f"got:{topic}") + return topic + + flow = TestFlow() + result = flow.kickoff() + assert result == "AI" + assert execution_log == ["got:AI"] + + def test_ask_in_listen_method(self) -> None: + """ask() works inside a @listen() method.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["detailed"]) + + @start() + def step1(self): + return "topic" + + @listen("step1") + def step2(self): + depth = self.ask("How deep?") + return f"researching at {depth} level" + + flow = TestFlow() + result = flow.kickoff() + assert result == "researching at detailed level" + + def test_ask_multiple_calls(self) -> None: + """Multiple ask() calls in one method return correct values in order.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["AI", "detailed", "english"]) + + @start() + def gather(self): + topic = self.ask("Topic?") + depth = self.ask("Depth?") + lang = self.ask("Language?") + return {"topic": topic, "depth": depth, "lang": lang} + + flow = TestFlow() + result = flow.kickoff() + assert result == {"topic": "AI", "depth": "detailed", "lang": "english"} + + def test_ask_conditional(self) -> None: + """ask() called conditionally based on previous answer.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["AI", "LLMs"]) + + @start() + def gather(self): + topic = self.ask("Topic?") + if topic == "AI": + focus = self.ask("Specific area?") + else: + focus = "general" + return {"topic": topic, "focus": focus} + + flow = TestFlow() + result = flow.kickoff() + assert result == {"topic": "AI", "focus": "LLMs"} + + def test_ask_returns_empty_string_on_enter(self) -> None: + """Empty string means user pressed Enter (intentional empty input).""" + + class TestFlow(Flow): + input_provider = MockInputProvider([""]) + + @start() + def my_method(self): + result = self.ask("Optional input:") + return result + + flow = TestFlow() + result = flow.kickoff() + assert result == "" + assert result is not None # Explicitly not None + + +# ── Timeout ────────────────────────────────────────────────────── + + +class TestAskTimeout: + """Tests for timeout behavior.""" + + def test_ask_timeout_returns_none(self) -> None: + """ask() returns None when timeout expires.""" + + class TestFlow(Flow): + input_provider = SlowMockProvider(delay=5.0) + + @start() + def my_method(self): + return self.ask("Question?", timeout=0.1) + + flow = TestFlow() + result = flow.kickoff() + assert result is None + + def test_ask_timeout_in_async_method(self) -> None: + """ask() timeout works inside an async flow method.""" + + class TestFlow(Flow): + input_provider = SlowMockProvider(delay=5.0) + + @start() + async def my_method(self): + return self.ask("Question?", timeout=0.1) + + flow = TestFlow() + result = flow.kickoff() + assert result is None + + def test_ask_loop_with_timeout_termination(self) -> None: + """while (msg := ask(...)) is not None pattern terminates on timeout.""" + messages_received: list[str] = [] + + class TestFlow(Flow): + input_provider = MockInputProvider(["hello", "world", None]) + + @start() + def chat(self): + while (msg := self.ask("You:")) is not None: + messages_received.append(msg) + return len(messages_received) + + flow = TestFlow() + result = flow.kickoff() + assert result == 2 + assert messages_received == ["hello", "world"] + + def test_ask_no_timeout_waits_indefinitely(self) -> None: + """ask() with no timeout blocks until provider returns.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["answer"]) + + @start() + def my_method(self): + return self.ask("Question?") # no timeout + + flow = TestFlow() + result = flow.kickoff() + assert result == "answer" + + +# ── Provider Resolution ────────────────────────────────────────── + + +class TestProviderResolution: + """Tests for provider resolution priority chain.""" + + def test_ask_uses_flow_level_provider(self) -> None: + """Per-flow input_provider is used when set.""" + provider = MockInputProvider(["from flow"]) + + class TestFlow(Flow): + input_provider = provider + + @start() + def my_method(self): + return self.ask("Q?") + + flow = TestFlow() + flow.kickoff() + assert provider.messages == ["Q?"] + + def test_ask_uses_global_config_provider(self) -> None: + """flow_config.input_provider is used as fallback.""" + provider = MockInputProvider(["from config"]) + + original = flow_config.input_provider + try: + flow_config.input_provider = provider + + class TestFlow(Flow): + @start() + def my_method(self): + return self.ask("Q?") + + flow = TestFlow() + result = flow.kickoff() + assert result == "from config" + assert provider.messages == ["Q?"] + finally: + flow_config.input_provider = original + + def test_ask_defaults_to_console_provider(self) -> None: + """When no provider configured, ConsoleProvider is used.""" + original = flow_config.input_provider + try: + flow_config.input_provider = None + + class TestFlow(Flow): + # No input_provider set + @start() + def my_method(self): + return self.ask("Q?") + + flow = TestFlow() + resolved = flow._resolve_input_provider() + assert isinstance(resolved, ConsoleProvider) + finally: + flow_config.input_provider = original + + def test_flow_provider_overrides_global(self) -> None: + """Per-flow provider takes precedence over global config.""" + flow_provider = MockInputProvider(["from flow"]) + global_provider = MockInputProvider(["from global"]) + + original = flow_config.input_provider + try: + flow_config.input_provider = global_provider + + class TestFlow(Flow): + input_provider = flow_provider + + @start() + def my_method(self): + return self.ask("Q?") + + flow = TestFlow() + result = flow.kickoff() + assert result == "from flow" + assert flow_provider.messages == ["Q?"] + assert global_provider.messages == [] # not called + finally: + flow_config.input_provider = original + + +# ── Events ─────────────────────────────────────────────────────── + + +class TestAskEvents: + """Tests for event emission during ask().""" + + def test_ask_emits_input_requested_event(self) -> None: + """FlowInputRequestedEvent is emitted when ask() is called.""" + from crewai.events.event_bus import crewai_event_bus + from crewai.events.types.flow_events import FlowInputRequestedEvent + + events_captured: list[FlowInputRequestedEvent] = [] + + class TestFlow(Flow): + input_provider = MockInputProvider(["answer"]) + + @start() + def my_method(self): + return self.ask("What topic?") + + flow = TestFlow() + + original_emit = crewai_event_bus.emit + + def capture_emit(source: Any, event: Any) -> Any: + if isinstance(event, FlowInputRequestedEvent): + events_captured.append(event) + return original_emit(source, event) + + with patch.object(crewai_event_bus, "emit", side_effect=capture_emit): + flow.kickoff() + + assert len(events_captured) == 1 + assert events_captured[0].message == "What topic?" + assert events_captured[0].type == "flow_input_requested" + + def test_ask_emits_input_received_event(self) -> None: + """FlowInputReceivedEvent is emitted after input is received.""" + from crewai.events.event_bus import crewai_event_bus + from crewai.events.types.flow_events import FlowInputReceivedEvent + + events_captured: list[FlowInputReceivedEvent] = [] + + class TestFlow(Flow): + input_provider = MockInputProvider(["my answer"]) + + @start() + def my_method(self): + return self.ask("Question?") + + flow = TestFlow() + + original_emit = crewai_event_bus.emit + + def capture_emit(source: Any, event: Any) -> Any: + if isinstance(event, FlowInputReceivedEvent): + events_captured.append(event) + return original_emit(source, event) + + with patch.object(crewai_event_bus, "emit", side_effect=capture_emit): + flow.kickoff() + + assert len(events_captured) == 1 + assert events_captured[0].message == "Question?" + assert events_captured[0].response == "my answer" + assert events_captured[0].type == "flow_input_received" + + def test_ask_timeout_emits_received_with_none(self) -> None: + """FlowInputReceivedEvent has response=None on timeout.""" + from crewai.events.event_bus import crewai_event_bus + from crewai.events.types.flow_events import FlowInputReceivedEvent + + events_captured: list[FlowInputReceivedEvent] = [] + + class TestFlow(Flow): + input_provider = SlowMockProvider(delay=5.0) + + @start() + def my_method(self): + return self.ask("Question?", timeout=0.1) + + flow = TestFlow() + + original_emit = crewai_event_bus.emit + + def capture_emit(source: Any, event: Any) -> Any: + if isinstance(event, FlowInputReceivedEvent): + events_captured.append(event) + return original_emit(source, event) + + with patch.object(crewai_event_bus, "emit", side_effect=capture_emit): + flow.kickoff() + + assert len(events_captured) == 1 + assert events_captured[0].response is None + + +# ── Auto-checkpoint (Durability) ───────────────────────────────── + + +class TestAskCheckpoint: + """Tests for auto-checkpoint durability before ask() waits.""" + + def test_ask_checkpoints_state_before_waiting(self) -> None: + """State is saved to persistence before waiting for input.""" + mock_persistence = MagicMock() + mock_persistence.load_state.return_value = None + + class TestFlow(Flow): + input_provider = MockInputProvider(["answer"]) + + @start() + def my_method(self): + self.state["important"] = "data" + return self.ask("Question?") + + flow = TestFlow(persistence=mock_persistence) + flow.kickoff() + + # Find the _ask_checkpoint call among save_state calls + checkpoint_calls = [ + c for c in mock_persistence.save_state.call_args_list + if c.kwargs.get("method_name") == "_ask_checkpoint" + or (len(c.args) >= 2 and c.args[1] == "_ask_checkpoint") + ] + assert len(checkpoint_calls) >= 1 + + def test_ask_no_checkpoint_without_persistence(self) -> None: + """No error when persistence is not configured.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["answer"]) + + @start() + def my_method(self): + return self.ask("Question?") + + flow = TestFlow() # No persistence + result = flow.kickoff() + assert result == "answer" # Works fine without persistence + + def test_state_recoverable_after_checkpoint(self) -> None: + """State set before ask() is checkpointed and recoverable. + + The auto-checkpoint happens *before* the provider is called, so + state values set prior to ask() are persisted. This means if the + server crashes while waiting for input, previously gathered data + is safe. + """ + mock_persistence = MagicMock() + mock_persistence.load_state.return_value = None + + class GatherFlow(Flow): + input_provider = MockInputProvider(["AI", "detailed"]) + + @start() + def gather(self): + # First ask: nothing in state yet + topic = self.ask("Topic?") + self.state["topic"] = topic + # Second ask: state now has topic, checkpoint saves it + depth = self.ask("Depth?") + self.state["depth"] = depth + return {"topic": topic, "depth": depth} + + flow = GatherFlow(persistence=mock_persistence) + result = flow.kickoff() + assert result == {"topic": "AI", "depth": "detailed"} + + # Find the checkpoint calls + checkpoint_calls = [ + c for c in mock_persistence.save_state.call_args_list + if c.kwargs.get("method_name") == "_ask_checkpoint" + or (len(c.args) >= 2 and c.args[1] == "_ask_checkpoint") + ] + assert len(checkpoint_calls) == 2 + + # The second checkpoint (before asking "Depth?") should have topic + second_checkpoint = checkpoint_calls[1] + # state_data is the third positional arg or keyword arg + if second_checkpoint.kwargs.get("state_data"): + state_data = second_checkpoint.kwargs["state_data"] + else: + state_data = second_checkpoint.args[2] + assert state_data.get("topic") == "AI" + + +# ── Input History ──────────────────────────────────────────────── + + +class TestInputHistory: + """Tests for _input_history tracking.""" + + def test_input_history_accumulated(self) -> None: + """_input_history tracks all ask/response pairs.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["AI", "detailed"]) + + @start() + def gather(self): + self.ask("Topic?") + self.ask("Depth?") + return "done" + + flow = TestFlow() + flow.kickoff() + + assert len(flow._input_history) == 2 + assert flow._input_history[0]["message"] == "Topic?" + assert flow._input_history[0]["response"] == "AI" + assert flow._input_history[1]["message"] == "Depth?" + assert flow._input_history[1]["response"] == "detailed" + + def test_input_history_includes_method_name(self) -> None: + """Input history records which method called ask().""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["AI"]) + + @start() + def gather_info(self): + self.ask("Topic?") + return "done" + + flow = TestFlow() + flow.kickoff() + + assert len(flow._input_history) == 1 + assert flow._input_history[0]["method_name"] == "gather_info" + + def test_input_history_includes_timestamp(self) -> None: + """Input history records timestamps.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["AI"]) + + @start() + def my_method(self): + self.ask("Topic?") + return "done" + + flow = TestFlow() + before = datetime.now() + flow.kickoff() + after = datetime.now() + + assert len(flow._input_history) == 1 + ts = flow._input_history[0]["timestamp"] + assert isinstance(ts, datetime) + assert before <= ts <= after + + def test_input_history_records_none_on_timeout(self) -> None: + """Input history records None response on timeout.""" + + class TestFlow(Flow): + input_provider = SlowMockProvider(delay=5.0) + + @start() + def my_method(self): + self.ask("Question?", timeout=0.1) + return "done" + + flow = TestFlow() + flow.kickoff() + + assert len(flow._input_history) == 1 + assert flow._input_history[0]["response"] is None + + +# ── Integration ────────────────────────────────────────────────── + + +class TestAskIntegration: + """Integration tests for ask() with other flow features.""" + + def test_ask_works_with_listen_chain(self) -> None: + """ask() in a start method, result flows to listener.""" + execution_log: list[str] = [] + + class TestFlow(Flow): + input_provider = MockInputProvider(["AI agents"]) + + @start() + def gather(self): + topic = self.ask("Topic?") + execution_log.append(f"gathered:{topic}") + return topic + + @listen("gather") + def process(self): + execution_log.append("processing") + return "processed" + + flow = TestFlow() + flow.kickoff() + assert "gathered:AI agents" in execution_log + assert "processing" in execution_log + + def test_ask_with_structured_state(self) -> None: + """ask() works with Pydantic-based flow state.""" + + class ResearchState(FlowState): + topic: str = "" + depth: str = "" + + class TestFlow(Flow[ResearchState]): + initial_state = ResearchState + input_provider = MockInputProvider(["AI", "detailed"]) + + @start() + def gather(self): + self.state.topic = self.ask("Topic?") + self.state.depth = self.ask("Depth?") + return {"topic": self.state.topic, "depth": self.state.depth} + + flow = TestFlow() + result = flow.kickoff() + assert result == {"topic": "AI", "depth": "detailed"} + assert flow.state.topic == "AI" + assert flow.state.depth == "detailed" + + def test_ask_in_async_method_with_listen_chain(self) -> None: + """ask() in an async start method, result flows to listener.""" + execution_log: list[str] = [] + + class TestFlow(Flow): + input_provider = MockInputProvider(["async topic"]) + + @start() + async def gather(self): + topic = self.ask("Topic?") + execution_log.append(f"gathered:{topic}") + return topic + + @listen("gather") + def process(self): + execution_log.append("processing") + return "processed" + + flow = TestFlow() + flow.kickoff() + assert "gathered:async topic" in execution_log + assert "processing" in execution_log + + def test_ask_with_state_persistence_recovery(self) -> None: + """Ask checkpoints state so previously gathered values survive.""" + mock_persistence = MagicMock() + mock_persistence.load_state.return_value = None + + class RecoverableFlow(Flow): + input_provider = MockInputProvider(["AI", "detailed"]) + + @start() + def gather(self): + if not self.state.get("topic"): + self.state["topic"] = self.ask("Topic?") + if not self.state.get("depth"): + self.state["depth"] = self.ask("Depth?") + return { + "topic": self.state["topic"], + "depth": self.state["depth"], + } + + flow = RecoverableFlow(persistence=mock_persistence) + result = flow.kickoff() + assert result["topic"] == "AI" + assert result["depth"] == "detailed" + + # Verify checkpoints were made + checkpoint_calls = [ + c for c in mock_persistence.save_state.call_args_list + if c.kwargs.get("method_name") == "_ask_checkpoint" + or (len(c.args) >= 2 and c.args[1] == "_ask_checkpoint") + ] + # Two ask() calls = two checkpoints + assert len(checkpoint_calls) == 2 + + def test_ask_and_human_feedback_coexist(self) -> None: + """ask() and @human_feedback can be used in the same flow.""" + from crewai.flow import human_feedback + + class TestFlow(Flow): + input_provider = MockInputProvider(["AI"]) + + @start() + def gather(self): + topic = self.ask("Topic?") + return topic + + @listen("gather") + @human_feedback(message="Review this topic:") + def review(self): + return f"Researching: {self.state.get('_last_topic', 'unknown')}" + + flow = TestFlow() + + with patch.object(flow, "_request_human_feedback", return_value="looks good"): + flow.kickoff() + + # Flow completed with both ask and human_feedback + assert flow.last_human_feedback is not None + + def test_ask_preserves_flow_lifecycle(self) -> None: + """Flow events (started, finished) still fire normally with ask().""" + from crewai.events.event_bus import crewai_event_bus + from crewai.events.types.flow_events import ( + FlowFinishedEvent, + FlowStartedEvent, + ) + + events_seen: list[str] = [] + + class TestFlow(Flow): + input_provider = MockInputProvider(["answer"]) + + @start() + def my_method(self): + return self.ask("Q?") + + flow = TestFlow() + + original_emit = crewai_event_bus.emit + + def capture_emit(source: Any, event: Any) -> Any: + if isinstance(event, FlowStartedEvent): + events_seen.append("started") + elif isinstance(event, FlowFinishedEvent): + events_seen.append("finished") + return original_emit(source, event) + + with patch.object(crewai_event_bus, "emit", side_effect=capture_emit): + flow.kickoff() + + assert "started" in events_seen + assert "finished" in events_seen + + +# ── Console Provider ───────────────────────────────────────────── + + +class TestConsoleProviderInput: + """Tests for ConsoleProvider.request_input() (used by Flow.ask()).""" + + def test_console_provider_pauses_live_updates(self) -> None: + """ConsoleProvider pauses and resumes formatter live updates.""" + from crewai.events.event_listener import event_listener + + mock_formatter = MagicMock() + mock_formatter.console = MagicMock() + + provider = ConsoleProvider(verbose=True) + + with ( + patch.object(event_listener, "formatter", mock_formatter), + patch("builtins.input", return_value="test input"), + ): + result = provider.request_input("Question?", MagicMock()) + + mock_formatter.pause_live_updates.assert_called_once() + mock_formatter.resume_live_updates.assert_called_once() + assert result == "test input" + + def test_console_provider_displays_message(self) -> None: + """ConsoleProvider displays the message with Rich console.""" + from crewai.events.event_listener import event_listener + + mock_formatter = MagicMock() + mock_console = MagicMock() + mock_formatter.console = mock_console + + provider = ConsoleProvider(verbose=True) + + with ( + patch.object(event_listener, "formatter", mock_formatter), + patch("builtins.input", return_value="answer"), + ): + provider.request_input("What topic?", MagicMock()) + + # Verify the message was printed + print_calls = [str(c) for c in mock_console.print.call_args_list] + assert any("What topic?" in c for c in print_calls) + + def test_console_provider_non_verbose(self) -> None: + """ConsoleProvider in non-verbose mode uses plain input.""" + from crewai.events.event_listener import event_listener + + mock_formatter = MagicMock() + mock_formatter.console = MagicMock() + + provider = ConsoleProvider(verbose=False) + + with ( + patch.object(event_listener, "formatter", mock_formatter), + patch("builtins.input", return_value="plain answer") as mock_input, + ): + result = provider.request_input("Q?", MagicMock()) + + assert result == "plain answer" + mock_input.assert_called_once_with("Q? ") + + def test_console_provider_strips_response(self) -> None: + """ConsoleProvider strips whitespace from response.""" + from crewai.events.event_listener import event_listener + + mock_formatter = MagicMock() + mock_formatter.console = MagicMock() + + provider = ConsoleProvider(verbose=False) + + with ( + patch.object(event_listener, "formatter", mock_formatter), + patch("builtins.input", return_value=" spaced answer "), + ): + result = provider.request_input("Q?", MagicMock()) + + assert result == "spaced answer" + + def test_console_provider_implements_protocol(self) -> None: + """ConsoleProvider satisfies the InputProvider protocol.""" + provider = ConsoleProvider() + assert isinstance(provider, InputProvider) + + +# ── InputProvider Protocol ─────────────────────────────────────── + + +class TestInputProviderProtocol: + """Tests for the InputProvider protocol.""" + + def test_custom_provider_satisfies_protocol(self) -> None: + """A class with request_input satisfies the InputProvider protocol.""" + + class MyProvider: + def request_input(self, message: str, flow: Flow[Any]) -> str | None: + return "custom" + + provider = MyProvider() + assert isinstance(provider, InputProvider) + + def test_mock_provider_satisfies_protocol(self) -> None: + """MockInputProvider satisfies the InputProvider protocol.""" + provider = MockInputProvider(["test"]) + assert isinstance(provider, InputProvider) + + +# ── Error Handling ─────────────────────────────────────────────── + + +class TestAskErrorHandling: + """Tests for error handling in ask().""" + + def test_ask_returns_none_on_provider_error(self) -> None: + """ask() returns None if provider raises an exception.""" + + class FailingProvider: + def request_input(self, message: str, flow: Flow[Any]) -> str | None: + raise RuntimeError("Provider failed") + + class TestFlow(Flow): + input_provider = FailingProvider() + + @start() + def my_method(self): + return self.ask("Question?") + + flow = TestFlow() + result = flow.kickoff() + assert result is None + + def test_ask_in_async_method_returns_none_on_provider_error(self) -> None: + """ask() returns None if provider raises in an async method.""" + + class FailingProvider: + def request_input(self, message: str, flow: Flow[Any]) -> str | None: + raise RuntimeError("Provider failed") + + class TestFlow(Flow): + input_provider = FailingProvider() + + @start() + async def my_method(self): + return self.ask("Question?") + + flow = TestFlow() + result = flow.kickoff() + assert result is None + + +# ── Metadata ───────────────────────────────────────────────────── + + +class TestAskMetadata: + """Tests for bidirectional metadata support in ask().""" + + def test_ask_passes_metadata_to_provider(self) -> None: + """Provider receives the metadata dict from ask().""" + provider = MockInputProvider(["answer"]) + + class TestFlow(Flow): + input_provider = provider + + @start() + def my_method(self): + return self.ask("Q?", metadata={"user_id": "u123"}) + + flow = TestFlow() + flow.kickoff() + assert provider.received_metadata == [{"user_id": "u123"}] + + def test_ask_metadata_none_by_default(self) -> None: + """Provider receives None metadata when not provided.""" + provider = MockInputProvider(["answer"]) + + class TestFlow(Flow): + input_provider = provider + + @start() + def my_method(self): + return self.ask("Q?") + + flow = TestFlow() + flow.kickoff() + assert provider.received_metadata == [None] + + def test_ask_provider_returns_input_response(self) -> None: + """Provider returns InputResponse with response metadata.""" + + class MetadataProvider: + def request_input( + self, message: str, flow: Flow[Any], metadata: dict[str, Any] | None = None + ) -> InputResponse: + return InputResponse( + text="the answer", + metadata={"responded_by": "u456", "thread_id": "t789"}, + ) + + class TestFlow(Flow): + input_provider = MetadataProvider() + + @start() + def my_method(self): + return self.ask("Q?", metadata={"user_id": "u123"}) + + flow = TestFlow() + result = flow.kickoff() + + # ask() still returns plain string + assert result == "the answer" + + # History has both metadata dicts + assert len(flow._input_history) == 1 + entry = flow._input_history[0] + assert entry["metadata"] == {"user_id": "u123"} + assert entry["response_metadata"] == {"responded_by": "u456", "thread_id": "t789"} + + def test_ask_provider_returns_string_with_metadata_sent(self) -> None: + """Provider returns plain string; history has metadata but no response_metadata.""" + + class TestFlow(Flow): + input_provider = MockInputProvider(["answer"]) + + @start() + def my_method(self): + return self.ask("Q?", metadata={"channel": "#research"}) + + flow = TestFlow() + flow.kickoff() + + entry = flow._input_history[0] + assert entry["metadata"] == {"channel": "#research"} + assert entry["response_metadata"] is None + + def test_ask_metadata_in_requested_event(self) -> None: + """FlowInputRequestedEvent carries metadata.""" + from crewai.events.event_bus import crewai_event_bus + from crewai.events.types.flow_events import FlowInputRequestedEvent + + events_captured: list[FlowInputRequestedEvent] = [] + + class TestFlow(Flow): + input_provider = MockInputProvider(["answer"]) + + @start() + def my_method(self): + return self.ask("Q?", metadata={"user_id": "u123"}) + + flow = TestFlow() + original_emit = crewai_event_bus.emit + + def capture_emit(source: Any, event: Any) -> Any: + if isinstance(event, FlowInputRequestedEvent): + events_captured.append(event) + return original_emit(source, event) + + with patch.object(crewai_event_bus, "emit", side_effect=capture_emit): + flow.kickoff() + + assert len(events_captured) == 1 + assert events_captured[0].metadata == {"user_id": "u123"} + + def test_ask_metadata_in_received_event(self) -> None: + """FlowInputReceivedEvent carries both metadata and response_metadata.""" + from crewai.events.event_bus import crewai_event_bus + from crewai.events.types.flow_events import FlowInputReceivedEvent + + events_captured: list[FlowInputReceivedEvent] = [] + + class MetadataProvider: + def request_input( + self, message: str, flow: Flow[Any], metadata: dict[str, Any] | None = None + ) -> InputResponse: + return InputResponse(text="answer", metadata={"responded_by": "u456"}) + + class TestFlow(Flow): + input_provider = MetadataProvider() + + @start() + def my_method(self): + return self.ask("Q?", metadata={"user_id": "u123"}) + + flow = TestFlow() + original_emit = crewai_event_bus.emit + + def capture_emit(source: Any, event: Any) -> Any: + if isinstance(event, FlowInputReceivedEvent): + events_captured.append(event) + return original_emit(source, event) + + with patch.object(crewai_event_bus, "emit", side_effect=capture_emit): + flow.kickoff() + + assert len(events_captured) == 1 + assert events_captured[0].metadata == {"user_id": "u123"} + assert events_captured[0].response_metadata == {"responded_by": "u456"} + assert events_captured[0].response == "answer" + + def test_ask_input_response_with_none_text(self) -> None: + """Provider returns InputResponse with text=None.""" + + class NoneTextProvider: + def request_input( + self, message: str, flow: Flow[Any], metadata: dict[str, Any] | None = None + ) -> InputResponse: + return InputResponse(text=None, metadata={"reason": "user_declined"}) + + class TestFlow(Flow): + input_provider = NoneTextProvider() + + @start() + def my_method(self): + return self.ask("Q?") + + flow = TestFlow() + result = flow.kickoff() + assert result is None + + entry = flow._input_history[0] + assert entry["response"] is None + assert entry["response_metadata"] == {"reason": "user_declined"} + + def test_ask_metadata_thread_safe(self) -> None: + """Concurrent ask() calls with different metadata don't cross-contaminate.""" + import threading + + call_log: list[dict[str, Any]] = [] + log_lock = threading.Lock() + + class TrackingProvider: + def request_input( + self, message: str, flow: Flow[Any], metadata: dict[str, Any] | None = None + ) -> InputResponse: + # Small delay to increase chance of interleaving + time.sleep(0.05) + with log_lock: + call_log.append({"message": message, "metadata": metadata}) + user = metadata.get("user", "unknown") if metadata else "unknown" + return InputResponse( + text=f"answer from {user}", + metadata={"responded_by": user}, + ) + + class TestFlow(Flow): + input_provider = TrackingProvider() + + @start() + def trigger(self): + return "go" + + @listen("trigger") + def listener_a(self): + return self.ask("Question A?", metadata={"user": "alice"}) + + @listen("trigger") + def listener_b(self): + return self.ask("Question B?", metadata={"user": "bob"}) + + flow = TestFlow() + flow.kickoff() + + # Both calls should have recorded their own metadata + assert len(flow._input_history) == 2 + + alice_entry = next( + (e for e in flow._input_history if e["metadata"] and e["metadata"].get("user") == "alice"), + None, + ) + bob_entry = next( + (e for e in flow._input_history if e["metadata"] and e["metadata"].get("user") == "bob"), + None, + ) + + assert alice_entry is not None + assert alice_entry["response"] == "answer from alice" + assert alice_entry["response_metadata"] == {"responded_by": "alice"} + + assert bob_entry is not None + assert bob_entry["response"] == "answer from bob" + assert bob_entry["response_metadata"] == {"responded_by": "bob"} diff --git a/lib/crewai/tests/test_human_feedback_decorator.py b/lib/crewai/tests/test_human_feedback_decorator.py index 0ae6adbbe..cd6919420 100644 --- a/lib/crewai/tests/test_human_feedback_decorator.py +++ b/lib/crewai/tests/test_human_feedback_decorator.py @@ -24,13 +24,13 @@ class TestHumanFeedbackValidation: """Tests for decorator parameter validation.""" def test_emit_requires_llm(self): - """Test that specifying emit without llm raises ValueError.""" + """Test that specifying emit with llm=None raises ValueError.""" with pytest.raises(ValueError) as exc_info: @human_feedback( message="Review this:", emit=["approve", "reject"], - # llm not provided + llm=None, # explicitly None ) def test_method(self): return "output" @@ -399,3 +399,156 @@ class TestCollapseToOutcome: ) assert result == "approved" # First in list + + +# -- HITL Learning tests -- + + +class TestHumanFeedbackLearn: + """Tests for the learn=True HITL learning feature.""" + + def test_learn_false_does_not_interact_with_memory(self): + """When learn=False (default), memory is never touched.""" + + class LearnOffFlow(Flow): + @start() + @human_feedback(message="Review:", learn=False) + def produce(self): + return "output" + + flow = LearnOffFlow() + flow.memory = MagicMock() + + with patch.object( + flow, "_request_human_feedback", return_value="looks good" + ): + flow.produce() + + # memory.recall and memory.remember_many should NOT be called + flow.memory.recall.assert_not_called() + flow.memory.remember_many.assert_not_called() + + def test_learn_true_stores_distilled_lessons(self): + """When learn=True and feedback has substance, lessons are distilled and stored.""" + + class LearnFlow(Flow): + @start() + @human_feedback(message="Review:", llm="gpt-4o-mini", learn=True) + def produce(self): + return "draft article" + + flow = LearnFlow() + flow.memory = MagicMock() + flow.memory.recall.return_value = [] # no prior lessons + + with ( + patch.object( + flow, "_request_human_feedback", return_value="Always add citations" + ), + patch("crewai.llm.LLM") as MockLLM, + ): + from crewai.flow.human_feedback import DistilledLessons + + mock_llm = MagicMock() + mock_llm.supports_function_calling.return_value = True + # Distillation call -> returns structured lessons + mock_llm.call.return_value = DistilledLessons( + lessons=["Always include source citations when making factual claims"] + ) + MockLLM.return_value = mock_llm + + flow.produce() + + # remember_many should be called with the distilled lesson + flow.memory.remember_many.assert_called_once() + lessons = flow.memory.remember_many.call_args.args[0] + assert len(lessons) == 1 + assert "citations" in lessons[0].lower() + # source should be "hitl" + assert flow.memory.remember_many.call_args.kwargs.get("source") == "hitl" + + def test_learn_true_pre_reviews_with_past_lessons(self): + """When learn=True and past lessons exist, output is pre-reviewed before human sees it.""" + from crewai.memory.types import MemoryMatch, MemoryRecord + + class LearnFlow(Flow): + @start() + @human_feedback(message="Review:", llm="gpt-4o-mini", learn=True) + def produce(self): + return "draft without citations" + + flow = LearnFlow() + # Mock memory with a past lesson + flow.memory = MagicMock() + flow.memory.recall.return_value = [ + MemoryMatch( + record=MemoryRecord( + content="Always include source citations when making factual claims", + embedding=[], + ), + score=0.9, + match_reasons=["semantic"], + ) + ] + + captured_output = {} + + def capture_feedback(message, output, metadata=None, emit=None): + captured_output["shown_to_human"] = output + return "approved" + + with ( + patch.object(flow, "_request_human_feedback", side_effect=capture_feedback), + patch("crewai.llm.LLM") as MockLLM, + ): + from crewai.flow.human_feedback import DistilledLessons, PreReviewResult + + mock_llm = MagicMock() + mock_llm.supports_function_calling.return_value = True + # Pre-review returns structured improved output, distillation returns empty lessons + mock_llm.call.side_effect = [ + PreReviewResult(improved_output="draft with citations added"), + DistilledLessons(lessons=[]), # "approved" has no new lessons + ] + MockLLM.return_value = mock_llm + + flow.produce() + + # The human should have seen the pre-reviewed output, not the raw output + assert captured_output["shown_to_human"] == "draft with citations added" + # recall was called to find past lessons + flow.memory.recall.assert_called_once() + + def test_learn_true_empty_feedback_does_not_store(self): + """When learn=True but feedback is empty, no lessons are stored.""" + + class LearnFlow(Flow): + @start() + @human_feedback(message="Review:", llm="gpt-4o-mini", learn=True) + def produce(self): + return "output" + + flow = LearnFlow() + flow.memory = MagicMock() + flow.memory.recall.return_value = [] + + with patch.object( + flow, "_request_human_feedback", return_value="" + ): + flow.produce() + + # Empty feedback -> no distillation, no storage + flow.memory.remember_many.assert_not_called() + + def test_learn_true_uses_default_llm(self): + """When learn=True and llm is not explicitly set, the default gpt-4o-mini is used.""" + + @human_feedback(message="Review:", learn=True) + def test_method(self): + return "output" + + config = test_method.__human_feedback_config__ + assert config is not None + assert config.learn is True + # llm defaults to "gpt-4o-mini" at the function level + assert config.llm == "gpt-4o-mini" diff --git a/lib/crewai/tests/test_human_feedback_integration.py b/lib/crewai/tests/test_human_feedback_integration.py index d2d6a6f31..15f1e364c 100644 --- a/lib/crewai/tests/test_human_feedback_integration.py +++ b/lib/crewai/tests/test_human_feedback_integration.py @@ -14,7 +14,7 @@ from unittest.mock import MagicMock, patch import pytest from pydantic import BaseModel -from crewai.flow import Flow, HumanFeedbackResult, human_feedback, listen, start +from crewai.flow import Flow, HumanFeedbackResult, human_feedback, listen, or_, start from crewai.flow.flow import FlowState @@ -271,6 +271,182 @@ class TestMultiStepFlows: assert len(flow.human_feedback_history) == 1 assert flow.human_feedback_history[0].outcome == "rejected" + def test_hitl_self_loop_routes_back_to_same_method(self): + """Test that a HITL router can loop back to itself via its own emit outcome. + + Pattern: review_work listens to or_("do_work", "review") and emits + ["review", "approved"]. When the human rejects (outcome="review"), + the method should re-execute. When approved, the flow should continue + to the approve_work listener. + """ + execution_order: list[str] = [] + + class SelfLoopFlow(Flow): + @start() + def initial_func(self): + execution_order.append("initial_func") + return "initial" + + @listen(initial_func) + def do_work(self): + execution_order.append("do_work") + return "work output" + + @human_feedback( + message="Do you approve this content?", + emit=["review", "approved"], + llm="gpt-4o-mini", + default_outcome="approved", + ) + @listen(or_("do_work", "review")) + def review_work(self): + execution_order.append("review_work") + return "content for review" + + @listen("approved") + def approve_work(self): + execution_order.append("approve_work") + return "published" + + flow = SelfLoopFlow() + + # First call: human rejects (outcome="review") -> self-loop + # Second call: human approves (outcome="approved") -> continue + with ( + patch.object( + flow, + "_request_human_feedback", + side_effect=["needs changes", "looks good"], + ), + patch.object( + flow, + "_collapse_to_outcome", + side_effect=["review", "approved"], + ), + ): + result = flow.kickoff() + + assert execution_order == [ + "initial_func", + "do_work", + "review_work", # first review -> rejected (review) + "review_work", # second review -> approved + "approve_work", + ] + assert result == "published" + assert len(flow.human_feedback_history) == 2 + assert flow.human_feedback_history[0].outcome == "review" + assert flow.human_feedback_history[1].outcome == "approved" + + def test_hitl_self_loop_multiple_rejections(self): + """Test that a HITL router can loop back multiple times before approving. + + Verifies the self-loop works for more than one rejection cycle. + """ + execution_order: list[str] = [] + + class MultiRejectFlow(Flow): + @start() + def generate(self): + execution_order.append("generate") + return "draft" + + @human_feedback( + message="Review this content:", + emit=["revise", "approved"], + llm="gpt-4o-mini", + default_outcome="approved", + ) + @listen(or_("generate", "revise")) + def review(self): + execution_order.append("review") + return "content v" + str(execution_order.count("review")) + + @listen("approved") + def publish(self): + execution_order.append("publish") + return "published" + + flow = MultiRejectFlow() + + # Three rejections, then approval + with ( + patch.object( + flow, + "_request_human_feedback", + side_effect=["bad", "still bad", "not yet", "great"], + ), + patch.object( + flow, + "_collapse_to_outcome", + side_effect=["revise", "revise", "revise", "approved"], + ), + ): + result = flow.kickoff() + + assert execution_order == [ + "generate", + "review", # 1st review -> revise + "review", # 2nd review -> revise + "review", # 3rd review -> revise + "review", # 4th review -> approved + "publish", + ] + assert result == "published" + assert len(flow.human_feedback_history) == 4 + assert [r.outcome for r in flow.human_feedback_history] == [ + "revise", "revise", "revise", "approved" + ] + + def test_hitl_self_loop_immediate_approval(self): + """Test that a HITL self-loop flow works when approved on the first try. + + No looping occurs -- the flow should proceed straight through. + """ + execution_order: list[str] = [] + + class ImmediateApprovalFlow(Flow): + @start() + def generate(self): + execution_order.append("generate") + return "perfect draft" + + @human_feedback( + message="Review:", + emit=["revise", "approved"], + llm="gpt-4o-mini", + ) + @listen(or_("generate", "revise")) + def review(self): + execution_order.append("review") + return "content" + + @listen("approved") + def publish(self): + execution_order.append("publish") + return "published" + + flow = ImmediateApprovalFlow() + + with ( + patch.object( + flow, + "_request_human_feedback", + return_value="perfect", + ), + patch.object( + flow, + "_collapse_to_outcome", + return_value="approved", + ), + ): + result = flow.kickoff() + + assert execution_order == ["generate", "review", "publish"] + assert result == "published" + assert len(flow.human_feedback_history) == 1 + assert flow.human_feedback_history[0].outcome == "approved" + def test_router_and_non_router_listeners_for_same_outcome(self): """Test that both router and non-router listeners fire for the same outcome.""" execution_order: list[str] = [] diff --git a/lib/crewai/tests/test_task.py b/lib/crewai/tests/test_task.py index 9a0010d89..21356c3b4 100644 --- a/lib/crewai/tests/test_task.py +++ b/lib/crewai/tests/test_task.py @@ -759,11 +759,11 @@ def test_custom_converter_cls(): crew = Crew(agents=[scorer], tasks=[task]) - with patch.object( - ScoreConverter, "to_pydantic", return_value=ScoreOutput(score=5) - ) as mock_to_pydantic: - crew.kickoff() - mock_to_pydantic.assert_called_once() + # With native structured output, the LLM returns a BaseModel directly, + # so the converter is bypassed. Verify the output is valid instead. + result = crew.kickoff() + assert isinstance(result.pydantic, ScoreOutput) + assert isinstance(result.pydantic.score, int) @pytest.mark.vcr() diff --git a/lib/crewai/tests/tools/test_base_tool.py b/lib/crewai/tests/tools/test_base_tool.py index 4a6850ce1..8f7ae877b 100644 --- a/lib/crewai/tests/tools/test_base_tool.py +++ b/lib/crewai/tests/tools/test_base_tool.py @@ -3,6 +3,8 @@ from typing import Callable from unittest.mock import patch import pytest +from pydantic import BaseModel, Field + from crewai.agent import Agent from crewai.crew import Crew from crewai.task import Task @@ -230,3 +232,218 @@ def test_max_usage_count_is_respected(): crew.kickoff() assert tool.max_usage_count == 5 assert tool.current_usage_count == 5 + + +# ============================================================================= +# Schema Validation in run() Tests +# ============================================================================= + + +class CodeExecutorInput(BaseModel): + code: str = Field(description="The code to execute") + language: str = Field(default="python", description="Programming language") + + +class CodeExecutorTool(BaseTool): + name: str = "code_executor" + description: str = "Execute code snippets" + args_schema: type[BaseModel] = CodeExecutorInput + + def _run(self, code: str, language: str = "python") -> str: + return f"Executed {language}: {code}" + + +class TestBaseToolRunValidation: + """Tests for args_schema validation in BaseTool.run().""" + + def test_run_with_valid_kwargs_passes_validation(self) -> None: + """Valid keyword arguments should pass schema validation and execute.""" + t = CodeExecutorTool() + result = t.run(code="print('hello')") + assert result == "Executed python: print('hello')" + + def test_run_with_all_kwargs_passes_validation(self) -> None: + """All keyword arguments including optional ones should pass.""" + t = CodeExecutorTool() + result = t.run(code="console.log('hi')", language="javascript") + assert result == "Executed javascript: console.log('hi')" + + def test_run_with_no_args_raises_validation_error(self) -> None: + """Calling run() with no arguments should raise a clear ValueError, + not a cryptic TypeError about missing positional arguments (GH-4611).""" + t = CodeExecutorTool() + with pytest.raises(ValueError, match="validation failed"): + t.run() + + def test_run_with_missing_required_kwarg_raises(self) -> None: + """Missing required kwargs should raise ValueError from schema validation.""" + t = CodeExecutorTool() + with pytest.raises(ValueError, match="validation failed"): + t.run(language="python") + + def test_run_with_wrong_field_name_raises(self) -> None: + """Kwargs not matching any schema field should trigger validation error + for missing required fields.""" + t = CodeExecutorTool() + with pytest.raises(ValueError, match="validation failed"): + t.run(wrong_arg="value") + + def test_run_with_positional_args_skips_validation(self) -> None: + """Positional-arg calls should bypass schema validation (backwards compat).""" + class SimpleTool(BaseTool): + name: str = "simple" + description: str = "A simple tool" + + def _run(self, question: str) -> str: + return question + + t = SimpleTool() + result = t.run("What is life?") + assert result == "What is life?" + + def test_run_strips_extra_kwargs_from_llm(self) -> None: + """Extra kwargs not in the schema should be silently stripped, + preventing unexpected-keyword crashes in _run.""" + t = CodeExecutorTool() + result = t.run(code="1+1", extra_hallucinated_field="junk") + assert result == "Executed python: 1+1" + + def test_run_increments_usage_after_validation(self) -> None: + """Usage count should still increment after validated execution.""" + t = CodeExecutorTool() + assert t.current_usage_count == 0 + t.run(code="x = 1") + assert t.current_usage_count == 1 + + def test_run_does_not_increment_usage_on_validation_error(self) -> None: + """Usage count should NOT increment when validation fails.""" + t = CodeExecutorTool() + assert t.current_usage_count == 0 + with pytest.raises(ValueError): + t.run(wrong="bad") + assert t.current_usage_count == 0 + + +class TestToolDecoratorRunValidation: + """Tests for args_schema validation in Tool.run() (decorator-based tools).""" + + def test_decorator_tool_run_validates_kwargs(self) -> None: + """Decorator-created tools should also validate kwargs against schema.""" + @tool("execute_code") + def execute_code(code: str, language: str = "python") -> str: + """Execute a code snippet.""" + return f"Executed {language}: {code}" + + result = execute_code.run(code="x = 1") + assert result == "Executed python: x = 1" + + def test_decorator_tool_run_rejects_missing_required(self) -> None: + """Decorator tools should reject missing required args via validation.""" + @tool("execute_code") + def execute_code(code: str) -> str: + """Execute a code snippet.""" + return f"Executed: {code}" + + with pytest.raises(ValueError, match="validation failed"): + execute_code.run(wrong_arg="value") + + def test_decorator_tool_positional_args_still_work(self) -> None: + """Positional args to decorator tools should bypass validation.""" + @tool("greet") + def greet(name: str) -> str: + """Greet someone.""" + return f"Hello, {name}!" + + result = greet.run("World") + assert result == "Hello, World!" + + +# ============================================================================= +# Async arun() Schema Validation Tests +# ============================================================================= + + +class AsyncCodeExecutorTool(BaseTool): + name: str = "async_code_executor" + description: str = "Execute code snippets asynchronously" + args_schema: type[BaseModel] = CodeExecutorInput + + async def _arun(self, code: str, language: str = "python") -> str: + return f"Async executed {language}: {code}" + + def _run(self, code: str, language: str = "python") -> str: + return f"Executed {language}: {code}" + + +class TestBaseToolArunValidation: + """Tests for args_schema validation in BaseTool.arun().""" + + @pytest.mark.asyncio + async def test_arun_with_valid_kwargs_passes_validation(self) -> None: + """Valid keyword arguments should pass schema validation in arun.""" + t = AsyncCodeExecutorTool() + result = await t.arun(code="print('hello')") + assert result == "Async executed python: print('hello')" + + @pytest.mark.asyncio + async def test_arun_with_no_args_raises_validation_error(self) -> None: + """Calling arun() with no arguments should raise a clear ValueError (GH-4611).""" + t = AsyncCodeExecutorTool() + with pytest.raises(ValueError, match="validation failed"): + await t.arun() + + @pytest.mark.asyncio + async def test_arun_with_missing_required_kwarg_raises(self) -> None: + """Missing required kwargs should raise ValueError in arun.""" + t = AsyncCodeExecutorTool() + with pytest.raises(ValueError, match="validation failed"): + await t.arun(language="python") + + @pytest.mark.asyncio + async def test_arun_with_wrong_field_name_raises(self) -> None: + """Kwargs not matching schema fields should trigger validation error in arun.""" + t = AsyncCodeExecutorTool() + with pytest.raises(ValueError, match="validation failed"): + await t.arun(wrong_arg="value") + + @pytest.mark.asyncio + async def test_arun_strips_extra_kwargs(self) -> None: + """Extra kwargs not in the schema should be stripped in arun.""" + t = AsyncCodeExecutorTool() + result = await t.arun(code="1+1", extra_field="junk") + assert result == "Async executed python: 1+1" + + @pytest.mark.asyncio + async def test_arun_does_not_increment_usage_on_validation_error(self) -> None: + """Usage count should NOT increment when arun validation fails.""" + t = AsyncCodeExecutorTool() + assert t.current_usage_count == 0 + with pytest.raises(ValueError): + await t.arun(wrong="bad") + assert t.current_usage_count == 0 + + +class TestToolDecoratorArunValidation: + """Tests for args_schema validation in Tool.arun() (decorator-based async tools).""" + + @pytest.mark.asyncio + async def test_async_decorator_tool_arun_validates_kwargs(self) -> None: + """Async decorator tools should validate kwargs in arun.""" + @tool("async_execute") + async def async_execute(code: str, language: str = "python") -> str: + """Execute code asynchronously.""" + return f"Async {language}: {code}" + + result = await async_execute.arun(code="x = 1") + assert result == "Async python: x = 1" + + @pytest.mark.asyncio + async def test_async_decorator_tool_arun_rejects_missing_required(self) -> None: + """Async decorator tools should reject missing required args in arun.""" + @tool("async_execute") + async def async_execute(code: str) -> str: + """Execute code asynchronously.""" + return f"Async: {code}" + + with pytest.raises(ValueError, match="validation failed"): + await async_execute.arun(wrong_arg="value") diff --git a/lib/crewai/tests/tracing/test_tracing.py b/lib/crewai/tests/tracing/test_tracing.py index 555446b26..ba49a37c8 100644 --- a/lib/crewai/tests/tracing/test_tracing.py +++ b/lib/crewai/tests/tracing/test_tracing.py @@ -840,3 +840,87 @@ class TestTraceListenerSetup: mock_mark_failed.assert_called_once_with( "test_batch_id_12345", "Internal Server Error" ) + + def test_ephemeral_batch_includes_anon_id(self): + """Test that ephemeral batch initialization sends anon_id from get_user_id()""" + fake_user_id = "abc123def456" + + with ( + patch( + "crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context", + return_value=True, + ), + patch( + "crewai.events.listeners.tracing.trace_batch_manager.get_user_id", + return_value=fake_user_id, + ), + patch( + "crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces", + return_value=False, + ), + ): + batch_manager = TraceBatchManager() + + mock_response = MagicMock( + status_code=201, + json=MagicMock(return_value={ + "ephemeral_trace_id": "test-trace-id", + "access_code": "TRACE-abc123", + }), + ) + + with patch.object( + batch_manager.plus_api, + "initialize_ephemeral_trace_batch", + return_value=mock_response, + ) as mock_init: + batch_manager.initialize_batch( + user_context={"privacy_level": "standard"}, + execution_metadata={ + "execution_type": "crew", + "crew_name": "test_crew", + }, + use_ephemeral=True, + ) + + mock_init.assert_called_once() + payload = mock_init.call_args[0][0] + assert payload["user_identifier"] == fake_user_id + assert "ephemeral_trace_id" in payload + + def test_non_ephemeral_batch_does_not_include_anon_id(self): + """Test that non-ephemeral batch initialization does not send anon_id""" + with ( + patch( + "crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context", + return_value=True, + ), + patch( + "crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces", + return_value=False, + ), + ): + batch_manager = TraceBatchManager() + + mock_response = MagicMock( + status_code=201, + json=MagicMock(return_value={"trace_id": "test-trace-id"}), + ) + + with patch.object( + batch_manager.plus_api, + "initialize_trace_batch", + return_value=mock_response, + ) as mock_init: + batch_manager.initialize_batch( + user_context={"privacy_level": "standard"}, + execution_metadata={ + "execution_type": "crew", + "crew_name": "test_crew", + }, + use_ephemeral=False, + ) + + mock_init.assert_called_once() + payload = mock_init.call_args[0][0] + assert "user_identifier" not in payload diff --git a/lib/crewai/tests/utilities/test_agent_utils.py b/lib/crewai/tests/utilities/test_agent_utils.py index 31d7b9705..3d249906a 100644 --- a/lib/crewai/tests/utilities/test_agent_utils.py +++ b/lib/crewai/tests/utilities/test_agent_utils.py @@ -3,7 +3,7 @@ from __future__ import annotations import asyncio -from typing import Any +from typing import Any, Literal, Optional from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -17,6 +17,7 @@ from crewai.utilities.agent_utils import ( _format_messages_for_summary, _split_messages_into_chunks, convert_tools_to_openai_schema, + parse_tool_call_args, summarize_messages, ) @@ -79,7 +80,7 @@ class TestConvertToolsToOpenaiSchema: def test_converts_single_tool(self) -> None: """Test converting a single tool to OpenAI schema.""" tools = [CalculatorTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) assert len(schemas) == 1 assert len(functions) == 1 @@ -94,7 +95,7 @@ class TestConvertToolsToOpenaiSchema: def test_converts_multiple_tools(self) -> None: """Test converting multiple tools to OpenAI schema.""" tools = [CalculatorTool(), SearchTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) assert len(schemas) == 2 assert len(functions) == 2 @@ -112,7 +113,7 @@ class TestConvertToolsToOpenaiSchema: def test_functions_dict_contains_callables(self) -> None: """Test that the functions dict maps names to callable run methods.""" tools = [CalculatorTool(), SearchTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) assert "calculator" in functions assert "web_search" in functions @@ -122,14 +123,14 @@ class TestConvertToolsToOpenaiSchema: def test_function_can_be_called(self) -> None: """Test that the returned function can be called.""" tools = [CalculatorTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) result = functions["calculator"](expression="2 + 2") assert result == "4" def test_empty_tools_list(self) -> None: """Test with an empty tools list.""" - schemas, functions = convert_tools_to_openai_schema([]) + schemas, functions, _ = convert_tools_to_openai_schema([]) assert schemas == [] assert functions == {} @@ -137,7 +138,7 @@ class TestConvertToolsToOpenaiSchema: def test_schema_has_required_fields(self) -> None: """Test that the schema includes required fields information.""" tools = [SearchTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) schema = schemas[0] params = schema["function"]["parameters"] @@ -157,7 +158,7 @@ class TestConvertToolsToOpenaiSchema: return "done" tools = [MinimalTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) assert len(schemas) == 1 schema = schemas[0] @@ -168,7 +169,7 @@ class TestConvertToolsToOpenaiSchema: def test_schema_structure_matches_openai_format(self) -> None: """Test that the schema structure matches OpenAI's expected format.""" tools = [CalculatorTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) schema = schemas[0] @@ -193,7 +194,7 @@ class TestConvertToolsToOpenaiSchema: def test_removes_redundant_schema_fields(self) -> None: """Test that redundant title and description are removed from parameters.""" tools = [CalculatorTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) params = schemas[0]["function"]["parameters"] # Title should be removed as it's redundant with function name @@ -202,7 +203,7 @@ class TestConvertToolsToOpenaiSchema: def test_preserves_field_descriptions(self) -> None: """Test that field descriptions are preserved in the schema.""" tools = [SearchTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) params = schemas[0]["function"]["parameters"] query_prop = params["properties"]["query"] @@ -214,7 +215,7 @@ class TestConvertToolsToOpenaiSchema: def test_preserves_default_values(self) -> None: """Test that default values are preserved in the schema.""" tools = [SearchTool()] - schemas, functions = convert_tools_to_openai_schema(tools) + schemas, functions, _ = convert_tools_to_openai_schema(tools) params = schemas[0]["function"]["parameters"] max_results_prop = params["properties"]["max_results"] @@ -234,6 +235,79 @@ def _make_mock_i18n() -> MagicMock: }.get(key, "") return mock_i18n +class MCPStyleInput(BaseModel): + """Input schema mimicking an MCP tool with optional fields.""" + + query: str = Field(description="Search query") + filter_type: Optional[Literal["internal", "user"]] = Field( + default=None, description="Filter type" + ) + page_id: Optional[str] = Field( + default=None, description="Page UUID" + ) + + +class MCPStyleTool(BaseTool): + """A tool mimicking MCP tool schemas with optional fields.""" + + name: str = "mcp_search" + description: str = "Search with optional filters" + args_schema: type[BaseModel] = MCPStyleInput + + def _run(self, **kwargs: Any) -> str: + return "result" + + +class TestOptionalFieldsPreserveNull: + """Tests that optional tool fields preserve null in the schema.""" + + def test_optional_string_allows_null(self) -> None: + """Optional[str] fields should include null in the schema so the LLM + can send null instead of being forced to guess a value.""" + tools = [MCPStyleTool()] + schemas, _, _ = convert_tools_to_openai_schema(tools) + + params = schemas[0]["function"]["parameters"] + page_id_prop = params["properties"]["page_id"] + + assert "anyOf" in page_id_prop + type_options = [opt.get("type") for opt in page_id_prop["anyOf"]] + assert "string" in type_options + assert "null" in type_options + + def test_optional_literal_allows_null(self) -> None: + """Optional[Literal[...]] fields should include null.""" + tools = [MCPStyleTool()] + schemas, _, _ = convert_tools_to_openai_schema(tools) + + params = schemas[0]["function"]["parameters"] + filter_prop = params["properties"]["filter_type"] + + assert "anyOf" in filter_prop + has_null = any(opt.get("type") == "null" for opt in filter_prop["anyOf"]) + assert has_null + + def test_required_field_stays_non_null(self) -> None: + """Required fields without Optional should NOT have null.""" + tools = [MCPStyleTool()] + schemas, _, _ = convert_tools_to_openai_schema(tools) + + params = schemas[0]["function"]["parameters"] + query_prop = params["properties"]["query"] + + assert query_prop.get("type") == "string" + assert "anyOf" not in query_prop + + def test_all_fields_in_required_for_strict_mode(self) -> None: + """All fields (including optional) must be in required for strict mode.""" + tools = [MCPStyleTool()] + schemas, _, _ = convert_tools_to_openai_schema(tools) + + params = schemas[0]["function"]["parameters"] + assert "query" in params["required"] + assert "filter_type" in params["required"] + assert "page_id" in params["required"] + class TestSummarizeMessages: """Tests for summarize_messages function.""" @@ -922,3 +996,56 @@ class TestParallelSummarizationVCR: assert summary_msg["role"] == "user" assert "files" in summary_msg assert "report.pdf" in summary_msg["files"] + + +class TestParseToolCallArgs: + """Unit tests for parse_tool_call_args.""" + + def test_valid_json_string_returns_dict(self) -> None: + args_dict, error = parse_tool_call_args('{"code": "print(1)"}', "run_code", "call_1") + assert error is None + assert args_dict == {"code": "print(1)"} + + def test_malformed_json_returns_error_dict(self) -> None: + args_dict, error = parse_tool_call_args('{"code": "print("hi")"}', "run_code", "call_1") + assert args_dict is None + assert error is not None + assert error["call_id"] == "call_1" + assert error["func_name"] == "run_code" + assert error["from_cache"] is False + assert "Failed to parse tool arguments as JSON" in error["result"] + assert "run_code" in error["result"] + + def test_malformed_json_preserves_original_tool(self) -> None: + mock_tool = object() + _, error = parse_tool_call_args("{bad}", "my_tool", "call_2", original_tool=mock_tool) + assert error is not None + assert error["original_tool"] is mock_tool + + def test_malformed_json_original_tool_defaults_to_none(self) -> None: + _, error = parse_tool_call_args("{bad}", "my_tool", "call_3") + assert error is not None + assert error["original_tool"] is None + + def test_dict_input_returned_directly(self) -> None: + func_args = {"code": "x = 42"} + args_dict, error = parse_tool_call_args(func_args, "run_code", "call_4") + assert error is None + assert args_dict == {"code": "x = 42"} + + def test_empty_dict_input_returned_directly(self) -> None: + args_dict, error = parse_tool_call_args({}, "run_code", "call_5") + assert error is None + assert args_dict == {} + + def test_valid_json_with_nested_values(self) -> None: + args_dict, error = parse_tool_call_args( + '{"query": "hello", "options": {"limit": 10}}', "search", "call_6" + ) + assert error is None + assert args_dict == {"query": "hello", "options": {"limit": 10}} + + def test_error_result_has_correct_keys(self) -> None: + _, error = parse_tool_call_args("{bad json}", "tool", "call_7") + assert error is not None + assert set(error.keys()) == {"call_id", "func_name", "result", "from_cache", "original_tool"} diff --git a/lib/crewai/tests/utilities/test_llm_utils.py b/lib/crewai/tests/utilities/test_llm_utils.py index e02173f8d..5d7d70b76 100644 --- a/lib/crewai/tests/utilities/test_llm_utils.py +++ b/lib/crewai/tests/utilities/test_llm_utils.py @@ -81,7 +81,7 @@ def test_create_llm_from_env_with_unaccepted_attributes() -> None: "OPENAI_API_KEY": "fake-key", "AWS_ACCESS_KEY_ID": "fake-access-key", "AWS_SECRET_ACCESS_KEY": "fake-secret-key", - "AWS_REGION_NAME": "us-west-2", + "AWS_DEFAULT_REGION": "us-west-2", }, ): llm = create_llm(llm_value=None) @@ -89,7 +89,7 @@ def test_create_llm_from_env_with_unaccepted_attributes() -> None: assert llm.model == "gpt-3.5-turbo" assert not hasattr(llm, "AWS_ACCESS_KEY_ID") assert not hasattr(llm, "AWS_SECRET_ACCESS_KEY") - assert not hasattr(llm, "AWS_REGION_NAME") + assert not hasattr(llm, "AWS_DEFAULT_REGION") def test_create_llm_with_partial_attributes() -> None: diff --git a/lib/crewai/tests/utilities/test_pydantic_schema_utils.py b/lib/crewai/tests/utilities/test_pydantic_schema_utils.py new file mode 100644 index 000000000..98a5e6aa5 --- /dev/null +++ b/lib/crewai/tests/utilities/test_pydantic_schema_utils.py @@ -0,0 +1,884 @@ +"""Tests for pydantic_schema_utils module. + +Covers: +- create_model_from_schema: type mapping, required/optional, enums, formats, + nested objects, arrays, unions, allOf, $ref, model_name, enrich_descriptions +- Schema transformation helpers: resolve_refs, force_additional_properties_false, + strip_unsupported_formats, ensure_type_in_schemas, convert_oneof_to_anyof, + ensure_all_properties_required, strip_null_from_types, build_rich_field_description +- End-to-end MCP tool schema conversion +""" + +from __future__ import annotations + +import datetime +from copy import deepcopy +from typing import Any + +import pytest +from pydantic import BaseModel + +from crewai.utilities.pydantic_schema_utils import ( + build_rich_field_description, + convert_oneof_to_anyof, + create_model_from_schema, + ensure_all_properties_required, + ensure_type_in_schemas, + force_additional_properties_false, + resolve_refs, + strip_null_from_types, + strip_unsupported_formats, +) + + +class TestSimpleTypes: + def test_string_field(self) -> None: + schema = { + "type": "object", + "properties": {"name": {"type": "string"}}, + "required": ["name"], + } + Model = create_model_from_schema(schema) + obj = Model(name="Alice") + assert obj.name == "Alice" + + def test_integer_field(self) -> None: + schema = { + "type": "object", + "properties": {"count": {"type": "integer"}}, + "required": ["count"], + } + Model = create_model_from_schema(schema) + obj = Model(count=42) + assert obj.count == 42 + + def test_number_field(self) -> None: + schema = { + "type": "object", + "properties": {"score": {"type": "number"}}, + "required": ["score"], + } + Model = create_model_from_schema(schema) + obj = Model(score=3.14) + assert obj.score == pytest.approx(3.14) + + def test_boolean_field(self) -> None: + schema = { + "type": "object", + "properties": {"active": {"type": "boolean"}}, + "required": ["active"], + } + Model = create_model_from_schema(schema) + assert Model(active=True).active is True + + def test_null_field(self) -> None: + schema = { + "type": "object", + "properties": {"value": {"type": "null"}}, + "required": ["value"], + } + Model = create_model_from_schema(schema) + obj = Model(value=None) + assert obj.value is None + + +class TestRequiredOptional: + def test_required_field_has_no_default(self) -> None: + schema = { + "type": "object", + "properties": {"name": {"type": "string"}}, + "required": ["name"], + } + Model = create_model_from_schema(schema) + with pytest.raises(Exception): + Model() + + def test_optional_field_defaults_to_none(self) -> None: + schema = { + "type": "object", + "properties": {"name": {"type": "string"}}, + "required": [], + } + Model = create_model_from_schema(schema) + obj = Model() + assert obj.name is None + + def test_mixed_required_optional(self) -> None: + schema = { + "type": "object", + "properties": { + "id": {"type": "integer"}, + "label": {"type": "string"}, + }, + "required": ["id"], + } + Model = create_model_from_schema(schema) + obj = Model(id=1) + assert obj.id == 1 + assert obj.label is None + + +class TestEnumLiteral: + def test_string_enum(self) -> None: + schema = { + "type": "object", + "properties": { + "color": {"type": "string", "enum": ["red", "green", "blue"]}, + }, + "required": ["color"], + } + Model = create_model_from_schema(schema) + obj = Model(color="red") + assert obj.color == "red" + + def test_string_enum_rejects_invalid(self) -> None: + schema = { + "type": "object", + "properties": { + "color": {"type": "string", "enum": ["red", "green", "blue"]}, + }, + "required": ["color"], + } + Model = create_model_from_schema(schema) + with pytest.raises(Exception): + Model(color="yellow") + + def test_const_value(self) -> None: + schema = { + "type": "object", + "properties": { + "kind": {"const": "fixed"}, + }, + "required": ["kind"], + } + Model = create_model_from_schema(schema) + obj = Model(kind="fixed") + assert obj.kind == "fixed" + + +class TestFormatMapping: + def test_date_format(self) -> None: + schema = { + "type": "object", + "properties": { + "birthday": {"type": "string", "format": "date"}, + }, + "required": ["birthday"], + } + Model = create_model_from_schema(schema) + obj = Model(birthday=datetime.date(2000, 1, 15)) + assert obj.birthday == datetime.date(2000, 1, 15) + + def test_datetime_format(self) -> None: + schema = { + "type": "object", + "properties": { + "created_at": {"type": "string", "format": "date-time"}, + }, + "required": ["created_at"], + } + Model = create_model_from_schema(schema) + dt = datetime.datetime(2025, 6, 1, 12, 0, 0) + obj = Model(created_at=dt) + assert obj.created_at == dt + + def test_time_format(self) -> None: + schema = { + "type": "object", + "properties": { + "alarm": {"type": "string", "format": "time"}, + }, + "required": ["alarm"], + } + Model = create_model_from_schema(schema) + t = datetime.time(8, 30) + obj = Model(alarm=t) + assert obj.alarm == t + + +class TestNestedObjects: + def test_nested_object_creates_model(self) -> None: + schema = { + "type": "object", + "properties": { + "address": { + "type": "object", + "properties": { + "street": {"type": "string"}, + "city": {"type": "string"}, + }, + "required": ["street", "city"], + }, + }, + "required": ["address"], + } + Model = create_model_from_schema(schema) + obj = Model(address={"street": "123 Main", "city": "Springfield"}) + assert obj.address.street == "123 Main" + assert obj.address.city == "Springfield" + + def test_object_without_properties_returns_dict(self) -> None: + schema = { + "type": "object", + "properties": { + "metadata": {"type": "object"}, + }, + "required": ["metadata"], + } + Model = create_model_from_schema(schema) + obj = Model(metadata={"key": "value"}) + assert obj.metadata == {"key": "value"} + + +class TestTypedArrays: + def test_array_of_strings(self) -> None: + schema = { + "type": "object", + "properties": { + "tags": {"type": "array", "items": {"type": "string"}}, + }, + "required": ["tags"], + } + Model = create_model_from_schema(schema) + obj = Model(tags=["a", "b", "c"]) + assert obj.tags == ["a", "b", "c"] + + def test_array_of_objects(self) -> None: + schema = { + "type": "object", + "properties": { + "items": { + "type": "array", + "items": { + "type": "object", + "properties": {"id": {"type": "integer"}}, + "required": ["id"], + }, + }, + }, + "required": ["items"], + } + Model = create_model_from_schema(schema) + obj = Model(items=[{"id": 1}, {"id": 2}]) + assert len(obj.items) == 2 + assert obj.items[0].id == 1 + + def test_untyped_array(self) -> None: + schema = { + "type": "object", + "properties": {"data": {"type": "array"}}, + "required": ["data"], + } + Model = create_model_from_schema(schema) + obj = Model(data=[1, "two", 3.0]) + assert obj.data == [1, "two", 3.0] + + +class TestUnionTypes: + def test_anyof_string_or_integer(self) -> None: + schema = { + "type": "object", + "properties": { + "value": { + "anyOf": [{"type": "string"}, {"type": "integer"}], + }, + }, + "required": ["value"], + } + Model = create_model_from_schema(schema) + assert Model(value="hello").value == "hello" + assert Model(value=42).value == 42 + + def test_oneof(self) -> None: + schema = { + "type": "object", + "properties": { + "value": { + "oneOf": [{"type": "string"}, {"type": "number"}], + }, + }, + "required": ["value"], + } + Model = create_model_from_schema(schema) + assert Model(value="hello").value == "hello" + assert Model(value=3.14).value == pytest.approx(3.14) + + +class TestAllOfMerging: + def test_allof_merges_properties(self) -> None: + schema = { + "type": "object", + "allOf": [ + { + "type": "object", + "properties": {"name": {"type": "string"}}, + "required": ["name"], + }, + { + "type": "object", + "properties": {"age": {"type": "integer"}}, + "required": ["age"], + }, + ], + } + Model = create_model_from_schema(schema) + obj = Model(name="Alice", age=30) + assert obj.name == "Alice" + assert obj.age == 30 + + def test_single_allof(self) -> None: + schema = { + "type": "object", + "properties": { + "item": { + "allOf": [ + { + "type": "object", + "properties": {"id": {"type": "integer"}}, + "required": ["id"], + } + ] + } + }, + "required": ["item"], + } + Model = create_model_from_schema(schema) + obj = Model(item={"id": 1}) + assert obj.item.id == 1 + + +# --------------------------------------------------------------------------- +# $ref resolution +# --------------------------------------------------------------------------- + + +class TestRefResolution: + def test_ref_in_property(self) -> None: + schema = { + "type": "object", + "properties": { + "item": {"$ref": "#/$defs/Item"}, + }, + "required": ["item"], + "$defs": { + "Item": { + "type": "object", + "title": "Item", + "properties": {"name": {"type": "string"}}, + "required": ["name"], + }, + }, + } + Model = create_model_from_schema(schema) + obj = Model(item={"name": "Widget"}) + assert obj.item.name == "Widget" + + +# --------------------------------------------------------------------------- +# model_name parameter +# --------------------------------------------------------------------------- + + +class TestModelName: + def test_model_name_override(self) -> None: + schema = { + "type": "object", + "title": "OriginalName", + "properties": {"x": {"type": "integer"}}, + "required": ["x"], + } + Model = create_model_from_schema(schema, model_name="CustomSchema") + assert Model.__name__ == "CustomSchema" + + def test_model_name_fallback_to_title(self) -> None: + schema = { + "type": "object", + "title": "FromTitle", + "properties": {"x": {"type": "integer"}}, + "required": ["x"], + } + Model = create_model_from_schema(schema) + assert Model.__name__ == "FromTitle" + + def test_model_name_fallback_to_dynamic(self) -> None: + schema = { + "type": "object", + "properties": {"x": {"type": "integer"}}, + "required": ["x"], + } + Model = create_model_from_schema(schema) + assert Model.__name__ == "DynamicModel" + + +# --------------------------------------------------------------------------- +# enrich_descriptions +# --------------------------------------------------------------------------- + + +class TestEnrichDescriptions: + def test_enriched_description_includes_constraints(self) -> None: + schema = { + "type": "object", + "properties": { + "score": { + "type": "integer", + "description": "The score value", + "minimum": 0, + "maximum": 100, + }, + }, + "required": ["score"], + } + Model = create_model_from_schema(schema, enrich_descriptions=True) + field_info = Model.model_fields["score"] + assert "Minimum: 0" in field_info.description + assert "Maximum: 100" in field_info.description + assert "The score value" in field_info.description + + def test_default_does_not_enrich(self) -> None: + schema = { + "type": "object", + "properties": { + "score": { + "type": "integer", + "description": "The score value", + "minimum": 0, + }, + }, + "required": ["score"], + } + Model = create_model_from_schema(schema, enrich_descriptions=False) + field_info = Model.model_fields["score"] + assert field_info.description == "The score value" + + def test_enriched_description_propagates_to_nested(self) -> None: + schema = { + "type": "object", + "properties": { + "config": { + "type": "object", + "properties": { + "level": { + "type": "integer", + "description": "Level", + "minimum": 1, + "maximum": 10, + }, + }, + "required": ["level"], + }, + }, + "required": ["config"], + } + Model = create_model_from_schema(schema, enrich_descriptions=True) + nested_model = Model.model_fields["config"].annotation + nested_field = nested_model.model_fields["level"] + assert "Minimum: 1" in nested_field.description + assert "Maximum: 10" in nested_field.description + + +# --------------------------------------------------------------------------- +# Edge cases +# --------------------------------------------------------------------------- + + +class TestEdgeCases: + def test_empty_properties(self) -> None: + schema = {"type": "object", "properties": {}, "required": []} + Model = create_model_from_schema(schema) + obj = Model() + assert obj is not None + + def test_no_properties_key(self) -> None: + schema = {"type": "object"} + Model = create_model_from_schema(schema) + obj = Model() + assert obj is not None + + def test_unknown_type_raises(self) -> None: + schema = { + "type": "object", + "properties": { + "weird": {"type": "hyperspace"}, + }, + "required": ["weird"], + } + with pytest.raises(ValueError, match="Unsupported JSON schema type"): + create_model_from_schema(schema) + + +# --------------------------------------------------------------------------- +# build_rich_field_description +# --------------------------------------------------------------------------- + + +class TestBuildRichFieldDescription: + def test_description_only(self) -> None: + assert build_rich_field_description({"description": "A name"}) == "A name" + + def test_empty_schema(self) -> None: + assert build_rich_field_description({}) == "" + + def test_format(self) -> None: + desc = build_rich_field_description({"format": "date-time"}) + assert "Format: date-time" in desc + + def test_enum(self) -> None: + desc = build_rich_field_description({"enum": ["a", "b"]}) + assert "Allowed values:" in desc + assert "'a'" in desc + assert "'b'" in desc + + def test_pattern(self) -> None: + desc = build_rich_field_description({"pattern": "^[a-z]+$"}) + assert "Pattern: ^[a-z]+$" in desc + + def test_min_max(self) -> None: + desc = build_rich_field_description({"minimum": 0, "maximum": 100}) + assert "Minimum: 0" in desc + assert "Maximum: 100" in desc + + def test_min_max_length(self) -> None: + desc = build_rich_field_description({"minLength": 1, "maxLength": 255}) + assert "Min length: 1" in desc + assert "Max length: 255" in desc + + def test_examples(self) -> None: + desc = build_rich_field_description({"examples": ["foo", "bar", "baz", "extra"]}) + assert "Examples:" in desc + assert "'foo'" in desc + assert "'baz'" in desc + # Only first 3 shown + assert "'extra'" not in desc + + def test_combined_constraints(self) -> None: + desc = build_rich_field_description({ + "description": "A score", + "minimum": 0, + "maximum": 10, + "format": "int32", + }) + assert desc.startswith("A score") + assert "Minimum: 0" in desc + assert "Maximum: 10" in desc + assert "Format: int32" in desc + + +# --------------------------------------------------------------------------- +# Schema transformation functions +# --------------------------------------------------------------------------- + + +class TestResolveRefs: + def test_basic_ref_resolution(self) -> None: + schema = { + "type": "object", + "properties": {"item": {"$ref": "#/$defs/Item"}}, + "$defs": { + "Item": {"type": "object", "properties": {"id": {"type": "integer"}}}, + }, + } + resolved = resolve_refs(schema) + assert "$ref" not in resolved["properties"]["item"] + assert resolved["properties"]["item"]["type"] == "object" + + def test_nested_ref_resolution(self) -> None: + schema = { + "type": "object", + "properties": {"wrapper": {"$ref": "#/$defs/Wrapper"}}, + "$defs": { + "Wrapper": { + "type": "object", + "properties": {"inner": {"$ref": "#/$defs/Inner"}}, + }, + "Inner": {"type": "string"}, + }, + } + resolved = resolve_refs(schema) + wrapper = resolved["properties"]["wrapper"] + assert wrapper["properties"]["inner"]["type"] == "string" + + def test_missing_ref_raises(self) -> None: + schema = { + "properties": {"x": {"$ref": "#/$defs/Missing"}}, + "$defs": {}, + } + with pytest.raises(KeyError, match="Missing"): + resolve_refs(schema) + + def test_no_refs_unchanged(self) -> None: + schema = { + "type": "object", + "properties": {"name": {"type": "string"}}, + } + resolved = resolve_refs(schema) + assert resolved == schema + + +class TestForceAdditionalPropertiesFalse: + def test_adds_to_object(self) -> None: + schema = {"type": "object", "properties": {"x": {"type": "integer"}}} + result = force_additional_properties_false(deepcopy(schema)) + assert result["additionalProperties"] is False + + def test_adds_empty_properties_and_required(self) -> None: + schema = {"type": "object"} + result = force_additional_properties_false(deepcopy(schema)) + assert result["properties"] == {} + assert result["required"] == [] + + def test_recursive_nested_objects(self) -> None: + schema = { + "type": "object", + "properties": { + "child": { + "type": "object", + "properties": {"id": {"type": "integer"}}, + }, + }, + } + result = force_additional_properties_false(deepcopy(schema)) + assert result["additionalProperties"] is False + assert result["properties"]["child"]["additionalProperties"] is False + + def test_does_not_affect_non_objects(self) -> None: + schema = {"type": "string"} + result = force_additional_properties_false(deepcopy(schema)) + assert "additionalProperties" not in result + + +class TestStripUnsupportedFormats: + def test_removes_email_format(self) -> None: + schema = {"type": "string", "format": "email"} + result = strip_unsupported_formats(deepcopy(schema)) + assert "format" not in result + + def test_keeps_date_time(self) -> None: + schema = {"type": "string", "format": "date-time"} + result = strip_unsupported_formats(deepcopy(schema)) + assert result["format"] == "date-time" + + def test_keeps_date(self) -> None: + schema = {"type": "string", "format": "date"} + result = strip_unsupported_formats(deepcopy(schema)) + assert result["format"] == "date" + + def test_removes_uri_format(self) -> None: + schema = {"type": "string", "format": "uri"} + result = strip_unsupported_formats(deepcopy(schema)) + assert "format" not in result + + def test_recursive(self) -> None: + schema = { + "type": "object", + "properties": { + "email": {"type": "string", "format": "email"}, + "created": {"type": "string", "format": "date-time"}, + }, + } + result = strip_unsupported_formats(deepcopy(schema)) + assert "format" not in result["properties"]["email"] + assert result["properties"]["created"]["format"] == "date-time" + + +class TestEnsureTypeInSchemas: + def test_empty_schema_in_anyof_gets_type(self) -> None: + schema = {"anyOf": [{}, {"type": "string"}]} + result = ensure_type_in_schemas(deepcopy(schema)) + assert result["anyOf"][0] == {"type": "object"} + + def test_empty_schema_in_oneof_gets_type(self) -> None: + schema = {"oneOf": [{}, {"type": "integer"}]} + result = ensure_type_in_schemas(deepcopy(schema)) + assert result["oneOf"][0] == {"type": "object"} + + def test_non_empty_unchanged(self) -> None: + schema = {"anyOf": [{"type": "string"}, {"type": "integer"}]} + result = ensure_type_in_schemas(deepcopy(schema)) + assert result == schema + + +class TestConvertOneofToAnyof: + def test_converts_top_level(self) -> None: + schema = {"oneOf": [{"type": "string"}, {"type": "integer"}]} + result = convert_oneof_to_anyof(deepcopy(schema)) + assert "oneOf" not in result + assert "anyOf" in result + assert len(result["anyOf"]) == 2 + + def test_converts_nested(self) -> None: + schema = { + "type": "object", + "properties": { + "value": {"oneOf": [{"type": "string"}, {"type": "number"}]}, + }, + } + result = convert_oneof_to_anyof(deepcopy(schema)) + assert "anyOf" in result["properties"]["value"] + assert "oneOf" not in result["properties"]["value"] + + +class TestEnsureAllPropertiesRequired: + def test_makes_all_required(self) -> None: + schema = { + "type": "object", + "properties": {"a": {"type": "string"}, "b": {"type": "integer"}}, + "required": ["a"], + } + result = ensure_all_properties_required(deepcopy(schema)) + assert set(result["required"]) == {"a", "b"} + + def test_recursive(self) -> None: + schema = { + "type": "object", + "properties": { + "child": { + "type": "object", + "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}, + "required": [], + }, + }, + } + result = ensure_all_properties_required(deepcopy(schema)) + assert set(result["properties"]["child"]["required"]) == {"x", "y"} + + +class TestStripNullFromTypes: + def test_strips_null_from_anyof(self) -> None: + schema = { + "anyOf": [{"type": "string"}, {"type": "null"}], + } + result = strip_null_from_types(deepcopy(schema)) + assert "anyOf" not in result + assert result["type"] == "string" + + def test_strips_null_from_type_array(self) -> None: + schema = {"type": ["string", "null"]} + result = strip_null_from_types(deepcopy(schema)) + assert result["type"] == "string" + + def test_multiple_non_null_in_anyof(self) -> None: + schema = { + "anyOf": [{"type": "string"}, {"type": "integer"}, {"type": "null"}], + } + result = strip_null_from_types(deepcopy(schema)) + assert len(result["anyOf"]) == 2 + + def test_no_null_unchanged(self) -> None: + schema = {"type": "string"} + result = strip_null_from_types(deepcopy(schema)) + assert result == schema + + +class TestEndToEndMCPSchema: + """Realistic MCP tool schema exercising multiple features simultaneously.""" + + MCP_SCHEMA: dict[str, Any] = { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "Search query", + "minLength": 1, + "maxLength": 500, + }, + "max_results": { + "type": "integer", + "description": "Maximum results", + "minimum": 1, + "maximum": 100, + }, + "format": { + "type": "string", + "enum": ["json", "csv", "xml"], + "description": "Output format", + }, + "filters": { + "type": "object", + "properties": { + "date_from": {"type": "string", "format": "date"}, + "date_to": {"type": "string", "format": "date"}, + "categories": { + "type": "array", + "items": {"type": "string"}, + }, + }, + "required": ["date_from"], + }, + "sort_order": { + "anyOf": [{"type": "string"}, {"type": "null"}], + }, + }, + "required": ["query", "format", "filters"], + } + + def test_model_creation(self) -> None: + Model = create_model_from_schema(self.MCP_SCHEMA) + assert Model is not None + assert issubclass(Model, BaseModel) + + def test_valid_input_accepted(self) -> None: + Model = create_model_from_schema(self.MCP_SCHEMA) + obj = Model( + query="test search", + format="json", + filters={"date_from": "2025-01-01"}, + ) + assert obj.query == "test search" + assert obj.format == "json" + + def test_invalid_enum_rejected(self) -> None: + Model = create_model_from_schema(self.MCP_SCHEMA) + with pytest.raises(Exception): + Model( + query="test", + format="yaml", + filters={"date_from": "2025-01-01"}, + ) + + def test_model_name_for_mcp_tool(self) -> None: + Model = create_model_from_schema( + self.MCP_SCHEMA, model_name="search_toolSchema" + ) + assert Model.__name__ == "search_toolSchema" + + def test_enriched_descriptions_for_mcp(self) -> None: + Model = create_model_from_schema( + self.MCP_SCHEMA, enrich_descriptions=True + ) + query_field = Model.model_fields["query"] + assert "Min length: 1" in query_field.description + assert "Max length: 500" in query_field.description + + max_results_field = Model.model_fields["max_results"] + assert "Minimum: 1" in max_results_field.description + assert "Maximum: 100" in max_results_field.description + + format_field = Model.model_fields["format"] + assert "Allowed values:" in format_field.description + + def test_optional_fields_accept_none(self) -> None: + Model = create_model_from_schema(self.MCP_SCHEMA) + obj = Model( + query="test", + format="csv", + filters={"date_from": "2025-01-01"}, + max_results=None, + sort_order=None, + ) + assert obj.max_results is None + assert obj.sort_order is None + + def test_nested_filters_validated(self) -> None: + Model = create_model_from_schema(self.MCP_SCHEMA) + obj = Model( + query="test", + format="xml", + filters={ + "date_from": "2025-01-01", + "date_to": "2025-12-31", + "categories": ["news", "tech"], + }, + ) + assert obj.filters.date_from == datetime.date(2025, 1, 1) + assert obj.filters.categories == ["news", "tech"] diff --git a/lib/devtools/pyproject.toml b/lib/devtools/pyproject.toml index ce407b3f9..58347585e 100644 --- a/lib/devtools/pyproject.toml +++ b/lib/devtools/pyproject.toml @@ -15,7 +15,7 @@ dependencies = [ "openai~=1.83.0", "python-dotenv~=1.1.1", "pygithub~=1.59.1", - "rich~=13.9.4", + "rich>=13.9.4", ] [project.scripts] diff --git a/lib/devtools/src/crewai_devtools/__init__.py b/lib/devtools/src/crewai_devtools/__init__.py index 4cf11a18b..4c45a4486 100644 --- a/lib/devtools/src/crewai_devtools/__init__.py +++ b/lib/devtools/src/crewai_devtools/__init__.py @@ -1,3 +1,3 @@ """CrewAI development tools.""" -__version__ = "1.9.3" +__version__ = "1.10.1" diff --git a/lib/devtools/src/crewai_devtools/cli.py b/lib/devtools/src/crewai_devtools/cli.py index abe3709a7..32950c39f 100644 --- a/lib/devtools/src/crewai_devtools/cli.py +++ b/lib/devtools/src/crewai_devtools/cli.py @@ -14,7 +14,7 @@ from rich.markdown import Markdown from rich.panel import Panel from rich.prompt import Confirm -from crewai_devtools.prompts import RELEASE_NOTES_PROMPT +from crewai_devtools.prompts import RELEASE_NOTES_PROMPT, TRANSLATE_RELEASE_NOTES_PROMPT load_dotenv() @@ -191,6 +191,248 @@ def update_pyproject_dependencies(file_path: Path, new_version: str) -> bool: return False +def add_docs_version(docs_json_path: Path, version: str) -> bool: + """Add a new version to the Mintlify docs.json versioning config. + + Copies the current default version's tabs into a new version entry, + sets the new version as default, and marks the previous default as + non-default. Operates on all languages. + + Args: + docs_json_path: Path to docs/docs.json. + version: Version string (e.g., "1.10.1b1"). + + Returns: + True if docs.json was updated, False otherwise. + """ + import json + + if not docs_json_path.exists(): + return False + + data = json.loads(docs_json_path.read_text()) + version_label = f"v{version}" + updated = False + + for lang in data.get("navigation", {}).get("languages", []): + versions = lang.get("versions", []) + if not versions: + continue + + # Skip if this version already exists for this language + if any(v.get("version") == version_label for v in versions): + continue + + # Find the current default and copy its tabs + default_version = next( + (v for v in versions if v.get("default")), + versions[0], + ) + + new_version = { + "version": version_label, + "default": True, + "tabs": default_version.get("tabs", []), + } + + # Remove default flag from old default + default_version.pop("default", None) + + # Insert new version at the beginning + versions.insert(0, new_version) + updated = True + + if not updated: + return False + + docs_json_path.write_text(json.dumps(data, indent=2, ensure_ascii=False) + "\n") + return True + + +_PT_BR_MONTHS = { + 1: "jan", + 2: "fev", + 3: "mar", + 4: "abr", + 5: "mai", + 6: "jun", + 7: "jul", + 8: "ago", + 9: "set", + 10: "out", + 11: "nov", + 12: "dez", +} + +_CHANGELOG_LOCALES: dict[str, dict[str, str]] = { + "en": { + "link_text": "View release on GitHub", + "language_name": "English", + }, + "pt-BR": { + "link_text": "Ver release no GitHub", + "language_name": "Brazilian Portuguese", + }, + "ko": { + "link_text": "GitHub 릴리스 보기", + "language_name": "Korean", + }, +} + + +def translate_release_notes( + release_notes: str, + lang: str, + client: OpenAI, +) -> str: + """Translate release notes into the target language using OpenAI. + + Args: + release_notes: English release notes markdown. + lang: Language code (e.g., "pt-BR", "ko"). + client: OpenAI client instance. + + Returns: + Translated release notes, or original on failure. + """ + locale_cfg = _CHANGELOG_LOCALES.get(lang) + if not locale_cfg: + return release_notes + + language_name = locale_cfg["language_name"] + prompt = TRANSLATE_RELEASE_NOTES_PROMPT.substitute( + language=language_name, + release_notes=release_notes, + ) + + try: + response = client.chat.completions.create( + model="gpt-4o-mini", + messages=[ + { + "role": "system", + "content": f"You are a professional translator. Translate technical documentation into {language_name}.", + }, + {"role": "user", "content": prompt}, + ], + temperature=0.3, + ) + return response.choices[0].message.content or release_notes + except Exception as e: + console.print( + f"[yellow]Warning:[/yellow] Could not translate to {language_name}: {e}" + ) + return release_notes + + +def _format_changelog_date(lang: str) -> str: + """Format today's date for a changelog entry in the given language.""" + from datetime import datetime + + now = datetime.now() + if lang == "ko": + return f"{now.year}년 {now.month}월 {now.day}일" + if lang == "pt-BR": + return f"{now.day:02d} {_PT_BR_MONTHS[now.month]} {now.year}" + return now.strftime("%b %d, %Y") + + +def update_changelog( + changelog_path: Path, + version: str, + release_notes: str, + lang: str = "en", +) -> bool: + """Prepend a new release entry to a docs changelog file. + + Args: + changelog_path: Path to the changelog.mdx file. + version: Version string (e.g., "1.9.3"). + release_notes: Markdown release notes content. + lang: Language code for localized date/link text. + + Returns: + True if changelog was updated, False otherwise. + """ + if not changelog_path.exists(): + return False + + locale_cfg = _CHANGELOG_LOCALES.get(lang, _CHANGELOG_LOCALES["en"]) + date_label = _format_changelog_date(lang) + link_text = locale_cfg["link_text"] + + # Indent each non-empty line with 2 spaces to match block format + indented_lines = [] + for line in release_notes.splitlines(): + if line.strip(): + indented_lines.append(f" {line}") + else: + indented_lines.append("") + indented_notes = "\n".join(indented_lines) + + entry = ( + f'\n' + f" ## v{version}\n" + f"\n" + f" [{link_text}]" + f"(https://github.com/crewAIInc/crewAI/releases/tag/{version})\n" + f"\n" + f"{indented_notes}\n" + f"\n" + f"" + ) + + content = changelog_path.read_text() + + # Insert after the frontmatter closing --- + parts = content.split("---", 2) + if len(parts) >= 3: + new_content = ( + parts[0] + + "---" + + parts[1] + + "---\n" + + entry + + "\n\n" + + parts[2].lstrip("\n") + ) + else: + new_content = entry + "\n\n" + content + + changelog_path.write_text(new_content) + return True + + +def update_template_dependencies(templates_dir: Path, new_version: str) -> list[Path]: + """Update crewai dependency versions in CLI template pyproject.toml files. + + Handles both pinned (==) and minimum (>=) version specifiers, + as well as extras like [tools]. + + Args: + templates_dir: Path to the CLI templates directory. + new_version: New version string. + + Returns: + List of paths that were updated. + """ + import re + + updated = [] + for pyproject in templates_dir.rglob("pyproject.toml"): + content = pyproject.read_text() + new_content = re.sub( + r'"crewai(\[tools\])?(==|>=)[^"]*"', + lambda m: f'"crewai{(m.group(1) or "")!s}=={new_version}"', + content, + ) + if new_content != content: + pyproject.write_text(new_content) + updated.append(pyproject) + + return updated + + def find_version_files(base_path: Path) -> list[Path]: """Find all __init__.py files that contain __version__. @@ -394,6 +636,22 @@ def bump(version: str, dry_run: bool, no_push: bool, no_commit: bool) -> None: "[yellow]Warning:[/yellow] No __version__ attributes found to update" ) + # Update CLI template pyproject.toml files + templates_dir = lib_dir / "crewai" / "src" / "crewai" / "cli" / "templates" + if templates_dir.exists(): + if dry_run: + for tpl in templates_dir.rglob("pyproject.toml"): + console.print( + f"[dim][DRY RUN][/dim] Would update template: {tpl.relative_to(cwd)}" + ) + else: + tpl_updated = update_template_dependencies(templates_dir, version) + for tpl in tpl_updated: + console.print( + f"[green]✓[/green] Updated template: {tpl.relative_to(cwd)}" + ) + updated_files.append(tpl) + if not dry_run: console.print("\nSyncing workspace...") run_command(["uv", "sync"]) @@ -575,9 +833,9 @@ def tag(dry_run: bool, no_edit: bool) -> None: github_contributors = get_github_contributors(commit_range) - if commits.strip(): - client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + if commits.strip(): contributors_section = "" if github_contributors: contributors_section = f"\n\n## Contributors\n\n{', '.join([f'@{u}' for u in github_contributors])}" @@ -588,7 +846,7 @@ def tag(dry_run: bool, no_edit: bool) -> None: contributors_section=contributors_section, ) - response = client.chat.completions.create( + response = openai_client.chat.completions.create( model="gpt-4o-mini", messages=[ { @@ -643,6 +901,143 @@ def tag(dry_run: bool, no_edit: bool) -> None: "\n[green]✓[/green] Using generated release notes without editing" ) + is_prerelease = any( + indicator in version.lower() + for indicator in ["a", "b", "rc", "alpha", "beta", "dev"] + ) + + # Update docs: changelogs + version switcher + docs_json_path = cwd / "docs" / "docs.json" + changelog_langs = ["en", "pt-BR", "ko"] + if not dry_run: + docs_files_staged = [] + + for lang in changelog_langs: + cl_path = cwd / "docs" / lang / "changelog.mdx" + if lang == "en": + notes_for_lang = release_notes + else: + console.print(f"[dim]Translating release notes to {lang}...[/dim]") + notes_for_lang = translate_release_notes( + release_notes, lang, openai_client + ) + if update_changelog(cl_path, version, notes_for_lang, lang=lang): + console.print( + f"[green]✓[/green] Updated {cl_path.relative_to(cwd)}" + ) + docs_files_staged.append(str(cl_path)) + else: + console.print( + f"[yellow]Warning:[/yellow] Changelog not found at {cl_path.relative_to(cwd)}" + ) + + if not is_prerelease: + if add_docs_version(docs_json_path, version): + console.print( + f"[green]✓[/green] Added v{version} to docs version switcher" + ) + docs_files_staged.append(str(docs_json_path)) + else: + console.print( + f"[yellow]Warning:[/yellow] docs.json not found at {docs_json_path.relative_to(cwd)}" + ) + + if docs_files_staged: + docs_branch = f"docs/changelog-v{version}" + run_command(["git", "checkout", "-b", docs_branch]) + for f in docs_files_staged: + run_command(["git", "add", f]) + run_command( + [ + "git", + "commit", + "-m", + f"docs: update changelog and version for v{version}", + ] + ) + console.print("[green]✓[/green] Committed docs updates") + + run_command(["git", "push", "-u", "origin", docs_branch]) + console.print(f"[green]✓[/green] Pushed branch {docs_branch}") + + run_command( + [ + "gh", + "pr", + "create", + "--base", + "main", + "--title", + f"docs: update changelog and version for v{version}", + "--body", + "", + ] + ) + console.print("[green]✓[/green] Created docs PR") + + run_command( + [ + "gh", + "pr", + "merge", + docs_branch, + "--squash", + "--auto", + "--delete-branch", + ] + ) + console.print("[green]✓[/green] Enabled auto-merge on docs PR") + + import time + + console.print("[cyan]Waiting for PR checks to pass and merge...[/cyan]") + while True: + time.sleep(10) + try: + state = run_command( + [ + "gh", + "pr", + "view", + docs_branch, + "--json", + "state", + "--jq", + ".state", + ] + ) + except subprocess.CalledProcessError: + state = "" + + if state == "MERGED": + break + + console.print("[dim]Still waiting for PR to merge...[/dim]") + + console.print("[green]✓[/green] Docs PR merged") + + run_command(["git", "checkout", "main"]) + run_command(["git", "pull"]) + console.print("[green]✓[/green] main branch updated with docs changes") + else: + for lang in changelog_langs: + cl_path = cwd / "docs" / lang / "changelog.mdx" + translated = " (translated)" if lang != "en" else "" + console.print( + f"[dim][DRY RUN][/dim] Would update {cl_path.relative_to(cwd)}{translated}" + ) + if not is_prerelease: + console.print( + f"[dim][DRY RUN][/dim] Would add v{version} to docs version switcher" + ) + else: + console.print( + "[dim][DRY RUN][/dim] Skipping docs version (pre-release)" + ) + console.print( + f"[dim][DRY RUN][/dim] Would create branch docs/changelog-v{version}, PR, and merge" + ) + if not dry_run: with console.status(f"[cyan]Creating tag {tag_name}..."): try: @@ -660,11 +1055,6 @@ def tag(dry_run: bool, no_edit: bool) -> None: sys.exit(1) console.print(f"[green]✓[/green] Pushed tag {tag_name}") - is_prerelease = any( - indicator in version.lower() - for indicator in ["a", "b", "rc", "alpha", "beta", "dev"] - ) - with console.status("[cyan]Creating GitHub Release..."): try: gh_cmd = [ diff --git a/lib/devtools/src/crewai_devtools/prompts.py b/lib/devtools/src/crewai_devtools/prompts.py index 1e96f03f4..6272972af 100644 --- a/lib/devtools/src/crewai_devtools/prompts.py +++ b/lib/devtools/src/crewai_devtools/prompts.py @@ -43,3 +43,18 @@ Instructions: Keep it professional and clear.""" ) + + +TRANSLATE_RELEASE_NOTES_PROMPT = Template( + """Translate the following release notes into $language. + +$release_notes + +Instructions: +- Translate all section headers and descriptions naturally +- Keep markdown formatting (##, ###, -, etc.) exactly as-is +- Keep all proper nouns, code identifiers, class names, and technical terms unchanged + (e.g. "CrewAI", "LiteAgent", "ChromaDB", "MCP", "@username") +- Keep the ## Contributors section and GitHub usernames unchanged +- Do not add or remove any content, only translate""" +) diff --git a/pyproject.toml b/pyproject.toml index 35ec3096b..335f51dae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,9 +8,9 @@ authors = [ [dependency-groups] dev = [ - "ruff==0.14.7", - "mypy==1.19.0", - "pre-commit==4.5.0", + "ruff==0.15.1", + "mypy==1.19.1", + "pre-commit==4.5.1", "bandit==1.9.2", "pytest==8.4.2", "pytest-asyncio==1.3.0", @@ -23,9 +23,9 @@ dev = [ "pytest-split==0.10.0", "types-requests~=2.31.0.6", "types-pyyaml==6.0.*", - "types-regex==2024.11.6.*", + "types-regex==2026.1.15.*", "types-appdirs==1.4.*", - "boto3-stubs[bedrock-runtime]==1.40.54", + "boto3-stubs[bedrock-runtime]==1.42.40", "types-psycopg2==2.9.21.20251012", "types-pymysql==1.1.0.20250916", "types-aiofiles~=25.1.0", @@ -142,6 +142,19 @@ python_files = "test_*.py" python_classes = "Test*" python_functions = "test_*" +[tool.uv] + +# composio-core pins rich<14 but textual requires rich>=14. +# onnxruntime 1.24+ dropped Python 3.10 wheels; cap it so qdrant[fastembed] resolves on 3.10. +# fastembed 0.7.x and docling 2.63 cap pillow<12; the removed APIs don't affect them. +# langchain-core 0.3.76 has a template-injection vuln (GHSA); force >=0.3.80. +override-dependencies = [ + "rich>=13.7.1", + "onnxruntime<1.24; python_version < '3.11'", + "pillow>=12.1.1", + "langchain-core>=0.3.80,<1", + "urllib3>=2.6.3", +] [tool.uv.workspace] members = [ diff --git a/uv.lock b/uv.lock index 6bef665cd..1a2b3d8f8 100644 --- a/uv.lock +++ b/uv.lock @@ -2,30 +2,14 @@ version = 1 revision = 3 requires-python = ">=3.10, <3.14" resolution-markers = [ - "python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", - "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", - "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform == 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and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", - "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", - "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform != 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'3.11' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version < '3.11' and platform_python_implementation == 'PyPy'", + "python_full_version < '3.11' and platform_python_implementation != 'PyPy'", + "python_full_version == '3.11.*' and platform_python_implementation == 'PyPy'", + "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy'", + "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy'", + "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy'", + "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'", + "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'", ] [manifest] @@ -35,13 +19,20 @@ members = [ "crewai-files", "crewai-tools", ] +overrides = [ + { name = "langchain-core", specifier = ">=0.3.80,<1" }, + { name = "onnxruntime", marker = "python_full_version < '3.11'", specifier = "<1.24" }, 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