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matcha/opt
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fix-gemini
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b371f97a2f |
@@ -12,6 +12,7 @@ from dotenv import load_dotenv
|
||||
import pytest
|
||||
from vcr.request import Request # type: ignore[import-untyped]
|
||||
|
||||
|
||||
try:
|
||||
import vcr.stubs.httpx_stubs as httpx_stubs # type: ignore[import-untyped]
|
||||
except ModuleNotFoundError:
|
||||
|
||||
127
docs/docs.json
127
docs/docs.json
@@ -65,9 +65,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "Welcome",
|
||||
"pages": [
|
||||
"index"
|
||||
]
|
||||
"pages": ["index"]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -89,23 +87,17 @@
|
||||
{
|
||||
"group": "Strategy",
|
||||
"icon": "compass",
|
||||
"pages": [
|
||||
"en/guides/concepts/evaluating-use-cases"
|
||||
]
|
||||
"pages": ["en/guides/concepts/evaluating-use-cases"]
|
||||
},
|
||||
{
|
||||
"group": "Agents",
|
||||
"icon": "user",
|
||||
"pages": [
|
||||
"en/guides/agents/crafting-effective-agents"
|
||||
]
|
||||
"pages": ["en/guides/agents/crafting-effective-agents"]
|
||||
},
|
||||
{
|
||||
"group": "Crews",
|
||||
"icon": "users",
|
||||
"pages": [
|
||||
"en/guides/crews/first-crew"
|
||||
]
|
||||
"pages": ["en/guides/crews/first-crew"]
|
||||
},
|
||||
{
|
||||
"group": "Flows",
|
||||
@@ -118,9 +110,7 @@
|
||||
{
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
]
|
||||
"pages": ["en/guides/coding-tools/agents-md"]
|
||||
},
|
||||
{
|
||||
"group": "Advanced",
|
||||
@@ -129,6 +119,13 @@
|
||||
"en/guides/advanced/customizing-prompts",
|
||||
"en/guides/advanced/fingerprinting"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Migration",
|
||||
"icon": "shuffle",
|
||||
"pages": [
|
||||
"en/guides/migration/migrating-from-langgraph"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -349,9 +346,7 @@
|
||||
},
|
||||
{
|
||||
"group": "Telemetry",
|
||||
"pages": [
|
||||
"en/telemetry"
|
||||
]
|
||||
"pages": ["en/telemetry"]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -361,9 +356,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "Getting Started",
|
||||
"pages": [
|
||||
"en/enterprise/introduction"
|
||||
]
|
||||
"pages": ["en/enterprise/introduction"]
|
||||
},
|
||||
{
|
||||
"group": "Build",
|
||||
@@ -387,9 +380,7 @@
|
||||
},
|
||||
{
|
||||
"group": "Manage",
|
||||
"pages": [
|
||||
"en/enterprise/features/rbac"
|
||||
]
|
||||
"pages": ["en/enterprise/features/rbac"]
|
||||
},
|
||||
{
|
||||
"group": "Integration Docs",
|
||||
@@ -487,10 +478,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "Examples",
|
||||
"pages": [
|
||||
"en/examples/example",
|
||||
"en/examples/cookbooks"
|
||||
]
|
||||
"pages": ["en/examples/example", "en/examples/cookbooks"]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -500,9 +488,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "Release Notes",
|
||||
"pages": [
|
||||
"en/changelog"
|
||||
]
|
||||
"pages": ["en/changelog"]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -547,9 +533,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "Bem-vindo",
|
||||
"pages": [
|
||||
"pt-BR/index"
|
||||
]
|
||||
"pages": ["pt-BR/index"]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -571,9 +555,7 @@
|
||||
{
|
||||
"group": "Estratégia",
|
||||
"icon": "compass",
|
||||
"pages": [
|
||||
"pt-BR/guides/concepts/evaluating-use-cases"
|
||||
]
|
||||
"pages": ["pt-BR/guides/concepts/evaluating-use-cases"]
|
||||
},
|
||||
{
|
||||
"group": "Agentes",
|
||||
@@ -585,9 +567,7 @@
|
||||
{
|
||||
"group": "Crews",
|
||||
"icon": "users",
|
||||
"pages": [
|
||||
"pt-BR/guides/crews/first-crew"
|
||||
]
|
||||
"pages": ["pt-BR/guides/crews/first-crew"]
|
||||
},
|
||||
{
|
||||
"group": "Flows",
|
||||
@@ -604,6 +584,13 @@
|
||||
"pt-BR/guides/advanced/customizing-prompts",
|
||||
"pt-BR/guides/advanced/fingerprinting"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Migração",
|
||||
"icon": "shuffle",
|
||||
"pages": [
|
||||
"pt-BR/guides/migration/migrating-from-langgraph"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -810,9 +797,7 @@
|
||||
},
|
||||
{
|
||||
"group": "Telemetria",
|
||||
"pages": [
|
||||
"pt-BR/telemetry"
|
||||
]
|
||||
"pages": ["pt-BR/telemetry"]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -822,9 +807,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/enterprise/introduction"
|
||||
]
|
||||
"pages": ["pt-BR/enterprise/introduction"]
|
||||
},
|
||||
{
|
||||
"group": "Construir",
|
||||
@@ -848,9 +831,7 @@
|
||||
},
|
||||
{
|
||||
"group": "Gerenciar",
|
||||
"pages": [
|
||||
"pt-BR/enterprise/features/rbac"
|
||||
]
|
||||
"pages": ["pt-BR/enterprise/features/rbac"]
|
||||
},
|
||||
{
|
||||
"group": "Documentação de Integração",
|
||||
@@ -960,9 +941,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "Notas de Versão",
|
||||
"pages": [
|
||||
"pt-BR/changelog"
|
||||
]
|
||||
"pages": ["pt-BR/changelog"]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1007,9 +986,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "환영합니다",
|
||||
"pages": [
|
||||
"ko/index"
|
||||
]
|
||||
"pages": ["ko/index"]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -1031,23 +1008,17 @@
|
||||
{
|
||||
"group": "전략",
|
||||
"icon": "compass",
|
||||
"pages": [
|
||||
"ko/guides/concepts/evaluating-use-cases"
|
||||
]
|
||||
"pages": ["ko/guides/concepts/evaluating-use-cases"]
|
||||
},
|
||||
{
|
||||
"group": "에이전트 (Agents)",
|
||||
"icon": "user",
|
||||
"pages": [
|
||||
"ko/guides/agents/crafting-effective-agents"
|
||||
]
|
||||
"pages": ["ko/guides/agents/crafting-effective-agents"]
|
||||
},
|
||||
{
|
||||
"group": "크루 (Crews)",
|
||||
"icon": "users",
|
||||
"pages": [
|
||||
"ko/guides/crews/first-crew"
|
||||
]
|
||||
"pages": ["ko/guides/crews/first-crew"]
|
||||
},
|
||||
{
|
||||
"group": "플로우 (Flows)",
|
||||
@@ -1064,6 +1035,13 @@
|
||||
"ko/guides/advanced/customizing-prompts",
|
||||
"ko/guides/advanced/fingerprinting"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "마이그레이션",
|
||||
"icon": "shuffle",
|
||||
"pages": [
|
||||
"ko/guides/migration/migrating-from-langgraph"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -1282,9 +1260,7 @@
|
||||
},
|
||||
{
|
||||
"group": "Telemetry",
|
||||
"pages": [
|
||||
"ko/telemetry"
|
||||
]
|
||||
"pages": ["ko/telemetry"]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -1294,9 +1270,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/enterprise/introduction"
|
||||
]
|
||||
"pages": ["ko/enterprise/introduction"]
|
||||
},
|
||||
{
|
||||
"group": "빌드",
|
||||
@@ -1320,9 +1294,7 @@
|
||||
},
|
||||
{
|
||||
"group": "관리",
|
||||
"pages": [
|
||||
"ko/enterprise/features/rbac"
|
||||
]
|
||||
"pages": ["ko/enterprise/features/rbac"]
|
||||
},
|
||||
{
|
||||
"group": "통합 문서",
|
||||
@@ -1419,10 +1391,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "예시",
|
||||
"pages": [
|
||||
"ko/examples/example",
|
||||
"ko/examples/cookbooks"
|
||||
]
|
||||
"pages": ["ko/examples/example", "ko/examples/cookbooks"]
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -1432,9 +1401,7 @@
|
||||
"groups": [
|
||||
{
|
||||
"group": "릴리스 노트",
|
||||
"pages": [
|
||||
"ko/changelog"
|
||||
]
|
||||
"pages": ["ko/changelog"]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -4,6 +4,106 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="Feb 27, 2026">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Feb 27, 2026">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Feb 26, 2026">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Jan 26, 2026">
|
||||
## v1.9.0
|
||||
|
||||
|
||||
@@ -106,6 +106,15 @@ There are different places in CrewAI code where you can specify the model to use
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Info>
|
||||
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]'
|
||||
```
|
||||
</Info>
|
||||
|
||||
## 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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Azure">
|
||||
@@ -766,6 +785,11 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
model="sagemaker/<my-endpoint>"
|
||||
)
|
||||
```
|
||||
|
||||
**Note:** This provider uses LiteLLM. Add it as a dependency to your project:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Mistral">
|
||||
@@ -781,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Nvidia NIM">
|
||||
@@ -867,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Local NVIDIA NIM Deployed using WSL2">
|
||||
@@ -907,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Groq">
|
||||
@@ -928,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="IBM watsonx.ai">
|
||||
@@ -950,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Ollama (Local LLMs)">
|
||||
@@ -963,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Fireworks AI">
|
||||
@@ -978,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Perplexity AI">
|
||||
@@ -993,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Hugging Face">
|
||||
@@ -1007,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="SambaNova">
|
||||
@@ -1030,6 +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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Cerebras">
|
||||
@@ -1055,6 +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
|
||||
</Info>
|
||||
|
||||
**Note:** This provider uses LiteLLM. Add it as a dependency to your project:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Open Router">
|
||||
@@ -1077,6 +1156,11 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
- openrouter/deepseek/deepseek-r1
|
||||
- openrouter/deepseek/deepseek-chat
|
||||
</Info>
|
||||
|
||||
**Note:** This provider uses LiteLLM. Add it as a dependency to your project:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Nebius AI Studio">
|
||||
@@ -1099,6 +1183,11 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
- Competitive pricing
|
||||
- Good balance of speed and quality
|
||||
</Info>
|
||||
|
||||
**Note:** This provider uses LiteLLM. Add it as a dependency to your project:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
|
||||
518
docs/en/guides/migration/migrating-from-langgraph.mdx
Normal file
518
docs/en/guides/migration/migrating-from-langgraph.mdx
Normal file
@@ -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.
|
||||
@@ -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.
|
||||
|
||||
<Note>
|
||||
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).
|
||||
|
||||
<Info>
|
||||
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).
|
||||
</Info>
|
||||
|
||||
## Changing the LLM
|
||||
|
||||
To use a different LLM with your CrewAI agents, you have several options:
|
||||
|
||||
@@ -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:
|
||||
|
||||
@@ -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
|
||||
<CodeGroup>
|
||||
```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)
|
||||
</CodeGroup>
|
||||
|
||||
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)
|
||||
```
|
||||
<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
|
||||
|
||||
- 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)
|
||||
|
||||
@@ -4,6 +4,106 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="2026년 2월 27일">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 2월 27일">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 2월 26일">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 1월 26일">
|
||||
## v1.9.0
|
||||
|
||||
|
||||
@@ -105,6 +105,15 @@ CrewAI 코드 내에는 사용할 모델을 지정할 수 있는 여러 위치
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Info>
|
||||
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]'
|
||||
```
|
||||
</Info>
|
||||
|
||||
## 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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Azure">
|
||||
@@ -439,6 +458,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
|
||||
model="sagemaker/<my-endpoint>"
|
||||
)
|
||||
```
|
||||
|
||||
**참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Mistral">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Nvidia NIM">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Local NVIDIA NIM Deployed using WSL2">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Groq">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="IBM watsonx.ai">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Ollama (Local LLMs)">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Fireworks AI">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Perplexity AI">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Hugging Face">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="SambaNova">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Cerebras">
|
||||
@@ -728,6 +802,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
|
||||
- 속도와 품질의 우수한 밸런스
|
||||
- 긴 컨텍스트 윈도우 지원
|
||||
</Info>
|
||||
|
||||
**참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Open Router">
|
||||
@@ -750,6 +829,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
|
||||
- openrouter/deepseek/deepseek-r1
|
||||
- openrouter/deepseek/deepseek-chat
|
||||
</Info>
|
||||
|
||||
**참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Nebius AI Studio">
|
||||
@@ -772,6 +856,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
|
||||
- 경쟁력 있는 가격
|
||||
- 속도와 품질의 우수한 밸런스
|
||||
</Info>
|
||||
|
||||
**참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
|
||||
518
docs/ko/guides/migration/migrating-from-langgraph.mdx
Normal file
518
docs/ko/guides/migration/migrating-from-langgraph.mdx
Normal file
@@ -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.
|
||||
@@ -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.
|
||||
|
||||
<Note>
|
||||
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.
|
||||
|
||||
<Info>
|
||||
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.
|
||||
</Info>
|
||||
|
||||
## LLM 변경하기
|
||||
|
||||
CrewAI agent에서 다른 LLM을 사용하려면 여러 가지 방법이 있습니다:
|
||||
|
||||
@@ -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:
|
||||
|
||||
@@ -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
|
||||
<CodeGroup>
|
||||
```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.
|
||||
</CodeGroup>
|
||||
|
||||
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)
|
||||
```
|
||||
<Tip>`advanced`를 True로 설정하면 복잡한 사용 사례를 위한 액션을 가져올 수 있습니다</Tip>
|
||||
|
||||
- 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.
|
||||
* 더욱 자세한 도구 목록은 [여기](https://docs.composio.dev/toolkits)에서 확인할 수 있습니다.
|
||||
@@ -4,6 +4,106 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="27 fev 2026">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="27 fev 2026">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="26 fev 2026">
|
||||
## 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
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="26 jan 2026">
|
||||
## v1.9.0
|
||||
|
||||
|
||||
@@ -105,6 +105,15 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Info>
|
||||
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]'
|
||||
```
|
||||
</Info>
|
||||
|
||||
## 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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Azure">
|
||||
@@ -438,6 +457,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
|
||||
model="sagemaker/<my-endpoint>"
|
||||
)
|
||||
```
|
||||
|
||||
**Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Mistral">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Nvidia NIM">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Local NVIDIA NIM Deployed using WSL2">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Groq">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="IBM watsonx.ai">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Ollama (LLMs Locais)">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Fireworks AI">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Perplexity AI">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Hugging Face">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="SambaNova">
|
||||
@@ -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]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Cerebras">
|
||||
@@ -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
|
||||
</Info>
|
||||
|
||||
**Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Open Router">
|
||||
@@ -749,6 +828,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
|
||||
- openrouter/deepseek/deepseek-r1
|
||||
- openrouter/deepseek/deepseek-chat
|
||||
</Info>
|
||||
|
||||
**Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
|
||||
518
docs/pt-BR/guides/migration/migrating-from-langgraph.mdx
Normal file
518
docs/pt-BR/guides/migration/migrating-from-langgraph.mdx
Normal file
@@ -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.
|
||||
@@ -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.
|
||||
|
||||
<Note>
|
||||
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).
|
||||
|
||||
<Info>
|
||||
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).
|
||||
</Info>
|
||||
|
||||
## Alterando a LLM
|
||||
|
||||
Para utilizar uma LLM diferente com seus agentes CrewAI, você tem várias opções:
|
||||
|
||||
@@ -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
|
||||
<CodeGroup>
|
||||
```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)
|
||||
</CodeGroup>
|
||||
|
||||
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)
|
||||
```
|
||||
<Tip>Defina `advanced` como True para obter ações para casos de uso complexos</Tip>
|
||||
|
||||
- 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)
|
||||
* Uma lista mais detalhada de ferramentas pode ser encontrada [aqui](https://docs.composio.dev/toolkits)
|
||||
@@ -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",
|
||||
|
||||
@@ -152,4 +152,4 @@ __all__ = [
|
||||
"wrap_file_source",
|
||||
]
|
||||
|
||||
__version__ = "1.9.3"
|
||||
__version__ = "1.10.1a1"
|
||||
|
||||
@@ -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==1.10.1a1",
|
||||
"tiktoken~=0.8.0",
|
||||
"beautifulsoup4~=4.13.4",
|
||||
"python-docx~=1.2.0",
|
||||
|
||||
@@ -291,4 +291,4 @@ __all__ = [
|
||||
"ZapierActionTools",
|
||||
]
|
||||
|
||||
__version__ = "1.9.3"
|
||||
__version__ = "1.10.1a1"
|
||||
|
||||
@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field
|
||||
from pydantic.types import StringConstraints
|
||||
import requests
|
||||
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
|
||||
@@ -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"
|
||||
|
||||
@@ -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
|
||||
|
||||
|
||||
|
||||
@@ -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": [
|
||||
{
|
||||
|
||||
@@ -21,7 +21,7 @@ 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",
|
||||
@@ -42,7 +42,7 @@ dependencies = [
|
||||
"mcp~=1.26.0",
|
||||
"uv~=0.9.13",
|
||||
"aiosqlite~=0.21.0",
|
||||
"lancedb>=0.4.0",
|
||||
"lancedb>=0.29.2",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
@@ -53,7 +53,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
|
||||
|
||||
[project.optional-dependencies]
|
||||
tools = [
|
||||
"crewai-tools==1.9.3",
|
||||
"crewai-tools==1.10.1a1",
|
||||
]
|
||||
embeddings = [
|
||||
"tiktoken~=0.8.0"
|
||||
@@ -66,7 +66,7 @@ openpyxl = [
|
||||
]
|
||||
mem0 = ["mem0ai~=0.1.94"]
|
||||
docling = [
|
||||
"docling~=2.63.0",
|
||||
"docling~=2.75.0",
|
||||
]
|
||||
qdrant = [
|
||||
"qdrant-client[fastembed]~=1.14.3",
|
||||
@@ -88,7 +88,7 @@ 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",
|
||||
|
||||
@@ -10,7 +10,6 @@ from crewai.flow.flow import Flow
|
||||
from crewai.knowledge.knowledge import Knowledge
|
||||
from crewai.llm import LLM
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
from crewai.memory.unified_memory import Memory
|
||||
from crewai.process import Process
|
||||
from crewai.task import Task
|
||||
from crewai.tasks.llm_guardrail import LLMGuardrail
|
||||
@@ -41,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
|
||||
|
||||
_suppress_pydantic_deprecation_warnings()
|
||||
|
||||
__version__ = "1.9.3"
|
||||
__version__ = "1.10.1a1"
|
||||
_telemetry_submitted = False
|
||||
|
||||
|
||||
@@ -72,6 +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",
|
||||
|
||||
@@ -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]
|
||||
|
||||
@@ -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(
|
||||
|
||||
@@ -8,11 +8,9 @@ import time
|
||||
from typing import (
|
||||
TYPE_CHECKING,
|
||||
Any,
|
||||
Final,
|
||||
Literal,
|
||||
cast,
|
||||
)
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from pydantic import (
|
||||
BaseModel,
|
||||
@@ -61,16 +59,8 @@ 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.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.tools.agent_tools.agent_tools import AgentTools
|
||||
@@ -111,18 +101,8 @@ 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.
|
||||
@@ -154,7 +134,7 @@ 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,
|
||||
@@ -384,10 +364,10 @@ class Agent(BaseAgent):
|
||||
)
|
||||
if unified_memory is not None:
|
||||
query = task.description
|
||||
matches = unified_memory.recall(query, limit=10)
|
||||
matches = unified_memory.recall(query, limit=5)
|
||||
if matches:
|
||||
memory = "Relevant memories:\n" + "\n".join(
|
||||
f"- {m.record.content}" for m in matches
|
||||
m.format() for m in matches
|
||||
)
|
||||
if memory.strip() != "":
|
||||
task_prompt += self.i18n.slice("memory").format(memory=memory)
|
||||
@@ -622,10 +602,10 @@ class Agent(BaseAgent):
|
||||
)
|
||||
if unified_memory is not None:
|
||||
query = task.description
|
||||
matches = unified_memory.recall(query, limit=10)
|
||||
matches = unified_memory.recall(query, limit=5)
|
||||
if matches:
|
||||
memory = "Relevant memories:\n" + "\n".join(
|
||||
f"- {m.record.content}" for m in matches
|
||||
m.format() for m in matches
|
||||
)
|
||||
if memory.strip() != "":
|
||||
task_prompt += self.i18n.slice("memory").format(memory=memory)
|
||||
@@ -864,7 +844,11 @@ 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(
|
||||
@@ -893,7 +877,11 @@ 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
|
||||
@@ -926,544 +914,17 @@ 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]:
|
||||
@@ -1695,11 +1156,15 @@ 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
|
||||
@@ -1712,7 +1177,8 @@ class Agent(BaseAgent):
|
||||
|
||||
existing_names = {sanitize_tool_name(t.name) for t in raw_tools}
|
||||
raw_tools.extend(
|
||||
mt for mt in create_memory_tools(agent_memory)
|
||||
mt
|
||||
for mt in create_memory_tools(agent_memory)
|
||||
if sanitize_tool_name(mt.name) not in existing_names
|
||||
)
|
||||
|
||||
@@ -1802,11 +1268,11 @@ class Agent(BaseAgent):
|
||||
),
|
||||
)
|
||||
start_time = time.time()
|
||||
matches = agent_memory.recall(formatted_messages, limit=10)
|
||||
matches = agent_memory.recall(formatted_messages, limit=5)
|
||||
memory_block = ""
|
||||
if matches:
|
||||
memory_block = "Relevant memories:\n" + "\n".join(
|
||||
f"- {m.record.content}" for m in matches
|
||||
m.format() for m in matches
|
||||
)
|
||||
if memory_block:
|
||||
formatted_messages += "\n\n" + self.i18n.slice("memory").format(
|
||||
@@ -1937,14 +1403,15 @@ class Agent(BaseAgent):
|
||||
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}\n"
|
||||
f"Agent: {self.role}\n"
|
||||
f"Result: {output_text}"
|
||||
)
|
||||
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)
|
||||
|
||||
@@ -4,7 +4,8 @@ from abc import ABC, abstractmethod
|
||||
from collections.abc import Callable
|
||||
from copy import copy as shallow_copy
|
||||
from hashlib import md5
|
||||
from typing import Any, Literal
|
||||
import re
|
||||
from typing import Any, Final, Literal
|
||||
import uuid
|
||||
|
||||
from pydantic import (
|
||||
@@ -36,6 +37,11 @@ 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",
|
||||
@@ -197,7 +203,7 @@ 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,
|
||||
@@ -276,14 +282,16 @@ 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:
|
||||
|
||||
@@ -30,7 +30,7 @@ class CrewAgentExecutorMixin:
|
||||
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:
|
||||
if memory is None or not self.task or getattr(memory, "_read_only", False):
|
||||
return
|
||||
if (
|
||||
f"Action: {sanitize_tool_name('Delegate work to coworker')}"
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
from crewai.agents.cache.cache_handler import CacheHandler
|
||||
|
||||
|
||||
|
||||
__all__ = ["CacheHandler"]
|
||||
|
||||
@@ -487,8 +487,8 @@ 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:
|
||||
@@ -700,9 +700,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
if not parsed_calls:
|
||||
return None
|
||||
|
||||
original_tools_by_name: dict[str, Any] = {}
|
||||
for tool in self.original_tools or []:
|
||||
original_tools_by_name[sanitize_tool_name(tool.name)] = tool
|
||||
original_tools_by_name: dict[str, Any] = dict(self._tool_name_mapping)
|
||||
|
||||
if len(parsed_calls) > 1:
|
||||
has_result_as_answer_in_batch = any(
|
||||
@@ -949,10 +947,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
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
|
||||
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(
|
||||
@@ -1259,7 +1263,7 @@ 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:
|
||||
@@ -1312,8 +1316,8 @@ 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:
|
||||
@@ -1374,7 +1378,7 @@ 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
|
||||
@@ -1386,7 +1390,7 @@ 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
|
||||
@@ -1397,7 +1401,7 @@ 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
|
||||
@@ -1491,7 +1495,7 @@ 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.
|
||||
@@ -1501,6 +1505,19 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
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"
|
||||
) -> None:
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
from crewai.cli.authentication.main import AuthenticationCommand
|
||||
|
||||
|
||||
|
||||
__all__ = ["AuthenticationCommand"]
|
||||
|
||||
@@ -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"
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
|
||||
import click
|
||||
|
||||
|
||||
@@ -290,13 +290,20 @@ class MemoryTUI(App[None]):
|
||||
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=200)
|
||||
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)
|
||||
entry_list.border_title = f"Entries ({len(self._entries)})"
|
||||
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(
|
||||
@@ -376,6 +383,11 @@ class MemoryTUI(App[None]):
|
||||
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. "
|
||||
|
||||
@@ -22,14 +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
|
||||
@@ -48,8 +49,13 @@ class PlusAPI:
|
||||
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) -> httpx.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) -> httpx.Response:
|
||||
return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
|
||||
@@ -190,6 +196,15 @@ class PlusAPI:
|
||||
timeout=30,
|
||||
)
|
||||
|
||||
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")
|
||||
|
||||
@@ -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]==1.10.1a1"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -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]==1.10.1a1"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -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]==1.10.1a1"
|
||||
]
|
||||
|
||||
[tool.crewai]
|
||||
|
||||
@@ -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(
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
from crewai.crews.crew_output import CrewOutput
|
||||
|
||||
|
||||
|
||||
__all__ = ["CrewOutput"]
|
||||
|
||||
@@ -63,6 +63,7 @@ from crewai.events.types.logging_events import (
|
||||
AgentLogsStartedEvent,
|
||||
)
|
||||
from crewai.events.types.mcp_events import (
|
||||
MCPConfigFetchFailedEvent,
|
||||
MCPConnectionCompletedEvent,
|
||||
MCPConnectionFailedEvent,
|
||||
MCPConnectionStartedEvent,
|
||||
@@ -165,6 +166,7 @@ __all__ = [
|
||||
"LiteAgentExecutionCompletedEvent",
|
||||
"LiteAgentExecutionErrorEvent",
|
||||
"LiteAgentExecutionStartedEvent",
|
||||
"MCPConfigFetchFailedEvent",
|
||||
"MCPConnectionCompletedEvent",
|
||||
"MCPConnectionFailedEvent",
|
||||
"MCPConnectionStartedEvent",
|
||||
|
||||
@@ -23,4 +23,3 @@ class BaseEventListener(ABC):
|
||||
Args:
|
||||
crewai_event_bus: The event bus to register listeners on.
|
||||
"""
|
||||
pass
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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
|
||||
)
|
||||
|
||||
@@ -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)
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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"
|
||||
|
||||
@@ -1512,6 +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,
|
||||
|
||||
@@ -52,6 +52,8 @@ 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,
|
||||
@@ -85,8 +87,6 @@ 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
|
||||
|
||||
@@ -321,7 +321,7 @@ 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)
|
||||
)
|
||||
|
||||
@@ -594,21 +594,19 @@ 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,
|
||||
@@ -622,24 +620,19 @@ 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",
|
||||
@@ -649,12 +642,26 @@ 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: LLMMessage = {
|
||||
"role": "user",
|
||||
"content": reasoning_prompt,
|
||||
}
|
||||
self.state.messages.append(reasoning_message)
|
||||
|
||||
return "tool_completed"
|
||||
|
||||
@listen("native_tool_calls")
|
||||
def execute_native_tool(
|
||||
@@ -728,7 +735,20 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
)
|
||||
for future in as_completed(future_to_idx):
|
||||
idx = future_to_idx[future]
|
||||
ordered_results[idx] = future.result()
|
||||
try:
|
||||
ordered_results[idx] = future.result()
|
||||
except Exception as e:
|
||||
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
|
||||
]
|
||||
@@ -824,11 +844,17 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
continue
|
||||
_, func_name, _ = info
|
||||
|
||||
original_tool = None
|
||||
for tool in self.original_tools or []:
|
||||
if sanitize_tool_name(tool.name) == func_name:
|
||||
original_tool = tool
|
||||
break
|
||||
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
|
||||
@@ -844,7 +870,18 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
"""Execute a single native tool call and return metadata/result."""
|
||||
info = extract_tool_call_info(tool_call)
|
||||
if not info:
|
||||
raise ValueError("Invalid native tool call format")
|
||||
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
|
||||
|
||||
@@ -856,12 +893,17 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
# 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
|
||||
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
|
||||
@@ -904,10 +946,16 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
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
|
||||
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(
|
||||
|
||||
@@ -16,7 +16,7 @@ from collections.abc import (
|
||||
Sequence,
|
||||
ValuesView,
|
||||
)
|
||||
from concurrent.futures import Future
|
||||
from concurrent.futures import Future, ThreadPoolExecutor
|
||||
import copy
|
||||
import enum
|
||||
import inspect
|
||||
@@ -1739,7 +1739,12 @@ 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,
|
||||
|
||||
@@ -2,10 +2,10 @@ from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from collections.abc import Callable
|
||||
import time
|
||||
from functools import wraps
|
||||
import inspect
|
||||
import json
|
||||
import time
|
||||
from types import MethodType
|
||||
from typing import (
|
||||
TYPE_CHECKING,
|
||||
@@ -49,15 +49,20 @@ from crewai.events.types.agent_events import (
|
||||
LiteAgentExecutionErrorEvent,
|
||||
LiteAgentExecutionStartedEvent,
|
||||
)
|
||||
from crewai.events.types.logging_events import AgentLogsExecutionEvent
|
||||
from crewai.events.types.memory_events import (
|
||||
MemoryRetrievalCompletedEvent,
|
||||
MemoryRetrievalFailedEvent,
|
||||
MemoryRetrievalStartedEvent,
|
||||
)
|
||||
from crewai.events.types.logging_events import AgentLogsExecutionEvent
|
||||
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
|
||||
@@ -270,11 +275,11 @@ 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)
|
||||
|
||||
@@ -440,12 +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
|
||||
|
||||
@@ -482,11 +491,12 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
# 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.agent_utils import sanitize_tool_name
|
||||
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)
|
||||
mt
|
||||
for mt in create_memory_tools(self._memory)
|
||||
if sanitize_tool_name(mt.name) not in existing_names
|
||||
]
|
||||
if memory_tools:
|
||||
@@ -565,9 +575,10 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
if memory_block:
|
||||
formatted = self.i18n.slice("memory").format(memory=memory_block)
|
||||
if self._messages and self._messages[0].get("role") == "system":
|
||||
self._messages[0]["content"] = (
|
||||
self._messages[0].get("content", "") + "\n\n" + formatted
|
||||
)
|
||||
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(
|
||||
@@ -588,16 +599,12 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
)
|
||||
|
||||
def _save_to_memory(self, output_text: str) -> None:
|
||||
"""Extract discrete memories from the run and remember each. No-op if _memory is None."""
|
||||
if self._memory is 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}\n"
|
||||
f"Agent: {self.role}\n"
|
||||
f"Result: {output_text}"
|
||||
)
|
||||
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)
|
||||
@@ -622,13 +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:
|
||||
self._save_to_memory(agent_finish.output)
|
||||
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)
|
||||
@@ -660,8 +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,
|
||||
@@ -838,10 +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.
|
||||
"""
|
||||
@@ -870,12 +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
|
||||
)
|
||||
@@ -901,7 +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...",
|
||||
|
||||
@@ -427,7 +427,7 @@ class LLM(BaseLLM):
|
||||
f"installed.\n\n"
|
||||
f"To fix this, either:\n"
|
||||
f" 1. Install LiteLLM for broad model support: "
|
||||
f"uv add litellm\n"
|
||||
f"uv add 'crewai[litellm]'\n"
|
||||
f"or\n"
|
||||
f"pip install litellm\n\n"
|
||||
f"For more details, see: "
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -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 +634,25 @@ 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])
|
||||
)
|
||||
# Gemini requires all parallel function responses in a single
|
||||
# Content object. When the previous Content already holds
|
||||
# function_response parts, merge into it instead of creating
|
||||
# a new Content.
|
||||
if (
|
||||
contents
|
||||
and contents[-1].role == "user"
|
||||
and contents[-1].parts
|
||||
and all(
|
||||
hasattr(p, "function_response")
|
||||
and p.function_response is not None
|
||||
for p in contents[-1].parts
|
||||
)
|
||||
):
|
||||
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 +926,7 @@ 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 +963,6 @@ 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 +999,21 @@ 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 +1367,7 @@ 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)
|
||||
|
||||
@@ -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",
|
||||
|
||||
@@ -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.
|
||||
|
||||
592
lib/crewai/src/crewai/mcp/tool_resolver.py
Normal file
592
lib/crewai/src/crewai/mcp/tool_resolver.py
Normal file
@@ -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,
|
||||
)
|
||||
@@ -1,6 +1,14 @@
|
||||
"""Memory module: unified Memory with LLM analysis and pluggable storage."""
|
||||
"""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.encoding_flow import EncodingFlow
|
||||
from crewai.memory.memory_scope import MemoryScope, MemorySlice
|
||||
from crewai.memory.types import (
|
||||
MemoryMatch,
|
||||
@@ -10,7 +18,25 @@ from crewai.memory.types import (
|
||||
embed_text,
|
||||
embed_texts,
|
||||
)
|
||||
from crewai.memory.unified_memory import Memory
|
||||
|
||||
|
||||
_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__ = [
|
||||
|
||||
@@ -145,7 +145,7 @@ class MemoryScope:
|
||||
|
||||
|
||||
class MemorySlice:
|
||||
"""View over multiple scopes: recall searches all, remember requires explicit scope unless read_only."""
|
||||
"""View over multiple scopes: recall searches all, remember is a no-op when read_only."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@@ -160,7 +160,7 @@ class MemorySlice:
|
||||
memory: The underlying Memory instance.
|
||||
scopes: List of scope paths to include.
|
||||
categories: Optional category filter for recall.
|
||||
read_only: If True, remember() raises PermissionError.
|
||||
read_only: If True, remember() is a silent no-op.
|
||||
"""
|
||||
self._memory = memory
|
||||
self._scopes = [s.rstrip("/") or "/" for s in scopes]
|
||||
@@ -176,10 +176,10 @@ class MemorySlice:
|
||||
importance: float | None = None,
|
||||
source: str | None = None,
|
||||
private: bool = False,
|
||||
) -> MemoryRecord:
|
||||
"""Remember into an explicit scope. Required when read_only=False."""
|
||||
) -> MemoryRecord | None:
|
||||
"""Remember into an explicit scope. No-op when read_only=True."""
|
||||
if self._read_only:
|
||||
raise PermissionError("This MemorySlice is read-only")
|
||||
return None
|
||||
return self._memory.remember(
|
||||
content,
|
||||
scope=scope,
|
||||
|
||||
@@ -53,6 +53,7 @@ class LanceDBStorage:
|
||||
path: str | Path | None = None,
|
||||
table_name: str = "memories",
|
||||
vector_dim: int | None = None,
|
||||
compact_every: int = 100,
|
||||
) -> None:
|
||||
"""Initialize LanceDB storage.
|
||||
|
||||
@@ -64,6 +65,10 @@ class LanceDBStorage:
|
||||
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")
|
||||
@@ -78,6 +83,22 @@ class LanceDBStorage:
|
||||
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:
|
||||
@@ -91,6 +112,11 @@ class LanceDBStorage:
|
||||
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
|
||||
@@ -178,6 +204,56 @@ class LanceDBStorage:
|
||||
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.
|
||||
|
||||
@@ -239,6 +315,7 @@ class LanceDBStorage:
|
||||
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]
|
||||
@@ -246,6 +323,13 @@ class LanceDBStorage:
|
||||
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."""
|
||||
@@ -261,6 +345,10 @@ class LanceDBStorage:
|
||||
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.
|
||||
"""
|
||||
@@ -268,25 +356,20 @@ class LanceDBStorage:
|
||||
return
|
||||
with self._write_lock:
|
||||
now = datetime.utcnow().isoformat()
|
||||
for rid in record_ids:
|
||||
safe_id = str(rid).replace("'", "''")
|
||||
rows = (
|
||||
self._table.search([0.0] * self._vector_dim)
|
||||
.where(f"id = '{safe_id}'")
|
||||
.limit(1)
|
||||
.to_list()
|
||||
)
|
||||
if rows:
|
||||
rows[0]["last_accessed"] = now
|
||||
self._retry_write("delete", f"id = '{safe_id}'")
|
||||
self._retry_write("add", [rows[0]])
|
||||
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([0.0] * self._vector_dim).where(f"id = '{safe_id}'").limit(1).to_list()
|
||||
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])
|
||||
@@ -374,13 +457,31 @@ class LanceDBStorage:
|
||||
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) -> list[dict[str, Any]]:
|
||||
"""Scan rows optionally filtered by scope prefix."""
|
||||
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([0.0] * self._vector_dim)
|
||||
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(
|
||||
@@ -406,7 +507,10 @@ class LanceDBStorage:
|
||||
prefix = scope if scope != "/" else ""
|
||||
if prefix and not prefix.startswith("/"):
|
||||
prefix = "/" + prefix
|
||||
rows = self._scan_rows(prefix or None)
|
||||
rows = self._scan_rows(
|
||||
prefix or None,
|
||||
columns=["scope", "categories_str", "created_at"],
|
||||
)
|
||||
if not rows:
|
||||
return ScopeInfo(
|
||||
path=scope or "/",
|
||||
@@ -453,7 +557,7 @@ class LanceDBStorage:
|
||||
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)
|
||||
rows = self._scan_rows(prefix if prefix != "/" else None, columns=["scope"])
|
||||
children: set[str] = set()
|
||||
for row in rows:
|
||||
sc = str(row.get("scope", ""))
|
||||
@@ -465,7 +569,7 @@ class LanceDBStorage:
|
||||
return sorted(children)
|
||||
|
||||
def list_categories(self, scope_prefix: str | None = None) -> dict[str, int]:
|
||||
rows = self._scan_rows(scope_prefix)
|
||||
rows = self._scan_rows(scope_prefix, columns=["categories_str"])
|
||||
counts: dict[str, int] = {}
|
||||
for row in rows:
|
||||
cat_str = row.get("categories_str") or "[]"
|
||||
@@ -498,6 +602,21 @@ class LanceDBStorage:
|
||||
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)
|
||||
|
||||
|
||||
@@ -87,6 +87,22 @@ class MemoryMatch(BaseModel):
|
||||
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."""
|
||||
@@ -291,7 +307,7 @@ def embed_text(embedder: Any, text: str) -> list[float]:
|
||||
return []
|
||||
first = result[0]
|
||||
if hasattr(first, "tolist"):
|
||||
return first.tolist()
|
||||
return list(first.tolist())
|
||||
if isinstance(first, list):
|
||||
return [float(x) for x in first]
|
||||
return list(first)
|
||||
|
||||
@@ -6,7 +6,7 @@ from concurrent.futures import Future, ThreadPoolExecutor
|
||||
from datetime import datetime
|
||||
import threading
|
||||
import time
|
||||
from typing import Any, Literal
|
||||
from typing import TYPE_CHECKING, Any, Literal
|
||||
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.types.memory_events import (
|
||||
@@ -21,7 +21,6 @@ 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.storage.lancedb_storage import LanceDBStorage
|
||||
from crewai.memory.types import (
|
||||
MemoryConfig,
|
||||
MemoryMatch,
|
||||
@@ -30,13 +29,20 @@ from crewai.memory.types import (
|
||||
compute_composite_score,
|
||||
embed_text,
|
||||
)
|
||||
from crewai.rag.embeddings.factory import build_embedder
|
||||
from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec
|
||||
|
||||
|
||||
def _default_embedder() -> Any:
|
||||
if TYPE_CHECKING:
|
||||
from chromadb.utils.embedding_functions.openai_embedding_function import (
|
||||
OpenAIEmbeddingFunction,
|
||||
)
|
||||
|
||||
|
||||
def _default_embedder() -> OpenAIEmbeddingFunction:
|
||||
"""Build default OpenAI embedder for memory."""
|
||||
from crewai.rag.embeddings.factory import build_embedder
|
||||
|
||||
return build_embedder({"provider": "openai", "config": {}})
|
||||
spec: OpenAIProviderSpec = {"provider": "openai", "config": {}}
|
||||
return build_embedder(spec)
|
||||
|
||||
|
||||
class Memory:
|
||||
@@ -88,6 +94,10 @@ class Memory:
|
||||
# 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.
|
||||
|
||||
@@ -107,7 +117,9 @@ class Memory:
|
||||
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,
|
||||
@@ -130,14 +142,15 @@ class Memory:
|
||||
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
|
||||
embedder
|
||||
if (embedder is not None and not isinstance(embedder, dict))
|
||||
else None
|
||||
)
|
||||
|
||||
# Storage is initialized eagerly (local, no API key needed).
|
||||
if storage == "lancedb":
|
||||
self._storage = LanceDBStorage()
|
||||
elif isinstance(storage, str):
|
||||
self._storage = LanceDBStorage(path=storage)
|
||||
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
|
||||
|
||||
@@ -160,12 +173,17 @@ class Memory:
|
||||
from crewai.llm import LLM
|
||||
|
||||
try:
|
||||
self._llm_instance = LLM(model=self._llm_config)
|
||||
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'
|
||||
" - 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"
|
||||
@@ -180,8 +198,6 @@ class Memory:
|
||||
if self._embedder_instance is None:
|
||||
try:
|
||||
if isinstance(self._embedder_config, dict):
|
||||
from crewai.rag.embeddings.factory import build_embedder
|
||||
|
||||
self._embedder_instance = build_embedder(self._embedder_config)
|
||||
else:
|
||||
self._embedder_instance = _default_embedder()
|
||||
@@ -317,7 +333,7 @@ class Memory:
|
||||
source: str | None = None,
|
||||
private: bool = False,
|
||||
agent_role: str | None = None,
|
||||
) -> MemoryRecord:
|
||||
) -> MemoryRecord | None:
|
||||
"""Store a single item in memory (synchronous).
|
||||
|
||||
Routes through the same serialized save pool as ``remember_many``
|
||||
@@ -335,11 +351,13 @@ class Memory:
|
||||
agent_role: Optional agent role for event metadata.
|
||||
|
||||
Returns:
|
||||
The created MemoryRecord.
|
||||
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(
|
||||
@@ -356,7 +374,13 @@ class Memory:
|
||||
# then immediately wait for the result.
|
||||
future = self._submit_save(
|
||||
self._encode_batch,
|
||||
[content], scope, categories, metadata, importance, source, private,
|
||||
[content],
|
||||
scope,
|
||||
categories,
|
||||
metadata,
|
||||
importance,
|
||||
source,
|
||||
private,
|
||||
)
|
||||
records = future.result()
|
||||
record = records[0] if records else None
|
||||
@@ -420,13 +444,19 @@ class Memory:
|
||||
Returns:
|
||||
Empty list (records are not available until the background save completes).
|
||||
"""
|
||||
if not contents:
|
||||
if not contents or self._read_only:
|
||||
return []
|
||||
|
||||
self._submit_save(
|
||||
self._background_encode_batch,
|
||||
contents, scope, categories, metadata,
|
||||
importance, source, private, agent_role,
|
||||
contents,
|
||||
scope,
|
||||
categories,
|
||||
metadata,
|
||||
importance,
|
||||
source,
|
||||
private,
|
||||
agent_role,
|
||||
)
|
||||
return []
|
||||
|
||||
@@ -566,14 +596,13 @@ class Memory:
|
||||
# Privacy filter
|
||||
if not include_private:
|
||||
raw = [
|
||||
(r, s) for r, s in 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
|
||||
)
|
||||
composite, reasons = compute_composite_score(r, s, self._config)
|
||||
results.append(
|
||||
MemoryMatch(
|
||||
record=r,
|
||||
@@ -739,7 +768,9 @@ class Memory:
|
||||
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)
|
||||
return self._storage.list_records(
|
||||
scope_prefix=scope, limit=limit, offset=offset
|
||||
)
|
||||
|
||||
def info(self, path: str = "/") -> ScopeInfo:
|
||||
"""Return scope info for path."""
|
||||
@@ -781,7 +812,7 @@ class Memory:
|
||||
importance: float | None = None,
|
||||
source: str | None = None,
|
||||
private: bool = False,
|
||||
) -> MemoryRecord:
|
||||
) -> MemoryRecord | None:
|
||||
"""Async remember: delegates to sync for now."""
|
||||
return self.remember(
|
||||
content,
|
||||
|
||||
@@ -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.
|
||||
|
||||
|
||||
@@ -586,16 +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,
|
||||
@@ -687,16 +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,
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
from crewai.telemetry.telemetry import Telemetry
|
||||
|
||||
|
||||
|
||||
__all__ = ["Telemetry"]
|
||||
|
||||
@@ -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"):
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from crewai.tools.base_tool import BaseTool, EnvVar, tool
|
||||
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BaseTool",
|
||||
"EnvVar",
|
||||
|
||||
@@ -18,13 +18,12 @@ from pydantic import (
|
||||
BaseModel as PydanticBaseModel,
|
||||
ConfigDict,
|
||||
Field,
|
||||
ValidationError,
|
||||
create_model,
|
||||
field_validator,
|
||||
)
|
||||
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
|
||||
@@ -163,13 +162,14 @@ class BaseTool(BaseModel, ABC):
|
||||
Raises:
|
||||
ValueError: If validation against args_schema fails.
|
||||
"""
|
||||
if kwargs and self.args_schema is not None and self.args_schema.model_fields:
|
||||
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}"
|
||||
f"Tool '{self.name}' arguments validation failed: {e}{hint}"
|
||||
) from e
|
||||
return kwargs
|
||||
|
||||
@@ -178,7 +178,8 @@ class BaseTool(BaseModel, ABC):
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
kwargs = self._validate_kwargs(kwargs)
|
||||
if not args:
|
||||
kwargs = self._validate_kwargs(kwargs)
|
||||
|
||||
result = self._run(*args, **kwargs)
|
||||
|
||||
@@ -203,7 +204,8 @@ class BaseTool(BaseModel, ABC):
|
||||
Returns:
|
||||
The result of the tool execution.
|
||||
"""
|
||||
kwargs = self._validate_kwargs(kwargs)
|
||||
if not args:
|
||||
kwargs = self._validate_kwargs(kwargs)
|
||||
result = await self._arun(*args, **kwargs)
|
||||
self.current_usage_count += 1
|
||||
return result
|
||||
@@ -356,7 +358,8 @@ class Tool(BaseTool, Generic[P, R]):
|
||||
Returns:
|
||||
The result of the tool execution.
|
||||
"""
|
||||
kwargs = self._validate_kwargs(kwargs)
|
||||
if not args:
|
||||
kwargs = self._validate_kwargs(kwargs) # type: ignore[assignment]
|
||||
|
||||
result = self.func(*args, **kwargs)
|
||||
|
||||
@@ -388,7 +391,8 @@ class Tool(BaseTool, Generic[P, R]):
|
||||
Returns:
|
||||
The result of the tool execution.
|
||||
"""
|
||||
kwargs = self._validate_kwargs(kwargs)
|
||||
if not args:
|
||||
kwargs = self._validate_kwargs(kwargs) # type: ignore[assignment]
|
||||
result = await self._arun(*args, **kwargs)
|
||||
self.current_usage_count += 1
|
||||
return result
|
||||
|
||||
@@ -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__)
|
||||
|
||||
|
||||
@@ -20,14 +20,6 @@ class RecallMemorySchema(BaseModel):
|
||||
"or multiple items to search for several things at once."
|
||||
),
|
||||
)
|
||||
scope: str | None = Field(
|
||||
default=None,
|
||||
description="Optional scope to narrow the search (e.g. /project/alpha)",
|
||||
)
|
||||
depth: str = Field(
|
||||
default="shallow",
|
||||
description="'shallow' for fast vector search, 'deep' for LLM-analyzed retrieval",
|
||||
)
|
||||
|
||||
|
||||
class RecallMemoryTool(BaseTool):
|
||||
@@ -41,32 +33,27 @@ class RecallMemoryTool(BaseTool):
|
||||
def _run(
|
||||
self,
|
||||
queries: list[str] | str,
|
||||
scope: str | None = None,
|
||||
depth: str = "shallow",
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Search memory for relevant information.
|
||||
|
||||
Args:
|
||||
queries: One or more search queries (string or list of strings).
|
||||
scope: Optional scope prefix to narrow the search.
|
||||
depth: "shallow" for fast vector search, "deep" for LLM-analyzed retrieval.
|
||||
|
||||
Returns:
|
||||
Formatted string of matching memories, or a message if none found.
|
||||
"""
|
||||
if isinstance(queries, str):
|
||||
queries = [queries]
|
||||
actual_depth = depth if depth in ("shallow", "deep") else "shallow"
|
||||
|
||||
all_lines: list[str] = []
|
||||
seen_ids: set[str] = set()
|
||||
for query in queries:
|
||||
matches = self.memory.recall(query, scope=scope, limit=5, depth=actual_depth)
|
||||
matches = self.memory.recall(query)
|
||||
for m in matches:
|
||||
if m.record.id not in seen_ids:
|
||||
seen_ids.add(m.record.id)
|
||||
all_lines.append(f"- (score={m.score:.2f}) {m.record.content}")
|
||||
all_lines.append(m.format())
|
||||
|
||||
if not all_lines:
|
||||
return "No relevant memories found."
|
||||
@@ -117,20 +104,28 @@ class RememberTool(BaseTool):
|
||||
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 a RememberTool.
|
||||
List containing a RecallMemoryTool and, if not read-only, a RememberTool.
|
||||
"""
|
||||
i18n = get_i18n()
|
||||
return [
|
||||
tools: list[BaseTool] = [
|
||||
RecallMemoryTool(
|
||||
memory=memory,
|
||||
description=i18n.tools("recall_memory"),
|
||||
),
|
||||
RememberTool(
|
||||
memory=memory,
|
||||
description=i18n.tools("save_to_memory"),
|
||||
),
|
||||
]
|
||||
if not getattr(memory, "_read_only", False):
|
||||
tools.append(
|
||||
RememberTool(
|
||||
memory=memory,
|
||||
description=i18n.tools("save_to_memory"),
|
||||
)
|
||||
)
|
||||
return tools
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -139,7 +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
|
||||
@@ -152,23 +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)
|
||||
@@ -184,6 +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": {
|
||||
@@ -195,8 +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:
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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
|
||||
|
||||
|
||||
@@ -659,7 +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"""
|
||||
@@ -691,7 +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"])
|
||||
|
||||
|
||||
# ============================================================================
|
||||
@@ -1136,6 +1136,7 @@ def test_lite_agent_memory_instance_recall_and_save_called():
|
||||
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."]
|
||||
|
||||
|
||||
@@ -1184,7 +1184,7 @@ class TestNativeToolCallingJsonParseError:
|
||||
executor = self._make_executor([tool])
|
||||
|
||||
from crewai.utilities.agent_utils import convert_tools_to_openai_schema
|
||||
_, available_functions = convert_tools_to_openai_schema([tool])
|
||||
_, available_functions, _ = convert_tools_to_openai_schema([tool])
|
||||
|
||||
malformed_json = '{"code": "print("hello")"}'
|
||||
|
||||
@@ -1212,7 +1212,7 @@ class TestNativeToolCallingJsonParseError:
|
||||
executor = self._make_executor([tool])
|
||||
|
||||
from crewai.utilities.agent_utils import convert_tools_to_openai_schema
|
||||
_, available_functions = convert_tools_to_openai_schema([tool])
|
||||
_, available_functions, _ = convert_tools_to_openai_schema([tool])
|
||||
|
||||
valid_json = '{"code": "print(1)"}'
|
||||
|
||||
@@ -1239,7 +1239,7 @@ class TestNativeToolCallingJsonParseError:
|
||||
executor = self._make_executor([tool])
|
||||
|
||||
from crewai.utilities.agent_utils import convert_tools_to_openai_schema
|
||||
_, available_functions = convert_tools_to_openai_schema([tool])
|
||||
_, available_functions, _ = convert_tools_to_openai_schema([tool])
|
||||
|
||||
result = executor._execute_single_native_tool_call(
|
||||
call_id="call_789",
|
||||
@@ -1265,7 +1265,7 @@ class TestNativeToolCallingJsonParseError:
|
||||
executor = self._make_executor([tool])
|
||||
|
||||
from crewai.utilities.agent_utils import convert_tools_to_openai_schema
|
||||
_, available_functions = convert_tools_to_openai_schema([tool])
|
||||
_, available_functions, _ = convert_tools_to_openai_schema([tool])
|
||||
|
||||
result = executor._execute_single_native_tool_call(
|
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call_id="call_schema",
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