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* feat: adopt directory-based docs versioning with Edge channel Switch docs.crewai.com from navigation-only versioning (every version selector entry rendered the same docs/<lang>/* source files) to Mintlify's directory-based versioning so each version selector entry renders its own snapshot. Add an "Edge" channel under docs/edge/<lang>/* that always reflects main HEAD for unreleased work, eliminating pre-release leakage onto frozen release labels. External links to canonical /<lang>/* URLs are preserved via wildcard redirects that always land on the current default version. Layout: - docs/edge/<lang>/* rolling source (you edit here) - docs/edge/enterprise-api.*.yaml - docs/v<X.Y.Z>/<lang>/* frozen, immutable snapshots - docs/v<X.Y.Z>/enterprise-api.*.yaml - docs/images/ shared, append-only - docs/docs.json nav + redirects URLs follow the Mintlify-idiomatic shape: /edge/<lang>/<page> for Edge, /v<X.Y.Z>/<lang>/<page> for every frozen snapshot. The wildcard redirects /<lang>/:slug* -> /<default>/<lang>/:slug* keep stale links working, and every freeze rewrites them (plus all per-section/per-page redirects) so destinations always resolve to the current default without depending on a second redirect hop. Release flow integration (devtools release): - New module crewai_devtools.docs_versioning.freeze() materialises docs/v<X.Y.Z>/ from docs/edge/, rewrites openapi: refs inside the snapshot, inserts the version into every language block in docs.json, and refreshes all redirect destinations. - _update_docs_and_create_pr() in cli.py now calls that freeze during Phase 2 of devtools release. Edge changelogs are updated first (so the snapshot freeze picks them up), then the snapshot is staged alongside docs.json, branched as docs/freeze-v<X.Y.Z>, and the PR is titled [docs-freeze] docs: snapshot and changelog for v<X.Y.Z> — the title prefix the new CI guard reads. - The PR still gates tag, GitHub release, PyPI publish, and the enterprise release as before; no new PRs are added. - Pre-releases (1.X.YaN, 1.X.YbN, ...) skip the snapshot — they ride Edge — and the docs PR title omits the [docs-freeze] prefix. - docs_check (AI-generated docs scaffolding) writes to docs/edge/<lang>/* so newly-generated unreleased docs land in Edge and never accidentally touch a frozen snapshot. Migration scripts (one-shot): - scripts/docs/freeze_historical_versions.py reconstructs all 16 historical snapshots (v1.10.0 .. v1.14.7) from git tags via git archive | tar, rewriting openapi: MDX refs so each snapshot reads its own enterprise-api YAML rather than the live one. - scripts/docs/prefix_version_paths.py one-shot-migrates docs.json: rewrites every page path in 16 versioned blocks to point under docs/v<X.Y.Z>/, inserts a new Edge entry per language, tags v1.14.7 as Latest (default), prunes pages whose target file doesn't exist in the snapshot (e.g. docs/ar/ didn't exist before v1.12.0), and writes the wildcard + per-section redirects. - scripts/docs/freeze_current_edge.py is now a thin CLI wrapper around docs_versioning.freeze for manual one-off freezes (e.g. retroactively snapshotting a forgotten release). CI guards (.github/workflows/docs-snapshots.yml): - Frozen snapshots under docs/v[0-9]*/ are immutable; only PRs whose title contains [docs-freeze] (i.e. release-cut PRs generated by devtools release or the manual wrapper) may modify them. - Images under docs/images/ are append-only since snapshots share a single image directory. Deleting or renaming an image breaks every historical snapshot that still references it. Restored docs/images/crewai-otel-export.png from PR #3673; it was deleted in PR #4908 but v1.10.0 / v1.10.1 snapshots still reference it. Restoring instead of editing the snapshots preserves historical rendering fidelity and validates the new append-only rule retroactively. Tests: - lib/devtools/tests/test_docs_versioning.py covers the freeze: file copy, openapi rewrite, version insertion, default demotion, redirect upserts, per-section redirect rewriting, idempotency, and invalid inputs. Verified locally with mintlify broken-links: 0 broken links across the full site (Edge + 16 frozen versions, 4 locales). AGENTS.md (repo root) is the contributor guide for the new model; RELEASING.md is the release-cut runbook; README's Contribution section links to both. Co-authored-by: Cursor <cursoragent@cursor.com> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
73 lines
3.3 KiB
Plaintext
73 lines
3.3 KiB
Plaintext
---
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title: "Visão Geral"
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description: "Conecte agentes CrewAI com automações externas e serviços de IA gerenciados"
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icon: "plug"
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mode: "wide"
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---
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As ferramentas de integração permitem que seus agentes deleguem trabalho para outras plataformas de automação e serviços de IA gerenciados. Use-as quando o fluxo precisar invocar uma automação já publicada no CrewAI Platform ou quando for necessário direcionar tarefas para provedores especializados, como Amazon Bedrock.
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## **Ferramentas Disponíveis**
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<CardGroup cols={2}>
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<Card title="Ferramenta de Execução de Automação CrewAI" icon="robot" href="/pt-BR/tools/integration/crewaiautomationtool">
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Invoque automações ativas do CrewAI Platform, envie entradas personalizadas e acompanhe o resultado diretamente do seu agente.
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</Card>
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<Card title="Ferramenta Bedrock Invoke Agent" icon="aws" href="/pt-BR/tools/integration/bedrockinvokeagenttool">
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Acesse agentes do Amazon Bedrock a partir das suas crews, reutilize guardrails existentes da AWS e traga as respostas de volta ao fluxo atual.
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</Card>
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</CardGroup>
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## **Casos de Uso Comuns**
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- **Encadear automações**: Inicie uma automação do CrewAI a partir de outra crew ou flow
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- **Hand-off corporativo**: Direcione tarefas para agentes Bedrock que já incorporam políticas e guardrails internos
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- **Workflows híbridos**: Combine o raciocínio do CrewAI com sistemas externos que expõem APIs de agente
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- **Tarefas demoradas**: Faça polling de automações externas e mescle o resultado final na execução atual
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## **Exemplo de Início Rápido**
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```python
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from crewai import Agent, Task, Crew
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from crewai_tools import InvokeCrewAIAutomationTool
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from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
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# Automação externa
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analysis_automation = InvokeCrewAIAutomationTool(
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crew_api_url="https://analysis-crew.acme.crewai.com",
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crew_bearer_token="YOUR_BEARER_TOKEN",
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crew_name="Analysis Automation",
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crew_description="Executa o pipeline de análise em produção",
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)
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# Agente gerenciado no Bedrock
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knowledge_router = BedrockInvokeAgentTool(
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agent_id="bedrock-agent-id",
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agent_alias_id="prod",
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)
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automation_strategist = Agent(
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role="Estrategista de Automação",
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goal="Orquestrar automações externas e resumir os resultados",
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backstory="Você coordena fluxos corporativos e sabe quando delegar tarefas a serviços especializados.",
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tools=[analysis_automation, knowledge_router],
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verbose=True,
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)
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execute_playbook = Task(
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description="Execute a automação de análise e peça ao agente Bedrock pontos principais para a diretoria.",
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agent=automation_strategist,
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)
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Crew(agents=[automation_strategist], tasks=[execute_playbook]).kickoff()
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```
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## **Boas Práticas**
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- **Proteja credenciais**: Armazene tokens e chaves de API em variáveis de ambiente ou no gerenciador de segredos
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- **Planeje a latência**: Automações externas podem levar mais tempo — configure intervalos e timeouts adequados
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- **Reutilize sessões**: Agentes Bedrock aceitam IDs de sessão para manter contexto entre chamadas
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- **Valide respostas**: Normalize a saída remota (JSON, texto, códigos de status) antes de enviá-la a etapas seguintes
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- **Monitore o uso**: Acompanhe logs no CrewAI Platform ou no AWS CloudWatch para evitar estouros de quota e falhas
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