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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>
146 lines
4.7 KiB
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146 lines
4.7 KiB
Plaintext
---
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title: Integração com a TrueFoundry
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icon: chart-line
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mode: "wide"
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---
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A TrueFoundry fornece um [AI Gateway](https://www.truefoundry.com/ai-gateway) pronto para uso empresarial, que pode ser usado para governança e observabilidade em frameworks agentivos como o CrewAI. O AI Gateway da TrueFoundry funciona como uma interface unificada para acesso a LLMs, oferecendo:
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- **Acesso unificado à API**: Conecte-se a 250+ LLMs (OpenAI, Claude, Gemini, Groq, Mistral) por meio de uma única API
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- **Baixa latência**: Latência interna abaixo de 3 ms com roteamento inteligente e balanceamento de carga
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- **Segurança corporativa**: Conformidade com SOC 2, HIPAA e GDPR, com RBAC e auditoria de logs
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- **Gestão de cotas e custos**: Cotas baseadas em tokens, rate limiting e rastreamento abrangente de uso
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- **Observabilidade**: Registro completo de requisições/respostas, métricas e traces com retenção personalizável
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## Como a TrueFoundry se integra ao CrewAI
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### Instalação e configuração
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<Steps>
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<Step title="Instalar o CrewAI">
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```bash
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pip install crewai
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```
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</Step>
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<Step title="Obter o token de acesso da TrueFoundry">
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1. Crie uma conta na [TrueFoundry](https://www.truefoundry.com/register)
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2. Siga os passos do [Início rápido](https://docs.truefoundry.com/gateway/quick-start)
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</Step>
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<Step title="Configurar o CrewAI com a TrueFoundry">
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```python
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from crewai import LLM
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# Criar uma instância de LLM com o AI Gateway da TrueFoundry
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truefoundry_llm = LLM(
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model="openai-main/gpt-4o", # Da mesma forma, você pode chamar qualquer modelo de qualquer provedor
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base_url="your_truefoundry_gateway_base_url",
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api_key="your_truefoundry_api_key"
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)
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# Usar nos seus agentes do CrewAI
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from crewai import Agent
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config['researcher'],
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llm=truefoundry_llm,
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verbose=True
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)
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```
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</Step>
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</Steps>
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### Exemplo completo do CrewAI
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```python
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from crewai import Agent, Task, Crew, LLM
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# Configurar o LLM com a TrueFoundry
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llm = LLM(
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model="openai-main/gpt-4o",
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base_url="your_truefoundry_gateway_base_url",
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api_key="your_truefoundry_api_key"
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)
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# Criar agentes
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researcher = Agent(
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role='Analista de Pesquisa',
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goal='Conduzir pesquisa de mercado detalhada',
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backstory='Analista de mercado especialista com atenção aos detalhes',
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llm=llm,
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verbose=True
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)
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writer = Agent(
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role='Redator de Conteúdo',
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goal='Criar relatórios abrangentes',
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backstory='Redator técnico experiente',
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llm=llm,
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verbose=True
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)
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# Criar tarefas
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research_task = Task(
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description='Pesquisar tendências do mercado de IA para 2024',
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agent=researcher,
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expected_output='Resumo de pesquisa abrangente'
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)
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writing_task = Task(
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description='Criar um relatório de pesquisa de mercado',
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agent=writer,
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expected_output='Relatório bem estruturado com insights',
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context=[research_task]
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)
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# Criar e executar a crew
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crew = Crew(
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agents=[researcher, writer],
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tasks=[research_task, writing_task],
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verbose=True
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)
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result = crew.kickoff()
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```
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### Observabilidade e governança
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Monitore seus agentes do CrewAI pela aba de métricas da TrueFoundry:
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Com o AI Gateway da TrueFoundry, você pode monitorar e analisar:
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- **Métricas de desempenho**: Acompanhe métricas-chave de latência como Latência da Requisição, Tempo até o Primeiro Token (TTFS) e Latência entre Tokens (ITL), com percentis P99, P90 e P50
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- **Custos e uso de tokens**: Tenha visibilidade dos custos da sua aplicação com detalhamento de tokens de entrada/saída e das despesas associadas a cada modelo
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- **Padrões de uso**: Entenda como sua aplicação está sendo utilizada com análises detalhadas sobre atividade de usuários, distribuição de modelos e uso por equipe
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- **Limite de taxa e balanceamento de carga**: Você pode configurar rate limiting, balanceamento de carga e fallback para seus modelos
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## Rastreamento
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Para uma compreensão mais detalhada sobre rastreamento, consulte [getting-started-tracing](https://docs.truefoundry.com/docs/tracing/tracing-getting-started). Para rastreamento, você pode adicionar o SDK do Traceloop:
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```bash
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pip install traceloop-sdk
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```
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```python
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from traceloop.sdk import Traceloop
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# Inicializar rastreamento avançado
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Traceloop.init(
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api_endpoint="https://your-truefoundry-endpoint/api/tracing",
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headers={
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"Authorization": f"Bearer {your_truefoundry_pat_token}",
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"TFY-Tracing-Project": "your_project_name",
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},
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)
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```
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Isso oferece correlação adicional de rastreamentos em todo o seu fluxo de trabalho com o CrewAI.
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