Files
crewAI/docs/edge/pt-BR/observability/truefoundry.mdx
Lucas Gomide a237ebabba feat: adopt directory-based docs versioning with Edge channel (#6202)
* 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>
2026-06-17 11:56:59 -04:00

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