Files
crewAI/docs/edge/pt-BR/tools/automation/apifyactorstool.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

100 lines
4.6 KiB
Plaintext

---
title: Apify Actors
description: "`ApifyActorsTool` permite que você execute Apify Actors para adicionar recursos de raspagem de dados na web, coleta, extração de dados e automação web aos seus fluxos de trabalho CrewAI."
# hack to use custom Apify icon
icon: "); -webkit-mask-image: url('https://upload.wikimedia.org/wikipedia/commons/a/ae/Apify.svg');/*"
mode: "wide"
---
# `ApifyActorsTool`
Integre [Apify Actors](https://apify.com/actors) nos seus fluxos de trabalho CrewAI.
## Descrição
O `ApifyActorsTool` conecta [Apify Actors](https://apify.com/actors), programas em nuvem para raspagem e automação web, aos seus fluxos de trabalho CrewAI.
Utilize qualquer um dos mais de 4.000 Actors disponíveis na [Apify Store](https://apify.com/store) para casos de uso como extração de dados de redes sociais, motores de busca, mapas online, sites de e-commerce, portais de viagem ou sites em geral.
Para mais detalhes, consulte a [integração Apify CrewAI](https://docs.apify.com/platform/integrations/crewai) na documentação do Apify.
## Passos para começar
<Steps>
<Step title="Instale as dependências">
Instale `crewai[tools]` e `langchain-apify` usando pip: `pip install 'crewai[tools]' langchain-apify`.
</Step>
<Step title="Obtenha um token de API do Apify">
Cadastre-se no [Apify Console](https://console.apify.com/) e obtenha seu [token de API do Apify](https://console.apify.com/settings/integrations).
</Step>
<Step title="Configure o ambiente">
Defina seu token de API do Apify na variável de ambiente `APIFY_API_TOKEN` para habilitar a funcionalidade da ferramenta.
</Step>
</Steps>
## Exemplo de uso
Use o `ApifyActorsTool` manualmente para executar o [RAG Web Browser Actor](https://apify.com/apify/rag-web-browser) e realizar uma busca na web:
```python
from crewai_tools import ApifyActorsTool
# Inicialize a ferramenta com um Apify Actor
tool = ApifyActorsTool(actor_name="apify/rag-web-browser")
# Execute a ferramenta com parâmetros de entrada
results = tool.run(run_input={"query": "What is CrewAI?", "maxResults": 5})
# Processe os resultados
for result in results:
print(f"URL: {result['metadata']['url']}")
print(f"Content: {result.get('markdown', 'N/A')[:100]}...")
```
### Saída esperada
Veja abaixo a saída do código acima:
```text
URL: https://www.example.com/crewai-intro
Content: CrewAI is a framework for building AI-powered workflows...
URL: https://docs.crewai.com/
Content: Official documentation for CrewAI...
```
O `ApifyActorsTool` busca automaticamente a definição do Actor e o esquema de entrada no Apify utilizando o `actor_name` fornecido e então constrói a descrição da ferramenta e o esquema dos argumentos. Isso significa que você só precisa informar um `actor_name` válido, e a ferramenta faz o resto quando usada com agentes—não é necessário especificar o `run_input`. Veja como funciona:
```python
from crewai import Agent
from crewai_tools import ApifyActorsTool
rag_browser = ApifyActorsTool(actor_name="apify/rag-web-browser")
agent = Agent(
role="Research Analyst",
goal="Find and summarize information about specific topics",
backstory="You are an experienced researcher with attention to detail",
tools=[rag_browser],
)
```
Você pode executar outros Actors da [Apify Store](https://apify.com/store) apenas alterando o `actor_name` e, ao usar manualmente, ajustando o `run_input` de acordo com o esquema de entrada do Actor.
Para um exemplo de uso com agentes, consulte o [template CrewAI Actor](https://apify.com/templates/python-crewai).
## Configuração
O `ApifyActorsTool` exige os seguintes inputs para funcionar:
- **`actor_name`**
O ID do Apify Actor a ser executado, por exemplo, `"apify/rag-web-browser"`. Explore todos os Actors na [Apify Store](https://apify.com/store).
- **`run_input`**
Um dicionário de parâmetros de entrada para o Actor ao executar a ferramenta manualmente.
- Por exemplo, para o Actor `apify/rag-web-browser`: `{"query": "search term", "maxResults": 5}`
- Veja o [schema de entrada do Actor](https://apify.com/apify/rag-web-browser/input-schema) para a lista de parâmetros de entrada.
## Recursos
- **[Apify](https://apify.com/)**: Explore a plataforma Apify.
- **[Como criar um agente de IA no Apify](https://blog.apify.com/how-to-build-an-ai-agent/)** - Um guia completo, passo a passo, para criar, publicar e monetizar agentes de IA na plataforma Apify.
- **[RAG Web Browser Actor](https://apify.com/apify/rag-web-browser)**: Um Actor popular para busca na web para LLMs.
- **[Guia de Integração CrewAI](https://docs.apify.com/platform/integrations/crewai)**: Siga o guia oficial para integrar Apify e CrewAI.