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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>
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100 lines
4.6 KiB
Plaintext
---
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title: Apify Actors
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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."
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# hack to use custom Apify icon
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icon: "); -webkit-mask-image: url('https://upload.wikimedia.org/wikipedia/commons/a/ae/Apify.svg');/*"
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mode: "wide"
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---
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# `ApifyActorsTool`
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Integre [Apify Actors](https://apify.com/actors) nos seus fluxos de trabalho CrewAI.
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## Descrição
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O `ApifyActorsTool` conecta [Apify Actors](https://apify.com/actors), programas em nuvem para raspagem e automação web, aos seus fluxos de trabalho CrewAI.
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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.
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Para mais detalhes, consulte a [integração Apify CrewAI](https://docs.apify.com/platform/integrations/crewai) na documentação do Apify.
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## Passos para começar
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<Steps>
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<Step title="Instale as dependências">
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Instale `crewai[tools]` e `langchain-apify` usando pip: `pip install 'crewai[tools]' langchain-apify`.
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</Step>
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<Step title="Obtenha um token de API do Apify">
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Cadastre-se no [Apify Console](https://console.apify.com/) e obtenha seu [token de API do Apify](https://console.apify.com/settings/integrations).
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</Step>
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<Step title="Configure o ambiente">
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Defina seu token de API do Apify na variável de ambiente `APIFY_API_TOKEN` para habilitar a funcionalidade da ferramenta.
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</Step>
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</Steps>
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## Exemplo de uso
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Use o `ApifyActorsTool` manualmente para executar o [RAG Web Browser Actor](https://apify.com/apify/rag-web-browser) e realizar uma busca na web:
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```python
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from crewai_tools import ApifyActorsTool
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# Inicialize a ferramenta com um Apify Actor
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tool = ApifyActorsTool(actor_name="apify/rag-web-browser")
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# Execute a ferramenta com parâmetros de entrada
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results = tool.run(run_input={"query": "What is CrewAI?", "maxResults": 5})
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# Processe os resultados
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for result in results:
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print(f"URL: {result['metadata']['url']}")
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print(f"Content: {result.get('markdown', 'N/A')[:100]}...")
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```
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### Saída esperada
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Veja abaixo a saída do código acima:
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```text
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URL: https://www.example.com/crewai-intro
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Content: CrewAI is a framework for building AI-powered workflows...
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URL: https://docs.crewai.com/
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Content: Official documentation for CrewAI...
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```
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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:
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```python
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from crewai import Agent
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from crewai_tools import ApifyActorsTool
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rag_browser = ApifyActorsTool(actor_name="apify/rag-web-browser")
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agent = Agent(
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role="Research Analyst",
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goal="Find and summarize information about specific topics",
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backstory="You are an experienced researcher with attention to detail",
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tools=[rag_browser],
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)
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```
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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.
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Para um exemplo de uso com agentes, consulte o [template CrewAI Actor](https://apify.com/templates/python-crewai).
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## Configuração
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O `ApifyActorsTool` exige os seguintes inputs para funcionar:
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- **`actor_name`**
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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).
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- **`run_input`**
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Um dicionário de parâmetros de entrada para o Actor ao executar a ferramenta manualmente.
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- Por exemplo, para o Actor `apify/rag-web-browser`: `{"query": "search term", "maxResults": 5}`
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- Veja o [schema de entrada do Actor](https://apify.com/apify/rag-web-browser/input-schema) para a lista de parâmetros de entrada.
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## Recursos
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- **[Apify](https://apify.com/)**: Explore a plataforma Apify.
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- **[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.
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- **[RAG Web Browser Actor](https://apify.com/apify/rag-web-browser)**: Um Actor popular para busca na web para LLMs.
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- **[Guia de Integração CrewAI](https://docs.apify.com/platform/integrations/crewai)**: Siga o guia oficial para integrar Apify e CrewAI. |