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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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---
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title: Apify 액터
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description: "`ApifyActorsTool`을(를) 사용하면 Apify 액터를 호출하여 CrewAI 워크플로우에 웹 스크래핑, 크롤링, 데이터 추출 및 웹 자동화 기능을 제공할 수 있습니다."
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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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[Apify Actors](https://apify.com/actors)를 CrewAI 워크플로우에 통합합니다.
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## 설명
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`ApifyActorsTool`은 [Apify Actors](https://apify.com/actors)와 CrewAI 워크플로우를 연결합니다. Apify Actors는 웹 스크래핑 및 자동화를 위한 클라우드 기반 프로그램입니다.
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[Apify Store](https://apify.com/store)에 있는 4,000개 이상의 Actor를 활용하여 소셜 미디어, 검색 엔진, 온라인 지도, 이커머스 사이트, 여행 포털 또는 일반 웹사이트에서 데이터를 추출하는 등 다양한 용도로 사용할 수 있습니다.
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자세한 내용은 Apify 문서의 [Apify CrewAI 통합](https://docs.apify.com/platform/integrations/crewai)을 참조하세요.
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## 시작 단계
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<Steps>
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<Step title="의존성 설치">
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`crewai[tools]`와 `langchain-apify`를 pip으로 설치하세요: `pip install 'crewai[tools]' langchain-apify`.
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</Step>
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<Step title="Apify API 토큰 받기">
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[Apify Console](https://console.apify.com/)에 회원가입하고 [Apify API 토큰](https://console.apify.com/settings/integrations)을 받아주세요.
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</Step>
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<Step title="환경 구성">
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Apify API 토큰을 `APIFY_API_TOKEN` 환경 변수로 설정해 도구의 기능을 활성화하세요.
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</Step>
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</Steps>
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## 사용 예시
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`ApifyActorsTool`을 수동으로 사용하여 [RAG Web Browser Actor](https://apify.com/apify/rag-web-browser)를 실행하고 웹 검색을 수행할 수 있습니다:
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```python
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from crewai_tools import ApifyActorsTool
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# Initialize the tool with an Apify Actor
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tool = ApifyActorsTool(actor_name="apify/rag-web-browser")
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# Run the tool with input parameters
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results = tool.run(run_input={"query": "What is CrewAI?", "maxResults": 5})
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# Process the results
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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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### 예상 출력
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위의 코드를 실행했을 때의 출력은 다음과 같습니다:
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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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`ApifyActorsTool`은 제공된 `actor_name`을 사용하여 Apify에서 Actor 정의와 입력 스키마를 자동으로 가져오고, 그 후 도구 설명과 인자 스키마를 생성합니다. 이는 유효한 `actor_name`만 지정하면 도구가 에이전트와 함께 사용할 때 나머지 과정을 처리하므로, 별도로 `run_input`을 지정할 필요가 없다는 의미입니다. 작동 방식은 다음과 같습니다:
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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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[Apify Store](https://apify.com/store)에 있는 다른 Actor도 `actor_name`만 변경하고, 수동으로 사용할 경우 Actor 입력 스키마에 따라 `run_input`을 조정하여 간단히 실행할 수 있습니다.
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에이전트와 함께 사용하는 예시는 [CrewAI Actor 템플릿](https://apify.com/templates/python-crewai)을 참고하세요.
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## 구성
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`ApifyActorsTool`을 사용하려면 다음 입력값이 필요합니다:
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- **`actor_name`**
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실행할 Apify Actor의 ID입니다. 예: `"apify/rag-web-browser"`. 모든 Actor는 [Apify Store](https://apify.com/store)에서 확인할 수 있습니다.
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- **`run_input`**
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도구를 수동으로 실행할 때 Actor에 전달할 입력 파라미터의 딕셔너리입니다.
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- 예를 들어, `apify/rag-web-browser` Actor의 경우: `{"query": "search term", "maxResults": 5}`
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- 입력 파라미터 목록은 Actor의 [input schema](https://apify.com/apify/rag-web-browser/input-schema)에서 확인할 수 있습니다.
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## 리소스
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- **[Apify](https://apify.com/)**: Apify 플랫폼을 살펴보세요.
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- **[Apify에서 AI 에이전트 구축하기](https://blog.apify.com/how-to-build-an-ai-agent/)** - Apify 플랫폼에서 AI 에이전트를 생성, 게시 및 수익화하는 단계별 완전 가이드입니다.
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- **[RAG Web Browser Actor](https://apify.com/apify/rag-web-browser)**: LLM을 위한 웹 검색에 많이 사용되는 Actor입니다.
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- **[CrewAI 통합 가이드](https://docs.apify.com/platform/integrations/crewai)**: Apify와 CrewAI를 통합하는 공식 가이드를 따라보세요. |