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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>
73 lines
3.4 KiB
Plaintext
73 lines
3.4 KiB
Plaintext
---
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title: "개요"
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description: "CrewAI 에이전트를 외부 자동화 및 관리형 AI 서비스와 연결"
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icon: "plug"
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mode: "wide"
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---
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통합 도구를 사용하면 에이전트가 다른 자동화 플랫폼이나 관리형 AI 서비스에 작업을 위임할 수 있습니다. 이미 운영 중인 CrewAI Platform 자동화를 호출하거나 Amazon Bedrock과 같은 전문 제공업체에 태스크를 넘겨야 할 때 활용하세요.
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## **사용 가능한 도구**
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<CardGroup cols={2}>
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<Card title="CrewAI 자동화 실행 도구" icon="robot" href="/ko/tools/integration/crewaiautomationtool">
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실행 중인 CrewAI Platform 자동화를 호출하고 사용자 입력을 전달하며, 결과를 에이전트로 다시 수집합니다.
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</Card>
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<Card title="Bedrock Invoke Agent 도구" icon="aws" href="/ko/tools/integration/bedrockinvokeagenttool">
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크루에서 Amazon Bedrock Agent를 호출하고 기존 AWS 가드레일을 재사용하며 응답을 현재 워크플로우로 되돌립니다.
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</Card>
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</CardGroup>
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## **주요 사용 사례**
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- **자동화 연결**: 한 크루 또는 플로우에서 다른 CrewAI 자동화를 연속 실행
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- **엔터프라이즈 핸드오프**: 사내 정책과 가드레일을 담고 있는 Bedrock Agent에 태스크 위임
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- **하이브리드 워크플로우**: CrewAI의 추론 능력과 외부의 에이전트 API를 결합
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- **장기 실행 작업**: 외부 자동화를 폴링하고 최종 결과를 현재 실행에 병합
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## **빠른 시작 예시**
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```python
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from crewai import Agent, Task, Crew
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from crewai_tools import InvokeCrewAIAutomationTool
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from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
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# 외부 자동화
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analysis_automation = InvokeCrewAIAutomationTool(
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crew_api_url="https://analysis-crew.acme.crewai.com",
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crew_bearer_token="YOUR_BEARER_TOKEN",
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crew_name="Analysis Automation",
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crew_description="프로덕션 분석 파이프라인을 실행",
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)
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# Bedrock 관리형 에이전트
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knowledge_router = BedrockInvokeAgentTool(
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agent_id="bedrock-agent-id",
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agent_alias_id="prod",
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)
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automation_strategist = Agent(
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role="자동화 전략가",
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goal="외부 자동화를 조율하고 결과를 요약",
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backstory="엔터프라이즈 워크플로우를 조정하고 전문 서비스에 태스크를 위임할 시점을 알고 있습니다.",
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tools=[analysis_automation, knowledge_router],
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verbose=True,
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)
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execute_playbook = Task(
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description="분석 자동화를 실행하고 Bedrock 에이전트에게 경영진 브리핑용 핵심 포인트를 요청하세요.",
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agent=automation_strategist,
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)
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Crew(agents=[automation_strategist], tasks=[execute_playbook]).kickoff()
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```
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## **모범 사례**
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- **자격 증명 보호**: API 키와 토큰은 환경 변수 또는 비밀 관리 솔루션에 저장하세요
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- **지연 시간 고려**: 외부 자동화는 시간이 더 걸릴 수 있으므로 폴링 주기와 타임아웃을 적절히 설정하세요
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- **세션 재사용**: Bedrock Agent는 세션 ID를 지원하므로 여러 호출 간에 컨텍스트를 유지할 수 있습니다
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- **응답 검증**: 후속 단계로 전달하기 전에 외부 출력(JSON, 텍스트, 상태 코드 등)을 정규화하세요
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- **사용량 모니터링**: CrewAI Platform 로그나 AWS CloudWatch를 통해 할당량 초과와 실패를 조기에 감지하세요
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