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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>
135 lines
5.3 KiB
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135 lines
5.3 KiB
Plaintext
---
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title: Stdio 전송
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description: Stdio(표준 입력/출력) 전송 메커니즘을 사용하여 CrewAI를 로컬 MCP 서버에 연결하는 방법을 알아보세요.
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icon: server
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mode: "wide"
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---
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## 개요
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Stdio(표준 입력/출력) 트랜스포트는 `MCPServerAdapter`를 로컬 MCP 서버에 연결하기 위해 설계되었습니다. 이 MCP 서버는 표준 입력 및 출력 스트림을 통해 통신합니다. 이는 일반적으로 MCP 서버가 CrewAI 애플리케이션과 동일한 머신에서 실행되는 스크립트나 실행 파일일 때 사용됩니다.
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## 주요 개념
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- **로컬 실행**: Stdio 전송은 MCP 서버를 위한 로컬에서 실행 중인 프로세스를 관리합니다.
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- **`StdioServerParameters`**: `mcp` 라이브러리의 이 클래스는 Stdio 서버를 실행하기 위한 명령어, 인수, 환경 변수를 구성하는 데 사용됩니다.
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## Stdio를 통한 연결
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연결 수명 주기를 관리하기 위한 두 가지 주요 접근 방식으로 Stdio 기반 MCP 서버에 연결할 수 있습니다.
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### 1. 완전 관리형 연결(권장)
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Python 컨텍스트 관리자(`with` 문)를 사용하는 것이 권장되는 방법입니다. 이 방식은 MCP 서버 프로세스의 시작과 컨텍스트 종료 시 자동 종료를 처리합니다.
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```python
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from crewai import Agent, Task, Crew, Process
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from crewai_tools import MCPServerAdapter
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from mcp import StdioServerParameters
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import os
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# Create a StdioServerParameters object
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server_params=StdioServerParameters(
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command="python3",
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args=["servers/your_stdio_server.py"],
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env={"UV_PYTHON": "3.12", **os.environ},
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)
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with MCPServerAdapter(server_params) as tools:
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print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}")
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# Example: Using the tools from the Stdio MCP server in a CrewAI Agent
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research_agent = Agent(
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role="Local Data Processor",
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goal="Process data using a local Stdio-based tool.",
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backstory="An AI that leverages local scripts via MCP for specialized tasks.",
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tools=tools,
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reasoning=True,
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verbose=True,
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)
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processing_task = Task(
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description="Process the input data file 'data.txt' and summarize its contents.",
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expected_output="A summary of the processed data.",
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agent=research_agent,
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markdown=True
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)
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data_crew = Crew(
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agents=[research_agent],
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tasks=[processing_task],
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verbose=True,
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process=Process.sequential
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)
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result = data_crew.kickoff()
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print("\nCrew Task Result (Stdio - Managed):\n", result)
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```
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### 2. 수동 연결 라이프사이클
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Stdio MCP 서버 프로세스가 시작되고 중지되는 시점을 더 세밀하게 제어해야 하는 경우, `MCPServerAdapter`의 라이프사이클을 수동으로 관리할 수 있습니다.
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<Info>
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서버 프로세스가 종료되고 자원이 해제되도록 **반드시** `mcp_server_adapter.stop()`을 호출해야 합니다. `try...finally` 블록을 사용하는 것을 강력히 추천합니다.
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</Info>
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```python
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from crewai import Agent, Task, Crew, Process
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from crewai_tools import MCPServerAdapter
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from mcp import StdioServerParameters
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import os
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# Create a StdioServerParameters object
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stdio_params=StdioServerParameters(
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command="python3",
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args=["servers/your_stdio_server.py"],
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env={"UV_PYTHON": "3.12", **os.environ},
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)
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mcp_server_adapter = MCPServerAdapter(server_params=stdio_params)
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try:
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mcp_server_adapter.start() # Manually start the connection and server process
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tools = mcp_server_adapter.tools
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print(f"Available tools (manual Stdio): {[tool.name for tool in tools]}")
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# Example: Using the tools with your Agent, Task, Crew setup
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manual_agent = Agent(
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role="Local Task Executor",
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goal="Execute a specific local task using a manually managed Stdio tool.",
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backstory="An AI proficient in controlling local processes via MCP.",
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tools=tools,
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verbose=True
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)
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manual_task = Task(
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description="Execute the 'perform_analysis' command via the Stdio tool.",
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expected_output="Results of the analysis.",
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agent=manual_agent
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)
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manual_crew = Crew(
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agents=[manual_agent],
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tasks=[manual_task],
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verbose=True,
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process=Process.sequential
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)
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result = manual_crew.kickoff() # Actual inputs depend on your tool
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print("\nCrew Task Result (Stdio - Manual):\n", result)
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except Exception as e:
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print(f"An error occurred during manual Stdio MCP integration: {e}")
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finally:
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if mcp_server_adapter and mcp_server_adapter.is_connected: # Check if connected before stopping
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print("Stopping Stdio MCP server connection (manual)...")
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mcp_server_adapter.stop() # **Crucial: Ensure stop is called**
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elif mcp_server_adapter: # If adapter exists but not connected (e.g. start failed)
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print("Stdio MCP server adapter was not connected. No stop needed or start failed.")
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```
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플레이스홀더 경로 및 명령어를 실제 Stdio 서버 정보로 교체해야 합니다. `StdioServerParameters`의 `env` 파라미터는
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서버 프로세스용 환경 변수를 설정할 때 사용할 수 있습니다. 이는 서버의 동작을 구성하거나 필요한 경로(예: `PYTHONPATH`)를 제공하는 데 유용할 수 있습니다. |