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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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136 lines
5.9 KiB
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---
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title: 스트리머블 HTTP 전송
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description: 유연한 스트리머블 HTTP 전송을 사용하여 CrewAI를 원격 MCP 서버에 연결하는 방법을 알아보세요.
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icon: globe
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mode: "wide"
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---
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## 개요
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Streamable HTTP 전송은 원격 MCP 서버에 연결할 수 있는 유연한 방법을 제공합니다. 이는 종종 HTTP를 기반으로 구축되며, 요청-응답 및 스트리밍을 포함한 다양한 통신 패턴을 지원할 수 있습니다. 때때로 더 넓은 HTTP 상호작용 내에서 서버-클라이언트 스트림을 위해 Server-Sent Events(SSE)를 활용하기도 합니다.
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## 주요 개념
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- **원격 서버**: 원격에 호스팅된 MCP 서버용으로 설계되었습니다.
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- **유연성**: 단순 SSE보다 더 복잡한 상호작용 패턴을 지원할 수 있으며, 서버가 구현한 경우 양방향 통신도 가능할 수 있습니다.
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- **`MCPServerAdapter` 구성**: MCP 통신을 위한 서버의 기본 URL을 제공하고, 전송 유형으로 `"streamable-http"`를 지정해야 합니다.
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## 스트리머블 HTTP를 통한 연결
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Streamable HTTP MCP 서버와의 연결 라이프사이클을 관리하는 주요 방법에는 두 가지가 있습니다:
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### 1. 완전히 관리되는 연결(추천)
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추천되는 방법은 Python 컨텍스트 매니저(`with` 문)을 사용하는 것으로, 연결의 설정과 해제를 자동으로 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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server_params = {
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"url": "http://localhost:8001/mcp", # 실제 Streamable HTTP 서버 URL로 교체하세요
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"transport": "streamable-http"
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}
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try:
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with MCPServerAdapter(server_params) as tools:
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print(f"Available tools from Streamable HTTP MCP server: {[tool.name for tool in tools]}")
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http_agent = Agent(
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role="HTTP Service Integrator",
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goal="Utilize tools from a remote MCP server via Streamable HTTP.",
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backstory="An AI agent adept at interacting with complex web services.",
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tools=tools,
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verbose=True,
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)
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http_task = Task(
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description="Perform a complex data query using a tool from the Streamable HTTP server.",
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expected_output="The result of the complex data query.",
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agent=http_agent,
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)
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http_crew = Crew(
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agents=[http_agent],
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tasks=[http_task],
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verbose=True,
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process=Process.sequential
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)
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result = http_crew.kickoff()
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print("\nCrew Task Result (Streamable HTTP - Managed):\n", result)
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except Exception as e:
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print(f"Error connecting to or using Streamable HTTP MCP server (Managed): {e}")
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print("Ensure the Streamable HTTP MCP server is running and accessible at the specified URL.")
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```
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**참고:** `"http://localhost:8001/mcp"`은 실제 사용 중인 Streamable HTTP MCP 서버의 URL로 교체해야 합니다.
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### 2. 수동 연결 라이프사이클
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보다 명시적인 제어가 필요한 시나리오에서는 `MCPServerAdapter` 연결을 직접 관리할 수 있습니다.
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<Info>
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연결을 종료하고 리소스를 해제하려면 작업이 끝난 후 반드시 `mcp_server_adapter.stop()`을 호출하는 것이 **매우 중요**합니다. 이를 보장하는 가장 안전한 방법은 `try...finally` 블록을 사용하는 것입니다.
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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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server_params = {
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"url": "http://localhost:8001/mcp", # Replace with your actual Streamable HTTP server URL
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"transport": "streamable-http"
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}
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mcp_server_adapter = None
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try:
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mcp_server_adapter = MCPServerAdapter(server_params)
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mcp_server_adapter.start()
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tools = mcp_server_adapter.tools
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print(f"Available tools (manual Streamable HTTP): {[tool.name for tool in tools]}")
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manual_http_agent = Agent(
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role="Advanced Web Service User",
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goal="Interact with an MCP server using manually managed Streamable HTTP connections.",
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backstory="An AI specialist in fine-tuning HTTP-based service integrations.",
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tools=tools,
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verbose=True
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)
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data_processing_task = Task(
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description="Submit data for processing and retrieve results via Streamable HTTP.",
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expected_output="Processed data or confirmation.",
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agent=manual_http_agent
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)
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data_crew = Crew(
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agents=[manual_http_agent],
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tasks=[data_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 (Streamable HTTP - Manual):\n", result)
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except Exception as e:
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print(f"An error occurred during manual Streamable HTTP MCP integration: {e}")
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print("Ensure the Streamable HTTP MCP server is running and accessible.")
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finally:
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if mcp_server_adapter and mcp_server_adapter.is_connected:
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print("Stopping Streamable HTTP 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:
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print("Streamable HTTP MCP server adapter was not connected. No stop needed or start failed.")
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```
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## 보안 고려사항
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Streamable HTTP 전송을 사용할 때는 일반적인 웹 보안 모범 사례가 매우 중요합니다:
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- **HTTPS 사용**: 데이터 전송을 암호화하기 위해 항상 MCP 서버 URL에 HTTPS(HTTP Secure)를 사용하는 것이 좋습니다.
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- **인증**: MCP 서버가 민감한 도구나 데이터를 노출하는 경우 강력한 인증 메커니즘을 구현하세요.
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- **입력 검증**: MCP 서버가 모든 수신 요청과 매개변수를 반드시 검증하도록 하십시오.
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MCP 통합 보안에 대한 종합적인 안내는 [보안 고려사항](./security.mdx) 페이지와 공식 [MCP 전송 보안 문서](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)를 참고하시기 바랍니다. |