AutogenをChatGPT、Claude、Geminiに接続

• 01 / 02コードレビューを自動化する方法
• 02 / 02会話のデバッグ方法

最速日

626ms

最遅日

934ms

14日間の傾向

安定+1%

Microsoft AutoGen Connector

準備完了です。MCPクライアントを選択し、セットアップ手順に従ってください。

コネクタリンクhttps://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcpコピー

Claude Desktop

以下の手順で数秒で接続できます。

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — Microsoft AutoGen capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "autogen-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

各クライアントの手順ガイドはこちらです。 接続方法

Claude向けのガイド付きセットアップ？ Microsoft AutoGenをClaudeや他のAIに設定する

このConnectorが対応するすべてのAIクライアントを見る ↑

• 01

  AIエンジニア

  実行中のエージェントトポロジーを監査したり、深い実行ログを追跡したり、コンテキストを切り替えることなくPythonスキルを改善させたい方に最適です。

• 02

  プロダクトマネージャー

  自動化されたバックエンドの群れ（スウォーム）の動作の健全性や実行ステップを検証する必要がある方に役立ちます。

• 03

  研究者

  実験的な境界を越えて、LLMからLLMへの会話履歴を容易に抽出・評価したい研究活動に貢献します。

• 01

  What is the difference between a 'session' and a 'workflow'?

  A session is the isolated memory space where the conversation happens. A workflow is the predefined topology or graph that dictates how the agents interact within that session. You create a session first, then run a workflow inside it.

• 02

  Can I debug the agents' conversations?

  Yes. You can use the message history capabilities to retrieve deep agent-to-agent conversation traces. This lets you see exactly which agent said what and why, which is critical for debugging.

• 03

  Do I need to write code to use this MCP?

  No. You interact with this MCP using natural conversation prompts through your AI client. The underlying complexity, like defining agents or listing skills, is handled by the capabilities you invoke.

• 04

  What if I want to change an agent's role?

  You can redefine agents using the create_agent capability. This lets you change their parameters, giving them new roles or specialized instructions mid-project.

• 05

  Does this MCP support multiple types of agents?

  Yes. You can define and manage multiple, specialized agents, such as a User Proxy, a Coder, and a Critic, allowing them to collaborate on a single task.

• 06

  Can my AI agent debug a looping multi-agent conversation?

  Yes. You can instruct your primary agent to retrieve the message traces for a specific AutoGen session ID. It will instantly unpack the internal LLM-to-LLM conversation, highlighting exactly which secondary agent is looping, throwing errors, or deviating from the constraints without manual log parsing.

• 07

  How do I add a new Python capability or skill dynamicly?

  Your agent can list currently mapped Python skills bound to the studio runtime. If you need a new capability, your primary AI can iterate on the script directly on your CLI/editor and once deployed in your studio, you can map it natively to customized agents via the creation parameters.

• 08

  Can it trigger a Workflow to start executing a new complex task?

  Absolutely. Ask your agent to create a fresh, blank, and completely isolated session, then dispatch a newly constructed 'human message' targeting an existing Multi-Agent workflow topology. It initiates the whole automated logic sequence securely and remotely.

• 

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• 

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  List, run, and manage workflow automations via Relay API.

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