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Open Multi-Agent Canvas MCP Server

@CopilotKit

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

CopilotKit

Open Multi-Agent Canvas MCP Server について

The open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add MCP servers for deep research

Open Multi-Agent Canvas MCP Server の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

Open Multi-Agent Canvas MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Open Multi-Agent Canvas?

Open Multi-Agent Canvas is an open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation. Built with Next.js, LangGraph, and CopilotKit, it supports travel planning, research, and general-purpose tasks through MCP servers.

How to use Open Multi-Agent Canvas?

Clone the repository, rename example.env to .env in the frontend folder and set NEXT_PUBLIC_CPK_PUBLIC_API_KEY from Copilot Cloud, install dependencies with pnpm i, then build and start with pnpm run build && pnpm run start. Optionally run the MCP Agent backend using Poetry and LangGraph.

Key features of Open Multi-Agent Canvas

  • Manage multiple agents in one dynamic conversation
  • Built-in MCP Agent for general-purpose tasks
  • Connect to custom MCP servers via Stdio or SSE
  • Public MCP server integration (e.g., mcp.composio.dev)
  • Supports travel planning and AI research agents
  • Open-source under MIT License

Use cases of Open Multi-Agent Canvas

  • Run multiple AI agents simultaneously for travel planning
  • Conduct web research with specialized researcher agents
  • Integrate custom MCP tools for task automation
  • Deploy in collaborative environments for multi-agent workflows

よくある質問

1

What is Open Multi-Agent Canvas and how does it differ from single-agent chats?

Open Multi-Agent Canvas allows multiple agents to interact in one conversation, enabling complex multi-step workflows that a single agent cannot handle.

2

What are the runtime requirements?

You need Node.js, pnpm, and a Copilot Cloud API key. The optional MCP Agent backend requires Python, Poetry, and an OpenAI API key.

3

Where does my data live?

Data is processed through Copilot Cloud and the configured MCP servers; refer to CopilotKit and MCP documentation for data handling details.

4

What transport and authentication methods are supported?

MCP servers can be connected via Standard IO (local commands) or SSE (Server-Sent Events). Authentication uses API keys for Copilot Cloud, OpenAI, and LangSmith.

5

Can I use existing agents with Open Multi-Agent Canvas?

Yes, the project includes reference agents for travel and AI research, and you can add custom agents through the MCP Agent configuration.

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