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.
@CopilotKit
Basic information
The open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add MCP servers for deep research
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
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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.
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.
Open Multi-Agent Canvas allows multiple agents to interact in one conversation, enabling complex multi-step workflows that a single agent cannot handle.
You need Node.js, pnpm, and a Copilot Cloud API key. The optional MCP Agent backend requires Python, Poetry, and an OpenAI API key.
Data is processed through Copilot Cloud and the configured MCP servers; refer to CopilotKit and MCP documentation for data handling details.
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.
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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