Open Multi Agent Canvas
The open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add MCP servers for deep research
What is Open Multi Agent Canvas?
Open Multi Agent Canvas is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add mcp servers for deep research
The open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add MCP servers for deep research
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- The open-source multi-agent chat interface that lets you man
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx open-multi-agent-canvasConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Open Multi Agent Canvas
Open Multi-Agent Canvas is an open-source, browser-based chat interface built with CopilotKit that lets users orchestrate multiple AI agents — including travel planners, research agents, and general-purpose assistants — within a single dynamic conversation. Users can extend the canvas by connecting any MCP server through the built-in configuration panel, enabling deep research and tool-augmented agent capabilities using Standard IO or SSE transport. Developers building multi-agent applications or researchers who want a flexible UI for coordinating specialized AI agents across complex workflows will find this canvas a strong foundation.
Prerequisites
- Node.js 18 or later and pnpm installed
- Python 3.10 or later with Poetry (for the optional agent backend)
- A CopilotKit Cloud account and public API key (NEXT_PUBLIC_CPK_PUBLIC_API_KEY)
- An OpenAI API key (OPENAI_API_KEY) for the LangGraph agent backend
- Optional: LangSmith API key (LANGSMITH_API_KEY) for agent tracing
Clone the repository
Clone the Open Multi-Agent Canvas repository to your local machine.
git clone https://github.com/CopilotKit/open-multi-agent-canvas
cd open-multi-agent-canvasConfigure frontend environment variables
Create a .env file in the frontend directory with your CopilotKit Cloud public API key.
cd frontend
cp .env.example .env
# Edit .env and set:
# NEXT_PUBLIC_CPK_PUBLIC_API_KEY=your_copilotkit_keyInstall frontend dependencies and start the app
Install pnpm dependencies and build and start the Next.js frontend.
pnpm i
pnpm run build && pnpm run startStart the optional MCP agent backend
For the MCP-enabled research agent, start the LangGraph backend in the agent directory with your OpenAI credentials.
cd ../agent
cp .env.example .env
# Set OPENAI_API_KEY and LANGSMITH_API_KEY in .env
poetry install
poetry run langgraph dev --host localhost --port 8123 --no-browserAdd MCP servers through the UI
In the running canvas app, click the 'MCP Servers' button in the top right. Add servers via Standard IO (local scripts) or SSE (remote MCP endpoints like mcp.composio.dev or mcp.run).
Open Multi Agent Canvas Examples
Client configuration
Frontend .env file configuration for Open Multi-Agent Canvas.
{
"mcpServers": {
"open-multi-agent-canvas": {
"command": "npx",
"args": ["open-multi-agent-canvas"],
"env": {
"NEXT_PUBLIC_CPK_PUBLIC_API_KEY": "your_copilotkit_key",
"OPENAI_API_KEY": "your_openai_key"
}
}
}
}Prompts to try
Example tasks to try in the multi-agent canvas once running.
- "Research the latest developments in MCP server tooling and summarize the key trends"
- "Plan a 5-day trip to Tokyo including flights, hotels, and daily itinerary"
- "Use the research agent to find three academic papers on multi-agent coordination"
- "Switch to the travel agent and find the cheapest flights from NYC to London next month"
- "Add the mcp.composio.dev GitHub server and then list my open pull requests"Troubleshooting Open Multi Agent Canvas
Frontend fails to start with 'NEXT_PUBLIC_CPK_PUBLIC_API_KEY is not defined'
Ensure you have created the .env file inside the frontend/ directory (not the repo root) and set NEXT_PUBLIC_CPK_PUBLIC_API_KEY to a valid key from your CopilotKit Cloud dashboard.
LangGraph agent backend fails to connect or agents do not respond
Verify the agent backend is running on port 8123 with 'poetry run langgraph dev --host localhost --port 8123 --no-browser'. Check that OPENAI_API_KEY is set correctly in the agent/.env file.
Adding an MCP server via SSE fails or shows connection error
Ensure the remote MCP server URL is publicly accessible and supports SSE transport. For local servers using Standard IO, verify the command and script path are correct and the script is executable.
Frequently Asked Questions about Open Multi Agent Canvas
What is Open Multi Agent Canvas?
Open Multi Agent Canvas is a Model Context Protocol (MCP) server that open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add mcp servers for deep research It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Open Multi Agent Canvas?
Follow the installation instructions on the Open Multi Agent Canvas GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Open Multi Agent Canvas?
Open Multi Agent Canvas works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Open Multi Agent Canvas free to use?
Yes, Open Multi Agent Canvas is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Browse More Coding Agents MCP Servers
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Set Up Open Multi Agent Canvas in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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