Open Multi-Agent Canvas MCP Server

Open Multi-Agent Canvas is an open‑source, web‑based interface that unifies multiple conversational agents into a single, dynamic chat experience. By leveraging the Model Context Protocol (MCP), the canvas allows developers to plug in any MCP‑compatible server—whether it’s a local tool, a cloud service, or a third‑party API—and have the agents interact seamlessly. The core problem it solves is the fragmentation of agent interactions: traditionally, each assistant or tool requires its own UI and integration logic. With the canvas, a single conversation thread can spawn, coordinate, and consume outputs from many agents, dramatically simplifying the development of complex workflows.

At its heart, the server exposes a rich set of MCP capabilities: resources for data exchange, tools that agents can invoke (e.g., running Python scripts or making HTTP calls), prompts for context sharing, and sampling strategies to control response generation. The interface includes built‑in agents such as the CoAgents Travel Agent and AI Researcher, but also offers a generic MCP Agent that can be configured on the fly. Users can add custom servers through a graphical panel—choosing between standard I/O for local execution or Server‑Sent Events (SSE) for remote services—and connect to public MCP endpoints like or . This plug‑and‑play model means developers can experiment with new tools without rewriting the front end or re‑architecting their agent logic.

Key features include:

• Unified multi‑agent orchestration: A single chat window that manages concurrent conversations, task delegation, and result aggregation.
• Dynamic server configuration: Add or remove MCP servers on demand via the UI, with support for both local and remote endpoints.
• Built‑in agent templates: Ready‑to‑use agents for travel planning, research, and general tasks that can be deployed locally or on LangSmith.
• Extensible MCP integration: Any tool that implements the MCP spec can be connected, enabling developers to incorporate new APIs or custom logic without changing the canvas code.
• Developer-friendly tooling: Built with Next.js, LangGraph, and CopilotKit, the project offers a modern stack that is easy to extend and deploy.

Real‑world use cases span from travel itinerary generation—where one agent gathers flight data while another suggests activities—to research pipelines, where a researcher agent queries academic databases and a summarization agent compiles findings. In enterprise settings, teams can chain agents that pull data from internal databases, process it with ML models, and surface actionable insights—all within a single conversational flow. The canvas’ ability to expose MCP servers as first‑class citizens makes it an ideal platform for rapid prototyping, collaborative development, and scalable deployment of multi‑agent systems.