MUXI MCP Server

MUXI is a next‑generation, open‑source framework that turns an AI assistant into a fully‑featured multi‑agent system. By exposing agents as Model Context Protocol (MCP) servers, it lets developers orchestrate dozens of specialized LLM‑powered bots that can talk to one another and to external services, all while preserving context across conversations. The result is a platform where complex workflows—such as data extraction, decision support, or customer‑facing chatbots—can be built from composable, independently deployable components.

At its core, MUXI solves the problem of scalable, context‑aware agent orchestration. Traditional single‑agent setups struggle with memory limits and lack a standardized way to share state or delegate tasks. MUXI introduces persistent, multi‑tier memory (FAISS for short‑term buffers, SQLite/PGvector for long‑term storage) and a declarative YAML/JSON configuration that lets you define agents, their capabilities, and how they should interact. Each agent can expose a set of tools—API calls, database queries, or custom logic—that are automatically discoverable by the MCP client. This makes it trivial to plug in new services without touching core code.

Key capabilities include:

• Agent‑to‑Agent (A2A) Communication: Structured message passing with capability registration, enabling dynamic task delegation and context sharing while maintaining isolation and authentication.
• Hybrid Transport: HTTP for configuration, SSE for token‑by‑token streaming, and WebSockets/WebRTC for bi‑directional multimodal exchanges.
• Intelligent Routing: The server can automatically forward user messages to the most suitable agent based on capability matching and context, reducing latency and improving accuracy.
• Extensible Memory & Knowledge: Built‑in support for short‑term buffers, long‑term vector stores, and lightweight Retrieval‑Augmented Generation (RAG) for domain knowledge.
• Multi‑User & Multi‑Tenant: Partitioned memory and authentication allow a single deployment to serve many users with isolated contexts.

Real‑world scenarios that benefit from MUXI include:

• Enterprise Knowledge Bases: A team of agents can ingest corporate documents, answer queries, and update knowledge graphs in real time.
• Customer Support Automation: Front‑end agents handle chat, while backend specialists fetch ticket data or trigger workflows through external APIs.
• Data‑Driven Decision Systems: Agents can pull from databases, run analytics, and present insights—all coordinated by a central MCP server.

Integrating MUXI into an AI workflow is straightforward: expose your agents as MCP servers, configure tool discovery, and let the client (e.g., Claude or any other MCP‑aware assistant) discover and invoke them. Because the framework supports both local executables and cloud deployments, developers can prototype locally with command‑line transports or scale to production behind HTTPS + SSE. MUXI’s modular architecture ensures you only ship the components you need, keeping resource usage lean while unlocking powerful orchestration features.