MCP Mesh: A Distributed Runtime for AI Agents with Auto-Discovery

I've been building MCP Mesh for the past 5 months — a distributed-first runtime for AI agents built on MCP protocol.

The Problem

Most agent frameworks assume monolithic deployment and manual wiring. You define agents, manually connect them, and hope it scales.

MCP Mesh takes a different approach.

What Makes It Different

• Distributed from day one — Agents are microservices, not threads in a monolith
• Auto-discovery — Agents register with a mesh registry and find each other by capability tags
• Dependency injection — Declare what your agent needs, the mesh provides it at runtime
• Deterministic behavior — Despite being distributed, agent interactions are predictable
• LLM failover — Switch providers without code changes, just registry config
• Native MCP — Built on the protocol, not wrapped around it

Observability is built in (Grafana + Tempo), and it's Kubernetes-ready with Helm charts.

Links

• 📖 Docs: https://dhyansraj.github.io/mcp-mesh/
• 🎥 Video tutorials (34 min total): https://www.youtube.com/watch?v=GpCB5OARtfM
• 💻 GitHub: https://github.com/dhyansraj/mcp-mesh

Would love feedback from anyone building agent systems. What problems are you hitting that current frameworks aren't solving?