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MCP Mesh - Distributed Service Mesh for AI Agents

MCP Mesh

Enterprise-Grade Distributed Service Mesh for AI Agents

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Enterprise-Grade Distributed Service Mesh for AI Agents

MCP Mesh transforms the Model Context Protocol (MCP) from a development protocol into an enterprise-grade distributed system. Build production-ready AI agent networks with zero boilerplate.

Complete Platform for AI Agents

MCP Mesh is a complete platform for building and deploying AI agents to production scale. See how MCP Mesh compares →

🚀 Quick Start

# Install MCP Mesh
pip install "mcp-mesh>=0.7,<0.8"

# Create your first agent
from fastmcp import FastMCP
import mesh

app = FastMCP("My Service")

@app.tool()
@mesh.tool(capability="greeting", dependencies=["date_service"])
async def greet(date_service: mesh.McpMeshAgent = None):
    return f"Hello! {await date_service()}"

@mesh.agent(name="my-service", auto_run=True)
class MyAgent:
    pass

That's it! No manual server setup, no connection management, no networking code.

✨ Key Features

🔌 Zero Boilerplate

Two decorators replace hundreds of lines of networking code. Just write business logic.

🎯 Smart Discovery

Tag-based service resolution with version constraints. Agents automatically find dependencies.

Kubernetes Native

Production-ready Helm charts with horizontal scaling, health checks, and observability.

🔄 Dynamic Updates

Hot dependency injection without restarts. Add, remove, or upgrade services seamlessly.

📊 Built-in Observability

Grafana dashboards, distributed tracing with Tempo, and Redis-backed session management.

🛡 Enterprise Ready

Graceful failure handling, auto-reconnection, RBAC support, and real-time monitoring.

🔥 Why MCP Mesh?

Stop fighting infrastructure. Start building intelligence.

  • Zero boilerplate networking code
  • Pure Python simplicity with FastMCP integration
  • End-to-end FastAPI integration with @mesh.route()
  • Same code runs locally, in Docker, and Kubernetes

Design intelligent systems, not complex integrations.

  • Agent-centric architecture with clear capabilities
  • Dynamic intelligence - agents get smarter automatically
  • Domain-driven design with focused, composable agents
  • Mix and match agents to create new capabilities

Production-ready AI infrastructure out of the box.

  • Kubernetes-native with battle-tested Helm charts
  • Enterprise observability with Grafana, Tempo, and Redis
  • Zero-touch operations with auto-discovery
  • Scale from 2 agents to 200+ with same complexity

Complete visibility and zero-downtime operations.

  • Real-Time Network Monitoring: See every agent, dependency, and health status in live dashboards
  • Intelligent Scaling: Agents scale independently based on demand - no cascading performance issues
  • Graceful Failure Handling: Agents degrade gracefully when dependencies are unavailable, automatically reconnect when services return
  • One-Click Diagnostics: meshctl status provides instant network health assessment with actionable insights

Transform AI experiments into production revenue.

  • Accelerated Time-to-Market: Move from PoC to production deployment in weeks, not months
  • Cross-Team Collaboration: Enable different departments to build agents that automatically enhance each other's capabilities
  • Risk Mitigation: Battle-tested enterprise patterns ensure reliable AI deployments that scale with your business
  • Future-Proof Architecture: Add new AI capabilities without disrupting existing systems

Turn your AI strategy from "promising experiments" to "competitive advantage in production."

📈 MCP vs MCP Mesh

Challenge Traditional MCP MCP Mesh
Connect 5 servers 200+ lines of networking code 2 decorators
Handle failures Manual error handling everywhere Automatic graceful degradation
Scale to production Custom Kubernetes setup helm install mcp-mesh
Monitor system Build custom dashboards Built-in observability stack
Add new capabilities Restart and reconfigure clients Auto-discovery, zero downtime

🆚 MCP Mesh vs Other Frameworks

Framework K8s Native Independent Scaling Service Discovery Best For
MCP Mesh ✅ Helm ✅ Per-agent pods ✅ Built-in Production deployment
LangGraph ❌ Manual ❌ Same process ❌ DIY Complex workflows
CrewAI ❌ Manual ❌ Limited ❌ None Rapid prototyping
AutoGen ❌ Manual ❌ Manual ❌ DIY Enterprise/Azure
OpenAI Agents ❌ Manual ❌ Manual ❌ None OpenAI-centric

Full comparison with code examples

📦 Installation

npm install -g @mcpmesh/cli

Command-line tool for managing agents, registry, and mesh operations.

npm install -g @mcpmesh/cli

Service discovery and coordination server. Included with the npm package above.

pip install "mcp-mesh>=0.7,<0.8"

Runtime for building agents with @mesh.agent and @mesh.tool decorators.

docker pull mcpmesh/registry:0.7
docker pull mcpmesh/python-runtime:0.7

Official container images for production deployments.

helm install mcp-mesh oci://ghcr.io/dhyansraj/mcp-mesh/charts/mcp-mesh

Kubernetes deployment with the umbrella chart. See Helm Deployment Guide.

🤝 Community & Support

⭐ Project Status

  • Latest Release: v0.7.21 (January 2026)
  • License: MIT
  • Language: Python 3.11+ (runtime), Go 1.23+ (registry)
  • Status: Production-ready, actively developed

🙏 Acknowledgments

  • Anthropic for creating the MCP protocol
  • FastMCP for excellent MCP server foundations
  • Kubernetes community for the infrastructure platform
  • All contributors who help make MCP Mesh better

Ready to get started?

Quick Tutorial View on GitHub

Star the repo if MCP Mesh helps you build better AI systems! ⭐