MCP Mesh

The future of AI is not one large model, but many specialized agents working together.

📚 Documentation · 🚀 Quick Start · 🎬 YouTube · 💬 Discord

# Install the CLI
npm install -g @mcpmesh/cli

# Explore commands
meshctl --help

# Built-in documentation
meshctl man

Python Quick Start → | Java Quick Start → | TypeScript Quick Start →

You write the agent logic. The mesh discovers, connects, heals, and traces — across languages, machines, and clouds.

Stop fighting infrastructure. Start building intelligence.

• Zero Boilerplate: Simple decorators/functions replace hundreds of lines of networking code
• Python, Java & TypeScript: Write MCP servers as simple functions in your preferred language - no manual client/server setup
• Web Framework Integration: Inject MCP agents directly into FastAPI (Python), Spring Boot (Java), or Express (TypeScript) APIs seamlessly
• LLM as Dependencies: Inject LLMs just like MCP agents - dynamic prompts with Jinja2 (Python), FreeMarker (Java), or Handlebars (TypeScript)
• Seamless Development Flow: Code locally, test with Docker Compose, deploy to Kubernetes - same code, zero changes
• kubectl-like Management: meshctl - a familiar command-line tool to run, monitor, and manage your entire agent network

from fastmcp import FastMCP
import mesh

app = FastMCP("TripPlanner")

@app.tool()
@mesh.tool(
    capability="plan_trip",
    dependencies=[
        {"capability": "weather", "tags": ["+claude"]},
        {"capability": "hotels",  "tags": ["+gpt"]},
        {"capability": "flights"},
        {"capability": "budget",  "tags": ["+claude"]},
    ],
)
async def plan_trip(
    destination: str,
    dates: str,
    weather: mesh.McpMeshTool = None,
    hotels:  mesh.McpMeshTool = None,
    flights: mesh.McpMeshTool = None,
    budget:  mesh.McpMeshTool = None,
) -> TripPlan:
    forecast = await weather(destination=destination, dates=dates)
    options  = await hotels(destination=destination, dates=dates)
    routes   = await flights(destination=destination, dates=dates)
    cost     = await budget(routes=routes, options=options)
    return TripPlan(forecast, options, routes, cost)

@mesh.agent(name="trip-planner", auto_run=True)
class TripAgent: pass

Four distributed calls, composed like a local function. Each dependency could live in this process, another machine, another language. Mesh handles discovery, transport, retry, and failover — your function stays a function. Each dep is just another @mesh.tool, defined the same way — in this agent or another.

Any dependency can be a plain tool or an LLM agent — your code can't tell the difference. weather could be a REST API or a Claude-powered reasoning agent returning a typed pydantic forecast. +claude means prefer the reasoning agent; if it dies, mesh auto-rewires to the API. When Claude recovers, mesh rewires back. No deploy, no config, no code change.

Routing stays in Python, not YAML. See how below.

from fastmcp import FastMCP
import mesh

app = FastMCP("ClaudeWeather")

@app.tool()
@mesh.llm(
    system_prompt="file://prompts/weather.j2",
    provider={"capability": "llm", "tags": ["+claude"]},
)
@mesh.tool(capability="weather", tags=["+claude"])
def weather(destination: str, dates: str,
            llm: mesh.MeshLlmAgent = None) -> Forecast:
    return llm(f"Forecast for {destination} on {dates}")

@mesh.agent(name="claude-weather", auto_run=True)
class Agent: pass

# Two providers of the same capability, wired at runtime
weather = reasoning_weather if user.wants_explanation else api_weather
forecast = await weather(destination, dates)

See the full TripPlanner tutorial →

Design intelligent systems, not complex integrations.

• Agent-Centric Architecture: Design specialized agents with clear capabilities and dependencies, not monolithic systems
• Dynamic Intelligence: Agents get smarter automatically when new capabilities come online - no reconfiguration needed
• Domain-Driven Design: Solve business problems with ecosystems of focused agents that can be designed and developed independently
• Composable Solutions: Mix and match agents to create new business capabilities without custom integration code

Example: Deploy a financial analysis agent that automatically discovers and uses risk assessment, market data, and compliance agents as they become available.

AI infrastructure out of the box.

• Kubernetes-Native: Deploy with Helm charts - horizontal scaling, health checks, and service discovery included
• Enterprise Observability: Built-in Grafana dashboards, distributed tracing, and centralized logging for complete system visibility
• Zero-Touch Operations: Agents self-register, auto-discover dependencies, and gracefully handle failures without network restarts
• Standards-Based: Leverage existing Kubernetes patterns - RBAC, network policies, service mesh integration, and security policies

Scale from 2 agents to 200+ with the same operational 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: Proven patterns help 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 Mesh handles the complexity so you don't have to:

• Zero Boilerplate: Just add @mesh.tool() - networking handled automatically
• Dynamic Everything: Add/remove/upgrade services without touching other code
• Complex Apps Made Simple: Financial services example shows 6+ interconnected agents
• Production Ready: Built-in resilience, distributed observability, and scaling

The Magic: Write simple functions in Python, Java, or TypeScript, get distributed systems.

• Distributed — dependencies span machines, clouds, and runtimes (Python/TypeScript/Java)
• Dynamic — services discovered and injected at runtime, not compile time
• Hot-swappable — dependencies update without restarts via heartbeat-driven re-resolution
• Pull-based discovery with runtime function injection — no networking code required
• Smart resolution with version constraints, capability matching, and tag scoring
• LLM as a dependency — treat LLMs as first-class injectable services with automatic tool discovery

• Registry as facilitator - agents communicate directly with fault tolerance
• Self-healing architecture - automatic reconnection when services return
• Graceful degradation - agents work standalone when dependencies unavailable
• Background orchestration - mesh coordinates without blocking business logic

• Complete observability stack - Grafana dashboards, Tempo tracing, Redis session management
• Distributed tracing with OTLP export and cross-agent context propagation
• Real-time trace streaming for multi-agent workflow monitoring
• Advanced session management with Redis-backed stickiness across pod replicas

• Near-complete MCP protocol support for distributed networks
• Enhanced proxy system with kwargs-driven auto-configuration for timeouts, retries, streaming
• meshctl CLI for lifecycle management and network insights
• Kubernetes native with scaling, health checks, and comprehensive observability

See full comparison →

We welcome contributions from the community! MCP Mesh is designed to be a collaborative effort to advance the state of distributed MCP applications.

1. Check the Issues - Find good first issues or suggest new features
2. Join Discussions - Share ideas and get help from the community
3. Submit Pull Requests - Contribute code, documentation, or examples
4. Follow our development guidelines - See project structure and coding standards below

• Discord - Real-time help and discussions
• GitHub Discussions - Share ideas and ask questions
• Issues - Report bugs or request features
• Examples - Working code examples and deployment patterns

This project is open source. License details will be provided in the LICENSE file.

• Anthropic for creating the MCP protocol that inspired this project
• FastMCP for providing excellent MCP server foundations
• Kubernetes community for building the infrastructure platform that makes this possible
• All the contributors who help make MCP Mesh better

1. 📚 Full Documentation - Complete guides and reference
2. ⚡ Quick Tutorial - Build your first distributed MCP agent
3. 💬 Join Discord - Connect with the community
4. 🔧 Contribute - Help build the future of AI orchestration

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