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mcp-agent-runtime-go

A modular Go runtime for the Model Context Protocol (MCP) — designed for intelligent agent coordination, model selection, prompt optimization, and lifecycle-aware execution.

This SDK helps you build systems that:

  • Coordinate multiple models (e.g., GPT-4o, Claude, Gemini)
  • Dynamically generate and execute agents
  • Route tasks based on trust, capability, or intent
  • Manage prompt formatting and lifecycle control

📖 Read the full article on LinkedIn →


mcp-agent-runtime-go Overview 1

🧠 Why this exists

Modern AI infrastructure is agentic: prompts now flow through models, memory, tools, and reasoning layers. MCP offers a universal protocol for managing that flow.

This Go implementation provides:

  • Concurrency-safe, production-ready execution
  • Symbolically annotated agent flows
  • Pluggable model and tool support
  • Session lifecycle and halting controls

🔧 Key Features

  • SessionCoordinator for request routing, lifecycle hooks
  • ModelRouter for dynamic model selection
  • AgentGenerator for building runtime agents on demand
  • PromptOptimizerStep to transform user input into optimized prompts
  • HaltingPolicy to bound execution
  • DescriptorHash() to lock and validate execution configuration

📦 Installation

go get github.com/modelcontextprotocol/mcp-agent-runtime-go

mcp-agent-runtime-go Overview 2

📁 Project Structure

/core/               # Protocol definitions, session types
/model/              # ModelSpec, Registry, Router logic
/agentgen/           # Dynamic agent generation
/promptopt/          # Prompt optimization steps and templates
/ext/                # Optional extensions (e.g., memory, auth)
/transport/          # HTTP/SSE/WebSocket integrations
roadmap.md           # Strategic plan for contributors
README.md            # This file

mcp-agent-runtime-go Overview 3

🗺️ Roadmap Overview

See roadmap.md for full scoring and implementation priority.

Core Module Priority Description
SessionLifecycle 10 Session entry points and lifecycle events
ModelRouter 10 Multi-model dispatch logic
AgentGenerator 10 Runtime agent composition and execution
HaltingPolicy 9 Safe runtime control
PromptOptimizerStep 9 Transform intent into optimized prompts
DescriptorHash() 10 Lock agent and routing structure

🤝 Contributing

This repo is open for discussion and design iteration.


📜 License

MIT License — open use encouraged with attribution.

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