🤖 MCP-Agent-Accelerator 🚀

A production ready framework for building AI agents using the Model Context Protocol (MCP). This accelerator eliminates integration boilerplate and provides for robust agent orchestration.

🚧 Status: Core framework is stable.

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🤔 The Problem

Building AI agents is more than just connecting to an LLM. Developers face significant hurdles when trying to make agents interact with real world tools:

• Complex Connections: Every new tool requires a custom, brittle integration.
• Coordination Chaos: Making multiple agents work together is a major challenge.
• Production Pitfalls: Moving from a simple demo to a reliable, production-grade system is difficult.
• Error Handling Hell: Managing failures and retries across different services is a nightmare.

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💡 The Solution

MCP-Agent-Accelerator provides a foundation that handles the complex infrastructure, allowing you to focus on your agent's unique logic and business value.

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✨ Core Capabilities

• 🔧 Universal Tool Integration: Connect to anything with an MCP server. Comes with pre configured connections for GitHub, databases, and local file systems.
• 🤝 Advanced Multi-Agent Orchestration: Implement sophisticated workflows with teams of specialized agents that collaborate to solve complex problems.
• ⚡ Production-Ready from Day One: Built with production in mind, featuring comprehensive logging, monitoring hooks, and full type safety.
• 📦 Containerized & Scalable: Includes Docker and Kubernetes manifests to get you deployed and scaled in any environment quickly.

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🛠️ Technology Stack

• Languages: TypeScript, Python
• Protocols: MCP, gRPC, REST
• AI Providers: OpenAI, Anthropic and more.
• Infrastructure: Docker, Kubernetes

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▶️ Example Usage

Here's a quick look at how you can orchestrate a team of agents for a code review task:

// Define a multi-agent code review workflow
const reviewTeam = new AgentOrchestrator([
  new Agent({ name: "security-scanner", servers: ["github", "sonarqube"] }),
  new Agent({ name: "style-checker", servers: ["github", "eslint"] }),
  new Agent({ name: "test-validator", servers: ["github", "jest"] })
]);

// Execute the workflow on a specific pull request
await reviewTeam.execute("review-pr", { repo: "my-org/project", pr: 123 });

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🏁 Getting Started

Prerequisites

• ✅ Node.js 18+
• ✅ GitHub Token
• ✅ LLM API Key (OpenAI, Anthropic, etc.)
• ✅ Docker (Optional)

Installation

# Clone the repository
git clone https://github.com/ATaylorAerospace/MCP-Agent-Accelerator.git
cd MCP-Agent-Accelerator

# Install dependencies
npm install

# Build the project
npm run build

# Run tests
npm test

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🙏 Contributing

This project aims to establish the best production grade patterns for the entire MCP ecosystem. We welcome contributions of all kinds

• New Connectors: Help us integrate with more tools.
• Orchestration Patterns: Share your workflow ideas.
• Examples & Docs: Improve the developer experience.
• Performance Tuning: Make the framework faster and MUCH more efficient.

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🏛️ Architecture

Built on proven, design patterns, the framework features a modular architecture that allows for selective adoption of components. It enforces a clean separation of concerns between agent logic, tool integration, and orchestration layers, making your codebase clean and maintainable.

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