MCP Agent Proxy is a Node.js-based HTTP proxy for OpenAI Chat and Embedding endpoints. It intercepts and forwards requests while providing streaming, response caching, detailed logging, and dynamic parameter overrides to streamline multi-agent development workflows.
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MCP Agent Proxy
MCP Agent Proxy is a Node.js-based HTTP proxy for OpenAI Chat and Embedding endpoints. It intercepts and forwards requests while providing streaming, response caching, detailed logging, and dynamic parameter overrides to streamline multi-agent development workflows.
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What is MCP Agent Proxy?
MCP Agent Proxy acts as a middleware service between your applications and the OpenAI API. It transparently forwards ChatCompletion and Embedding calls, handles streaming responses to clients, caches results to improve performance and reduce costs, logs request and response metadata for debugging, and allows on-the-fly customization of API parameters. Developers can integrate it into existing agent frameworks to simplify multi-channel processing and maintain a single managed endpoint for all AI interactions.
Who will use MCP Agent Proxy?
- AI developers building multi-agent chatbots
- Data scientists experimenting with OpenAI APIs
- DevOps engineers managing AI request pipelines
- Product teams needing centralized AI request logging
How to use the MCP Agent Proxy?
- Step1: Clone the repo: git clone https://github.com/mashh-lab/mcp-agent-proxy.git
- Step2: Install dependencies: cd mcp-agent-proxy && npm install
- Step3: Configure environment variables: set OPENAI_API_KEY, PROXY_PORT, UPSTREAM_URL
- Step4: Start the proxy: npm start
- Step5: Point your application’s OpenAI endpoint to http://localhost:PROXY_PORT
Platform
- Linux
- Mac
- Windows
MCP Agent Proxy's Core Features & Benefits
The Core Features
- HTTP proxy for ChatCompletion and Embedding endpoints
- Real-time streaming of API responses
- Response caching with configurable TTL
- Request and response logging
- Dynamic override of API parameters
- Support for high concurrency
The Benefits
- Reduces API costs via caching
- Centralizes request monitoring and logging
- Enables experimentation with parameters without code changes
- Simplifies multi-agent integration
- Improves development and debugging workflows
MCP Agent Proxy's Main Use Cases & Applications
- Building multi-agent conversational platforms
- Logging and auditing AI requests in production
- Rapid prototyping with parameter tuning
- Reducing redundant embedding calls through caching
FAQs of MCP Agent Proxy
MCP Agent Proxy Company Information
- mashh-lab
MCP Agent Proxy Reviews
5/5
MCP Agent Proxy's Main Competitors and alternatives?
LangChain Serve
Azure OpenAI Proxy
OpenAI CLI Proxy
ProxyCrawl
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