nest-llm-aigent
by luis1232023
This project acts as an AI agent, configuring multiple MCP servers and providing a unified HTTP service for large language models. It addresses the challenge of integrating MCP servers into existing business applications by providing a forwarding solution.
Last updated: N/A
What is nest-llm-aigent?
nest-llm-aigent is a forwarding solution that acts as an AI agent, allowing you to configure multiple MCP (Model Context Protocol) servers and expose them as a unified HTTP service for large language models. It simplifies the integration of MCP servers into existing web services, particularly those built with NestJS.
How to use nest-llm-aigent?
- Integrate the adapter layer into your existing web service.
- Package MCP Servers as private NPM packages for distribution and management.
- Install MCP Server packages using
npm install. - Configure the MCP servers using a configuration file (e.g.,
mcp.config.json). - Use the provided HTTP endpoints to interact with the LLMs through the configured MCP servers.
Key features of nest-llm-aigent
Easy Integration with existing web services (e.g., NestJS).
Seamless extension through private NPM packages for MCP Server management.
Convenient deployment via centralized management and version control of MCP Servers.
Supports almost all large language model calls.
Provides standard HTTP interfaces for accessing tools, resources, and prompts.
Supports unified management of multiple MCP Servers.
Use cases of nest-llm-aigent
Integrating large language models into existing web applications.
Building AI-powered chatbots and virtual assistants.
Creating custom AI agents with access to specific tools and resources.
Managing and deploying multiple LLM services in a centralized manner.
FAQ from nest-llm-aigent
What is MCP?
What is MCP?
MCP stands for Model Context Protocol, a protocol designed to standardize the interaction with large language models and their extensions.
How does this project help with MCP integration?
How does this project help with MCP integration?
This project provides a forwarding solution that acts as an AI agent, allowing you to configure multiple MCP servers and expose them as a unified HTTP service, simplifying integration into existing web services.
What kind of models are supported?
What kind of models are supported?
The project supports almost all large language model calls.
How are MCP servers deployed?
How are MCP servers deployed?
The recommended approach is to package MCP servers as private NPM packages and manage them through a private NPM repository.
What are the future plans for this project?
What are the future plans for this project?
Future plans include improving SSE protocol support, enabling context sharing across layers, and potentially encapsulating the HTTP interfaces into a socket for better chat support. Also consider creating a common class based on the original MCP protocol to support both MCP client calls and function calls.