nest-llm-aigent
by luis1232023
nest-llm-aigent is a forwarding solution that acts as an AI agent, allowing you to configure multiple MCP servers and provide a unified HTTP service for large language models. It solves the integration challenges of MCP's CS architecture into existing business applications.
Last updated: N/A
What is nest-llm-aigent?
nest-llm-aigent is an AI agent that provides a unified HTTP interface for accessing multiple MCP (Model Context Protocol) servers. It simplifies the integration of large language models into existing web services by acting as a forwarding layer.
How to use nest-llm-aigent?
- Integrate the adapter layer with your web service (e.g., NestJS). 2. Package MCP servers as private NPM packages for distribution and management. 3. Install MCP server packages using
npm installand configure them using a configuration file (e.g.,mcp.config.json). 4. Use the provided HTTP API endpoints to interact with the LLMs through the agent.
Key features of nest-llm-aigent
Easy integration with existing web services
Seamless extension through private NPM packages
Convenient deployment with centralized management and version control
Supports almost all large language model calls
Standardized API endpoints for various functionalities (tools, resources, prompts, LLM calls)
Configuration via JSON file
Use cases of nest-llm-aigent
Integrating LLMs into existing NestJS applications
Managing and deploying multiple MCP servers in a centralized manner
Providing a unified HTTP interface for accessing different LLMs
Building AI agents that can interact with various tools and resources
Creating chat applications with context sharing and global variables
FAQ from nest-llm-aigent
What is MCP?
What is MCP?
MCP (Model Context Protocol) is a protocol designed to standardize the interaction with large language models and their extensions.
How do I deploy MCP servers?
How do I deploy MCP servers?
It is recommended to package MCP servers as private NPM packages and manage them through a private NPM repository.
How do I configure the agent?
How do I configure the agent?
The agent is configured using a JSON file (e.g., mcp.config.json) that specifies the MCP servers and their configurations.
What kind of models are supported?
What kind of models are supported?
The agent supports almost all large language model calls.
What are the future plans for this project?
What are the future plans for this project?
Future plans include supporting SSE protocol for MCP servers, enabling context sharing across layers, encapsulating interfaces into a socket for better chat scenarios, and potentially creating a common class for class-based MCP servers to support both MCP client calls and function calls.