LangGraph solution template for MCP
LangGraph solution template for MCP
Overview
Universal Assistant with LangGraph and Model Context Protocol (MCP)
Key Highlights:
- Purpose: Seamless integration of language models into complex workflows and applications, enabling AI-powered assistants, IDEs, and custom AI workflows.
- Features:
- Modular and flexible LangGraph framework for representing workflows as graphs.
- Standardized MCP protocol for connecting language models to external data sources and tools.
- Multi-agent pattern for routing user requests to the appropriate agent and tool.
- Generic MCP wrapper with extensible implementation for various operations.
- Target Audience: Developers and teams building AI-powered applications, assistants, and workflows.
- Uniqueness: Combines the power of LangGraph and MCP to create a reusable, extensible, and standardized approach to integrating language models into complex systems.
Overview:
The project presents a "Universal Assistant" built using the LangGraph framework and the Model Context Protocol (MCP). LangGraph provides a structured yet dynamic way to execute tasks, making it ideal for building AI applications involving natural language understanding, automation, and decision-making. MCP, on the other hand, enables seamless integration between language models and external data sources and tools, similar to how USB-C provides a standardized way to connect devices to various peripherals.
The key components of the solution include a Router, the Assistant, and a generic MCP wrapper. The Router collects and indexes routing information from various MCP servers, while the Assistant uses a multi-agent pattern to route user requests to the appropriate agent and tool. The MCP wrapper employs a Strategy Pattern to provide a common interface for executing various operations on MCP servers, making the solution highly extensible.
This project serves as a reusable template for developers and teams looking to build AI-powered applications, assistants, and workflows that leverage the power of language models while maintaining a modular and standardized approach.