MCP-AI-Infra-Real-Time-Agent
by junfanz1
This project demonstrates a decoupled real-time agent architecture connecting LangGraph agents to remote tools served by custom MCP servers. It enables a flexible and scalable multi-agent system with modular, cloud-deployable tool execution.
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What is MCP-AI-Infra-Real-Time-Agent?
This project implements an MCP-based AI infrastructure for real-time tool execution, structured knowledge retrieval, and dynamic agentic interactions. It focuses on integrating LangGraph agents with remote tools served by custom MCP (Modular Command Protocol) servers, enabling AI models to access structured knowledge and invoke specific tools dynamically.
How to use MCP-AI-Infra-Real-Time-Agent?
To use this project, you can spin up LangChain-compatible MCP tool servers (e.g., math_server.py, weather_server.py) and integrate them with LangGraph ReAct agents. The multiserver_client.py and stdio_client.py provide examples of how to connect agents to MCP servers using different transport protocols (SSE and STDIO). Follow the instructions in the README to set up the servers and clients, ensuring proper API key management and environment configuration.
Key features of MCP-AI-Infra-Real-Time-Agent
Decoupled Architecture: LangGraph agents orchestrate LLM workflows while delegating tool execution to remote MCP servers.
Advanced Asynchronous Programming: Utilizes Python’s async/await for concurrent execution and scalable real-time communication.
MCP & LangGraph Integration: Seamless transformation and invocation of distributed tools using Modular Command Protocol (MCP) with LangGraph and LangChain.
Flexible Multi-Server Connectivity: Supports 1:1 bindings to various tool servers, integrating diverse environments (local, cloud, containerized).
Use cases of MCP-AI-Infra-Real-Time-Agent
Real-time data retrieval and analysis using external APIs (e.g., weather forecasts, stock prices).
Automated task execution by invoking tools with user approval.
Building composable AI agents for multi-layer agentic interactions.
Creating adaptive AI systems that dynamically discover and register new tools.
FAQ from MCP-AI-Infra-Real-Time-Agent
What is MCP?
What is MCP?
MCP (Modular Command Protocol) is a protocol for defining and invoking modular tools, enabling seamless communication between AI agents and external services.
What is LangGraph?
What is LangGraph?
LangGraph is a state-machine inspired framework for agentic reasoning, allowing you to build complex agent workflows.
How does this project achieve real-time communication?
How does this project achieve real-time communication?
The project uses SSE (Server-Sent Events) and STDIO (Standard Input/Output) transports for real-time interaction between agents and tool servers, leveraging async/await for non-blocking I/O.
What are the benefits of using a decoupled architecture?
What are the benefits of using a decoupled architecture?
A decoupled architecture allows for modularity and scalability, enabling tool servers to scale independently and supporting language-agnostic tool integration.
What is the Agent2Agent Protocol?
What is the Agent2Agent Protocol?
The Agent2Agent Protocol defines a standard way for agents to discover capabilities, share information, coordinate actions, and securely exchange context and data across different enterprise applications.