From Models to Agents: The Missing Layer Between AI and Real Problems

The integration of LangChain (LangGraph), MCP (Machine Context Provider), RAG (Retrieval-Augmented Generation), and Knowledge Graphs into a cohesive system is indeed powerful, enabling sophisticated question-answering capabilities that leverage live data, document retrieval, and semantic relationships. Here’s an overview with key details on how to build such a system:

1. LangChain (LangGraph)

Purpose: Manages the flow of information between different components.

• Architecture: LangGraph orchestrates requests from users, deciding which MCP tools or RAG systems are needed based on the query type and context.

Key Components:

• User Interface: A chatbot interface where users can input their questions.
• Query Routing Logic: Determines whether to fetch live data (MCP), retrieve relevant documents (RAG), or traverse knowledge graphs for relationship-based answers.

2. Machine Context Provider (MCP)

Purpose: Provides structured, real-time data from databases and APIs.

• Architecture: MCP acts as an interface between the LLM and various backend systems, fetching live data such as usage logs, cost breakdowns, etc.

Key Components:

• API Endpoints: Define endpoints

Read the full article at Towards AI - Medium

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