🔧 Dynamic AI Assistant with LangGraph, RAG, and MCP Tool Scaling | Docker-in-Docker Architecture
In this video, we showcase a working LangGraph pipeline that dynamically discovers and invokes tools using an advanced Model Context Protocol (MCP) architecture.
🧠 What’s Inside:
🚀 New select_tools node at the start: using RAG (Retrieval Augmented Generation) to intelligently filter relevant tools before prompting the assistant.
🤖 LangGraph-based agent loop with assistant, tools, and handle_tool_results nodes for dynamic interaction.
📦 Docker-in-Docker deployment to scale MCP servers, each exposing tools via JSON-RPC.
🧰 Tool scaling: discover and invoke tools across multiple MCP containers (Python, Node.js, etc.) using a unified pipeline.
🌐 Smart routing: assistant only sees the most relevant tools, reducing overcalls and improving performance.
⚙️ Why It Matters: This architecture supports scalable, modular, and context-aware tool invocation—perfect for production-grade AI assistants with evolving capabilities.
#LangGraph #AIagents #MCP #RAG #DockerInDocker #ToolScaling #AIautomation #LLM #LangChain #OpenAI #Gemini #JSONRPC #DevOps #DynamicTools #ReActAgents #VectorSearch