LangGraph + MCP + Ollama The Key To Powerful Agentic AI

LangGraph + MCP + Ollama The Key To Powerful Agentic AI

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AI Summary

This video provides a tutorial on creating a multi-agent chatbot using Langraph, MCP, and open-source tools. It discusses the importance of the Model Context Protocol (MCP), which simplifies API use and allows integration of various AI tools without extensive coding. Key points include:

• Comparison of MCP and Function Calling: MCP allows for greater flexibility and autonomy for AI agents compared to function calling, which is more structured and controllable.

• When to Use: Use function calling for simple, predictable tasks and MCP for complex, multi-step workflows.

• Building an Agent: The tutorial outlines installing necessary libraries and provides detailed steps:

  • Use pip install requirements to install required libraries.
  • Implement an async function for the AI agent and integrate multi-server MCP client.
  • Create a chatbot node using the create chatbot function to process user interactions dynamically.

• Features of the Chatbot: It supports generating outputs based on user queries, including producing Python scripts and handling tool calls dynamically.

• Error Handling and Responses: The chatbot is designed to manage errors effectively, maintain conversational flow, and ensure stability and context-aware interactions throughout the conversation.

The video emphasizes that MCP serves as a common standard, enabling AI systems to interact seamlessly with various services, thus enhancing the integration experience.