A meticulously curated collection of hands-on Jupyter notebooks, designed to illuminate the comprehensive application of MLflow across a spectrum ranging from foundational Machine Learning principles to pioneering Generative AI paradigms.
🧪 Explore and test MCP servers with fastmcp and FastAgent. List tools, invoke APIs, and augment LLMs with real-time capabilities using OpenAI, Claude, and Brave Search.
A Jupyter Notebook showing how to make LLMs use custom Python functions with LangChain's Tool Calling feature
A Jupyter Notebook guide to building a simple but powerful autonomous AI Agent with LangChain that can reason and use tools like web search to answer questions.
Add a description, image, and links to the tool-calling topic page so that developers can more easily learn about it.
To associate your repository with the tool-calling topic, visit your repo's landing page and select "manage topics."