Use the powerful and extensible LangChain framework, using prompts, parsing, memory, chains, question answering, and agents.

Learn Microsoft's open source orchestrator, Semantic Kernel and use LLM building blocks such as memory, connectors, chains and planners in your apps.

Learn about the latest advancements in LLM APIs and use LangChain Expression Language (LCEL) to compose and customize chains and agents.

Expand your toolkit with LangChain.js, a JavaScript framework for building with LLMs. Understand the fundamentals of using LangChain to orchestrate and chain modules.

Build a full-stack web application that uses RAG capabilities to chat with your data. Learn to build a RAG application in JavaScript, using an intelligent agent to answer queries.

Explore Mistral's open-source and commercial models, and leverage Mistral's JSON mode to generate structured LLM responses. Use Mistral's API to call user-defined functions for enhanced LLM capabilities.

Build autonomous agents that intelligently navigate and analyze your data. Learn to develop agentic RAG systems using LlamaIndex, enabling powerful document Q&A and summarization. Gain valuable skills in guiding agent reasoning and debugging.

Optimize the efficiency, security, query processing speed, and cost of your RAG applications.

Learn a flexible framework to build a variety of complex AI applications.

Build systems with MemGPT agents that can autonomously manage their memory.

Move your LLM-powered applications beyond proof-of-concept and into production with the added control of guardrails.

Build LLM apps that can process very long documents using the Jamba model

Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management.

Build, debug, and optimize AI agents using DSPy and MLflow.

Build agents that communicate and collaborate across different frameworks using ACP.