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

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

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.

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

Interact with tabular data and SQL databases using natural language, enabling more efficient and accessible data analysis.

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

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

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

Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.

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.