GenAI Agents is a tutorial and implementation collection for building generative AI agents, ranging from simple conversational bots to complex multi-agent systems.
The repository addresses the challenge of learning to build production-ready AI agents by providing hands-on implementations across the spectrum of agent complexity. It covers foundational concepts like prompt engineering and structured outputs, progresses through single-agent patterns with tool use and retrieval-augmented generation, and extends to multi-agent architectures and evaluation techniques. The tutorials use frameworks like LangChain and LangGraph alongside large language models from providers like OpenAI, demonstrating practical patterns for real-world deployment.
Developers should choose this resource if they want to move beyond toy examples toward production systems. The collection suits teams building conversational interfaces, autonomous agents with tool access, multi-agent orchestration systems, and document processing pipelines. The repository explicitly positions itself as a learning path from initial prototyping through deployment, making it valuable for developers at various skill levels who want to understand both the theory and implementation details of agent systems.
The project maintains an active collection of tutorials with recent additions spanning trace-based evaluation, human-in-the-loop approval workflows, document intake systems, and specialized agents like HR assistants and tour guides. The repository is structured primarily as Jupyter Notebooks, making implementations directly executable and modifiable for learning purposes. The maintainer provides supplementary video content breaking down concepts into focused episodes, and offers a structured course pairing video lectures with hands-on labs for developers seeking systematic instruction on building production AI systems.
