Gain control with LangGraph to design agents that reliably handle complex tasks.
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At its core, a compiled LangGraph is a Runnable. This template utilizes LangChain’s built-in streaming support through astream_events, granting programmatic access to every stage of the Agentic Workflow. You can observe and interact with key components—LLM, prompt, and tool—throughout their full execution lifecycle: start, stream, and end. For a comprehensive list of event types and usage examples, refer to the Event Reference.
LangGraph offers built-in state management and persistence via the AsyncPostgresSaver, enabling faster iteration on agentic workflows. Since LLMs are inherently stateless, chat history must typically be injected as context for each query—but LangGraph abstracts this away, requiring only a thread_id. It seamlessly handles chat history and metadata serialization/deserialization, simplifying development. Learn more about its advanced persistence capabilities here.