Human in the loop!
This lesson demonstrates LangGraph's "time travel" debugging capabilities, allowing users to replay and fork past states of LangChain agents using checkpoints and thread IDs. Key functions like `graph.get_state()`, `graph.get_state_history()`, `graph.update_state()`, and `graph.stream()` are used to navigate and modify the execution history, enabling efficient debugging and exploration of alternative computation paths.
Master LangGraph's dynamic breakpoints using `NodeInterrupt` to control graph execution based on internal conditions, enabling debugging and sophisticated workflow management. Learn how to implement, handle, and effectively utilize these dynamic breakpoints for seamless graph control and state manipulation.
This lesson teaches you how to use LangGraph's breakpoints for human-in-the-loop control of LangChain graphs. Master techniques like state editing with `update_state` and `HumanMessage` to dynamically adjust graph execution and achieve desired outcomes.
This module enhances a chatbot with human-in-the-loop streaming interactions using LangGraph and GPT-4. It leverages LangGraph's streaming methods to provide real-time feedback and updates, showcasing different streaming modes for efficient graph state management.
This lesson teaches you how to build interactive chatbots using LangGraph's streaming capabilities. It covers different streaming modes ("updates" and "values"), demonstrates handling streamed responses from LLMs like GPT-4, and shows how to access and process individual tokens in real-time.
Master seamless human-agent collaboration by strategically integrating human oversight into automated workflows using breakpoints. LangChain Academy's Module 3 teaches you how to manage sensitive tasks, delegate work, and debug effectively through human-in-the-loop techniques.
