LangGraph Streaming Modes and Custom Data - Part 17/21 скачать в хорошем качестве

LangGraph Streaming Modes and Custom Data - Part 17/21

Learn how LangGraph handles async streaming with custom data modes for real-time tool call feedback. This tutorial covers astream with stream_mode="custom" to process incremental outputs from tool invocations, demonstrating dynamic interaction patterns in Python. #LangGraph #Python #Async #Streaming #AI #ToolCalls #CustomData Zen Koan Explanation: The code demonstrates a streaming execution where a function call (`get_items`) with arguments (`{"place":"bedroom"}`) is processed in a loop, with each chunk of output being printed sequentially. This mirrors the koan's theme of a self-referential cycle—like a "tail-biting loop"—where the function's arguments (the "whispered" bedroom context) perpetuate the streaming process until completion. The repetitive, chunked output evokes the idea of a continuous, introspective flow. Source: https://docs.langchain.com/oss/python...