All about LangGraph
This lesson teaches you how to build a conversational AI chatbot with memory using Langchain and LLMs. It covers techniques for summarizing conversations, managing message history efficiently, and leveraging checkpoints for long-running interactions, all while demonstrating practical applications with real-world examples.
This lesson teaches you to build efficient chatbots with long-term memory using Langchain and LangGraph, focusing on managing message history to avoid exceeding token limits. Key techniques include customizing state schemas, employing reducers like `RemoveMessage` and `add_messages`, and leveraging LangSmith for debugging and tracing.
This lesson teaches how to manage complex data flows in LangGraph using multiple schemas within graph nodes. It demonstrates using `OverallState` and `PrivateState` TypedDicts to control internal node communication and filter outputs, enhancing graph flexibility and code clarity.
This lesson explores LangGraph's state management, showcasing how to define and update state schemas using Python's `TypedDict` and handle concurrent state updates using reducers and the `Annotated` type. It further demonstrates efficient message management with the `add_messages` reducer, including adding, updating, and removing messages using unique IDs.
This lesson teaches you to build memory-enabled agents in LangGraph, deploying them via LangGraph Cloud. It then shows how to manage agent state effectively using Python's `TypedDict`, `dataclass`, and Pydantic for flexible schema definition, type hinting, and runtime validation.
Master LangChain's agentic applications by learning how memory and state management enhance chatbot performance. This module explores efficient message history handling, database integration (like Postgres and SQLite), and techniques to optimize context windows.
