Combining control flow (edges) and state updates (nodes) can be very useful. For instance, you might want to execute state updates and decide which node to transition to next within the same node. LangGraph provides a way to achieve this by returning an object from the Command node function: def my_node(state: State) -> Command[Literal["my_other_node"]]: return … Read more
LangGraph provides a high level of control over the execution of charts. How to Create Parallel Execution Branches The parallel execution of nodes is crucial for speeding up overall graph operations. LangGraph offers native support for parallel execution of nodes, which can significantly enhance the performance of graph-based workflows. This parallelization is achieved through fan-out … Read more
LangGraph Persistence allows for easy state persistence between graph execution (thread-level persistence) and threads (cross-thread persistence). This tutorial demonstrates how to add persistence to a graph. LangGraph has a built-in persistence layer that is implemented through checkpoints. When compiling a graph with a checkpoint, the checkpoint saves a snapshot of the graph’s state at each … Read more
How to Update Graph State via Tool A common use case is to update the graph state from within the tool. For example, in a customer support application, you may want to look up the customer account or ID at the start of the conversation. To update the graph state from the tool, you can … Read more
Map-reduce operations are crucial for efficient task decomposition and parallel processing. This method involves breaking down tasks into smaller sub-tasks, processing each sub-task in parallel, and aggregating the results of all completed sub-tasks. Consider this example: Given a general topic from the user, generate a list of related topics, create a joke for each topic, … Read more
Click the “Blue Word” to Follow Us In today’s rapidly evolving AI field, building Agents (Which Multi-Agent Framework is Better? A Deep Dive into Five Popular Options) has become an important means of achieving automation and decision optimization. LangGraph, as a powerful tool, provides developers with an intuitive and efficient way to build AI Agents … Read more
Overview When building AI applications, we often need to handle complex workflows, including serial and parallel execution of multiple steps, state management, event notifications, etc. LangGraph provides a powerful event stream mechanism and a flexible node return value system to support these needs. This article will delve into the event streams and node return value … Read more
LangGraph is a library developed by LangChainAI for creating workflows for agents and multi-agent systems. It offers the following core advantages: cycles, controllability, and persistence, which undoubtedly reduce the workload for agent developers. This article will highlight the key points and usage methods of LangGraph from my perspective during the development process. Basic Introduction The … Read more
Introduction on how to use LangGraph to improve RAG. Long press to follow “Python Learning Base”, join the reader group, and share more wonderful content. 1. Introduction LangGraph is the latest member of the LangChain, LangServe, and LangSmith series, aimed at building generative AI applications using LLMs. Remember, all these are independent packages and must … Read more
Introduction Langchain recently launched an impressive course focused on LangGraph and its key features in developing powerful agent and multi-agent workflows. In this series, we will explore the essential insights from the course and create applications that leverage agent workflows. In the first part, we will cover the basic concepts of LangGraph and how to … Read more