LangGraph

In this blog piece, you’ll learn why and how we built LangGraph for production agents—focusing on control, durability, and the core features needed to scale.

DeerFlow is a community-driven framework for deep research, combining language models with tools like web search, crawling, and Python execution, while contributing back to the open-source communit...

Understanding multi-agent handoffs Handoffs are a central concept in multi-agent systems. LangGraph swarm is built on them. But, they can be hard to understand. Here, we break-down the swarm handoff mechanism. 📽️: https://t.co/YkSCFeg9A8

Contribute to langchain-ai/langgraph-supervisor development by creating an account on GitHub.

Counting tokens is a useful task in natural language processing (NLP) that allows us to measure the length and complexity of a text. The two important use cases for counting the tokens are: controlling the length of the prompt - models has limit …

LangGraph is a multi-agent framework. This means not only interacting with other LangGraph agents, but all other types of agents as well, regardless of how they are built. Today we are taking a few steps to to build towards this vision. We are announcing: * Agent Protocol: a common interface for

I just wanted to validate if it's ok to initialize/compile the graph once and then use it to serve multiple parallel requests in a web application. In other words is the shared state passed fro...

✒️Kiroku is a multi-agent system that helps you organize and write documents Really complex agent (see the diagram below!) that HEAVILY involves a "human in…

This post will explain how you can implement complex agentic ReAct flows using LangGraph and LangGraph Studio. Basic Python knowledge is…

Build language agents as graphs

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CoAgents, the building blocks for the next generation of AI-native apps.

In CopilotKit's blog, see how to use: • Real-time state sharing to match user… — LangChain (@LangChainAI)

LangGraph is one of the most versatile Python libraries for building AI agents. We can combine LangChain's LangGraph with Ollama and Llama 3.1 to build highl...

At Sequoia’s AI Ascent conference in March, I talked about three limitations for agents: planning, UX, and memory. Check out that talk here. In this post I will dive more into memory. See the previous post on planning here, and the previous posts on UX here, here, and here.

We released a bunch of new functionality for memory in LangGraph, and in doing so we thought hard about what memory actually means, and was is useful today Some highlights 👇 🛃Memory is application specific The best memory today… — Harrison Chase (@hwchase17)

Build language agents as graphs

Build resilient language agents as graphs.

Build resilient language agents as graphs. Contribute to langchain-ai/langgraph development by creating an account on GitHub.

Desktop app for prototyping and debugging LangGraph applications locally. - langchain-ai/langgraph-studio