LangGraph
A library for building stateful, multi-agent applications with LLMs, built on LangChain.
LangGraph is a tool that helps developers build complex AI applications. Think of it like a flowchart for AI agents, where each agent can perform a task and then pass information to the next agent.
This allows for more sophisticated AI systems that can handle multi-step processes, like planning a trip or managing a customer service interaction. It's built on top of LangChain, another popular tool for working with AI models.
LangGraph is an extension of LangChain designed for building agentic workflows that require state management and conditional branching. It provides a framework for defining graphs where nodes represent LLM calls or other tools, and edges represent the flow of control. This enables the creation of complex, multi-agent systems capable of iterative reasoning and dynamic decision-making.
Key features include the ability to define custom state, implement conditional logic for routing, and manage execution history. This makes it suitable for applications requiring persistent memory and complex interaction patterns between multiple AI agents.
LangGraph is appearing in AI news as labs focus on building more complex, multi-agent systems. These systems require tools that can manage state and orchestrate interactions between different AI agents over multiple turns, which LangGraph provides.
- Why Your AI Agent Fails in Production (And How to Catch It)Google · Google Cloud Tech on YouTube ·
- Agentic AI – Complete Course for BeginnersOpenAI · freeCodeCamp ·
- Google Gemini
- Google Gemini's content mentions automating AI agent testing, suggesting LangGraph could be used to build and test such agents.
- OpenAI ChatGPT
- OpenAI's content directly references LangGraph as a tool for building advanced multi-agent AI systems.
- LangChain
- LangChain is a broader framework for developing LLM applications, whi
LangGraph was developed by the team behind LangChain to address the need for building more complex, stateful agentic applications. Source ↗
Explore the LangGraph documentation and tutorials. Look for examples that demonstrate building a simple multi-agent system, such as a basic chatbot that can remember previous turns in the conversation or a tool that plans a sequence of actions.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- ToolLangChain and LangGraph for building multi-agent AI systemsFrameworks for building production-ready multi-agent AI systems and automating workflows. · 15 min
- Case studyBuilding custom ChatGPT, trip planner, and content agentsReal-world projects demonstrating agentic AI system development with LangChain and LangGraph. · 30 min
- PromptRole-play a student struggling with math for AI tutor simulationSimulate a student's math struggles and uncertainty for AI tutor evaluation. · 5 min
- TemplateReusable workflow templates for healthcare teamsTeams can create and share templates for common workflows like documentation or prior authorizations. · 15 min
- TemplateTemplate for building an AI tool tailored to your workA structured approach to defining and building a custom AI tool for specific business needs. · 15 min
Questions people ask
- What is the main benefit of using LangGraph?
- LangGraph allows you to build complex AI applications with multiple agents that can communicate and manage state over time, enabling more sophisticated workflows.
- Is LangGraph a standalone tool?
- No, LangGraph is built on top of LangChain, extending its capabilities for creating agentic graphs.
First seen on Subvolts Jul 30, 2026. Updated Oct 7, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.