LangGraph is a low-level Python orchestration framework for building stateful, long-running agents, aimed at developers and platform teams who need durable execution, human-in-the-loop control, and persistent memory instead of a turnkey agent application.

What it is

LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. It is maintained in Python under the MIT licence, sits in the AI & Machine Learning / AI Development Platforms category, and can be used standalone even though it integrates with other LangChain products. A JavaScript/TypeScript equivalent exists as LangGraph.js, and the framework is positioned as supporting infrastructure for any long-running, stateful workflow or agent, not only conversational ones.

The concrete problem it solves is the absence of orchestration infrastructure beneath an agent loop. Without a framework at this level, teams must build their own state handling, failure recovery, and resume logic around model calls. LangGraph supplies that layer directly: agents persist through failures and resume from exactly where they left off, human oversight can inspect and modify agent state at any point during execution, and memory is handled both as short-term working memory for ongoing reasoning and as long-term persistent memory across sessions. It therefore replaces bespoke orchestration code rather than a competing product, and it stays low-level by design.

Key capabilities

• Durable execution: agents persist through failures and resume from exactly where they left off, which allows runs to continue for extended periods.
• Human-in-the-loop through interrupts: human oversight can inspect and modify agent state at any point during execution.
• Comprehensive memory: short-term working memory for ongoing reasoning plus long-term persistent memory that survives across sessions.
• Debugging with LangSmith: visualization tools trace execution paths, capture state transitions, and report detailed runtime metrics.
• Production-ready deployment through LangSmith Deployment: scalable infrastructure built for stateful, long-running workflows.
• Ecosystem pairing: LangChain supplies integrations and composable components, and Deep Agents builds on LangGraph for agents that plan, use subagents, and leverage file systems.
• Cross-language option: LangGraph.js provides an equivalent library for JavaScript and TypeScript.

Who uses it and how

• Enterprises building long-running agent systems, a use case reflected in the enterprise topic and in the framework's stated use by Klarna, Replit, and Elastic.
• Teams that need agents to survive process restarts and infrastructure failures rather than losing in-flight work, which is what durable execution targets.
• Applications requiring approval gates or manual review mid-run, where interrupts let a human inspect and modify state before the agent continues.
• Long-lived assistants that must remember context across separate sessions, using persistent memory alongside working memory.
• Organizations already working in the LangChain ecosystem that want to pair orchestration with LangSmith for evaluation, observability, and deployment.

Getting started

Install the Python package with pip install -U langgraph. Full conceptual overviews, API reference, and a quickstart guide are published at docs.langchain.com, and a JavaScript/TypeScript equivalent is available through LangGraph.js.

How it compares

Within its own ecosystem, LangGraph is the low-level orchestration layer, while LangChain provides integrations and composable components, Deep Agents is a higher-level package for agents that plan and use subagents, and LangSmith covers evaluation and observability. LangSmith Deployment is the hosted platform for deploying and scaling long-running stateful workflows, and LangGraph.js mirrors the core library for JavaScript and TypeScript teams. The choice is therefore between levels of abstraction and between the Python and JS/TS implementations, not between vendors.

When to use it — and when not to

LangGraph is the right choice when the orchestration itself must be controlled, because it deliberately supplies supporting infrastructure rather than a finished agent. Teams that want something assembled quickly should look at Deep Agents instead, and anyone unwilling to depend on LangSmith for observability and deployment should note that the README routes production deployment through LangSmith Deployment. The project also carries a substantial open-issue count of 786, so adopters should expect active development and should plan for reading the documentation closely rather than assuming stable, hands-off operation.

pip install -U langgraph