langchain-ai/LangGraph
Build resilient agents.
LangGraph is an open-source framework from LangChain for building stateful, multi-step AI agents as graphs with loops, branching, and persistence. It gives developers low-level orchestration that simple chains lack: durable state, human-in-the-loop checkpoints, and multi-agent coordination.
Our Review
LangGraph comes from the LangChain team and has gathered 33,000 GitHub stars as the orchestration layer underneath many production agents. Where a basic prompt chain runs once and stops, LangGraph treats an agent as a stateful graph it can pause, resume, loop, and hand to a human, which is what makes agents reliable enough to ship.
What LangGraph does:
- •Agents as graphs model an agent as nodes and edges with shared state, so control flow is explicit instead of hidden in prompt logic.
- •Loops and branching support cycles and conditional routing, so agents can retry, reflect, and take different paths.
- •Durable state and persistence checkpoint agent state so a run can pause, resume, and survive failures.
- •Human-in-the-loop pause for human approval or edits at any step before continuing.
- •Multi-agent coordination compose multiple agents and subgraphs that pass control and state between them.
- •Works with any model and LangChain model-agnostic, and it plugs into the wider LangChain and LangSmith tooling.
Getting started:
Install with pip install langgraph (or the JS package), define a graph of nodes and edges, compile it, and run. Docs at docs.langchain.com.
Limitations:
LangGraph is a low-level framework, so it trades simplicity for control: there is a learning curve in modeling agents as graphs, and small tasks may be simpler with a plain LLM call. It is a developer library, not a visual builder. Production features like managed persistence and deployment lean on the paid LangGraph Platform, and the fast-moving LangChain ecosystem means APIs evolve.
Our Verdict
LangGraph is the default low-level framework in 2026 for developers building agents that need to be reliable rather than throwaway demos. If your agent has to loop, call tools, wait for a human, and recover from failure, LangGraph's stateful-graph model gives you that control, with 33,000 stars and the LangChain team behind it.
For developers, the value is explicit control: you define the agent's steps, state, and transitions instead of hoping a prompt chain behaves. Built-in persistence and human-in-the-loop make it suited to production, and because it is model-agnostic and integrates with LangChain and LangSmith, it fits an existing stack.
Skip LangGraph if your use case is a single prompt or a simple chain; the graph model adds overhead you would not need, and a lighter framework or direct API call is faster. If you want a visual, no-code agent builder, a canvas tool like Sim is a better starting point than a code-first framework.
Frequently Asked Questions
What is LangGraph?
LangGraph is an open-source framework from the LangChain team for building stateful, multi-step AI agents. It models an agent as a graph of nodes and edges with shared state, which lets you add loops, branching, persistence, and human-in-the-loop checkpoints. It is the low-level orchestration layer many production agent systems are built on, and it works with any model.
Is LangGraph free and open source?
Yes. LangGraph is released under the MIT license and is free and open source as of 2026. You install the Python or JavaScript package at no cost. The optional LangGraph Platform, a paid managed service for deployment, persistence, and monitoring, is separate; the core framework you self-run is free.
How is LangGraph different from LangChain?
LangChain is a broad toolkit of components for LLM apps, while LangGraph is a focused framework for orchestrating agents as stateful graphs. LangGraph is lower-level and more explicit about control flow and state. Choose plain LangChain for straightforward chains and integrations; choose LangGraph when you need loops, durable state, and reliable multi-step agent behavior.
When should I use LangGraph?
Use LangGraph when an agent must do more than one model call: retry on failure, branch on conditions, call tools across steps, persist progress, or pause for human review. It is built for reliability and complex control flow. For a single prompt or a simple linear chain as of 2026, a lighter approach is usually enough.
Does LangGraph work with models other than OpenAI?
Yes. LangGraph is model-agnostic. You can use OpenAI, Anthropic, Google Gemini, open models, or anything you can call from code, and it integrates with the wider LangChain ecosystem for model and tool connections. The graph orchestration is independent of which model runs at each node, so you can mix providers within one agent.
How do I install LangGraph?
Visit the GitHub repository at https://github.com/langchain-ai/langgraph for installation instructions.
What license does LangGraph use?
LangGraph uses the MIT license.
What are alternatives to LangGraph?
Explore related tools and alternatives on My AI Guide.
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