OpenAI builds and deploys advanced AI models like GPT-4o for autonomous agents and workflows.
LangGraph
$0.07Starting price
0Popularity
About LangGraph
LangGraph is an open-source orchestration framework by LangChain designed for building resilient, long-running, stateful AI agents. It enables developers to model agent workflows as graphs with controllable nodes, edges, branches, subgraphs, and state transitions. The framework supports durable execution by persisting agent state, allowing workflows to resume from checkpoints and recover from failures. It includes built-in short-term and long-term memory to support persistent reasoning across sessions, as well as human-in-the-loop controls for inspecting and modifying agent state during execution. LangGraph integrates with LangSmith for debugging, tracing, evaluation, and deployment, making it suitable for production LLM applications. It is ideal for AI engineers, agent developers, backend engineers, platform teams, and developers building reliable tool-using agents, multi-agent systems, and production workflows.
GitHub, Inc.
San Francisco, California, US · Founded 2008
- Founders
- Tom Preston-Werner, Chris Wanstrath, PJ Hyett, Scott Chacon
- Founded
- 2008
- Headquarters
- San Francisco, California, US
- Legal status
- Subsidiary of Microsoft (NASDAQ: MSFT)
Key features
- Graph-based agent workflows with nodes, edges, branches, and subgraphs
- Durable execution with checkpointing, retries, and failure recovery
- Short-term and long-term memory for persistent reasoning
- Human-in-the-loop review and state inspection during execution
- Integration with LangSmith for debugging, tracing, and evaluation
- Support for tool calls and multi-agent coordination
- Stateful agent orchestration with persistent execution state
- Python and JavaScript/TypeScript support via LangGraph and LangGraph.js
- Production-ready deployment capabilities
- Streaming and tool integration within workflows
Use cases
- Orchestrating complex AI agent workflows with graph-based state machines
- Building long-running, tool-using, multi-step, and multi-agent systems
- Supporting durable execution, checkpointing, retries, and failure recovery in production workflows
Pros
- Low-level orchestration framework for building stateful, long-running agents
- Supports durable execution with automatic recovery from failures
- Includes built-in short-term and long-term memory for persistent reasoning
- Integrates with LangSmith for debugging, tracing, evaluation, and deployment
- Compatible with LangChain ecosystem for streamlined LLM application development
Cons
- Requires technical expertise to implement complex agent workflows
- May involve additional setup for production deployment compared to higher-level tools
- Dependency on LangChain ecosystem for full feature utilization
Frequently asked questions about LangGraph
What is LangGraph and what does it do?
LangGraph is an open-source orchestration framework for building resilient, stateful AI agents. It enables developers to model agent workflows as graphs with controllable nodes, edges, and state transitions, supporting durable execution and human-in-the-loop controls.
Who is LangGraph suitable for?
LangGraph is ideal for AI engineers, agent developers, backend engineers, platform teams, and developers building reliable tool-using agents, multi-agent systems, and production workflows.
How does LangGraph handle agent state persistence?
LangGraph provides durable execution by persisting agent state, allowing workflows to resume from checkpoints and recover from failures. It includes both short-term and long-term memory to support persistent reasoning across sessions.
Does LangGraph integrate with other tools?
Yes, LangGraph integrates seamlessly with the LangChain ecosystem, including LangSmith for debugging and deployment, and Deep Agents for higher-level agent development.
What are the main limitations of LangGraph?
LangGraph requires technical expertise to implement complex workflows and may involve additional setup for production deployment. Full feature utilization depends on the LangChain ecosystem.
How do I get started with LangGraph?
Start by installing LangGraph via pip and exploring the documentation or quickstart guides. For higher-level agent development, consider using Deep Agents built on top of LangGraph.
