-
+
-[](https://pypi.org/project/langgraph/)
-[](https://pepy.tech/project/langgraph)
-[](https://github.com/langchain-ai/langgraph/issues)
-[](https://docs.langchain.com/oss/python/langgraph/overview)
+
+
Low-level orchestration framework for building stateful agents.
+
+
+
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
-## Get started
-
-Install LangGraph:
-
-```
+```bash
pip install -U langgraph
```
-Create a simple workflow:
+If you're looking to quickly build agents with LangChain's `create_agent` (built on LangGraph), check out the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
-```python
-from langgraph.graph import START, StateGraph
-from typing_extensions import TypedDict
+---
+**Documentation**:
-class State(TypedDict):
- text: str
+- [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) – Comprehensive documentation, including conceptual overviews and guides
+- [reference.langchain.com/python/langgraph](https://reference.langchain.com/python/langgraph) – API reference docs for LangGraph packages
+- [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart) – Get started building with LangGraph
+**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
-def node_a(state: State) -> dict:
- return {"text": state["text"] + "a"}
+> [!NOTE]
+> Looking for the JS/TS library? Check out [LangGraph.js](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
+## Why use LangGraph?
-def node_b(state: State) -> dict:
- return {"text": state["text"] + "b"}
+LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
+- **[Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution)**. Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
+- **[Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts)**. Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
+- **[Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory)**. Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
+- **[Debugging with LangSmith](https://www.langchain.com/langsmith)**. Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
+- **[Production-ready deployment](https://docs.langchain.com/langsmith/deployments)**. Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
-graph = StateGraph(State)
-graph.add_node("node_a", node_a)
-graph.add_node("node_b", node_b)
-graph.add_edge(START, "node_a")
-graph.add_edge("node_a", "node_b")
+## LangGraph ecosystem
-print(graph.compile().invoke({"text": ""}))
-# {'text': 'ab'}
-```
+While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
-Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
+To improve your LLM application development, pair LangGraph with:
-To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
+- [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
+- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
+- [LangSmith](https://www.langchain.com/langsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
-## Core benefits
-
-LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
-
-- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
-- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
-- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
-- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
-- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
-
-## LangGraph’s ecosystem
-
-While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
-
-- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
-- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
-
-> [!NOTE]
-> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
-
## Additional resources
-- [Guides](https://docs.langchain.com/oss/python/langgraph/overview): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
-- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
-- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
+- [Guides](https://docs.langchain.com/oss/python/learn) – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [API Reference](https://reference.langchain.com/python/langgraph) – Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag) – Guided examples on getting started with LangGraph.
+- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph) – Learn the basics of LangGraph in our free, structured course.
+- [Case studies](https://www.langchain.com/built-with-langgraph) – Hear how industry leaders use LangGraph to ship AI applications at scale.
+- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – Learn how to contribute to LangChain projects and find good first issues.
## Acknowledgements
-
+ 
+ 
+ 
+ 
+