124 lines
7.2 KiB
Plaintext
124 lines
7.2 KiB
Plaintext
Metadata-Version: 2.4
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Name: langgraph
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Version: 1.0.5
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Summary: Building stateful, multi-actor applications with LLMs
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Project-URL: Homepage, https://docs.langchain.com/oss/python/langgraph/overview
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Project-URL: Documentation, https://reference.langchain.com/python/langgraph/
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Project-URL: Source, https://github.com/langchain-ai/langgraph/tree/main/libs/langgraph
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Project-URL: Changelog, https://github.com/langchain-ai/langgraph/releases
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Project-URL: Twitter, https://x.com/LangChainAI
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Project-URL: Slack, https://www.langchain.com/join-community
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Project-URL: Reddit, https://www.reddit.com/r/LangChain/
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License-Expression: MIT
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License-File: LICENSE
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3 :: Only
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Programming Language :: Python :: 3.11
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Classifier: Programming Language :: Python :: 3.12
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Classifier: Programming Language :: Python :: 3.13
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Classifier: Programming Language :: Python :: Implementation :: CPython
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Classifier: Programming Language :: Python :: Implementation :: PyPy
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Requires-Python: >=3.10
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Requires-Dist: langchain-core>=0.1
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Requires-Dist: langgraph-checkpoint<4.0.0,>=2.1.0
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Requires-Dist: langgraph-prebuilt<1.1.0,>=1.0.2
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Requires-Dist: langgraph-sdk<0.4.0,>=0.3.0
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Requires-Dist: pydantic>=2.7.4
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Requires-Dist: xxhash>=3.5.0
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Description-Content-Type: text/markdown
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<picture class="github-only">
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<source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
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<source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
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<img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
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</picture>
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<div>
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<br>
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</div>
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[](https://pypi.org/project/langgraph/)
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[](https://pepy.tech/project/langgraph)
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[](https://github.com/langchain-ai/langgraph/issues)
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[](https://docs.langchain.com/oss/python/langgraph/overview)
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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.
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## Get started
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Install LangGraph:
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```
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pip install -U langgraph
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```
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Create a simple workflow:
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```python
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from langgraph.graph import START, StateGraph
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from typing_extensions import TypedDict
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class State(TypedDict):
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text: str
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def node_a(state: State) -> dict:
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return {"text": state["text"] + "a"}
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def node_b(state: State) -> dict:
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return {"text": state["text"] + "b"}
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graph = StateGraph(State)
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graph.add_node("node_a", node_a)
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graph.add_node("node_b", node_b)
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graph.add_edge(START, "node_a")
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graph.add_edge("node_a", "node_b")
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print(graph.compile().invoke({"text": ""}))
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# {'text': 'ab'}
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```
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Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
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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).
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## Core benefits
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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:
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- [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.
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- [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.
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- [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.
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- [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.
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- [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.
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## LangGraph’s ecosystem
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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:
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- [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.
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- [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).
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- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
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> [!NOTE]
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> 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).
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## Additional resources
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- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
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- [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.
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- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
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- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
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- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
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- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
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## Acknowledgements
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LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
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