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docs: restyle README.md to match langchain repo layout (#7187)
Restyle `README.md` to match the `langchain` monorepo's layout: centered logo with link, tagline `<h3>`, centered badges wrapped in `<a>` tags, dedicated **Documentation** / **Discussions** callout sections, and consistent heading/bullet formatting. Add `.markdownlint.json` (ported from `langchain`) to suppress expected inline-HTML lint warnings.
This commit is contained in:
Mason Daugherty
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{
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"MD013": false,
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"MD024": {
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"siblings_only": true
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},
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"MD025": false,
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"MD033": false,
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"MD034": false,
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"MD036": false,
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"MD041": false,
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"MD046": {
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"style": "fenced"
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}
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}
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@@ -1,93 +1,78 @@
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<picture class="github-only">
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<source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
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<source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
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<img alt="LangGraph Logo" src=".github/images/logo-dark.svg" width="50%">
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</picture>
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<div>
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<br>
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<div align="center">
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<a href="https://www.langchain.com/langgraph">
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<picture>
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<source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
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<source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
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<img alt="LangGraph Logo" src=".github/images/logo-dark.svg" width="50%">
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</picture>
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</a>
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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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<div align="center">
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<h3>Low-level orchestration framework for building stateful agents.</h3>
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</div>
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<div align="center">
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<a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
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<a href="https://pypistats.org/packages/langgraph" target="_blank"><img src="https://img.shields.io/pepy/dt/langgraph" alt="PyPI - Downloads"></a>
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<a href="https://github.com/langchain-ai/langgraph/issues" target="_blank"><img src="https://img.shields.io/github/issues-raw/langchain-ai/langgraph" alt="Open Issues"></a>
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<a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langgraph" alt="PyPI - License"></a>
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<a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
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</div>
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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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```bash
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pip install -U langgraph
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```
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Create a simple workflow:
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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).
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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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---
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**Documentation**:
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class State(TypedDict):
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text: str
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- [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) – Comprehensive documentation, including conceptual overviews and guides
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- [reference.langchain.com/python/langgraph](https://reference.langchain.com/python/langgraph) – API reference docs for LangGraph packages
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- [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart) – Get started building with LangGraph
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**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
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def node_a(state: State) -> dict:
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return {"text": state["text"] + "a"}
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> [!NOTE]
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> 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).
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## Why use LangGraph?
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def node_b(state: State) -> dict:
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return {"text": state["text"] + "b"}
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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](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.
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- **[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.
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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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## LangGraph ecosystem
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print(graph.compile().invoke({"text": ""}))
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# {'text': 'ab'}
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```
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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.
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Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
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To improve your LLM application development, pair LangGraph with:
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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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- [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
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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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- [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.
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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 [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
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> [!TIP]
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> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
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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/overview): 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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- [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.).
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- [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.
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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 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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- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – Learn how to contribute to LangChain projects and find good first issues.
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## Acknowledgements
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