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Bumps [langgraph](https://github.com/langchain-ai/langgraph) from 1.0.8 to 1.0.10rc1. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/langchain-ai/langgraph/releases">langgraph's releases</a>.</em></p> <blockquote> <h2>langgraph==1.0.10rc1</h2> <p>Changes since 1.0.9</p> <ul> <li>release: Candidate (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6947">#6947</a>)</li> <li>Merge commit from fork</li> <li>chore: add tests to confirm expected subgraph persistence behavior (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6943">#6943</a>)</li> <li>fix(langgraph): correct ParentCommand bubbling when checkpoint_ns includes numeric task segments (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6864">#6864</a>)</li> <li>chore: add <code>make type</code> target for type checking (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6748">#6748</a>)</li> </ul> <h2>langgraph==1.0.9</h2> <p>Changes since 1.0.8</p> <ul> <li>release: langgraph + prebuilt (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6875">#6875</a>)</li> <li>fix: sequential interrupt handling w/ functional API (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6863">#6863</a>)</li> <li>chore: state_updated_at sort by (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6857">#6857</a>)</li> <li>chore: bump orjson (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6852">#6852</a>)</li> <li>chore: conformance testing (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6842">#6842</a>)</li> <li>chore(deps): bump the all-dependencies group in /libs/langgraph with 6 updates (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6815">#6815</a>)</li> <li>chore(deps): bump protobuf from 6.33.4 to 6.33.5 in /libs/langgraph (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6833">#6833</a>)</li> <li>chore(deps): bump cryptography from 46.0.3 to 46.0.5 in /libs/langgraph (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6837">#6837</a>)</li> <li>chore(deps): bump nbconvert from 7.16.6 to 7.17.0 in /libs/langgraph (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6832">#6832</a>)</li> <li>chore: server runtime type (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6774">#6774</a>)</li> <li>refactor: replace bare except with BaseException in AsyncQueue (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6765">#6765</a>)</li> </ul> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href="a04ec5d6f0"><code>a04ec5d</code></a> release: Candidate (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6947">#6947</a>)</li> <li><a href="50df7d423a"><code>50df7d4</code></a> Merge commit from fork</li> <li><a href="c4a4a46473"><code>c4a4a46</code></a> chore: add tests to confirm expected subgraph persistence behavior (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6943">#6943</a>)</li> <li><a href="f178eb821e"><code>f178eb8</code></a> fix(langgraph): correct ParentCommand bubbling when checkpoint_ns includes nu...</li> <li><a href="48167d7fec"><code>48167d7</code></a> chore(deps): bump the all-dependencies group in /libs/cli with 2 updates (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6920">#6920</a>)</li> <li><a href="806878a421"><code>806878a</code></a> chore(deps): bump the all-dependencies group in /libs/checkpoint-postgres wit...</li> <li><a href="8087e6a42c"><code>8087e6a</code></a> docs(sdk-py): update auth docstrings to default-deny pattern (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6933">#6933</a>)</li> <li><a href="8fbdb14487"><code>8fbdb14</code></a> release(sdk-py): 0.3.9 (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6932">#6932</a>)</li> <li><a href="5093802f31"><code>5093802</code></a> chore(deps): bump the all-dependencies group in /libs/checkpoint with 2 updat...</li> <li><a href="b89ef60b91"><code>b89ef60</code></a> feat(sdk-py): add extract parameter to threads.search() (<a href="https://redirect.github.com/langchain-ai/langgraph/issues/6880">#6880</a>)</li> <li>Additional commits viewable in <a href="https://github.com/langchain-ai/langgraph/compare/1.0.8...1.0.10rc1">compare view</a></li> </ul> </details> <br /> [](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. 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The agent engineering platform.
LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development – all while future-proofing decisions as the underlying technology evolves.
pip install langchain
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Documentation:
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python – API reference docs for LangChain packages
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Note
Looking for the JS/TS library? Check out LangChain.js.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- Real-time data augmentation. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- Model interoperability. Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly – LangChain's abstractions keep you moving without losing momentum.
- Rapid prototyping. Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle.
- Production-ready features. Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices.
- Vibrant community and ecosystem. Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community.
- Flexible abstraction layers. Work at the level of abstraction that suits your needs - from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity.
LangChain ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
To improve your LLM application development, pair LangChain with:
- Deep Agents (new!) – Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph – Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows – and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- Integrations – List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
- 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 – 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.
Additional resources
- API Reference – Detailed reference on navigating base packages and integrations for LangChain.
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
- LangChain Academy – Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.