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Bumps [nltk](https://github.com/nltk/nltk) from 3.9.2 to 3.9.3. <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/nltk/nltk/blob/develop/ChangeLog">nltk's changelog</a>.</em></p> <blockquote> <p>Version 3.9.3 2026-02-21</p> <ul> <li>Fix CVE-2025-14009: secure ZIP extraction in nltk.downloader (<a href="https://redirect.github.com/nltk/nltk/issues/3468">#3468</a>)</li> <li>Block path traversal/arbitrary reads in nltk.data for protocol-less refs (<a href="https://redirect.github.com/nltk/nltk/issues/3467">#3467</a>)</li> <li>Block path traversal/abs paths in corpus readers and FS pointers (<a href="https://redirect.github.com/nltk/nltk/issues/3479">#3479</a>, <a href="https://redirect.github.com/nltk/nltk/issues/3480">#3480</a>)</li> <li>Validate external StanfordSegmenter JARs using SHA256 (<a href="https://redirect.github.com/nltk/nltk/issues/3477">#3477</a>)</li> <li>Add optional sandbox enforcement for filestring() (<a href="https://redirect.github.com/nltk/nltk/issues/3485">#3485</a>)</li> <li>Maintenance: downloader/zipped models, CI/tooling updates</li> </ul> <p>Thanks to the following contributors to 3.9.3: Chris Clauss, Eric Kafe, HyperPS, purificant, Shivansh-Game, Christopher Smith</p> <p>Version 3.9.2 2025-10-01</p> <ul> <li>Update download checksums to use SHA256 in built index</li> <li>Fix percentage escape in new-style string formatting</li> <li>replace shortened URLs using goo.gl</li> <li>Make Wordnet interoperable with various taggers and tagged corpora</li> <li>Fix saving PerceptronTagger</li> <li>Document how to reproduce old Wordnet studies</li> <li>properly initialize Portuguese corpus reader</li> <li>support for mixed rules conversion into Chomsky Normal Form</li> <li>only import tkinter if a GUI is needed</li> <li>issue <a href="https://redirect.github.com/nltk/nltk/issues/2112">#2112</a> with Corenlp</li> <li>new environment variable NLTK_DOWNLOADER_FORCE_INTERACTIVE_SHELL</li> <li>Lesk defaults to most frequent sense in case of ties</li> </ul> <p>Thanks to the following contributors to 3.9.2: Jose Cols, Peter de Blanc, GeneralPoxter, Eric Kafe, William LaCroix, Jason Liu, Samer Masterson, Mike014, purificant, Andrew Ernest Ritz, samertm, Ikram Ul Haq, Christopher Smith, Ryan Mannion</p> <p>Version 3.9.1 2024-08-19</p> <ul> <li>Fixed bug that prevented wordnet from loading</li> </ul> <p>Version 3.9 2024-08-18</p> <ul> <li>Fix security vulnerability CVE-2024-39705 (breaking change)</li> <li>Replace pickled models (punkt, chunker, taggers) by new pickle-free "_tab" packages</li> <li>No longer sort Wordnet synsets and relations (sort in calling function when required)</li> <li>Only strip the last suffix in Wordnet Morphy, thus restricting synsets() results</li> <li>Add Python 3.12 support</li> <li>Many other minor fixes</li> </ul> <p>Thanks to the following contributors to 3.8.2: Tom Aarsen, Cat Lee Ball, Veralara Bernhard, Carlos Brandt, Konstantin Chernyshev, Michael Higgins, Eric Kafe, Vivek Kalyan, David Lukes, Rob Malouf, purificant, Alex Rudnick, Liling Tan, Akihiro Yamazaki.</p> <p>Version 3.8.1 2023-01-02</p> <ul> <li>Resolve RCE vulnerability in localhost WordNet Browser (<a href="https://redirect.github.com/nltk/nltk/issues/3100">#3100</a>)</li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="4154eb85e8"><code>4154eb8</code></a> Merge pull request <a href="https://redirect.github.com/nltk/nltk/issues/3503">#3503</a> from ekaf/hotfix-3501</li> <li><a href="7a710cbc8b"><code>7a710cb</code></a> Prepare release 3.9.3</li> <li><a href="1056b323af"><code>1056b32</code></a> Merge pull request <a href="https://redirect.github.com/nltk/nltk/issues/3468">#3468</a> from HyperPS/fix/secure-unzip-rce</li> <li><a href="7dc5baa98f"><code>7dc5baa</code></a> Resolve merge conflict in tag mapping using normalized nltk resource URL</li> <li><a href="7ef38b8aa6"><code>7ef38b8</code></a> Merge pull request <a href="https://redirect.github.com/nltk/nltk/issues/3467">#3467</a> from HyperPS/develop</li> <li><a href="b2e1164bf8"><code>b2e1164</code></a> Merge pull request <a href="https://redirect.github.com/nltk/nltk/issues/3485">#3485</a> from HyperPS/fix-filestring-sandbox-update</li> <li><a href="ac0ce55daa"><code>ac0ce55</code></a> Merge pull request <a href="https://redirect.github.com/nltk/nltk/issues/3480">#3480</a> from HyperPS/fix/filesystem-sandbox-security</li> <li><a href="603e34d25a"><code>603e34d</code></a> Merge pull request <a href="https://redirect.github.com/nltk/nltk/issues/3479">#3479</a> from HyperPS/fix/corpusreader-path-traversal</li> <li><a href="b63a5014aa"><code>b63a501</code></a> Merge pull request <a href="https://redirect.github.com/nltk/nltk/issues/3477">#3477</a> from HyperPS/fix/stanford-segmenter-rce-sha256</li> <li><a href="df38955e50"><code>df38955</code></a> Merge pull request <a href="https://redirect.github.com/nltk/nltk/issues/3494">#3494</a> from ekaf/ewnv</li> <li>Additional commits viewable in <a href="https://github.com/nltk/nltk/compare/3.9.2...3.9.3">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. You can also trigger a rebase manually by commenting `@dependabot rebase`. 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The platform for reliable agents.
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.