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Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 5 to 6. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/astral-sh/setup-uv/releases">astral-sh/setup-uv's releases</a>.</em></p> <blockquote> <h2>v6.0.0 🌈 activate-environment and working-directory</h2> <h2>Changes</h2> <p>This version contains some breaking changes which have been gathering up for a while. Lets dive into them:</p> <ul> <li><a href="https://github.com/astral-sh/setup-uv/blob/HEAD/#activate-environment">Activate environment</a></li> <li><a href="https://github.com/astral-sh/setup-uv/blob/HEAD/#working-directory">Working Directory</a></li> <li><a href="https://github.com/astral-sh/setup-uv/blob/HEAD/#default-cache-dependency-glob">Default <code>cache-dependency-glob</code></a></li> <li><a href="https://github.com/astral-sh/setup-uv/blob/HEAD/#use-default-cache-dir-on-self-hosted-runners">Use default cache dir on self hosted runners</a></li> </ul> <h3>Activate environment</h3> <p>In previous versions using the input <code>python-version</code> automatically activated a venv at the repository root. This led to some unwanted side-effects, was sometimes unexpected and not flexible enough.</p> <p>The venv activation is now explicitly controlled with the new input <code>activate-environment</code> (false by default):</p> <pre lang="yaml"><code>- name: Install the latest version of uv and activate the environment uses: astral-sh/setup-uv@v6 with: activate-environment: true - run: uv pip install pip </code></pre> <p>The venv gets created by the <a href="https://docs.astral.sh/uv/pip/environments/"><code>uv venv</code></a> command so the python version is controlled by the <code>python-version</code> input or the files <code>pyproject.toml</code>, <code>uv.toml</code>, <code>.python-version</code> in the <code>working-directory</code>.</p> <h3>Working Directory</h3> <p>The new input <code>working-directory</code> controls where we look for <code>pyproject.toml</code>, <code>uv.toml</code> and <code>.python-version</code> files which are used to determine the version of uv and python to install.</p> <p>It can also be used to control where the venv gets created.</p> <pre lang="yaml"><code>- name: Install uv based on the config files in the working-directory uses: astral-sh/setup-uv@v6 with: working-directory: my/subproject/dir </code></pre> <blockquote> <p>[!CAUTION]</p> <p>The inputs <code>pyproject-file</code> and <code>uv-file</code> have been removed.</p> </blockquote> <h3>Default <code>cache-dependency-glob</code></h3> <p><a href="https://github.com/ssbarnea"><code>@ssbarnea</code></a> found out that the default <code>cache-dependency-glob</code> was not suitable for a lot of users.</p> <p>The old default</p> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="6b9c6063ab"><code>6b9c606</code></a> Bump dependencies (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/389">#389</a>)</li> <li><a href="ef6bcdff59"><code>ef6bcdf</code></a> Fix default cache dependency glob (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/388">#388</a>)</li> <li><a href="9a311713f4"><code>9a31171</code></a> chore: update known checksums for 0.6.17 (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/384">#384</a>)</li> <li><a href="c7f87aa956"><code>c7f87aa</code></a> bump to v6 in README (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/382">#382</a>)</li> <li><a href="aadfaf08d6"><code>aadfaf0</code></a> Change default cache-dependency-glob (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/352">#352</a>)</li> <li><a href="a0f9da6273"><code>a0f9da6</code></a> No default UV_CACHE_DIR on selfhosted runners (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/380">#380</a>)</li> <li><a href="ec4c691628"><code>ec4c691</code></a> new inputs activate-environment and working-directory (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/381">#381</a>)</li> <li><a href="aa1290542e"><code>aa12905</code></a> chore: update known checksums for 0.6.16 (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/378">#378</a>)</li> <li><a href="fcaddda076"><code>fcaddda</code></a> chore: update known checksums for 0.6.15 (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/377">#377</a>)</li> <li><a href="fb3a0a97fa"><code>fb3a0a9</code></a> log info on venv activation (<a href="https://redirect.github.com/astral-sh/setup-uv/issues/375">#375</a>)</li> <li>See full diff in <a href="https://github.com/astral-sh/setup-uv/compare/v5...v6">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`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Note
Looking for the JS/TS library? Check out LangChain.js.
LangChain is a framework for building 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 -U langchain
To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
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.
LangChain’s 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:
- 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.
- 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.
- LangGraph Platform - 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.
Additional resources
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- How-to Guides: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.