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6965c87a68
Extract the model profile refresh logic into a reusable `workflow_call`
workflow so external repos like `langchain-google` and `langchain-aws`
can run the same daily profile refresh and get auto-PRs without
duplicating the pipeline. The in-monorepo caller becomes a thin wrapper
passing provider JSON.
## Changes
- Add `_refresh_model_profiles.yml` as a reusable `workflow_call`
workflow — accepts a `providers` JSON array of `{provider, data_dir}`
pairs, optional `cli-path` (skips cloning the CLI repo when the caller
already has it), and configurable PR metadata inputs
- External callers get the `langchain-profiles` CLI via sparse checkout
of `langchain-ai/langchain` at a configurable `cli-ref`; the in-monorepo
caller short-circuits with `cli-path: libs/model-profiles`
- Add input validation step using `jq` — rejects non-array JSON and
entries missing `provider`/`data_dir` keys with `::error::` annotations
- Replace the piped `while read` loop with `mapfile`/`for` +
per-provider error handling: one provider failure no longer kills the
rest, and all failures are collected and reported at the end
- Route all `${{ inputs.* }}` expressions through `env:` bindings in
`run:` blocks to prevent script injection from caller-controlled values
- Validate `cli-path` existence before use, with a clear error if the
directory is missing
- Summary step now runs with `if: always()` and handles
failure/success/no-op states separately
- Refactor `refresh_model_profiles.yml` into a thin caller that passes
the 10 in-monorepo providers as JSON
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.
Note
Looking for the JS/TS library? Check out LangChain.js.
Quickstart
pip install langchain
# or
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.4")
result = model.invoke("Hello, world!")
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
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.
- Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- Integrations — Chat & embedding models, tools & toolkits, and more
- LangSmith — Agent evals, observability, and debugging for LLM apps
- LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
- 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
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.
Additional resources
- 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.