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Deepak Thorat
2dd956b8ad
Closes #40793 --- Docs-only repair from a full audit of developer-facing markdown against the repository as source of truth. A new contributor following `AGENTS.md` from the repo root currently hits missing files and commands that cannot run; package docs and a workflow comment also drift from reality. **What was wrong** - `AGENTS.md` listed root-level `pyproject.toml`, `uv.lock`, and `Makefile` as key config files — none exist at the repo root (config is per package under `libs/*/`). - Setup/test/lint examples (`uv sync --all-groups`, `make test` / `lint` / `format`) had no working directory, so they fail if copy-pasted from the root. - The monorepo structure tree omitted `openwiki/` and `AGENTS.md`. - `libs/README.md` omitted the `model-profiles/` package from its directory list. - Root `README.md` linked Deep Agents with `http://` while every other docs link uses `https://`. - The PR-title paragraph claimed scopes are mandatory “with no exceptions”, but `pr_lint.yml` sets `requireScope: false` (only empty `type():` parens are rejected). - Grammar (“require” → “requires”), an unfinished editable-installs sentence, incomplete `pr_lint.yml` scope comment (missing `openrouter`, `typesafe`), and a stale `make help` line pointing at a non-existent top-level Makefile. **What changed** - Clarified per-package config layout and required `cd` into `libs/<package>` before `uv` / `make` commands. - Completed the structure diagram; added `model-profiles/` to `libs/README.md`. - Switched Deep Agents links to `https://`. - Aligned the scope guidance with actual CI behavior (documented, did not change `requireScope`). - Tightened the `pr_lint.yml` comment and removed the dead Makefile help line. No runtime code, tests, or CI logic changed — comments and markdown only. AI assistance was used to prepare this change; I reviewed the diff against the repository layout.
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.
Tip
Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilities for common usage patterns such as planning, subagents, file system usage, and more.
Quickstart
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.5")
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.
For an equivalent JS/TS library, check out LangChain.js.
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
Resources
- Documentation — conceptual overviews and guides
- LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together
- API reference — complete reference for all public classes, functions, and types
- Discussions — community forum for technical questions, ideas, and feedback
- LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
- Contributing Guide — how to contribute and find good first issues
- Code of Conduct — community guidelines and standards