{ "markdown": "
\n \n \n \n \n \"LangChain\n \n \n
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The agent engineering platform.

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\n\nLangChain 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.\n\n> [!TIP]\n> Just getting started? Check out **[Deep Agents](http://docs.langchain.com/oss/python/deepagents/)** — 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.\n\n## Quickstart\n\n```bash\nuv add langchain\n```\n\n```python\nfrom langchain.chat_models import init_chat_model\n\nmodel = init_chat_model(\"openai:gpt-5.5\")\nresult = model.invoke(\"Hello, world!\")\n```\n\nIf you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://github.com/langchain-ai/langgraph), our framework for building controllable agent workflows.\n\nFor an equivalent JS/TS library, check out [LangChain.js](https://github.com/langchain-ai/langchainjs).\n\n> [!TIP]\n> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).\n\n## LangChain ecosystem\n\nWhile 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.\n\n- **[Deep Agents](http://docs.langchain.com/oss/python/deepagents/)** — Build agents that can plan, use subagents, and leverage file systems for complex tasks\n- **[LangGraph](https://docs.langchain.com/oss/python/langgraph/overview)** — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework\n- **[Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview)** — Chat & embedding models, tools & toolkits, and more\n- **[LangSmith](https://www.langchain.com/langsmith)** — Agent evals, observability, and debugging for LLM apps\n- **[LangSmith Deployment](https://docs.langchain.com/langsmith/deployments)** — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows\n\n## Why use LangChain?\n\nLangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.\n\n- **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\n- **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\n- **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\n- **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\n- **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\n- **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\n\n---\n\n## Resources\n\n- [Documentation](https://docs.langchain.com/oss/python/langchain/overview) — conceptual overviews and guides\n- [LangChain ecosystem overview](https://docs.langchain.com/oss/python/concepts/products) — how LangChain, LangGraph, and Deep Agents fit together\n- [API reference](https://reference.langchain.com/python) — complete reference for all public classes, functions, and types\n- [Discussions](https://forum.langchain.com/c/oss-product-help-lc-and-lg/langchain/14) — community forum for technical questions, ideas, and feedback\n- [LangChain Academy](https://academy.langchain.com/) — comprehensive, free courses on LangChain libraries and products, made by the LangChain team\n- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) — how to contribute and find good first issues\n- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) — community guidelines and standards\n", "bytes": 5889, "sha": "7e8f7983153194ce3e1d277e03c14623a2f7e3c6238ef71f066403a9f3398ad3", "repo_slug": "srk-3/langchain", "fonte": "repo", "truncated": false, "api": "https://agentalog.com/api/listings/okf_srk_3_langchain_openwiki_index_md_7d1b0b15/readme" }