mirror of
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f7dbdab5ba
Fix several docstring inaccuracies in langchain-core and update outdated LangSmith URLs across three README files. **Docstring fixes (libs/core):** - `tap_output_iter`: docstring says "async iterator" but method accepts sync `Iterator` - `agenerate_from_stream`: docstring says "Iterator" but method accepts `AsyncIterator` - `BaseLLM.OutputType`: docstring says "input type" but property returns output type - Grammar: "or deprecated" → "or be deprecated", "relies" → "rely", "whose the" → "whose" **URL fixes (libs/core, libs/langchain, libs/langchain_v1):** - Updated `smith.langchain.com` → `www.langchain.com/langsmith` (root README already uses the correct URL) Verified with `make lint` and `make format` in libs/core — no new issues introduced. Changes are docs-only with no code logic impact. *This PR was created with assistance from an AI coding tool.*
40 lines
2.8 KiB
Markdown
40 lines
2.8 KiB
Markdown
# 🦜️🔗 LangChain
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[](https://pypi.org/project/langchain/#history)
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[](https://opensource.org/licenses/MIT)
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[](https://pypistats.org/packages/langchain)
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[](https://x.com/langchain)
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Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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To help you ship LangChain apps to production faster, check out [LangSmith](https://www.langchain.com/langsmith).
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[LangSmith](https://www.langchain.com/langsmith) is a unified developer platform for building, testing, and monitoring LLM applications.
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## Quick Install
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```bash
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pip install langchain
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```
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## 🤔 What is this?
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LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and [more](https://docs.langchain.com/oss/python/integrations/providers/overview). LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.
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We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our low-level agent orchestration framework and runtime, when you have more advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.
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LangChain [agents](https://docs.langchain.com/oss/python/langchain/agents) are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. (You do not need to know LangGraph for basic LangChain agent usage.)
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## 📖 Documentation
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For full documentation, see the [API reference](https://reference.langchain.com/python/langchain/langchain/). For conceptual guides, tutorials, and examples on using LangChain, see the [LangChain Docs](https://docs.langchain.com/oss/python/langchain/overview). You can also chat with the docs using [Chat LangChain](https://chat.langchain.com).
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## 📕 Releases & Versioning
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See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
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## 💁 Contributing
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As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
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For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).
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