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# 🦜️🔗 LangChain
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⚡ Building applications with LLMs through composability ⚡
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[](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [](https://opensource.org/licenses/MIT) [](https://twitter.com/langchainai) [](https://discord.gg/6adMQxSpJS)
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## Quick Install
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`pip install langchain`
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## 🤔 What is this?
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Large language models (LLMs) are emerging as a transformative technology, enabling
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developers to build applications that they previously could not.
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But using these LLMs in isolation is often not enough to
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create a truly powerful app - the real power comes when you are able to
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combine them with other sources of computation or knowledge.
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This library is aimed at assisting in the development of those types of applications.
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## 📖 Documentation
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Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documentation on:
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- Getting started (installation, setting up environment, simple examples)
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- How-To examples (demos, integrations, helper functions)
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- Reference (full API docs)
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- Resources (high level explanation of core concepts)
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## 🚀 What can this help with?
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There are four main areas that LangChain is designed to help with.
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These are, in increasing order of complexity:
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1. LLM and Prompts
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2. Chains
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3. Agents
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4. Memory
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For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/?).
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## 🤖 Contributing
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As an open source project in a rapidly developing field, we are extremely open
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to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
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For detailed information on how to contribute, see [here](CONTRIBUTING.md).
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