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# Welcome to LangChain

LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:

- Be data-aware: connect a language model to other sources of data

- Be agentic: allow a language model to interact with its environment

The LangChain framework is designed with the above principles in mind.

This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see here. For the JavaScript documentation, see here.

## Getting Started

Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.

- Getting Started Documentation

## Modules

There are several main modules that LangChain provides support for. For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides. These modules are, in increasing order of complexity:

- Models: The various model types and model integrations LangChain supports.

- Prompts: This includes prompt management, prompt optimization, and prompt serialization.

- Memory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.

- Indexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.

- Chains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.

- Agents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.

## Use Cases

The above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.

- Autonomous Agents: Autonomous agents are long running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.

- Agent Simulations: Putting agents in a sandbox and observing how they interact with each other or to events can be an interesting way to observe their long-term memory abilities.

- Personal Assistants: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data.

- Question Answering: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.

- Chatbots: Since language models are good at producing text, that makes them ideal for creating chatbots.

- Querying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.

- Code Understanding: If you want to understand how to use LLMs to query source code from github, you should read this page.

- Interacting with APIs: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions.

- Extraction: Extract structured information from text.

- Summarization: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.

- Evaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.

## Reference Docs

All of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.

- Reference Documentation

## LangChain Ecosystem

Guides for how other companies/products can be used with LangChain

- LangChain Ecosystem

## Additional Resources

Additional collection of resources we think may be useful as you develop your application!

- LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.

- Glossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!

- Gallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.

- Deployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.

- Tracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.

- Model Laboratory: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.

- Discord: Join us on our Discord to discuss all things LangChain!

- YouTube: A collection of the LangChain tutorials and videos.

- Production Support: As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.