#### Use cases Below are some of the common use cases LangChain supports. [Source](https://python.langchain.com/en/latest/index.html#use-cases) - [Autonomous Agents](https://python.langchain.com/en/latest/use_cases/autonomous_agents.html): Autonomous agents are long running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI. - [Agent Simulations](https://python.langchain.com/en/latest/use_cases/agent_simulations.html): 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](https://python.langchain.com/en/latest/use_cases/personal_assistants.html): The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data. - [Question Answering](https://python.langchain.com/en/latest/use_cases/question_answering.html): The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer. - [Chatbots](https://python.langchain.com/en/latest/use_cases/chatbots.html): Since language models are good at producing text, that makes them ideal for creating chatbots. - [Querying Tabular Data](https://python.langchain.com/en/latest/use_cases/tabular.html): 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](https://python.langchain.com/en/latest/use_cases/code.html): If you want to understand how to use LLMs to query source code from github, you should read this page. - [Interacting with APIs](https://python.langchain.com/en/latest/use_cases/apis.html): 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](https://python.langchain.com/en/latest/use_cases/extraction.html): Extract structured information from text. - [Summarization](https://python.langchain.com/en/latest/use_cases/summarization.html): Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation. - [Evaluation](https://python.langchain.com/en/latest/use_cases/evaluation.html): 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