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title: Chatbots | LlamaIndex OSS Documentation
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Chatbots are another extremely popular use case for LLMs. Instead of single-shot question-answering, a chatbot can handle multiple back-and-forth queries and answers, getting clarification or answering follow-up questions.

LlamaIndex gives you the tools to build knowledge-augmented chatbots and agents. This use case builds upon the [Q\&A](/python/framework/use_cases/q_and_a/index.md) use case, make sure to check that out first!

## Resources

The central module guide you’ll want to check out is our [Chat Engines](/python/framework/module_guides/deploying/chat_engines/index.md).

Here are some additional relevant resources to build full-stack chatbot apps:

- [Building a chatbot](/python/framework/understanding/putting_it_all_together/chatbots/building_a_chatbot/index.md) tutorial
- [create-llama](https://blog.llamaindex.ai/create-llama-a-command-line-tool-to-generate-llamaindex-apps-8f7683021191), a command line tool that generates a full-stack chatbot application for you
- [SECinsights.ai](https://www.secinsights.ai/), an open-source application that uses LlamaIndex to build a chatbot that answers questions about SEC filings
- [RAGs](https://blog.llamaindex.ai/introducing-rags-your-personalized-chatgpt-experience-over-your-data-2b9d140769b1), a project inspired by OpenAI’s GPTs that lets you build a low-code chatbot over your data using Streamlit
- Our [OpenAI agents](/python/framework/module_guides/deploying/agents/modules/index.md) are all chat bots in nature

## External sources

- [Building a chatbot with Streamlit](https://blog.streamlit.io/build-a-chatbot-with-custom-data-sources-powered-by-llamaindex/)
- [Building a chatbot with CopilotKit](https://docs.copilotkit.ai/llamaindex/quickstart)