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| # Chatbots | |
| > [Conceptual Guide](https://docs.langchain.com/docs/use-cases/chatbots) | |
| Since language models are good at producing text, that makes them ideal for creating chatbots. | |
| Aside from the base prompts/LLMs, an important concept to know for Chatbots is `memory`. | |
| Most chat based applications rely on remembering what happened in previous interactions, which `memory` is designed to help with. | |
| The following resources exist: | |
| - [ChatGPT Clone](../modules/agents/agent_executors/examples/chatgpt_clone.ipynb): A notebook walking through how to recreate a ChatGPT-like experience with LangChain. | |
| - [Conversation Memory](../modules/memory/getting_started.ipynb): A notebook walking through how to use different types of conversational memory. | |
| - [Conversation Agent](../modules/agents/agents/examples/conversational_agent.ipynb): A notebook walking through how to create an agent optimized for conversation. | |
| Additional related resources include: | |
| - [Memory Key Concepts](../modules/memory.rst): Explanation of key concepts related to memory. | |
| - [Memory Examples](../modules/memory/how_to_guides.rst): A collection of how-to examples for working with memory. | |
| More end-to-end examples include: | |
| - [Voice Assistant](chatbots/voice_assistant.ipynb): A notebook walking through how to create a voice assistant using LangChain. | |