GitHub

Lobe Chat

Lobe Chat is an open-source, modern-designed user interface (UI) and framework for interacting with large language models (LLMs) like ChatGPT, Claude, Gemini, Groq, and Ollama. It offers a streamlined and feature-rich way to chat with these AI models.

What is the project about?

It's a web application that provides a user-friendly interface to interact with various LLMs. It acts as a central hub for accessing and chatting with different AI models, offering features beyond basic text interaction.

What problem does it solve?

  • Provides a single, well-designed interface for multiple LLMs, eliminating the need to switch between different platforms.
  • Simplifies the deployment of a private chat application using these models.
  • Enhances the chat experience with features like voice interaction, visual recognition, and plugin extensions.
  • Offers a more organized and feature-rich alternative to basic chat interfaces.
  • Allows for self-hosting, giving users more control over their data and privacy.

What are the features of the project?

  • Chain of Thought (CoT): Visualizes the AI's reasoning process step-by-step.
  • Branching Conversations: Allows for non-linear conversations, exploring different topics while preserving context.
  • Artifacts Support: Enables real-time creation and visualization of content like SVGs, HTML, and documents.
  • File Upload / Knowledge Base: Supports uploading files and creating knowledge bases for richer dialogue.
  • Multi-Model Service Provider Support: Works with a wide range of LLMs, including OpenAI, Ollama, Anthropic, Google, and many others (36 providers in total).
  • Local LLM Support: Integrates with Ollama for running models locally.
  • Model Visual Recognition: Can "see" and understand images uploaded by the user (using models like GPT-4 Vision).
  • TTS & STT Voice Conversation: Text-to-Speech and Speech-to-Text for voice interaction.
  • Text to Image Generation: Integrates with image generation tools like DALL-E 3 and Midjourney.
  • Plugin System (Function Calling): Extends functionality with plugins that can access real-time information and perform actions.
  • Agent Market (GPTs): A marketplace to discover, share, and use pre-designed AI agents.
  • Database Support: Supports both local and remote databases (PostgreSQL) for data storage.
  • Multi-User Management: Supports user authentication and management via next-auth and Clerk.
  • Progressive Web App (PWA): Can be installed as a desktop or mobile application for a native-like experience.
  • Mobile Device Adaptation: Optimized for use on mobile devices.
  • Custom Themes: Allows users to personalize the appearance with themes and color customization.
  • Quick Deployment: Easy one-click deployment options (Vercel, Docker, etc.).
  • Privacy Protection: Data can be stored locally in the user's browser.

What are the technologies used in the project?

  • Frontend Framework (implied, likely React based on ecosystem packages)
  • Large Language Models (OpenAI, Anthropic, Google, Ollama, etc.)
  • Docker (for containerized deployment)
  • Vercel, Zeabur, Sealos, Alibaba Cloud (for one-click deployment)
  • PostgreSQL (for server-side database)
  • CRDT (Conflict-Free Replicated Data Type) for multi-device synchronization (experimental)
  • next-auth, Clerk (for user authentication)
  • TTS/STT libraries (OpenAI Audio, Microsoft Edge Speech)

What are the benefits of the project?

  • User-Friendly: Provides a clean, intuitive interface for interacting with LLMs.
  • Extensible: The plugin system allows for significant expansion of capabilities.
  • Versatile: Supports a wide variety of LLMs and use cases.
  • Private and Secure: Offers self-hosting and local data storage options.
  • Efficient: Optimized for performance and quick deployment.
  • Customizable: Allows for personalization through themes and settings.
  • Community-Driven: Open-source with active development and community contributions.

What are the use cases of the project?

  • Personal Chatbot: A private AI assistant for various tasks.
  • Customer Support: Building AI-powered customer service agents.
  • Content Creation: Generating text, images, and other content.
  • Research and Development: Experimenting with different LLMs and their capabilities.
  • Education: Learning about and interacting with AI models.
  • Data Analysis: Using AI to analyze and understand data.
  • Automation: Automating tasks through AI-powered agents.
  • Any application where interaction with LLMs is beneficial.
lobe-chat screenshot