Prompt
How does Big AGI compare to LibreChat in terms of maturity? Does it support storing conversations in custom storage? Are there any other self-hostable solutions like this? Note: by "self-hostable" I mean that it can be self-hosted on a web server to deliver a managed web UI (and not on a desktop environment)
Output
Big AGI and LibreChat are two self-hostable AI chat platforms, each with its own strengths and maturity level. Here's a detailed comparison between the two:
Maturity
- Big AGI: Big AGI is a feature-rich platform designed for professionals, offering a wide range of capabilities such as multi-model chats, AI personas, text-to-image generation, voice interactions, and more. It supports multiple AI models from various vendors (e.g., OpenAI, Anthropic, Google Gemini) and provides advanced features like conversation branching, message editing, and chat cost calculation. However, it stores all chat data locally in the browser by default, which can be a limitation for users who want more robust data storage solutions. While it does support exporting data and has MongoDB integration in development, it still lacks some advanced document storage or retrieval features like RAG (retrieval-augmented generation)[4][9]. Overall, Big AGI is quite mature but may still be evolving in terms of deployment flexibility and storage options.
- LibreChat: LibreChat is also a highly functional platform that mirrors the ChatGPT interface and offers compatibility with multiple AI services (e.g., OpenAI, Azure). It allows users to interact with files (e.g., PDFs) and images and supports advanced agents capable of handling file operations and code interpretation. LibreChat uses MongoDB for storing conversation histories and user data across devices, providing a more robust solution for persistent data storage[5][10]. Additionally, it has features like conversation export in multiple formats (JSON, Markdown) and supports API-based integrations. LibreChat is considered quite mature in terms of both functionality and storage capabilities.
Support for Storing Conversations in Custom Storage
- Big AGI: By default, Big AGI stores conversations locally in the browser, which can be problematic for users who want persistent storage across sessions or devices. However, it does offer an option to store conversations in a database (like MongoDB), although this feature may not extend to settings or other configurations yet[4][9]. This makes Big AGI less ideal for users who require comprehensive custom storage solutions out of the box.
- LibreChat: LibreChat natively supports storing conversations in MongoDB. This allows for persistent storage across devices and sessions, making it easier to manage user data securely and efficiently. Users can delete conversations from the UI, but the underlying data remains stored unless explicitly removed from MongoDB[6][10]. This makes LibreChat more suitable for users needing reliable custom storage solutions.
Other Self-Hostable Solutions
If you're looking for other self-hostable AI chat platforms that can be hosted on a web server with managed web UIs (not just desktop environments), here are some alternatives:
- Open WebUI: A polished front-end that supports multiple backends like Ollama and LiteLLM. It offers features like multi-modal support (image input), document context integration (PDFs), and administrative settings for multi-user scenarios[1][2].
- Oobabooga Text Generation WebUI: A popular option for running local models like Llama2 or Mistral. It provides a flexible interface but has some limitations regarding conversation persistence[1][2].
- Ollama: A self-hosted solution that integrates well with various LLMs like Llama3. It offers good scalability and customization options for users who need to manage multiple models or integrate with their own data sources[7].
- LlamaGPT: A fully offline alternative that ensures complete privacy by running models locally without any external API calls[8].
In summary:
- Big AGI is feature-rich but still evolving in terms of custom storage options.
- LibreChat is more mature regarding persistent data storage via MongoDB.\ Both platforms are viable self-hostable solutions depending on your specific needs around model flexibility and data management.
The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.