AnythingLLM vs SillyTavern

AnythingLLM is a private AI assistant for desktop, mobile, cloud, and self-hosted use. Its desktop product runs on macOS, Windows, and Linux, and the free Desktop plan requires no account or API key and has no token limits. It can turn documents into an agent knowledge base while keeping the data on the device. Desktop tools include file-system capabilities, web search and scraping, model selection, background jobs, custom agent skills, and a Meeting Assistant that records, transcribes, summarizes, and indexes meetings on the computer without a bot joining or cloud processing. The mobile app runs small models on-device and includes web search, document chat, file generation, scheduled jobs, and memory. Supported providers range from Ollama and LM Studio to OpenAI, Anthropic, and Google Gemini Pro. Cloud instances support multiple users, tenant isolation, admin controls, and white-labeling. Self-hosted data remains on the user's infrastructure, according to the maker. The free plan is available; Basic costs $50 a month and Pro costs $99 a month.

SillyTavern is a locally installed interface for interacting with text-generation models, image-generation engines, and text-to-speech voice models. Users can create, import, and switch between character cards, then return to chat histories or review them. The interface includes group chats, lorebooks, personas, prompt controls, chat commands, scripting, and built-in retrieval-augmented generation. Extensions can add character sprites, chat summarization and translation, image generation, text-to-speech, and web search. SillyTavern works with local and cloud model backends, including KoboldCpp, llama.cpp, Ollama, OpenAI, Claude, Mistral, OpenRouter, and AI Horde. The project says it will remain free and open source under AGPL-3.0 and provides no hosted service. It says it does not programmatically track user data, though cloud providers may log information sent to them. Local use requires a system supporting NodeJS 20 or higher; documentation recommends an NVIDIA 3000-series GPU with at least 6GB of VRAM for local inference. The app was not designed for very large character libraries, and performance can degrade above 1,000 characters.