README.md

AnythingLLM: The all-in-one AI app you were looking for.
Chat with your docs, use AI Agents, hyper-configurable, multi-user, & no frustrating set up required.

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AnythingLLM for desktop (Mac, Windows, & Linux)! Download Now

A full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions.

Watch the demo!

Product Overview

AnythingLLM is a full-stack application where you can use commercial off-the-shelf LLMs or popular open source LLMs and vectorDB solutions to build a private ChatGPT with no compromises that you can run locally as well as host remotely and be able to chat intelligently with any documents you provide it.

AnythingLLM divides your documents into objects called workspaces. A Workspace functions a lot like a thread, but with the addition of containerization of your documents. Workspaces can share documents, but they do not talk to each other so you can keep your context for each workspace clean.

Cool features of AnythingLLM

• Custom AI Agents
• Multi-modal support (both closed and open-source LLMs!)
• Multi-user instance support and permissioning Docker version only
• Agents inside your workspace (browse the web, run code, etc)
• Custom Embeddable Chat widget for your website Docker version only
• Multiple document type support (PDF, TXT, DOCX, etc)
• Simple chat UI with Drag-n-Drop funcitonality and clear citations.
• 100% Cloud deployment ready.
• Works with all popular closed and open-source LLM providers.
• Built-in cost & time-saving measures for managing very large documents compared to any other chat UI.
• Full Developer API for custom integrations!
• Much more...install and find out!

Supported LLMs, Embedder Models, Speech models, and Vector Databases

Large Language Models (LLMs):

• Any open-source llama.cpp compatible model
• OpenAI
• OpenAI (Generic)
• Azure OpenAI
• AWS Bedrock
• Anthropic
• Google Gemini Pro
• Hugging Face (chat models)
• Ollama (chat models)
• LM Studio (all models)
• LocalAi (all models)
• Together AI (chat models)
• Fireworks AI (chat models)
• Perplexity (chat models)
• OpenRouter (chat models)
• DeepSeek (chat models)
• Mistral
• Groq
• Cohere
• KoboldCPP
• LiteLLM
• Text Generation Web UI
• Apipie
• xAI
• Novita AI (chat models)

Embedder models:

• AnythingLLM Native Embedder (default)
• OpenAI
• Azure OpenAI
• LocalAi (all)
• Ollama (all)
• LM Studio (all)
• Cohere

Audio Transcription models:

• AnythingLLM Built-in (default)
• OpenAI

TTS (text-to-speech) support:

• Native Browser Built-in (default)
• PiperTTSLocal - runs in browser
• OpenAI TTS
• ElevenLabs
• Any OpenAI Compatible TTS service.

STT (speech-to-text) support:

• Native Browser Built-in (default)

Vector Databases:

• LanceDB (default)
• Astra DB
• Pinecone
• Chroma
• Weaviate
• Qdrant
• Milvus
• Zilliz

Technical Overview

This monorepo consists of three main sections:

• frontend: A viteJS + React frontend that you can run to easily create and manage all your content the LLM can use.
• server: A NodeJS express server to handle all the interactions and do all the vectorDB management and LLM interactions.
• collector: NodeJS express server that process and parses documents from the UI.
• docker: Docker instructions and build process + information for building from source.
• embed: Submodule for generation & creation of the web embed widget.
• browser-extension: Submodule for the chrome browser extension.

Self Hosting

Mintplex Labs & the community maintain a number of deployment methods, scripts, and templates that you can use to run AnythingLLM locally. Refer to the table below to read how to deploy on your preferred environment or to automatically deploy.

Docker	AWS	GCP	Digital Ocean	Render.com

Railway	RepoCloud	Elestio

or set up a production AnythingLLM instance without Docker →

How to setup for development

• yarn setup To fill in the required .env files you'll need in each of the application sections (from root of repo).
  • Go fill those out before proceeding. Ensure server/.env.development is filled or else things won't work right.
• yarn dev:server To boot the server locally (from root of repo).
• yarn dev:frontend To boot the frontend locally (from root of repo).
• yarn dev:collector To then run the document collector (from root of repo).

Learn about documents

Learn about vector caching