Overview
AnythingLLM is a full-stack application built by Mintplex Labs to make it easy to create private, document-aware chat assistants. The project enables you to ingest documents (PDF, DOCX, TXT, etc.), split and embed their content, and use any supported LLM or vector database to answer questions or hold contextual conversations. It targets both local (desktop) and server/Docker deployments and is designed for flexibility, multi-user scenarios, and extensibility.
Key Features
- Retrieval-Augmented Generation (RAG): Built-in support to turn documents into retrievable context so LLMs can provide accurate, citation-backed responses.
- No-code AI Agent Builder: Create custom agents and workflows without coding; agents can perform actions like web browsing from inside a workspace.
- Multi-model & multi-provider support: Works with many closed-source and open-source LLM providers (OpenAI, Anthropic, Hugging Face, local llama.cpp-compatible models, LM Studio, LocalAI, etc.).
- Embedder & Vector DB flexibility: Supports native embedder plus OpenAI, Cohere, LocalAI, Ollama, LM Studio, and vector DBs like LanceDB (default), PGVector, Pinecone, Chroma, Weaviate, Qdrant, Milvus, and more.
- Multi-modal & audio: Multi-modal model support, built-in and external audio transcription, TTS integrations (browser-native, OpenAI TTS, ElevenLabs, Piper), and STT options.
- Multi-user & permissions: Docker deployment supports multi-user instances with permissioning; desktop builds are available for individual use.
- Developer API, embeddable chat widget, and browser extension for integration into other apps and workflows.
- Deployment options: Desktop apps for Mac/Windows/Linux, Docker, and cloud deployment templates (AWS, GCP, DigitalOcean, Render, Railway, etc.).
- Telemetry & privacy controls: Anonymous telemetry powered by PostHog with an opt-out (DISABLE_TELEMETRY) and transparent details in the repo.
- Open-source license: MIT-licensed, actively maintained repository with a large star count and community contributions.
Architecture & Components
- frontend: ViteJS + React UI for workspace/document/chat management and embed widget support.
- server: Node.js/Express server handling LLM interactions, vector DB management, and API endpoints.
- collector: Document processing service to parse and chunk documents for embedding.
- docker: Docker build/configuration and deployment helpers.
- embed & browser-extension: Submodules for web-embed chat widget and a Chrome extension.
Use Cases
- Internal knowledge bases and private document chat assistants for teams.
- Self-hosted alternatives to hosted chat services, allowing use of local or preferred LLM providers and vector stores.
- Rapid prototyping of AI Agents and workflows via the no-code builder.
- Embedding a document-aware chat widget into websites or integrating with other applications via the provided API.
Notable Info
- Created and maintained by Mintplex Labs. The project README advertises desktop downloads and hosted instances, and the repo is MIT licensed.
- The project emphasizes compatibility (MCP), cost/time-saving measures for large documents, and clear citation-based responses.
Getting Started
Clone the repo or download desktop builds, configure provider keys and .env files, run the collector/server/frontend or use the Docker deployment templates, then ingest documents into workspaces to enable contextual chat.
