What is AnythingLLM?
AnythingLLM is an open-source application built by Mintplex Labs, a Y Combinator alum, that bundles retrieval-augmented generation, AI agents, and multi-model chat into a single self-hosted or desktop tool. It connects to local models via Ollama, LM Studio, or llama.cpp, or to cloud providers, and ingests documents into a private knowledge base that never leaves the user's device by default.
Built-in agent skills can browse the web, transcribe and summarize meetings entirely on-device, and automate multi-step workflows. It ships as a one-click desktop app for macOS, Windows, and Linux, or as a Docker image for multi-user, self-hosted deployments with white-labeling and access controls.
Key features
- Chat with local documents using built-in RAG, with a vector database of your choice
- Built-in AI agents and "agent skills" for web search, file access, and automation
- On-device meeting transcription and summarization with no cloud processing
- Works with any local model (Ollama, LM Studio, llama.cpp) or cloud API key
- Multi-user support, password protection, and white-labeling on the Docker deployment
- Full developer API and embeddable chat widget for websites
How to get started
- Download the desktop app or deploy the Docker image
- Pick a local or cloud LLM and embedding model
- Create a workspace and upload documents to build its knowledge base
- Enable agent skills you need, such as web search or file tools
- Chat, run agent tasks, or embed the chat widget in your product
AnythingLLM pricing
The desktop and Docker apps are free and MIT-licensed with unlimited local use; you only pay for any cloud model API you connect. Multi-user and white-label features are free on Docker, with paid Workspaces add-ons for teams that want managed hosting.
| Plan | Price | Best for |
|---|---|---|
| Free / Open Source | $0 | Individuals and self-hosters running locally |
| Workspaces | Contact for pricing | Teams wanting managed multi-user hosting |
Our take: Its "own, don't rent" pitch is backed by a genuinely large open-source project (65k+ GitHub stars), making it one of the few local-first AI agent tools with real community traction rather than a thin wrapper.
Prices verified 2026-09. Plans change often — confirm on the official site above before you buy.
Use cases
- Privacy-conscious teams wanting a self-hosted ChatGPT-like tool
- Developers building document Q&A or internal knowledge bases
- Individuals running fully offline AI on their own hardware
- Organizations needing multi-user AI access without per-seat API costs
AnythingLLM vs. alternatives
| Tool | How it compares |
|---|---|
| AnythingLLM | An open-source, local-first AI app that lets you chat with your documents, build AI agents, and run any LLM without sending data to the cloud. |
| Dify | Dify is a cloud-hosted agent-building platform rather than a local-first desktop app. |
| Flowise | Flowise focuses on visual workflow building rather than document chat and local models. |
| LangGraph | LangGraph is a code-first framework for developers, without AnythingLLM's no-code desktop app. |
FAQ
Is AnythingLLM free to use?
AnythingLLM's plans: Free & open-source (desktop/Docker); paid team features via Workspaces. Pricing and free-tier limits change over time, so check the official site above for the latest details.
What is AnythingLLM used for?
An open-source, local-first AI app that lets you chat with your documents, build AI agents, and run any LLM without sending data to the cloud.
Who is AnythingLLM best for?
AnythingLLM is a good fit for privacy-conscious teams wanting a self-hosted ChatGPT-like tool, or developers building document Q&A or internal knowledge bases.
What are the alternatives to AnythingLLM?
Commonly compared alternatives include Dify, Flowise, and LangGraph, see the comparison above for how they differ.
How much does AnythingLLM cost?
AnythingLLM itself costs nothing — it's free and open source for desktop or self-hosted use. The only ongoing cost is optional: whatever you pay a cloud LLM provider if you choose not to run models locally.
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Last updated: 2026-09 · Reviewed by AIKetra editors · How we evaluate tools · Embed a Featured badge
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