What is AnythingLLM used for?

AnythingLLM is an all-in-one AI application for chatting with your documents and running AI agents locally, with multi-user support and desktop and Docker versions. It is a good fit for private document question answering, team AI workspace with per-user access and local-first AI assistant.

Is AnythingLLM open source?

Yes. AnythingLLM is open source under the MIT licence. Its source code is on GitHub at Mintplex-Labs/anything-llm and is written mainly in JavaScript.

Yes. AnythingLLM is open source, so the software itself is free to use. A managed cloud version is also available, with paid plans from $50 per month.

Can I self-host AnythingLLM?

Yes. AnythingLLM can be self-hosted on your own server or infrastructure.

What is AnythingLLM an alternative to?

AnythingLLM is an open-source alternative to ChatGPT, NotebookLM, Google Gemini and DeepSeek. Other open-source alternatives to ChatGPT include Open WebUI, LobeHub and LibreChat.

Is AnythingLLM actively maintained?

Yes. The most recent commit to AnythingLLM was on 2 October 2026, and the latest release is v1.17.0, published on 1 October 2026. The project has 67k stars on GitHub.

AnythingLLM: open-source alternative to ChatGPT, NotebookLM

About AnythingLLM

AnythingLLM is an all-in-one AI application for chatting with your own documents and automating work with AI agents. It runs locally by default, lets you connect local or cloud language models, and is written in JavaScript under the MIT license. The README describes it as a way to build a private, fully featured assistant in the style of ChatGPT.

Vector databases, document processing pipelines, multi-user support and agents all come included. Newer features listed include dynamic model routing based on rules you define, automatic and user-managed memories, scheduled tasks with agent capabilities, skill selection, a no-code agent builder, MCP compatibility and multi-modal support for both closed and open-source models.

It is available as a desktop app for Mac, Windows and Linux, as a Docker version that adds multi-user access control, and as a hosted instance, and there is an open-source Android app. Administrators can control access and experience per user. It suits individuals and teams who want a private document-chat and agent workspace without much setup.

Key features

Chat with your documents using RAG
Built-in AI agents and no-code agent builder
Multi-user access control in Docker version
Connects to local or cloud LLMs
Vector database and document pipeline support
MCP compatibility and scheduled tasks
Desktop apps for Mac, Windows and Linux

Good fit for

→Private document question answering
→Team AI workspace with per-user access
→Local-first AI assistant

Built with: JavaScript
Tags: rag; ai-agents; llm; local-ai; vector-database; self-hosted; document-chat; mcp

AnythingLLM: questions and answers

What is AnythingLLM used for?: AnythingLLM is an all-in-one AI application for chatting with your documents and running AI agents locally, with multi-user support and desktop and Docker versions. It is a good fit for private document question answering, team AI workspace with per-user access and local-first AI assistant.
Is AnythingLLM open source?: Yes. AnythingLLM is open source under the MIT licence. Its source code is on GitHub at Mintplex-Labs/anything-llm and is written mainly in JavaScript.
Is AnythingLLM free?: Yes. AnythingLLM is open source, so the software itself is free to use. A managed cloud version is also available, with paid plans from $50 per month.
Can I self-host AnythingLLM?: Yes. AnythingLLM can be self-hosted on your own server or infrastructure.
What is AnythingLLM an alternative to?: AnythingLLM is an open-source alternative to ChatGPT, NotebookLM, Google Gemini and DeepSeek. Other open-source alternatives to ChatGPT include Open WebUI, LobeHub and LibreChat.
Is AnythingLLM actively maintained?: Yes. The most recent commit to AnythingLLM was on 2 October 2026, and the latest release is v1.17.0, published on 1 October 2026. The project has 67k stars on GitHub.