anything-llm README

The project's README from github.com/Mintplex-Labs/anything-llm. As one markdown file: README.md.

[!NOTE] We are also working on Open Computer which gives an entire computer environment for AI Agents to use.

This will bring AnythingLLM's agent capabilities to a new level and a novel UX paradigm for AI Agent use.

⭐ Star the repo to stay updated!

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Mintplex-Labs%2Fanything-llm | Trendshift

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

Discord | License | Docs | Hosted Instance

English · 简体中文 · 日本語

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

Chat with your docs. Automate complex workflows with AI Agents. Hyper-configurable, multi-user ready, battle-tested—and runs locally by default with zero setup friction.

Chatting

Watch the demo!

Watch the video

Product Overview

AnythingLLM is the all-in-one AI application that lets you build a private, fully-featured ChatGPT—without compromises. Connect your favorite local or cloud LLM, ingest your documents, and start chatting in minutes. Out of the box you get built-in agents, multi-user support, vector databases, and document pipelines — no extra configuration required.

AnythingLLM supports multiple users as well where you can control the access and experience per user without compromising the security or privacy of the instance or your intellectual property.

Cool Features of AnythingLLM

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

Large Language Models (LLMs):

Embedder models:

Audio Transcription models:

TTS (text-to-speech) support:

STT (speech-to-text) support:

Vector Databases:

Technical Overview

This monorepo consists of six main sections:

🛳 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
[![Deploy on Docker][docker-btn]][docker-deploy] [![Deploy on AWS][aws-btn]][aws-deploy] [![Deploy on GCP][gcp-btn]][gcp-deploy] [![Deploy on DigitalOcean][do-btn]][do-deploy] [![Deploy on Render.com][render-btn]][render-deploy]
Railway RepoCloud Elestio Northflank Sealos
[![Deploy on Railway][railway-btn]][railway-deploy] [![Deploy on RepoCloud][repocloud-btn]][repocloud-deploy] [![Deploy on Elestio][elestio-btn]][elestio-deploy] [![Deploy on Northflank][northflank-btn]][northflank-deploy] [![Deploy on Sealos][sealos-btn]][sealos-deploy]

or set up a production AnythingLLM instance without Docker →

How to setup for development

Learn about documents

Telemetry & Privacy

AnythingLLM by Mintplex Labs Inc contains a telemetry feature that collects anonymous usage information.

More about Telemetry & Privacy for AnythingLLM

Why?

We use this information to help us understand how AnythingLLM is used, to help us prioritize work on new features and bug fixes, and to help us improve AnythingLLM's performance and stability.

Opting out

Set DISABLE_TELEMETRY in your server or docker .env settings to "true" to opt out of telemetry. You can also do this in-app by going to the sidebar > Privacy and disabling telemetry.

What do you explicitly track?

We will only track usage details that help us make product and roadmap decisions, specifically:

  • Type of your installation (Docker or Desktop)

  • When a document is added or removed. No information about the document. Just that the event occurred. This gives us an idea of use.

  • Type of vector database in use. This helps us prioritize changes when updates arrive for that provider.

  • Type of LLM provider & model tag in use. This helps us prioritize changes when updates arrive for that provider or model, or combination thereof. eg: reasoning vs regular, multi-modal models, etc.

  • When a chat is sent. This is the most regular "event" and gives us an idea of the daily-activity of this project across all installations. Again, only the event is sent - we have no information on the nature or content of the chat itself.

You can verify these claims by finding all locations Telemetry.sendTelemetry is called. Additionally these events are written to the output log so you can also see the specific data which was sent - if enabled. No IP or other identifying information is collected. The Telemetry provider is PostHog - an open-source telemetry collection service.

We take privacy very seriously, and we hope you understand that we want to learn how our tool is used, without using annoying popup surveys, so we can build something worth using. The anonymous data is never shared with third parties, ever.

View all telemetry events in source code

Other outbound connections

If you disable telemetry, you would still see outbound connections to the following services:

  • If using an external tool, LLM, Embedding models, or Vector databases, you will still see outbound connections to the respective service provider.
  • cdn.anythingllm.com for pulling models from our mirror CDN. This is not tracked by telemetry and is actually useful for those in VPN restricted regions.
  • github/githubusercontent.com There are some various flat files that are downloaded from these domains for context window caching.

Basically, if telemetry is disabled we don't collect anything. However, depending on your setup you may still see outbound connections and would be subject to the terms of service of the respective service provider.

👋 Contributing

💖 Sponsors

All Sponsors

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anything-llm (Mintplex-Labs/anything-llm)

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Anything-llm
What is this?AnythingLLM is an all-in-one, self-hosted AI application that lets you chat with your documents, run AI agents, and build a private ChatGPT-like experience using local or cloud LLMs.
Is this a library I embed in code, or a standalone app?It's a standalone app: a monorepo with a frontend (ViteJS + React), a Node.js server, and a document collector service, available as Docker deployment or a native desktop app for Mac, Windows, and Linux.
How do I get it running?Easiest is to download the desktop app for Mac/Windows/Linux, or deploy the Docker version with one click on providers like AWS, GCP, DigitalOcean, or Render. For development, run 'yarn setup' to install dependencies and fill env files, then start server, collector, and frontend separately.
Do I need an API key or can I run everything locally?No API key is required to run it — it works with local LLMs (like llama.cpp-compatible models, Ollama, LM Studio, LocalAI) out of the box, but you can also connect cloud providers like OpenAI, Anthropic, or Azure if you prefer.
Does multi-user support work on the desktop app too?No, multi-user permissioning, the embeddable chat widget, and in-workspace agents browsing the web are explicitly Docker-version-only features; the desktop app is single-instance focused.
Who is this for — developers only, or regular users too?It targets both: regular users can just download the desktop app or use a hosted instance, while developers can self-host via Docker, use the full developer API, and build custom integrations.
Which platforms does the desktop app support?Mac, Windows, and Linux.
Can it run fully offline without any cloud dependency?Yes, it's designed to run locally by default with zero setup friction, using local LLMs, embedders, and vector databases like LanceDB.
What vector databases does it support?LanceDB is the default, with support for PGVector, Astra DB, Pinecone, Chroma, Weaviate, Qdrant, Milvus, and Zilliz.
Can AI agents in AnythingLLM browse the web or run on a schedule?Yes — it includes built-in agents that can browse the web, plus Scheduled Tasks for running recurring prompts on a cron schedule with full agent capabilities.
Does it support the Model Context Protocol (MCP)?Yes, AnythingLLM is MCP-compatible, letting you plug in external tools for its agents.
What document types can it ingest?It supports multiple document types including PDF, TXT, and DOCX, with an intuitive drag-and-drop chat UI showing source citations.
What are similar projects?ruflo, ODS, ragflow, SurfSense, brigade, dify, sim, llama-gpt
Which topics is it in?localai, self-hosted-ai, agentic-workflow, hermes-agent, local-ai, computer-use, agent-harness, vector-database, multimodal, local-llm, agent-orchestration, chatbot
Creator
Mintplex-Labs
Size
92.8 MB
Stars
67.2K
Updated
CommitOct 15Releasev1.18.0Oct 13
Age
3y
Commits · 90 Days