AnythingLLM
The all-in-one AI app: private RAG workspaces with documents, agents, and multi-LLM support
About
AnythingLLM is a full-stack, self-hosted AI application for building private ChatGPT-like experiences over your own documents. It wraps any LLM (OpenAI, Anthropic, Ollama, LM Studio, or a local model) with workspace-based RAG: upload PDFs/docs/websites, chat against them with chunk citations, spin up multiple workspaces and users, build agents with tool use, web browsing and MCP, and embed chat into your own products via the developer API. It ships as a Docker container, a desktop installer, or a managed cloud service. The entire codebase is MIT-licensed; Mintplex Labs also sells embedded and hosted tiers.
What it can do
- **Workspace-based RAG** — upload documents (PDF, text, URLs, GitHub repos), chat against them with chunk-level citations.
- **Multi-LLM support** — OpenAI, Anthropic, Gemini, Ollama, LM Studio, local models, and more.
- **Agents** — tool use, web browsing, and MCP integrations; cron-scheduled flows (per third-party analysis; hands-on testing not done here).
- **Multi-user + API** — user management, workspaces, and a full developer API for embedding chat.
- **Embeddable** — iframe/embeddable chat widgets for your own products.
- **Desktop + cloud** — native desktop apps alongside Docker self-host and a managed cloud tier.
Setup
Docker (recommended)
```bash
export STORAGE_LOCATION=$HOME/anythingllm && \
mkdir -p $STORAGE_LOCATION && \
touch "$STORAGE_LOCATION/.env" && \
docker run -d -p 3001:3001 \
--cap-add SYS_ADMIN \
-v ${STORAGE_LOCATION}:/app/server/storage \
-v ${STORAGE_LOCATION}/.env:/app/server/.env \
-e STORAGE_DIR="/app/server/storage" \
mintplexlabs/anythingllm:latest
```
Then open http://localhost:3001. All data persists between container rebuilds.
Desktop
Download the installer for your OS from anythingllm.com/desktop.
From source (not recommended for casual use)
```bash
git clone https://github.com/Mintplex-Labs/anything-llm && cd anything-llm
touch server/storage/anythingllm.db
cd docker && cp .env.example .env
docker-compose up -d --build
```
Prerequisites
- **Docker** (for the Docker route) on any modern OS; CPU note: the default vector DB needs a CPU with AVX2 — older CPUs or misconfigured VM guests may crash with `Illegal instruction (core dumped)` (documented in their troubleshooting). - An LLM provider: OpenAI/Anthropic API key, or a local server like Ollama / LM Studio. - UID/GID defaults to 1000 in the container; mismatch with the host user can cause permission errors.
Use cases
Works with
Trust score — 9/10
Scored 9/10 (verified tier, metrics-v1). Evidence: 66,981 GitHub stars (log-scaled vs catalog peers); last push 0d ago; 16 releases/12mo; 331 open issues. Manual review: PostHog-telemetry-on-by-default claim is third-party, unverified; native OCR/agents not hands-on tested. License: MIT.
★ 66,981 GitHub stars
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 8.9/10
- Maintenance · recency, releases, issue load
- 9.8/10
- Openness · license + docs
- 10.0/10
- Manual review · sanity read + red-flag check
- 9.0/10
- · 66,981 GitHub stars (log-scaled vs catalog peers)
- · last push 0d ago; 16 releases/12mo; 331 open issues
- · manual review: PostHog-telemetry-on-by-default claim is third-party, unverified; native OCR/agents not hands-on tested
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.