AnythingLLM¶

What it is¶

AnythingLLM is a privacy-first workspace application for running AI assistants with documents, vector search, agent workflows, and multiple local or hosted model backends.

What problem it solves¶

It gives teams an "all-in-one" application surface for internal knowledge work without forcing them to assemble chat UI, retrieval, vector storage, and model connectors from scratch.

Where it fits in the stack¶

AI & Knowledge / Internal AI Workspace. It is an application layer for internal knowledge assistants, document-grounded chat, and lightweight agent workflows.

Typical use cases¶

Internal knowledge base chat over company docs
Team workspaces with document upload and retrieval
Privacy-first AI assistant deployments using local or self-hosted models

Strengths¶

Strong out-of-the-box internal assistant experience
Works with local and hosted model backends
Useful bridge between prototype and internal deployment

Limitations¶

Less flexible than building your own fully custom app stack
Product conventions may not match every enterprise workflow

When to use it¶

When you want a fast internal AI workspace for teams
When document-grounded assistants matter more than bespoke product UX

When not to use it¶

When you need a fully custom application architecture
When a simple RAG API service is enough and a full workspace UI is unnecessary

Getting started¶

Installation¶

AnythingLLM can be installed as a Desktop application or run via Docker.

Desktop: Download the installer from the official website.

Docker (Linux/macOS/Windows):

docker pull mintplexlabs/anythingllm
export STORAGE_LOCATION=$HOME/anythingllm && mkdir -p $STORAGE_LOCATION && touch "$STORAGE_LOCATION/.env"
docker run -d -p 3001:3001 --cap-add SYS_ADMIN -v "$STORAGE_LOCATION:/app/storage" -v "$STORAGE_LOCATION/.env:/app/server/.env" --name anythingllm mintplexlabs/anythingllm

CLI examples¶

1. Run via Docker Compose¶

Create a docker-compose.yml:

services:
  anythingllm:
    image: mintplexlabs/anythingllm
    container_name: anythingllm
    ports:
      - "3001:3001"
    cap_add:
      - SYS_ADMIN
    volumes:
      - ./storage:/app/storage
      - .env:/app/server/.env
    restart: always

Then run: docker compose up -d

2. View Container Logs¶

docker logs -f anythingllm

3. Access Container Shell¶

docker exec -it anythingllm /bin/bash

API examples¶

REST API (Python/curl)¶

AnythingLLM provides a Developer API (enabled in Settings -> Developer API).

# Get all workspaces
curl -X GET 'http://localhost:3001/api/v1/workspaces' \
  -H "Authorization: Bearer $ANYTHINGLLM_API_KEY"

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "http://localhost:3001/api/v1"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Chat with a specific workspace
workspace_slug = "my-internal-kb"
data = {
    "message": "What is our policy on remote work?",
    "mode": "query" # 'query' for RAG, 'chat' for conversation
}

response = requests.post(f"{BASE_URL}/workspace/{workspace_slug}/chat", json=data, headers=headers)
print(response.json()['textResponse'])

Sources / References¶

Contribution Metadata¶

Last reviewed: 2026-05-28
Confidence: high