Open WebUI: Your Local ChatGPT

Transform your local LLM into a beautiful, full-featured web interface — like ChatGPT, but running entirely on your machine.

What Is Open WebUI?

Open WebUI is a self-hosted web interface for Ollama. It gives you:

• 🖥️ A ChatGPT-like chat interface in your browser
• 🔄 Switch between models mid-conversation
• 📁 Upload documents and chat with them (RAG)
• 🖼️ Image generation (via Automatic1111 / ComfyUI)
• 🎤 Voice input / text-to-speech
• 👥 Multi-user support (share with family or team)
• 📱 Mobile-friendly (works on phone browsers)
• 🔌 Plugins for images, web search, and more

Best of all: It connects to your local Ollama instance — no data ever leaves your machine.

Prerequisites

• ✅ Ollama installed and working (see Getting Started)
• ✅ At least one model pulled (e.g., qwen2.5:7b)
• ✅ Docker installed (recommended) OR Python 3.11+

Option A: Install with Docker (Recommended — 2 Minutes)

Docker is the easiest way. One command and you're done:

docker run -d \
  -p 3000:8080 \
  -v open-webui:/app/backend/data \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

What this does:

• -p 3000:8080 — makes it available at http://localhost:3000
• -v open-webui:/app/backend/data — keeps your chats saved even if you restart
• -e OLLAMA_BASE_URL — tells it where your Ollama is running
• --restart always — auto-starts when your computer boots

Verify It's Running

# Check logs — you should see "Application startup complete"
docker logs open-webui --tail 20

Then open http://localhost:3000 in your browser.

First time? Create an account. Don't worry — it's local only. Your data stays on your machine.

Option B: Install with pip (No Docker)

If you don't have Docker:

# Install
pip install open-webui

# Run
open-webui serve

Then open http://localhost:8080.

What You'll See

After logging in, Open WebUI looks and feels like ChatGPT:

Key areas:

Cool Things to Try

1. Switch Models Mid-Chat

In the top dropdown, you can switch models during a conversation. Each model sees the same chat history.

• Start with qwen2.5:7b for general chat
• Switch to deepseek-r1:14b when you need hard reasoning
• Switch to codellama for code tasks

2. Upload Documents (Built-in RAG)

Click the paperclip icon and upload a PDF, Word doc, or text file. The model can then answer questions about it.

Use cases:

• Upload a research paper and ask questions
• Upload your company's handbook
• Upload a textbook chapter for study help

3. Use Voice Input

Click the microphone icon to speak instead of type. This works in Chrome and Edge.

4. Customize the Model's Behavior

In Settings → Model, you can adjust:

• Temperature: 0.2 (precise) to 1.0 (creative)
• Context length: How much the model remembers
• System prompt: The model's persona

Advanced: Connecting to Other Services

Image Generation

Open WebUI can integrate with local image generators:

# Add Automatic1111 (Stable Diffusion)
docker run -d \
  -p 7860:7860 \
  -v sd-models:/models \
  --gpus all \
  asd/stable-diffusion-webui:latest

Then configure in Open WebUI Settings → Image Generation.

Web Search (Experimental)

Enable web search in Settings → Web Search. Open WebUI will search the internet when answering questions.

Production Setup

With HTTPS

For secure remote access (behind a VPN or tunnel):

# Using Caddy as a reverse proxy
docker run -d \
  -p 443:443 \
  -v open-webui:/app/backend/data \
  -e OLLAMA_BASE_URL=http://ollama:11434 \
  -e WEBUI_SECRET_KEY=your-secret-here \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

Multi-User Setup

Open WebUI supports multiple users out of the box. Each user:

• Gets their own chat history
• Can't see other users' chats
• Can choose from any model you've pulled

To add users: Go to Settings → Admin Panel → Users → Create User.

Troubleshooting

Resources

• Official docs: docs.openwebui.com
• GitHub: github.com/open-webui/open-webui
• Docker Hub: ghcr.io/open-webui/open-webui

Next step: Now that you have a GUI, try setting up Local RAG — let your LLM answer questions about your own documents.

Part of the Local LLM Guide — the definitive resource for running AI on your own hardware.