Your private AI that knows everything
AI assistant that sees your screen, understands your work, never sends data to the cloud. Runs 100% locally with Ollama or any local LLM.
See it in action
Cloud AI has problems
ChatGPT and Claude are powerful, but come with tradeoffs.
Sending screen content to OpenAI raises privacy concerns
Corporate policies may prohibit sharing with cloud services
Cloud AI doesn't know what you're working on without manual context
Paying for API calls every time you ask a question
No AI when you're offline
100% local AI with desktop context
screenpipe captures your screen and feeds context to a local LLM running on your machine. Ask questions, search history, get help - all without internet.
Complete privacy
Screen data and conversations never leave your computer. No cloud, no tracking.
Works offline
No internet required. Your AI works completely offline once set up.
Desktop aware
Ask 'what's this error about?' and it can search your screen history for context.
Your choice of model
Use Ollama, LM Studio, or any OpenAI-compatible local server.
How it works
Install Ollama
Download Ollama and pull a model. Llama 3.2 or Mistral work well for most tasks.
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull a model (choose one)
ollama pull llama3.2 # 3B params, fast
ollama pull mistral # 7B params, balanced
ollama pull deepseek-r1:8b # 8B params, good reasoning
# Verify it's running
ollama listInstall and configure screenpipe
Download screenpipe and point it to your Ollama instance in Settings.
# Download screenpipe from screenpi.pe
# Or install via CLI:
curl -fsSL https://screenpi.pe/install.sh | sh
# Start screenpipe
screenpipe
# In Settings → AI Provider:
# - Select "Ollama"
# - Model: llama3.2 (or your chosen model)
# - URL: http://localhost:11434Ask anything
Use the screenpipe chat or integrate with your own app. The LLM has access to your screen context.
# Example: Query screenpipe API with context
curl -X POST "http://localhost:3030/chat" \
-H "Content-Type: application/json" \
-d '{
"message": "What error did I see in the terminal?",
"use_context": true
}'Code examples
Ollama models for different use cases
Choose based on your hardware and needs
# Fast responses (4GB RAM)
ollama pull llama3.2 # 3B, general purpose
ollama pull phi3:mini # 3.8B, Microsoft's efficient model
# Balanced (8GB RAM)
ollama pull mistral # 7B, great for coding
ollama pull deepseek-r1:8b # 8B, strong reasoning
# Maximum quality (16GB+ RAM)
ollama pull llama3.1:70b # 70B, best quality
ollama pull deepseek-r1:32b # 32B, excellent reasoning
# Coding focused
ollama pull codellama # Optimized for code
ollama pull deepseek-coder # Strong at programmingVerify your setup
Check that everything is working
# Check Ollama is running
curl http://localhost:11434/api/tags
# Check screenpipe is running
curl http://localhost:3030/health
# Test a simple query
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt": "Hello, how are you?",
"stream": false
}'