Open Interpreter: Let AI Run Code Directly on Your Machine

Open Interpreter gives AI models the ability to execute code on your computer. Where ChatGPT Code Interpreter runs in a sandboxed cloud environment, Open Interpreter runs natively — with access to your files, packages, internet, and system commands. You approve every block of code before it runs.

What Makes It Different

Standard AI coding assistants generate code you copy and paste. Open Interpreter: Executes code directly (Python, JS, Bash, HTML), shows you the output (stdout, plots, file changes), requires your approval before each step (no autonomous execution), has full system access (read/write files, install packages, run commands), works locally with Ollama.

Installation

# via pip
pip install open-interpreter

# via Homebrew
brew install open-interpreter

First Run with Ollama

ollama serve &
interpreter --model llama3

On first run, select Ollama as the backend. It connects to http://localhost:11434.

Safety Model

Open Interpreter uses a confirmation-first approach: AI generates code, you see what it will do, you approve (y), deny (n), or edit (e), then output is shown and the next step begins. Auto-run mode (use carefully): interpreter --auto_run

Configuration with Ollama

mkdir -p ~/.config/open-interpreter
cat > ~/.config/open-interpreter/config.yaml << EOF
llm:
  provider: ollama
  model: llama3
  api_base: http://localhost:11434
  temperature: 0.7
EOF

Practical Examples

Data analysis: Upload CSV, generate matplotlib plots, statistical summaries.

Web scraping: Fetch pages, parse with BeautifulSoup, save to files.

API development: Write FastAPI endpoints, test them with requests.

System admin: Find large files, check disk usage, parse logs.

Language Support

Python (best supported), JavaScript/Node, Bash (default on Linux), HTML. Switch mid-conversation: >>> Use JavaScript to parse this JSON

Safety Features

Approved commands whitelist, blocked commands (rm -rf /, dd, mkfs), directory restrictions, dry run mode (interpreter --dry_run).

Troubleshooting

Model not found: Verify with ollama list, pull if missing: ollama pull llama3

Slow generation: Use smaller model for simple tasks: interpreter --model phi3