Open Interpreter – Natural Language Computer Control

Open Interpreter is a powerful open-source tool that lets large language models run code on your computer to complete tasks. It provides a natural language interface to your computer’s general-purpose capabilities, allowing you to create and edit files, control a browser, analyze data, and automate workflows through simple conversation.

Key Features

• Natural Language Interface – Control your computer through conversation
• Code Execution – Runs Python, JavaScript, Shell, and more
• Local Models – Works with Ollama, LM Studio, and local LLMs
• Cloud Models – Supports GPT-4, Claude, and other APIs
• File Operations – Create, edit, and manage files
• Vision Support – Analyze images and screenshots
• Interactive Mode – Confirm actions before execution

Installation

# Install with pip
pip install open-interpreter

# Run Open Interpreter
interpreter

# Run with local model (Ollama)
interpreter --local

# Run with specific model
interpreter --model gpt-4-turbo

Usage Examples

# Start interpreter
$ interpreter

> "Summarize all PDF files in my Downloads folder"
> "Create a Python script that monitors CPU usage"
> "Find all images larger than 5MB and compress them"
> "Set up a new React project called my-app"
> "Analyze this CSV file and create a chart"

Python SDK

from interpreter import interpreter

# Configure
interpreter.llm.model = "gpt-4-turbo"
interpreter.auto_run = True  # Skip confirmations

# Chat
interpreter.chat("Create a bar chart of my system's memory usage")

# Programmatic use
for chunk in interpreter.chat("List all running processes", stream=True):
    print(chunk)

Using with Local Models

# With Ollama
interpreter --local --model ollama/codellama

# With LM Studio
interpreter --api_base http://localhost:1234/v1 --model local-model

# With custom endpoint
interpreter --api_base http://localhost:8080/v1 --api_key "not-needed"

Safety Features

• Confirmation Mode – Review code before execution (default)
• Safe Mode – Restricted operations for sensitive environments
• Sandboxing – Run in Docker containers for isolation

Use Cases

• System Administration – Automate server tasks with natural language
• Data Analysis – Process and visualize data conversationally
• File Management – Organize, convert, and batch process files
• Development – Scaffold projects, write boilerplate code
• Automation – Create scripts for repetitive tasks

Download Open Interpreter