Open Interpreter

Open Interpreter lets language models run code, browse the web, and edit files on your machine through a natural language chat interface. With 57K+ GitHub stars, it's the leading open-source alternative to ChatGPT's Code Interpreter — but without sandbox limits.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

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What is Open Interpreter?

Open Interpreter brings the power of an AI coding assistant directly to your terminal. Instead of copy-pasting between ChatGPT and your shell, you chat naturally and the model executes code in real time:

• Run Python, JS, shell, R, AppleScript — directly on your server

• Browse the web — fetch pages, fill forms, extract data

• Edit files — create, modify, and manage any file on disk

• Persistent state — variables, imports, and results survive across messages

• Multiple LLM backends — OpenAI, Anthropic, local models via Ollama/LlamaCpp

Open Interpreter is designed for developers and researchers who want a conversational interface to their entire compute environment. On a Clore.ai GPU server, you get a powerful machine with full internet access and no execution limits.

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Server Requirements

Component	Minimum	Recommended
GPU	Any (CPU mode available)	RTX 3090 / A100 for local LLMs
VRAM	—	24 GB+ for local 13B models
RAM	8 GB	16 GB+
CPU	4 cores	8+ cores
Storage	20 GB	50 GB+
OS	Ubuntu 20.04+	Ubuntu 22.04
Python	3.10+	3.11
Network	Required	High-speed for web browsing

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Ports

Port	Service	Notes
22	SSH	Terminal access, tunnel for web UI
8000	Open Interpreter Server	REST API & optional web UI

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Quick Start with Docker

Open Interpreter doesn't have an official Docker image, so we build a clean one. This approach gives you a reproducible, isolated environment on any Clore.ai server.

Dockerfile

FROM python:3.11-slim

# System dependencies
RUN apt-get update && apt-get install -y \
    git \
    curl \
    wget \
    build-essential \
    nodejs \
    npm \
    chromium \
    chromium-driver \
    && rm -rf /var/lib/apt/lists/*

# Install Open Interpreter with all extras
RUN pip install --no-cache-dir \
    open-interpreter \
    'open-interpreter[local]' \
    playwright \
    jupyter

# Install Playwright browsers
RUN playwright install chromium

WORKDIR /workspace

# Expose server port
EXPOSE 8000

# Default: interactive terminal
CMD ["interpreter"]

Build & Run

# Build the image
docker build -t open-interpreter:latest .

# Run interactive mode (terminal chat)
docker run -it --rm \
  -e OPENAI_API_KEY=sk-... \
  -v $(pwd)/workspace:/workspace \
  open-interpreter:latest

# Run as REST API server
docker run -d \
  --name open-interpreter \
  -p 8000:8000 \
  -e OPENAI_API_KEY=sk-... \
  -v $(pwd)/workspace:/workspace \
  open-interpreter:latest \
  interpreter --server --port 8000 --host 0.0.0.0

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Installation on Clore.ai (Bare Metal)

If you prefer to run directly on a Clore.ai server without Docker:

Step 1 — Rent a Server

1. Go to Clore.ai Marketplace

2. Filter by RAM ≥ 16 GB, GPU (optional but useful for local models)

3. Choose a server with a PyTorch or Ubuntu base image

4. Open SSH port 22 and optionally 8000 in your order

Step 2 — Connect via SSH

ssh root@<server-ip> -p <ssh-port>

Step 3 — Install Dependencies

# Update system
apt-get update && apt-get upgrade -y

# Install Python 3.11
apt-get install -y python3.11 python3.11-venv python3.11-pip nodejs npm curl

# Create virtual environment
python3.11 -m venv /opt/open-interpreter
source /opt/open-interpreter/bin/activate

Step 4 — Install Open Interpreter

# Basic install
pip install open-interpreter

# With local LLM support (Ollama, LlamaCpp)
pip install 'open-interpreter[local]'

# With browser/web support
pip install playwright
playwright install chromium

Step 5 — Configure API Key

# Set environment variable (add to ~/.bashrc for persistence)
export OPENAI_API_KEY=sk-your-key-here
export ANTHROPIC_API_KEY=sk-ant-your-key-here

# Or use a .env file
cat > /workspace/.env << 'EOF'
OPENAI_API_KEY=sk-your-key-here
ANTHROPIC_API_KEY=sk-ant-your-key-here
EOF

Step 6 — First Run

# Activate environment
source /opt/open-interpreter/bin/activate

# Start interactive chat
interpreter

# Or with specific model
interpreter --model gpt-4o
interpreter --model claude-3-5-sonnet-20241022

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Using Local LLMs (No API Key Required)

One of Open Interpreter's killer features on Clore.ai GPU servers is running entirely local models:

Option A: Ollama Backend

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a code-capable model
ollama pull codellama:13b
ollama pull deepseek-coder:6.7b
ollama pull mistral:7b

# Run Open Interpreter with Ollama
interpreter --model ollama/codellama:13b

Option B: LlamaCpp Backend

# Install llama-cpp-python with CUDA support
pip install 'llama-cpp-python[server]' --extra-index-url https://jllllll.github.io/llama-cpp-python-cuBLAS-wheels/AVX2/cu121

# Download a GGUF model
wget https://huggingface.co/TheBloke/CodeLlama-13B-GGUF/resolve/main/codellama-13b.Q4_K_M.gguf

# Run with the local model
interpreter --local --model /path/to/codellama-13b.Q4_K_M.gguf

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Running as a Server (REST API)

Open Interpreter 0.2+ includes a built-in HTTP server for programmatic access:

# Start the server
interpreter --server --port 8000 --host 0.0.0.0

# In another terminal or client, send requests:
curl -X POST http://<server-ip>:8000/run \
  -H "Content-Type: application/json" \
  -d '{"language": "python", "code": "import os; print(os.listdir(\".\"))"}'

# Chat endpoint
curl -X POST http://<server-ip>:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "List all Python files in /workspace and count lines of code"}'

SSH Tunnel for Local Access

If port 8000 is not publicly exposed, use SSH tunneling:

# On your local machine
ssh -L 8000:localhost:8000 root@<server-ip> -p <ssh-port> -N
# Then open http://localhost:8000

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Practical Examples

Example 1: Data Analysis Pipeline

User: Download the MNIST dataset, train a simple CNN, and plot accuracy curves. Save the plot as mnist_results.png

Open Interpreter: Sure! I'll do this step by step...
[executes Python code in real time]

# Open Interpreter generates and runs:
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# ... trains model, plots results, saves PNG

Example 2: Web Scraping

User: Scrape the top 10 trending GitHub repositories today and save them to a CSV with name, stars, and description.

Example 3: File Management

User: Find all .log files in /var/log older than 7 days and compress them into a tarball at /backup/logs-$(date).tar.gz

Example 4: System Monitoring Script

User: Write me a Python script that monitors GPU memory usage every 5 seconds and alerts if it exceeds 90%. Run it in the background.

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Configuration File

Create ~/.interpreter/config.yaml to set defaults:

model: gpt-4o
temperature: 0
system_message: |
  You are a helpful AI assistant running on a Clore.ai GPU server.
  Always prefer efficient, production-ready code.
  Save important outputs to /workspace/.
safe_mode: false
auto_run: true
verbose: false

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Running with systemd (Persistent Service)

# /etc/systemd/system/open-interpreter.service
[Unit]
Description=Open Interpreter Server
After=network.target

[Service]
Type=simple
User=root
WorkingDirectory=/workspace
Environment=OPENAI_API_KEY=sk-your-key
ExecStart=/opt/open-interpreter/bin/interpreter --server --port 8000 --host 0.0.0.0
Restart=on-failure
RestartSec=10

[Install]
WantedBy=multi-user.target

systemctl daemon-reload
systemctl enable open-interpreter
systemctl start open-interpreter
systemctl status open-interpreter

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Troubleshooting

interpreter command not found

# Make sure the venv is activated
source /opt/open-interpreter/bin/activate

# Or install globally
pip install open-interpreter

# Check PATH
which interpreter
echo $PATH

Code execution is blocked / safety mode

# Disable safe mode (use with caution on trusted servers)
interpreter --safe_mode false

# Or in config.yaml:
# safe_mode: false
# auto_run: true

Playwright / browser errors

# Install system deps for Chromium
apt-get install -y \
    libnss3 libatk1.0-0 libatk-bridge2.0-0 \
    libcups2 libxcomposite1 libxdamage1 \
    libxrandr2 libgbm1 libxkbcommon0

playwright install chromium
playwright install-deps

Out of memory with local LLMs

# Use a smaller quantized model
ollama pull codellama:7b  # instead of 13b

# Or reduce context window
interpreter --model ollama/codellama:7b --context_window 4096

Connection refused on port 8000

# Check if server is running
ss -tlnp | grep 8000

# Check firewall
ufw allow 8000/tcp

# Restart service
systemctl restart open-interpreter

API rate limits

# Switch to Anthropic Claude for higher limits
export ANTHROPIC_API_KEY=sk-ant-...
interpreter --model claude-3-5-sonnet-20241022

# Or use a local model to avoid API limits entirely
interpreter --model ollama/codellama:13b

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Security Considerations

Open Interpreter executes code directly on your server. Always:

• Run in a Docker container or VM for production use

• Never expose port 8000 publicly without authentication

• Use SSH tunneling for remote access

• Audit the code before enabling auto_run: true for untrusted inputs

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Clore.ai GPU Recommendations

Open Interpreter itself is lightweight — the GPU need is driven by whichever local model you run as the backend.

GPU	VRAM	Clore.ai Price	Local Model Recommendation
RTX 3090	24 GB	~$0.12/hr	CodeLlama 13B Q8, Llama 3 8B, Mistral 7B — solid coding quality
RTX 4090	24 GB	~$0.70/hr	CodeLlama 34B Q4, DeepSeek Coder 33B Q4 — near GPT-4 coding quality
A100 40GB	40 GB	~$1.20/hr	Llama 3 70B Q4 — production-grade autonomous coding agent
CPU-only	—	~$0.02/hr	Any model via OpenAI/Anthropic API — no local GPU needed

If you're using OpenAI/Anthropic API: You only need a CPU instance (~$0.02/hr) — the GPU is irrelevant since inference runs in the cloud. Choose GPU instances only when running local models to avoid per-token API costs.

Best local model setup: RTX 3090 + Ollama running codellama:13b gives you a fully autonomous, privacy-preserving coding agent with no API costs for ~$0.12/hr.

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Useful Links

• GitHub: https://github.com/OpenInterpreter/open-interpreter ⭐ 57K+

• Documentation: https://docs.openinterpreter.com

• Discord: https://discord.gg/Hvz9Axh84z

• Clore.ai Marketplace: https://clore.ai/marketplace

• Ollama Models: https://ollama.ai/library

• HuggingFace GGUF Models: https://huggingface.co/TheBloke