# 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. {% hint style="success" %} All examples can be run on GPU servers rented through [CLORE.AI Marketplace](https://clore.ai/marketplace). {% endhint %} *** ## 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 {% hint style="info" %} 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. {% endhint %} *** ## 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 | *** ## Ports | Port | Service | Notes | | ---- | ----------------------- | ---------------------------------- | | 22 | SSH | Terminal access, tunnel for web UI | | 8000 | Open Interpreter Server | REST API & optional web UI | *** ## 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 ```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 ```bash # 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 ``` *** ## 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](https://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 ```bash ssh root@ -p ``` ### Step 3 — Install Dependencies ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ``` *** ## 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 ```bash # 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 ```bash # 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 ``` *** ## Running as a Server (REST API) Open Interpreter 0.2+ includes a built-in HTTP server for programmatic access: ```bash # Start the server interpreter --server --port 8000 --host 0.0.0.0 # In another terminal or client, send requests: curl -X POST http://:8000/run \ -H "Content-Type: application/json" \ -d '{"language": "python", "code": "import os; print(os.listdir(\".\"))"}' # Chat endpoint curl -X POST http://: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: ```bash # On your local machine ssh -L 8000:localhost:8000 root@ -p -N # Then open http://localhost:8000 ``` *** ## 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] ``` ```python # 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. ``` *** ## Configuration File Create `~/.interpreter/config.yaml` to set defaults: ```yaml 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 ``` *** ## Running with systemd (Persistent Service) ```ini # /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 ``` ```bash systemctl daemon-reload systemctl enable open-interpreter systemctl start open-interpreter systemctl status open-interpreter ``` *** ## Troubleshooting ### `interpreter` command not found ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # Check if server is running ss -tlnp | grep 8000 # Check firewall ufw allow 8000/tcp # Restart service systemctl restart open-interpreter ``` ### API rate limits ```bash # 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 ``` *** ## Security Considerations {% hint style="warning" %} 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 {% endhint %} *** ## 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 | {% hint style="info" %} **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. {% endhint %} *** ## Useful Links * **GitHub**: ⭐ 57K+ * **Documentation**: * **Discord**: * **Clore.ai Marketplace**: * **Ollama Models**: * **HuggingFace GGUF Models**: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.clore.ai/guides/ai-platforms-and-agents/open-interpreter.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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