The open-source personal AI assistant ecosystem
OpenClaw is the largest open-source AI assistant platform with 238K+ GitHub stars. Browse the full ecosystem — core alternatives, skills, plugins, memory systems, infrastructure, and desktop apps.
pip install open-interpreterRequires Python 3.10+. The core runtime for OpenClaw-compatible assistants.
interpreterOpens an interactive terminal session. The assistant can execute code, browse the web, and manage files.
Add skills, connect local LLMs, configure memory systems, and install plugins from the ecosystem below.
Check your current version
python3 --versionIf you see 3.10.x or 3.11.x, skip to step 2. If you see 3.8 or 3.9, or "command not found", keep going.
Install Python 3.11
Mac: brew install [email protected] (install Homebrew first if you don't have it)
Windows: Download from python.org. Check "Add Python to PATH" during install.
Linux: sudo apt install python3.11 python3.11-venv
Create the environment
python3 -m venv interpreter_envThis creates a folder called interpreter_env with its own Python. Your system Python stays untouched.
Activate it
Mac/Linux:
source interpreter_env/bin/activateWindows:
interpreter_env\Scripts\activateYou'll see (interpreter_env) in your terminal prompt. That means it's working.
Install via pip
pip install open-interpreterMake sure your virtual environment is activated first (you should see (interpreter_env) in your prompt).
Verify it installed
interpreter --versionYou should see a version number. If you get "command not found", try python3 -m interpreter instead.
Get an OpenAI API key
Go to platform.openai.com/api-keys and create one. You'll need to add a payment method — but costs are low (~$0.01-0.05 per conversation).
Set the key in your terminal
Mac/Linux (add to ~/.zshrc or ~/.bashrc to persist):
export OPENAI_API_KEY='sk-your-key-here'Windows:
setx OPENAI_API_KEY sk-your-key-hereSave money with GPT-3.5
interpreter --model gpt-3.5-turboGPT-4 is the default and costs ~10x more. GPT-3.5 is fine for most tasks. You can also use interpreter --fast as a shortcut.
Launch it
interpreterYou'll see a chat prompt. Type naturally — it understands plain English.
Things to try
> What files are in my Downloads folder?> Create a Python script that renames all .jpg files in ~/Photos to include today's date> Make a bar chart of my monthly expenses from expenses.csv> Search Google for the weather in Tokyo and summarize itIt writes and runs code to accomplish each task. You'll be asked to approve before it executes anything.
Auto-approve mode (skip confirmations)
interpreter -yThe -y flag auto-approves all code execution. Only use this when you trust the task — it can modify files and run shell commands.
Install Ollama
Download from ollama.com. One-click install on Mac/Windows/Linux.
Pull a model
ollama pull llama3.1Llama 3.1 8B is a great starting point (~4.7GB download). For better results with more RAM, try ollama pull llama3.1:70b.
Run Open Interpreter with your local model
interpreter --localIt auto-detects Ollama and uses your downloaded model. Zero API costs. Fully private — nothing leaves your machine.
Hardware tip: 8B models need ~8GB RAM. 70B models need ~48GB RAM. If your machine struggles, stick with 8B — it handles most tasks well.
Once you're comfortable with the basics, explore the tools below. The OpenClaw ecosystem includes:
Skills
Custom commands that teach your assistant new abilities — from web scraping to data analysis.
Plugins
Connect to external services — Slack, email, calendars, databases, and more.
Memory Systems
Give your assistant long-term memory so it remembers your preferences and past conversations.
Desktop Apps
GUI interfaces that make Open Interpreter feel like a native app instead of a terminal tool.
Pick a use case. Get the command. Start building in 60 seconds.
Control your screen — fill PDFs, manipulate Excel, automate native apps. OS Mode gives the agent eyes and hands via PyAutoGUI + screenshots. macOS: enable Screen Recording in Privacy settings. Linux: install scrot. Single monitor only.
pip install open-interpreter[os] && interpreter --osTry this
"Open my Downloads folder, find all PDFs from this month, fill in the 'Name' field on each form with 'John Doe', and save them to a new folder called 'filled'"
Agent watches your issues board, creates a branch, writes code, runs tests, and opens a PR. The #1 most-requested autonomous workflow on Reddit. Use --loop to keep it running until done. Pair with --safe_mode for guardrails.
pip install open-interpreter && interpreter -y --loopTry this
"Look at the open issues in this repo, pick the one labeled 'good first issue', create a branch, implement the fix, write a test, and open a pull request"
Fully private — nothing leaves your machine. Zero API costs. Requires Ollama + a model download. 8B models need ~8GB RAM. Codestral recommended for code tasks, Llama 3.1 for general use. Fix JSON spam with --no-llm_supports_functions.
pip install open-interpreter[local] && ollama pull codestral && interpreter --api_base http://localhost:11434/v1 --model ollama/codestralTry this
"Analyze the Python files in this project, find potential bugs, and generate a report — all running locally with no API calls"
Team of specialized agents that collaborate via CrewAI — researcher, writer, reviewer, each with defined roles. Requires uv package manager. Windows users need Visual Studio Build Tools for chromadb compilation.
pip install uv && uv tool install crewai && crewai create crew my_project && cd my_project && crewai installTry this
"Create a crew: researcher finds the top 5 competitors, analyst compares their pricing, writer produces a markdown report with recommendations"
Chain analysis steps: load data → clean → analyze → visualize → export PDF. The agent writes and runs Python (pandas, matplotlib) autonomously. Use GPT-4o for complex statistical reasoning. Known issue: long outputs get truncated — break tasks into steps.
pip install open-interpreter && interpreter -y --model gpt-4oTry this
"Load sales_data.csv, remove duplicate rows, calculate monthly revenue trends, create a bar chart for each quarter, and export everything as a PDF report"
Real-time filesystem monitoring with watchdog — auto-sort downloads, rename files, deduplicate, archive old files. Schedule runs periodic maintenance. Use interpreter to generate the watchdog script, then run it standalone.
pip install open-interpreter watchdog schedule && interpreter -yTry this
"Write me a Python script using watchdog that monitors my Downloads folder, automatically moves .pdf files to ~/Documents/PDFs and .jpg files to ~/Pictures, and runs 24/7"
The strongest lanes in this ecosystem, ranked by visible public proof.
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