Update: ● 0.1.12 supports --vision (experimental).

pip install open-interpreter

Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.

This provides a natural-language interface to your computer’s general-purpose capabilities:

• Create and edit photos, videos, PDFs, etc.
• Control a Chrome browser to perform research
• Plot, clean, and analyze large datasets
• …etc.

⚠️ Note: You’ll be asked to approve code before it’s run.

Demo

An interactive demo is also available on Google Colab:

Along with an example implementation of a voice interface (inspired by Her):

Quick Start

pip install open-interpreter

Terminal

After installation, simply run interpreter:

Python

import interpreter

interpreter.chat("Plot AAPL and META's normalized stock prices") # Executes a single command
interpreter.chat() # Starts an interactive chat

Comparison to ChatGPT’s Code Interpreter

OpenAI’s release of Code Interpreter with GPT-4 presents a fantastic opportunity to accomplish real-world tasks with ChatGPT.

However, OpenAI’s service is hosted, closed-source, and heavily restricted:

• No internet access.
• Limited set of pre-installed packages.
• 100 MB maximum upload, 120.0 second runtime limit.
• State is cleared (along with any generated files or links) when the environment dies.

Open Interpreter overcomes these limitations by running in your local environment. It has full access to the internet, isn’t restricted by time or file size, and can utilize any package or library.

This combines the power of GPT-4’s Code Interpreter with the flexibility of your local development environment.

Commands

Update: The Generator Update (0.1.5) introduced streaming:

message = "What operating system are we on?"

for chunk in interpreter.chat(message, display=False, stream=True):
  print(chunk)

Interactive Chat

To start an interactive chat in your terminal, either run interpreter from the command line:

Or interpreter.chat() from a .py file:

You can also stream each chunk:

message = "What operating system are we on?"

for chunk in interpreter.chat(message, display=False, stream=True):
  print(chunk)

Programmatic Chat

For more precise control, you can pass messages directly to .chat(message):

interpreter.chat("Add subtitles to all videos in /videos.")

# ... Streams output to your terminal, completes task ...

interpreter.chat("These look great but can you make the subtitles bigger?")

# ...

Start a New Chat

In Python, Open Interpreter remembers conversation history. If you want to start fresh, you can reset it:

Save and Restore Chats

interpreter.chat() returns a List of messages, which can be used to resume a conversation with interpreter.messages = messages:

messages = interpreter.chat("My name is Killian.") # Save messages to 'messages'
interpreter.reset() # Reset interpreter ("Killian" will be forgotten)

interpreter.messages = messages # Resume chat from 'messages' ("Killian" will be remembered)

Customize System Message

You can inspect and configure Open Interpreter’s system message to extend its functionality, modify permissions, or give it more context.

interpreter.system_message += """
Run shell commands with -y so the user doesn't have to confirm them.
"""
print(interpreter.system_message)

Change your Language Model

Open Interpreter uses LiteLLM to connect to hosted language models.

You can change the model by setting the model parameter:

interpreter --model gpt-3.5-turbo
interpreter --model claude-2
interpreter --model command-nightly

In Python, set the model on the object:

interpreter.model = "gpt-3.5-turbo"

Find the appropriate “model” string for your language model here.

Running Open Interpreter locally

Open Interpreter uses LM Studio to connect to local language models (experimental).

Simply run interpreter in local mode from the command line:

You will need to run LM Studio in the background.

1. Download https://lmstudio.ai/ then start it.
2. Select a model then click ↓ Download.
3. Click the ↔️ button on the left (below 💬).
4. Select your model at the top, then click Start Server.

Once the server is running, you can begin your conversation with Open Interpreter.

(When you run the command interpreter --local, the steps above will be displayed.)

Note: Local mode sets your context_window to 3000, and your max_tokens to 1000. If your model has different requirements, set these parameters manually (see below).

Context Window, Max Tokens

You can modify the max_tokens and context_window (in tokens) of locally running models.

For local mode, smaller context windows will use less RAM, so we recommend trying a much shorter window (~1000) if it’s is failing / if it’s slow. Make sure max_tokens is less than context_window.

interpreter --local --max_tokens 1000 --context_window 3000

Debug mode

To help contributors inspect Open Interpreter, --debug mode is highly verbose.

You can activate debug mode by using it’s flag (interpreter --debug), or mid-chat:

$ interpreter
...
> %debug true <- Turns on debug mode

> %debug false