GitHub

Open Interpreter

What is the project about?

Open Interpreter is a project that allows Large Language Models (LLMs) to run code locally on a user's computer. It provides a natural-language interface, similar to ChatGPT, through which users can interact with their computer's capabilities.

What problem does it solve?

It overcomes the limitations of hosted, closed-source, and restricted environments like OpenAI's Code Interpreter. It gives LLMs the ability to interact with the local environment, execute code, access the internet, and use any available packages or libraries without file size or runtime restrictions.

What are the features of the project?

  • Runs code (Python, Javascript, Shell, etc.) locally.
  • ChatGPT-like interface in the terminal.
  • Ability to create and edit files (photos, videos, PDFs, etc.).
  • Control a Chrome browser for web tasks.
  • Plot, clean, and analyze datasets.
  • Full internet access.
  • No restrictions on file size or runtime.
  • Can use any package or library.
  • Interactive and programmatic chat modes.
  • Save and restore chat sessions.
  • Customizable system message.
  • Support for multiple language models (via LiteLLM).
  • Local model support (LM Studio, jan.ai, ollama, Llamafile).
  • Verbose mode for debugging.
  • Configuration profiles.
  • FastAPI server for HTTP REST endpoint control.
  • Android support via Termux.

What are the technologies used in the project?

  • Python
  • Large Language Models (LLMs) - GPT-4, GPT-3.5-turbo, Claude-2, etc.
  • LiteLLM
  • LM Studio, jan.ai, ollama, Llamafile (for local models)
  • FastAPI
  • JavaScript
  • Shell scripting
  • YAML (for configuration)
  • Markdown

What are the benefits of the project?

  • Flexibility: Runs in the local environment, removing restrictions of hosted services.
  • Power: Combines the capabilities of LLMs with the full power of a local development environment.
  • Accessibility: Provides a natural language interface to computer functions.
  • Extensibility: Supports various language models and local execution.
  • Open-Source: Allows for community contributions and transparency.

What are the use cases of the project?

  • Automating tasks involving file manipulation, data analysis, and web interaction.
  • Creating and editing multimedia content.
  • Performing research and data analysis.
  • Developing and testing code.
  • Controlling a computer through natural language.
  • Building custom applications that leverage LLMs and local code execution.
  • Rapid prototyping.
open-interpreter screenshot