Introducing the Google Colab CLI

Google announced the Google Colab Command-Line Interface (CLI), a tool that connects local terminals to remote Colab runtimes for zero-friction execution by developers and AI agents. The CLI enables users to provision GPU instances, run scripts, and download results with simple terminal commands, integrating seamlessly into standard environments for agent use. This release aims to accelerate development by making Colab compute accessible and programmable for terminal-based workflows.

Today we are announcing the Google Colab Command-Line Interface https://github.com/googlecolab/google-colab-cli CLI , which bridges the gap between your local terminal and remote Colab runtimes, providing a zero-friction execution platform for both developers and AI agents. The Colab CLI offers: colab --gpu A100 or colab --gpu T4 . colab exec . .ipynb logs via colab download and colab log . colab repl or colab console .Because the Colab CLI integrates seamlessly into standard terminal environments, it can be used by any agent with terminal access. To ensure your AI assistants can hit the ground running, the CLI includes a prepackaged Colab skill file https://github.com/googlecolab/google-colab-cli/blob/main/COLAB SKILL.md that provides agents with instant, built-in context on exactly how to leverage the CLI. Let's look at a real life example of something a user or agent might try with the Colab CLI. Note that while the example below highlights Antigravity https://antigravity.google/ agent using Colab CLI as a tool, Colab CLI can easily be used by Claude Code, Codex, and other agents. Here is how an Agent can use the Colab CLI for a real-world ML workflow: The CLI can be used to run a real QLoRA pipeline that runs end-to-end with just a handful of commands. Offload heavy computational lifting to a GPU without typing a single cloud provisioning command by Instructing Antigravity or your agent of choice to build a remote fine-tuning job. In this scenario, we ask our agent to use the Colab CLI to fine-tune google/gemma-3-1b-it https://huggingface.co/google/gemma-3-1b-it on a Text-to-SQL dataset https://huggingface.co/datasets/philschmid/gretel-synthetic-text-to-sql to make the model better at writing SQL queries. The Antigravity prompt: Use the Colab CLI https://github.com/googlecolab/google-colab-cli to fine-tune Gemma 3 1B using QLoRA. Provision a Colab T4 GPU instance, install the necessary ML packages transformers, datasets, peft, trl, etc. , run my local ~ finetune run https://github.com/googlecolab/google-colab-cli/blob/main/examples/finetune run.py .py https://gist.github.com/spencersgoogle/05be7d5b8a86785284a72032d11e7214 script remotely, download the resulting safetensors adapter, save the notebook log, and cleanup. Antigravity executes: bash $ colab new --gpu T4 $ colab install transformers datasets peft trl bitsandbytes accelerate $ colab exec -f finetune run.py $ colab log --output gemma finetune log.ipynb $ colab stop Antigravity also uses the "colab download" command to download the adapter model, adapter config, tokenizer config, and tokenizer, which can be used to load and run your fine-tuned model locally. Now you have a remotely fine-tuned model ready to serve from your local device The Colab CLI makes powerful Colab compute accessible, programmable, and agent-ready. It is lightweight and easily accessible to any terminal-based AI agent. To start using the Colab CLI yourself, head over to the Google Colab CLI GitHub repository https://github.com/googlecolab/google-colab-cli for setup instructions. We are excited to see how this accelerates your development process and look forward to seeing what you and your agents build