colabctl 0.2.0

Programmatic control of Google Colab for developers and AI agents — allocate GPUs, run code & notebooks, sync files, without the website.

colabctl

Drive Google Colab from code, the terminal, or an AI agent — allocate GPU/TPU runtimes, run code and notebooks, stream outputs, and sync files, without ever touching the Colab website. And when Colab isn't the right fit, run the same job on Modal, Vertex AI, or Hugging Face through one interface.

import asyncio
from colabctl import ColabClient

async def main():
    async with ColabClient() as colab:
        async with await colab.allocate(gpu="T4") as gpu:
            r = await gpu.run("import torch; print(torch.cuda.get_device_name(0))")
            print(r.text)          # → Tesla T4

asyncio.run(main())

Status: alpha. The Colab paths (official-CLI transport + a from-scratch /tun/m/* transport) and the Modal backend are validated against real accounts; Vertex / Hugging Face / the browser-bridge are implemented and unit-tested but not yet live-validated. See ROADMAP.md for the honest, detailed status.

Install

pip install "colabctl[cli,sdk,native,secrets]"
# or as a CLI tool (exposes `colabctl` and `colabctl-mcp`):
uv tool install "colabctl[cli,sdk]"

Bleeding edge from source: pip install "colabctl[all] @ git+https://github.com/mandipadk/colabctl.git".

Extras: cli, sdk, native, secrets, mcp, drive, modal, vertex, hf, browser (or all).

Authenticate (Colab)

The sanctioned Colab path uses Google Application Default Credentials:

gcloud auth application-default login \
  --scopes=openid,https://www.googleapis.com/auth/cloud-platform,\
https://www.googleapis.com/auth/userinfo.email,\
https://www.googleapis.com/auth/colaboratory,\
https://www.googleapis.com/auth/drive.file

(Other backends use their own credentials — MODAL_TOKEN_*, HF_TOKEN, GCP for Vertex.)

Use it

Python SDK — allocate a GPU, run code, get typed results, auto-release:

async with ColabClient() as colab:
    async with await colab.allocate(gpu="A100") as gpu:
        await gpu.upload("train.py", "content/train.py")
        result = await gpu.run("exec(open('content/train.py').read())")
        await gpu.download("content/model.pt", "model.pt")

@remote — ship a local function to a GPU and get its return value back:

from colabctl import remote

@remote(gpu="A100")
def train():
    import torch
    return torch.cuda.get_device_name(0)

print(train())          # blocks, runs on an A100, returns the device name

CLI:

colabctl run train.py --gpu T4               # one-shot: allocate → run → release
colabctl new --gpu A100 --name myjob         # keep a runtime; attach later
colabctl exec -s myjob -c "print(2**10)"
colabctl job run train.py --backend modal --gpu A100 --req torch   # any backend
colabctl job backends                        # list backends + capabilities

From an AI agent (MCP) — let Claude / Codex drive Colab and run jobs on Modal/Vertex/HF:

{ "mcpServers": { "colabctl": { "command": "colabctl-mcp" } } }

Backends

One job API (submit / status / logs / result / cancel) with capability-based routing and automatic failover — a Colab outage or quota block degrades to another backend instead of failing.

Backend	What it's for	ToS posture	Live-validated
Colab (CLI + native)	Your Colab Pro GPUs, interactive or batch	sanctioned (native is opt-in)	✅
Modal	gVisor-isolated GPU sandboxes; great for agent code	sanctioned	✅
Vertex AI	Headless, deadline-bound production jobs	sanctioned	⏳ impl + tests
Hugging Face Jobs	Durable, cheap GPU jobs	sanctioned	⏳ impl + tests

How it works

colabctl wraps Google's official google-colab-cli/colab-mcp as the sanctioned default, keeps a from-scratch /tun/m/* transport as a co-equal opt-in path (so you're never hostage to an immature dependency), and puts the durable engineering into a capability-detecting provider abstraction so the product survives Colab churn and abuse-detection bans by routing elsewhere.

• Architecture: SPEC.md · design decisions: DECISIONS.md · research: RESEARCH.md
• Docs: docs/ (uvx mkdocs serve)
• Contributing: CONTRIBUTING.md · Roadmap & status: ROADMAP.md

A note on Terms of Service

colabctl defaults to Google's sanctioned tooling on paid Colab Pro, where automated use is permitted with a positive compute-unit balance. The reverse-engineered native transport is disabled by default (COLABCTL_ENABLE_NATIVE=1 to opt in). Opaque abuse-detection bans can still affect any account; colabctl treats that as a disclosed, first-class fact and lets you fail over to other backends. Don't share/resell access, and respect each backend's terms.

License

Apache-2.0.