Local AI Models in the Antigravity SDK

In many cases we see that an Architect-Builder pattern is a great way of combining cloud model scale with local model advantages. In the hybrid demo video below, built with the updated Antigravity SDK, a cloud architect (Gemini 3.8 Flash) acts as the planner and conductor, while a local swarm of Gemma 4 26B instances handles the heavy lifting entirely on-device.

When tasked with auditing and patching three vulnerable modules (auth.py, billing.py, and database.py), the workflow maintains strict data privacy and allows us to make the most of our token utilization:

No code uploaded: Gemini 3.8 Flash plans the strategy and decomposes the work based purely on filenames and task descriptions - spending just 95 cloud tokens without any source code ever leaving the machine.

Autonomous local gauntlet: Local Gemma 4 26B models take over on the local GPU to execute an adversarial audit loop: reproducing security vulnerabilities, authoring candidate fixes, critiquing patches, and validating against regression test suites.

Massive cost and privacy wins: In this recorded run, 97.2% of all tokens (3,322 tokens) run locally and offline without calling a cloud API, delivering fully verified, green patches while keeping proprietary code completely secure on-device.

Check out the example project here to run the built-in 3-file gauntlet or point it at your own Python modules and test suite.