Google Antigravity SDK adds local model support via LiteRT
10/04/2026 — 10/04, 15:57·1 sources·1 reports
Story overview
On October 4, 2026, the Google Developers Blog announced that the Google Antigravity SDK now supports local AI models. According to the post, developers can run offline, agentic workflows on-device using models such as Gemma 4 26B A4B through LiteRT.
The update is aimed at hybrid orchestration architectures. In this setup, a cloud model acts as a lightweight planner, while local models take on token-intensive work such as code auditing and patching directly on the device. The blog post frames this split as a way to let those heavier tasks be handled locally and securely, with planning still delegated to the cloud.
The SDK can also connect directly to OpenAI-compatible inference services, including Ollama and vLLM.
That is the extent of what the single available report covers. No release timeline, availability details, or pricing were included in the announcement summary, and no further updates on the feature have been reported so far. The information described here therefore reflects the capabilities Google outlined in that post rather than any confirmed rollout.
AI-generated from 1 reports · updated 2 hours ago
Latest turnGoogle's Antigravity SDK now lets developers run offline agentic workflows locally with models such as Gemma 4 26B A4B via LiteRT, enabling hybrid setups where a cloud model plans while local models handle token-heavy work like code auditing and patching. The SDK also adds drop-in support for OpenAI-compatible inference servers including Ollama and vLLM.
Reports on this story headlines open the original
Google's Antigravity SDK now lets developers run offline agentic workflows locally with models such as Gemma 4 26B A4B via LiteRT, enabling hybrid setups where a cloud model plans while local models handle token-heavy work like code auditing and patching. The SDK also adds drop-in support for OpenAI-compatible inference servers including Ollama and vLLM.
Google Developers BlogFirst-partyAI score 55
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