{"slug": "google-antigravity-sdk-adds-local-model-support-for-greater-token-efficiency-and", "title": "Google Antigravity SDK Adds Local Model Support for Greater Token Efficiency and Code Privacy", "summary": "Google added local model support to its Antigravity SDK, letting developers run agentic workflows on their own machines while optionally mixing in cloud models, with an initial implementation focused on Gemma 4 26B A4B via Google's LiteRT. In a demonstration auditing and patching three vulnerable Python modules, Google reported that only 95 of roughly 3,400 tokens were spent on the cloud for planning while the remaining 97.2% ran locally, and that no source code was uploaded to the cloud. The SDK also supports OpenAI-compatible local inference servers through LocalOpenAIAgentConfig, including Ollama, LM Studio, and vLLM, and is distinct from the Antigravity IDE, which is built on the same agent infrastructure.", "body_md": "[Google has introduced support for local models in the Antigravity SDK](https://developers.googleblog.com/introducing-support-for-local-ai-models-in-the-antigravity-sdk/), enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based models for higher-level tasks. This approach gives developers greater control over data privacy, inference costs, network dependencies, and computational workload distribution.\n\nOne of the most compelling capabilities of the Antigravity SDK is hybrid agent orchestration, which adapts Google's proposed *Architect-Builder* pattern to a mixed cloud-local compute environment.\n\nAccording to the Architect-Builder pattern, one primary, high-context AI agent, the *architect*, holds the macro-level system view, including dependencies, architectural guardrails, and detailed specifications or implementation plans. Once the architect has defined a plan, several parallel, multiple specialized AI agents, the *builders*, work independently in isolated environments to execute discrete, narrow tasks mapped out by the specification.\n\nGoogle says that the Antigravity SDK enables the architect to operate in the cloud, devising a strategy and breaking the task into smaller units using filenames and task descriptions, without requiring access to the source code itself. The builders, instead, run locally and perform tasks such as reproducing security vulnerabilities, proposing code fixes, reviewing candidate patches, and running regression tests. Additional specialized local agents can validate proposed changes against the provided test suites, keeping the code-intensive stages of the workflow on-device.\n\nIn a demonstration involving the auditing and patching ot three vulnerable Python modules, Google reports that only 95 out of approximately 3400 tokens were used on the cloud for planning, while the remaining 97.2% were spent locally. Google also states that no source code was uploaded to the cloud.\n\nThe initial implementation focuses on running Gemma 4 26B A4B through Google's LiteRT technology. However, developers are not limited to LiteRT exclusively, as the Antigravity SDK also supports OpenAI-compatible local inference servers through `LocalOpenAIAgentConfig`, including Ollama, LM Studio, and vLLM.\n\nCommenting on the announcement, [Reddit user ambassadortim highlighted](https://www.reddit.com/r/LocalLLaMA/comments/1wof9kk/comment/pbmqoky/) that \"companies can have more control over their AI model and when to change or at least have more control over that\". In a separate discussion, [FollowingTop3534 questioned whether the cloud-token share metrics tells the whole story](https://www.reddit.com/r/google_antigravity/comments/1wom9gb/comment/pbqw54e/):\n\nI'd benchmark patch acceptance, wall time, and energy alongside the cloud-token share. '97.2% of tokens ran locally' is interesting, but it doesn't tell you whether the run was faster or cheaper on your hardware, and the recommended 24GB+ VRAM or unified memory is a real gate.\n\nAs a final note from the community, user [Latter_Crazy expressed enthusiasm](https://www.reddit.com/r/google_antigravity/comments/1wom9gb/comment/pbz5wg2/)for the new capabilities:\n\nit can do things I could not do before with the cloud rules. Cost, speed, quality are all going up and this is just the start. Highly recommend looking into this.\n\nThe Antigravity SDK should not be confused with the [Antigravity IDE](https://antigravity.google), which is an IDE powered by the Antigravity SDK. They share the same underlying agent infrastructure, but serve different purposes, with the SDK letting you build your own AI agents programmatically rather than simply using them for coding tasks.", "url": "https://wpnews.pro/news/google-antigravity-sdk-adds-local-model-support-for-greater-token-efficiency-and", "canonical_source": "https://www.infoq.com/news/2026/10/antigravity-sdk-local-models/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global", "published_at": "2026-10-14 09:00:00+00:00", "updated_at": "2026-10-15 00:36:57.955068+00:00", "lang": "en", "topics": ["ai-agents", "developer-tools", "large-language-models", "ai-tools"], "entities": ["Google", "Antigravity SDK", "Antigravity IDE", "Gemma 4 26B A4B", "LiteRT", "LocalOpenAIAgentConfig", "Ollama", "LM Studio"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/google-antigravity-sdk-adds-local-model-support-for-greater-token-efficiency-and", "markdown": "https://wpnews.pro/news/google-antigravity-sdk-adds-local-model-support-for-greater-token-efficiency-and.md", "text": "https://wpnews.pro/news/google-antigravity-sdk-adds-local-model-support-for-greater-token-efficiency-and.txt", "jsonld": "https://wpnews.pro/news/google-antigravity-sdk-adds-local-model-support-for-greater-token-efficiency-and.jsonld"}}