Google's Antigravity SDK Now Runs Local Models Offline

Google’s Antigravity SDK can now use local AI models for agent workflows. The September 23 announcement gives developers a way to keep inference on their own machine, including when the network is unavailable. That creates a useful option for code and documents that should stay local, provided the tools around the model follow the same boundary.

Editorial note: Prepared October 1 from information published by September 26, 2026. Later product developments are outside this article’s scope.

Google’s launch post highlights Gemma 4 26B A4B through LiteRT and recommends more than 24 GB of VRAM or unified memory for that setup. It also identifies OpenAI-compatible local servers through LocalOpenAIAgentConfig. This is SDK support: it does not mean every Antigravity editor session has switched to local inference.

An SDK is a library used to build an application. The model produces responses, while the surrounding agent code decides how to call tools and continue the task. Moving the model changes where those responses are generated. It does not automatically decide which files a tool can read or where a tool can send results. For the wider product context, see our Antigravity desktop overview.

Google demonstrates a hybrid workflow in which a cloud planner receives filenames and task descriptions while local workers handle source code. That is a vendor demonstration, not proof that an arbitrary hybrid application keeps every sensitive detail local. We have not run an independent performance or privacy benchmark of it.

The design question is concrete: which exact fields cross the boundary? A filename can expose a customer name. An error message can include a path, a query or a fragment of a document. A “summary only” return value can still disclose the content it summarizes. Review the outbound payload rather than treating the absence of a full source file as sufficient evidence.

For a hybrid pilot, use synthetic filenames and a disposable repository. Capture the requests sent to the cloud planner and inspect them before using real work. If the organization cannot authorize even that limited transfer, choose the fully local path instead of trying to phrase a prompt that promises privacy.

The official Python SDK repository is the implementation reference linked by Google’s launch post. Record the package version, model artifact and runtime used for your pilot. A later package install can change behavior, so “we used the SDK” is not enough detail to reproduce the result.

Use Google’s dated setup instructions for the exact installation and configuration syntax. The LiteRT route uses LiteRTAgentConfig; the compatible-server route uses LocalOpenAIAgentConfig. Check that the endpoint you configure is actually local. A localhost address inside a container refers to that container, which may not be where the model server runs.

Start with reading a small test directory and producing a summary. Add file writing only when that simple loop behaves as expected. Keep the first write task recoverable: a generated report or a patch in a throwaway checkout. The purpose is to establish that configuration, inference and tool execution work together before evaluating a larger task.

A local model can operate on valuable files if its tools can reach them. Our sandbox and filesystem boundary guide explains why a working directory alone should not be treated as an access-control guarantee. Give the runtime only the files and credentials the task needs.

Keep secrets out of the test environment. Review mounted directories, environment variables and inherited credentials, then check whether a permitted command can invoke another program with broader access. For a truly offline requirement, verify network behavior at the runtime or operating-system boundary. A promise in the prompt is not a network policy.

Compare a fixed set of tasks with your existing process. For each, record whether the result passed, how long it took, peak memory use and the time a reviewer spent fixing it. Add the cost of hardware and operations to the decision, even though local inference avoids a per-request cloud API charge. An idle machine you already own and a new dedicated workstation have different economics.

Start with a task whose correctness is observable, such as making a small change that must pass an existing test. Avoid ranking the setup by how quickly it begins producing text. The useful result is the accepted patch or report, including any retries. Our model-routing guide covers that workload-based comparison; our AI transformation service helps teams design the evaluation.