LibreAI / AI Agents / antigravity-sdk-python
// agents · google-antigravity/antigravity-sdk-python

antigravity-sdk-python

Google's Python SDK for building AI agents on the Antigravity platform and Gemini models.

★ 3.7k stars⑂ 1.5k forksApache-2.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

antigravity-sdk-python is an open-source ai agents project by google-antigravity (google-antigravity/antigravity-sdk-python on GitHub). It has 3,664 GitHub stars and 1,456 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 7 October 2026.

GitHub stars
3,664
Forks
1,456
License
Apache-2.0
Last updated on GitHub
7 October 2026
Category
AI Agents
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Clarify your goal

    Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.

  2. Get the project

    Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.

  3. Install and configure

    Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.

  4. Run your first task

    Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.

// features

Why people love it

One-class agents

The Agent class handles lifecycle, tool wiring, hook registration and policy defaults behind an async context manager.

Streaming everywhere

Stream responses, reasoning/thinking deltas and strongly-typed ToolCall events without blocking.

Custom tools and MCP

Define custom tools and connect MCP servers to extend what agents can do.

Multimodal ingestion

Send mixed text and content objects — filesystem paths resolve automatically to document types.

Hooks, policies and triggers

Control agent behaviour with hooks and policies, and react to triggers.

Local models too

Run agents against locally imported LiteRT-LM models by pointing at the model path.

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// run it

Run it yourself

  1. Clone the repository

    Get the source so you can inspect, configure and run it locally.

    git clone https://github.com/google-antigravity/antigravity-sdk-python.git
    cd antigravity-sdk-python
  2. Install the SDK

    Install the package from PyPI.

    pip install google-antigravity
  3. Set your key and run the quickstart

    Export a Gemini API key for express mode (or use ADC project/location for Vertex standard mode), then run the quickstart Agent example.

    export GEMINI_API_KEY=your-key  # see README quickstart for the agent code
// use cases

What you can build

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// faq

Questions, answered

Who maintains antigravity-sdk-python?

It is maintained by google-antigravity on GitHub (google-antigravity/antigravity-sdk-python), where the source, issues and releases live.

Is it free to use?

Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.

Does it need a paid API key?

Express mode uses a Gemini API key (usage is billed by Google); standard mode routes through a Google Cloud project with ADC, and local LiteRT-LM models avoid API calls.

How does it compare to Google's genai SDK used directly, or frameworks like LangGraph?

Compared with Google's genai SDK used directly, or frameworks like LangGraph, antigravity-sdk-python's distinguishing focus is: google's Python SDK for building AI agents on the Antigravity platform and Gemini models. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.

How do I get started?

Clone https://github.com/google-antigravity/antigravity-sdk-python (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.

// keep exploring

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