Google Antigravity SDK

The Google Antigravity SDK is a Python SDK for building AI agents powered by Antigravity and Gemini. It provides a secure, scalable, and stateful infrastructure layer that abstracts the agentic loop, letting you focus on what your agent does rather than how it runs.

Installation

pip install google-antigravity

[!IMPORTANT] The Google Antigravity SDK relies on a compiled runtime binary that is included in the platform-specific wheels published to PyPI. Cloning this repository alone is not sufficient to run the SDK. Always install from PyPI with pip install google-antigravity to obtain the binary.

Quickstart

Get started by running one of the examples/, such as the hello_world example with:

export GEMINI_API_KEY="your_api_key_here"
python ./examples/getting_started/hello_world.py

Gemini Enterprise Agent Platform (formerly Vertex AI)

To use the SDK with Gemini Enterprise Agent Platform (formerly Vertex AI), the SDK supports two authentication modes:

1. Express Mode (API Key)

For fast setup without requiring Google Cloud projects, regional configuration, or Application Default Credentials (ADC), provide an API key with vertex=True:

from google.antigravity import Agent, LocalAgentConfig

config = LocalAgentConfig(
    vertex=True,
    api_key="your_api_key_here",
)

async with Agent(config) as agent:
    response = await agent.chat("Hello!")
    print(await response.text())

2. Standard Mode (Project & Location with ADC)

For enterprise deployments routing to regional endpoints, configure LocalAgentConfig with vertex=True, project, and location. By default, this mode authenticates via Application Default Credentials (ADC).

from google.antigravity import Agent, LocalAgentConfig

config = LocalAgentConfig(
    vertex=True,
    project="your-gcp-project",
    location="us-central1",
)

async with Agent(config) as agent:
    response = await agent.chat("Hello!")
    print(await response.text())

Alternatively, you can leave these fields unset in LocalAgentConfig and export the environment variables instead:

# Either GOOGLE_GENAI_USE_VERTEXAI or GOOGLE_GENAI_USE_ENTERPRISE enable Vertex.
export GOOGLE_GENAI_USE_VERTEXAI=True
export GOOGLE_CLOUD_PROJECT="your-gcp-project"
export GOOGLE_CLOUD_LOCATION="us-central1"

Explicit kwargs always take precedence over env vars.

Ensure you have authenticated locally before running the agent in Standard Mode:

gcloud auth application-default login

See vertex.py for a complete example.

Concepts

Simple Agent

The Agent class is the easiest way to get started. It manages the full lifecycle — binary discovery, tool wiring, hook registration, and policy defaults — behind a single async context manager.

The system_instructions parameter is optional.

import asyncio
from google.antigravity import Agent, LocalAgentConfig

async def main():
    config = LocalAgentConfig(
        system_instructions="You are an expert assistant for codebase navigation.",
        # api_key="your_api_key_here",
    )
    async with Agent(config) as agent:
        response = await agent.chat("What files are in the current directory?")
        print(await response.text())

async def run():
    await main()