Google Antigravity Agents: API, SDK, and ADK Integration Guide

Building a production‑grade AI agent traditionally required a lot of infrastructure work such as writing orchestration loops, creating sandbox environments, wiring tools, handling state and retries. The article introduces three components that eliminate most of that effort: the Antigravity engine (Google’s core production agent runtime), the managed Agent API (preview) and the Agent Development Kit (ADK).

It explains how to choose between the API and ADK based on two dimensions: the degree of control over the orchestration loop and the shape of the business task (result‑oriented vs workflow‑oriented). The API runs the Antigravity engine directly and is suited for simple result‑driven tasks, while ADK lets teams write custom loops, integrate the engine into their own applications, and handle complex scheduling graphs.

Step‑by‑step usage of the Agent API is shown. After installing the google-genai SDK and initializing a Client, an agent is created with client.agents.create(...) specifying an ID, base agent, description, system instruction and a list of tools (code execution, filesystem, Google search, URL context). Interaction is started with client.interactions.create(...) using background=True, stream=True and store=True. The article displays a loop that prints streamed events and extracts the final status and environment ID.

The Antigravity SDK ( pip install google-antigravity) provides the same engine locally. An async example creates a LocalAgentConfig, starts an Agent, sends a chat request and streams the response token‑by‑token. The SDK adds capabilities not present in the managed service, such as streaming internal thoughts, intercepting tool calls, custom Python tools, MCP integration and declarative permission policies.

ADK is described as a high‑code‑volume framework for teams that need explicit, testable control over tool‑call timing and multi‑role scheduling graphs. The engine is now a native agent type inside ADK, and ADK workflows can invoke the Agent API as sub‑agents via the A2A protocol. The article lists design principles—composability, extensibility, bidirectional bridging—and shows how the same underlying Antigravity engine can be used interchangeably through API, SDK or ADK.

Finally, a practical selection guide lists scenarios where the Agent API is preferred (quick rollout, result‑driven tasks, need for built‑in code execution, filesystem and search) and where ADK is preferred (complex scheduling graphs, explicit control, compliance/audit, long‑running workflows). The conclusion emphasizes that users can start with the API and later migrate parts of the system to ADK without rewriting the core engine.