Google Antigravity SDK

Installation & Setup

Before proceeding with any Google Antigravity tasks, ensure the environment is ready:

• Verify Applicability: If operating in an existing codebase, verify that

using this Python SDK is possible and appropriate for the project.

• Check Dependencies: Check if google-antigravity is listed in the

project's dependencies (e.g., requirements.txt, pyproject.toml).

• Install Package: Ensure the google-antigravity Python package is

installed.

• Authentication Setup: Check for a valid GEMINIAPIKEY environment

variable or a .env file (required to access Gemini models). - If credentials are missing, you MUST actively help the user get set up with an API key by providing the following link: - Default to Google AI Studio: https://aistudio.google.com/app/api-keys - Explain that the API key can be passed explicitly in code as shorthand (e.g., LocalAgentConfig(apikey="...")) or automatically read from the environment. - For Gemini Enterprise Agent Platform (formerly Vertex AI) authentication, the SDK supports both Standard Mode and Express Mode: - Standard Mode (ADC): Instruct the user to run gcloud auth application-default login and configure the agent with vertex=True along with project and location in LocalAgentConfig. - Express Mode (API Key): Configure the agent with vertex=True along with apikey="your-express-api-key" in LocalAgentConfig (no ADC or regional project/location needed). - Note: For local models (LiteRTAgentConfig or LocalOpenAIAgentConfig), no API key or cloud credentials are needed. See references/local_models.md for setup details.

Routing Table

Use the following information to dig deeper into specific topics based on the user request. Read the referenced files or explore the directories to find relevant information.

References

• If the user needs to understand the high-level overview and core concepts of

the Google Antigravity SDK (Agent, Conversation, Connection), read references/architecture.md.

• If the user needs to perform advanced agent configuration (e.g., selecting

appropriate models, configuring execution behavior via agentbehavior—defaulting to autonomous vs interactive—or configuring connection reliability), or understand the critical rules for model identifiers to avoid assumptions, read references/agentconfiguration.md.

• If the user needs to extend an agent's capabilities by integrating Model

Context Protocol (MCP) servers, or configure tool permissions for the agent, read references/mcp_integration.md.

• If the user needs to define safety policies, resolve execution order,

restrict agent actions using predicates, or run terminal commands inside an OS-level sandbox, read references/safety_policies.md.

• If the user needs to debug failed agents, stream logs, or implement error

recovery using hooks to make agents robust, read references/error_handling.md.

• If the user needs to monitor costs, track token usage (including thinking

tokens), or build custom audit logs for advanced monitoring, read references/observability.md.

• If the user needs to see a list of built-in tools and understand their default state, read references/builtintools.md.
• If the user needs to run agents locally using on-device models (e.g., Gemma

via LiteRT, or via OpenAI-compatible APIs), understand hardware requirements, or set up a local model environment, read references/local_models.md.

Examples

• If the user needs to implement basic agent behavior, streaming responses, or

expose internal thoughts, read examples/gettingstarted/helloworld.md.

• If the user needs to customize or override default retry behavior and

exponential backoff for API errors or schema validation, read examples/gettingstarted/customizingretries.md.

• If the user needs to equip an agent with custom capabilities (tools) derived

from Python functions, or maintain agent state across tool execution, read examples/gettingstarted/customtool.md.

• If the user needs to shape an agent's persona, define its system

instructions, or dynamically adapt its behavior, read examples/gettingstarted/personaconfig.md.

• If the user needs to build multimodal agents capable of processing images

and PDFs, or generating visual content, read examples/getting_started/multimodal.md.

• If the user needs to implement multi-agent delegation, allowing a main agent

to spawn and orchestrate subagents, or configure multi-tier nested subagent hierarchies (using maxsubagentdepth and allowedsubagents), read examples/gettingstarted/subagents.md.

• If the user needs to connect an agent to external services via MCP (Stdio or

SSE), read examples/gettingstarted/mcptools.md.

• If the user needs to create proactive agents that respond to time-based

events or file system triggers in the background, read examples/gettingstarted/periodictrigger.md.

• If the user needs to intercept agent lifecycle events (e.g., pre/post turn,

stop, tool execution, errors) to customize execution flow, read examples/getting_started/hooks.md.

• If the user needs to implement turn-level cancellation or programmatic

stream aborts, read examples/getting_started/cancellation.md.

• If the user needs to implement persistent agents that remember past

interactions across sessions, read examples/getting_started/persistence.md.

• If the user needs to override the default application data directory

for agent artifacts, scratch files, and media storage, read examples/gettingstarted/appdatadiroverride.md.

• If the user needs an agent to output structured data (e.g., JSON matching a

Pydantic schema) for reliable integration, read examples/gettingstarted/structuredoutput.md.

• If the user needs to add, configure, or load agent skills into the Google

Antigravity SDK agent, read examples/gettingstarted/agentskills.md.

• If the user needs to enable and use built-in web tools (like Google Search

or URL fetching) with the agent, read examples/gettingstarted/webtools.md. (Note: when fetching massive web pages or articles, pair readurlcontent with view_file to inspect cached disk files).

• If the user needs to enforce session operational limits (model or

tool calls) or proactive token budget controls (input, output, or total tokens) and handle StopReason, read examples/gettingstarted/budgetlimits.md.

• If the user needs to set up and run a local model agent (LiteRT with Gemma,

or an OpenAI-compatible server like Ollama), including model download, hardware requirements, and context window configuration, read examples/gettingstarted/localmodels.md.

• If the user needs to configure conversation context limits or background

trajectory checkpointing (compaction cadence) to handle long-running sessions, read examples/getting_started/compaction.md.