Google Antigravity Sdk

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 GEMINI_API_KEY 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(api_key="...")) 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 api_key="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 agent_behavior—defaulting to autonomous vs interactive—or configuring connection reliability), or understand the critical rules for model identifiers to avoid assumptions, read references/agent_configuration.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/built_in_tools.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/getting_started/hello_world.md.
• If the user needs to customize or override default retry behavior and exponential backoff for API errors or schema validation, read examples/getting_started/customizing_retries.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/getting_started/custom_tool.md.
• If the user needs to shape an agent's persona, define its system instructions, or dynamically adapt its behavior, read examples/getting_started/persona_config.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 max_subagent_depth and allowed_subagents), read examples/getting_started/subagents.md.
• If the user needs to connect an agent to external services via MCP (Stdio or SSE), read examples/getting_started/mcp_tools.md.
• If the user needs to create proactive agents that respond to time-based events or file system triggers in the background, read examples/getting_started/periodic_trigger.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/getting_started/app_data_dir_override.md.
• If the user needs an agent to output structured data (e.g., JSON matching a Pydantic schema) for reliable integration, read examples/getting_started/structured_output.md.
• If the user needs to add, configure, or load agent skills into the Google Antigravity SDK agent, read examples/getting_started/agent_skills.md.
• If the user needs to enable and use built-in web tools (like Google Search or URL fetching) with the agent, read examples/getting_started/web_tools.md. (Note: when fetching massive web pages or articles, pair read_url_content 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/getting_started/budget_limits.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/getting_started/local_models.md.
• If the user needs to configure conversation context limits and compaction thresholds to handle long-running sessions, read examples/getting_started/compaction.md.