antigravity-sdk-python: A Python library for building AI agents that leverage the full power of Google Antigravity

by google-antigravity · MCP Server · ★ 3.7k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 69/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsantigravitygeminigemini-apigooglellm-agentsmcppython-sdkskills

Quick Facts

Stars3,724
Forks1,472
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score69/100
Open Issues33
Last Updated2026-10-13
Created2026-04-29
Platformsgemini, mcp, python
Est. Tokens~15k

About antigravity-sdk-python

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.

Compatible Skills

These tools work well together with antigravity-sdk-python for enhanced workflows:

  • Agentic-SEO-Skill — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (57%)
  • gcli2api — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (55%)
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  • AntiHub-ALL — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (51%)
  • gemini-for-claude-code — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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