MCP Servers · google-antigravity

antigravity-sdk-python

The Google Antigravity SDK is a Python library for building AI agents powered by Google's Gemini LLM. It abstracts the complexity of agentic loops, tool integration, and state management, allowing developers to focus on agent behavior rather than infrastructure.

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Source: GitHub — github.com/google-antigravity/antigravity-sdk-python

2.3k

GitHub stars

833

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	google-antigravity/antigravity-sdk-python
Owner	google-antigravity
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	2.3k
Forks	833
Open issues	22
Latest release	Unknown
Last updated	2026-06-25
Source	https://github.com/google-antigravity/antigravity-sdk-python

What antigravity-sdk-python is

Python SDK providing async-first agent orchestration with Gemini integration, MCP server support, multimodal input handling, and a three-layer architecture (Agent > Conversation > ConnectionStrategy). Ships with a compiled runtime binary via PyPI wheels; local setup requires binary distribution, not source compilation.

Quickstart

Get the antigravity-sdk-python source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/google-antigravity/antigravity-sdk-python.git2cd antigravity-sdk-python3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

AI Agent Development with Tool Orchestration

Build conversational agents that call custom Python functions, MCP servers, or file system tools with declarative policy controls and stateful conversation history.

Multimodal Document Analysis

Process mixed content (PDFs, images, video, audio) alongside text prompts for architectural review, compliance checks, or design validation tasks.

Interactive CLI and Automation Tools

Create read-write agentic loops with user confirmation gates, background triggers, and real-time streaming output for ops automation or interactive code assistance.

Implementation considerations

SDK requires PyPI wheel installation (not source clone) to obtain compiled runtime binary; build infrastructure must support platform-specific wheel downloads.
Async-first API design demands asyncio/await familiarity; synchronous code paths not evident in examples or core API surface.
Tool registration and policy enforcement rely on decorator and configuration patterns; custom tool signatures must match expected type hints.
Multimodal content requires explicit MIME type handling or `from_file()` helper; streaming responses need explicit async iteration.
GCP/Vertex AI authentication defaults to Application Default Credentials (ADC); local development requires `gcloud auth application-default login`.

When to avoid it — and what to weigh

Offline/Air-Gapped Deployments — SDK requires runtime binary from PyPI and connectivity to Google Gemini API; not suitable for fully offline or restricted-network environments.
Non-Python Ecosystems — Python-only SDK; teams requiring Java, Go, Node.js, or other language bindings will need alternative solutions or wrapper implementations.
Embedded or Resource-Constrained Devices — Compiled binary footprint and async runtime requirements suggest unsuitability for IoT, edge devices, or minimal resource settings.
Vendor Lock-In Concerns — Tight coupling to Google Gemini API; switching to alternative LLMs (Claude, GPT, open-source) requires significant refactoring.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing modification, redistribution, and commercial use provided original copyright and license text are retained.

Apache-2.0 permits commercial use, modification, and closed-source redistribution. No additional license or attribution beyond Apache terms required. However, verify Google's Gemini API terms separately; SDK license does not cover backend service ToS.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

SDK defaults to read-only mode (safeguard against accidental tool execution). Policies provide deny/allow/ask_user enforcement; review tool whitelist and policy chains before enabling write capabilities. No explicit discussion of: input sanitization, prompt injection risks, tool call parameter validation, or credential handling best practices. Assess multimodal content sources (file upload permissions, MIME type validation) and MCP server origin/authenticity before deployment.

Alternatives to consider

LangChain (Python)

Broader LLM model support, mature agent frameworks (ReAct, tool use), extensive integrations (databases, APIs, file stores), and larger community. Trade-off: higher complexity, less opinionated architecture.

Anthropic Claude SDK + Tool Use

Native tool-use and vision APIs for Claude models. Simpler than LangChain for focused Claude-only agents. Trade-off: no MCP built-in, less stateful conversation scaffolding, Google-specific features unavailable.

Vertex AI Agent Builder (No-Code/Low-Code)

GCP-native visual workflow builder for agents, no Python required, integrated monitoring/logging. Trade-off: less flexibility, vendor lock-in, limited customization vs. SDK approach.

Software development agency

Build on antigravity-sdk-python with DEV.co software developers

Antigravity SDK simplifies agent orchestration for Python teams on GCP. Review architecture fit, credential strategy, and tool policies before production rollout.

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antigravity-sdk-python FAQ

Can I use Antigravity SDK with models other than Gemini?

Not evident from documentation. SDK is tightly coupled to Google Gemini and Vertex AI (Gemini Enterprise Agent Platform). Alternative LLM support would require custom ConnectionStrategy implementation (Layer 3 architecture).

What is the compiled binary and why can't I clone and run locally?

SDK ships with a platform-specific compiled runtime (likely agent orchestration engine). PyPI wheels bundle this binary for Linux, macOS, Windows. Source clone lacks the binary; PyPI installation is mandatory.

Does the SDK handle conversation persistence (database storage)?

Not explicitly. `Conversation` class maintains in-memory history via `conversation.history`. Persistence to database, file, or external store requires custom serialization or wrapper implementation.

How does streaming work with tool calls and thinking?

ChatResponse exposes separate async generators: `response.thoughts` for reasoning, `response.tool_calls` for tool invocations, and iteration over response itself for text tokens. Caller orchestrates interleaving.

Software development & web development with DEV.co

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If antigravity-sdk-python is part of your mcp servers roadmap, our team can implement, customize, migrate, and maintain it.

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Antigravity SDK simplifies agent orchestration for Python teams on GCP. Review architecture fit, credential strategy, and tool policies before production rollout.

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