What is the Gemini Interactions API?

It is the modern, recommended interface for Gemini models that replaces the legacy generateContent methods, offering better support for agents, streaming, and stateful conversations.

Which SDK versions are supported by this skill?

This skill supports the 'google-genai' (Python) and '@google/genai' (JS/TS) SDKs version 2.3.0 or higher.

Does this skill support the Antigravity agent?

Yes, it provides implementation patterns for the Antigravity managed agent, allowing for code execution and file management in sandboxed environments.

How does it handle conversation history?

Conversations are stored by default; you can resume a dialogue by passing the 'previous_interaction_id' to maintain context across multiple turns.

Is it compatible with Gemini 1.5 or 2.0 models?

No, those models are considered legacy. This skill prioritizes Gemini 3.5 Flash and 3.1 variants for optimal performance and reasoning.

Gemini Interactions API | Claude Code Skill for AI Dev

The Gemini Interactions API skill provides a standardized framework for developers to implement Google's latest generative AI capabilities, including the advanced Gemini 3.5 Flash and 3.1 Pro models. It focuses on the recommended Interactions API, which simplifies multi-turn stateful conversations, multimodal processing of images and video, and the deployment of managed agents like Antigravity. This skill is particularly valuable for teams migrating from legacy SDKs to the current google-genai and @google/genai packages, offering immediate access to features like background research tasks, sandboxed code execution, and real-time streaming responses.

Key Features

01Multimodal support for image, video, and audio generation and understanding.

02Standardized migration patterns from legacy generateContent APIs.

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04Integration with Managed Agents for sandboxed code execution and web browsing.

05Native support for Deep Research and Deep Research Max agents.

06Stateful multi-turn chat management with server-side context storage.

Use Cases

01Building conversational AI agents with persistent memory and tool-calling capabilities.

02Automating complex research tasks using specialized high-exhaustiveness agents.

03Developing multimodal applications that generate and process diverse media types.

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