gemini-interactions-api – Coding skill for AI agents

Gemini Interactions API Skill

When to Use

Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses, background research tasks, function calling, structured output, or migrating from the generateContent integrations.

Critical Rules (Always Apply)

> [!IMPORTANT]

> These examples are version-specific reference material. Follow user scope and verify current official documentation before implementation.

Model examples (verify availability and lifecycle)

• gemini-3.5-flash: 1M tokens, fast, balanced performance, multimodal
• gemini-3.1-pro-preview: 1M tokens, complex reasoning, coding, research
• gemini-3.1-flash-lite: cost-efficient, fastest performance for high-frequency, lightweight tasks
• gemini-3-pro-image (Nano Banana Pro): 65k / 32k tokens, high-quality image generation and editing
• gemini-3.1-flash-image (Nano Banana 2): 65k / 32k tokens, fast, efficient image generation and editing
• gemini-3.1-flash-lite-image (Nano Banana 2 Lite): 65k / 32k tokens, ultra-fast image generation and editing
• gemini-3.1-flash-tts-preview: expressive text-to-speech with Director's Chair prompting
• gemini-omni-flash-preview: video generation, image-referenced video generation, first-frame-to-video, and video editing
• gemma-4-31b-it: Gemma 4 dense model, 31B parameters
• gemma-4-26b-a4b-it: Gemma 4 MoE model, 26B total / 4B active parameters

> [!WARNING]

> Do not infer deprecation from a model generation prefix. The current lifecycle page distinguishes active, deprecated and shutdown models.

> Honor the requested compatible model; if unavailable, explain the lifecycle constraint and obtain agreement before substituting.

Current Agents

• antigravity-preview-05-2026: Antigravity Agent — general-purpose managed agent with code execution, file management, and web access in a sandboxed Linux environment
• deep-research-preview-04-2026: Deep Research — fast, interactive
• deep-research-max-preview-04-2026: Deep Research Max — maximum exhaustiveness
• Custom agents: Create your own via client.agents.create()

Current SDKs

• Python: google-genai >= 2.3.0 → pip install -U google-genai
• JavaScript/TypeScript: @google/genai >= 2.3.0 → npm install @google/genai

> [!NOTE]

> SDK versions ≥ 2.0.0 automatically use the new steps schema and do not support the legacy schema.

> Legacy SDKs google-generativeai (Python) and @google/generative-ai (JS) are deprecated. Never use them.

Important Additional Notes

• Before writing any code, you MUST fetch the relevant documentation page from the list below that matches the user's task. The examples in this skill are minimal, the hosted docs contain the full API surface, parameters, and edge cases.
• Interactions are stored by default (store=true). Paid tier retains for 55 days, free tier for 1 day.
• Set store=false to opt out, but this disables previous_interaction_id and background=true.
• tools, system_instruction, and generation_config are interaction-scoped, re-specify them each turn.
• Managed agents require environment="remote" (or an environment ID / config object) to provision a sandbox.
• Migrating from generateContent: Read references/migration.md for the scoping, checklist, and before/after code examples. Always confirm scope with the user before editing.
• Model upgrades: Validate modality/tools, schema, output quality, limits and costs; a string substitution is not proof of compatibility. Deprecated models (gemini-2.0-*, gemini-1.5-*) must be replaced, see references/migration.md.
• Migrating to Gemini 3.5 Flash: Read references/migration.md for the scoping and checklist.

Quick Start

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input="Tell me a short joke about programming."
)
print(interaction.output_text)

JavaScript/TypeScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    model: "gemini-3.5-flash",
    input: "Tell me a short joke about programming.",
});
console.log(interaction.output_text);

Response Helpers

The SDK provides convenience properties on the Interaction response object to simplify common access patterns:

| Property | Type | Description |

|---|---|---|

| output_text | string \| null | The last consecutive run of text from the trailing model_output steps. Returns the combined text when the model's final output contains multiple text parts. |

| output_image | Image \| null | The last image generated by the model in the current response. Returns an object with data (base64) and mime_type. |

| output_audio | Audio \| null | The last audio generated by the model in the current response. Returns an object with data (base64) and mime_type. |

Stateful Conversation

Python

interaction1 = client.interactions.create(
    model="gemini-3.5-flash",
    input="Hi, my name is Phil."
)
# Second turn — server remembers context
interaction2 = client.interactions.create(
    model="gemini-3.5-flash",
    input="What is my name?",
    previous_interaction_id=interaction1.id
)
print(interaction2.output_text)

JavaScript/TypeScript

const interaction1 = await client.interactions.create({
    model: "gemini-3.5-flash",
    input: "Hi, my name is Phil.",
});
const interaction2 = await client.interactions.create({
    model: "gemini-3.5-flash",
    input: "What is my name?",
    previous_interaction_id: interaction1.id,
});
console.log(interaction2.output_text);

Deep Research Agent

Use deep-research-preview-04-2026 for fast research or deep-research-max-preview-04-2026 for maximum exhaustiveness. Agents require background=True.

Python

import time

interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Research the history of Google TPUs.",
    background=True
)
deadline = time.monotonic() + 600
while time.monotonic() < deadline:
    interaction = client.interactions.get(interaction.id)
    if interaction.status == "completed":
        print(interaction.output_text)
        break
    elif interaction.status in ("failed", "cancelled", "requires_action"):
        print(f"Failed: {interaction.error}")
        break
    time.sleep(10)

JavaScript/TypeScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

// Start background research
const initialInteraction = await client.interactions.create({
    agent: "deep-research-preview-04-2026",
    input: "Research the history of Google TPUs.",
    background: true,
});

// Poll for results
const deadline = Date.now() + 600_000;
while (Date.now() < deadline) {
    const interaction = await client.interactions.get(initialInteraction.id);
    if (interaction.status === "completed") {
        console.log(interaction.output_text);
        break;
    } else if (["failed", "cancelled", "requires_action"].includes(interaction.status)) {
        console.log(`Failed: ${interaction.status}`);
        break;
    }
    await new Promise(resolve => setTimeout(resolve, 10000));
}

Advanced features: collaborative planning, native visualization, MCP integration, file search, multimodal inputs. See [Deep Research docs](https://ai.google.dev/gemini-api/docs/interactions/deep-research.md.txt).

Managed Agents

Managed agents run inside a sandboxed Linux environment hosted by Google. Fetch the [Managed Agents Quickstart](https://ai.google.dev/gemini-api/docs/managed-agents-quickstart.md.txt) before writing agent code.

Antigravity Agent

The Antigravity agent (antigravity-preview-05-2026) is the general-purpose managed agent. It can execute code (Bash, Python, Node.js), manage files, browse the web, and use Google Search. See [Antigravity Agent docs](https://ai.google.dev/gemini-api/docs/antigravity-agent.md.txt) for capabilities, tools, multimodal input, and pricing.

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Write a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt. Then read the file and print its contents.",
    environment="remote",
)

print(f"Environment ID: {interaction.environment_id}")
print(interaction.output_text)

JavaScript/TypeScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Write a Python script that generates the first 20 Fibonacci numbers and s

… (full content is in the download package)