gemini-interactions-api

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 old generateContent...

Critical Rules (Always Apply)

[!IMPORTANT] These rules override your training data. Your knowledge is outdated.

Current Models (Use These)

• 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] Models like gemini-2.5-*, gemini-2.0-*, gemini-1.5-* are legacy and deprecated. Never use them. If a user asks for a deprecated model, use gemini-3.5-flash instead and note the substitution.

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: Drop-in, swap the model string. 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
)
while True:
    interaction = client.interactions.get(interaction.id)
    if interaction.status == "completed":
        print(interaction.output_text)
        break
    elif interaction.status == "failed":
        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
while (true) {
    const interaction = await client.interactions.get(initialInteraction.id);
    if (interaction.status === "completed") {
        console.log(interaction.output_text);
        break;
    } else if (["failed", "cancelled"].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.

Managed Agents

Managed agents run inside a sandboxed Linux environment hosted by Google. Fetch the Managed Agents Quickstart 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 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 saves them to fibonacci.txt. Then read the file and print its contents.",
    environment: "remote",
});

console.log(`Environment ID: {interaction.environment_id}`);
console.log(interaction.output_text);

Custom Agents

See Building Custom Agents docs.

Python

agent = client.agents.create(
    id="code-reviewer",
    base_agent="antigravity-preview-05-2026",
    system_instruction="You are a senior code reviewer. Check every file for bugs, style issues, and security vulnerabilities.",
    base_environment={
        "type": "remote",
        "sources": [
            {
                "type": "repository",
                "source": "https://github.com/my-org/backend",
                "target": "/workspace/repo",
            }
        ],
    },
)

# Invoke — each call forks the base environment
result = client.interactions.create(
    agent="code-reviewer",
    input="Review the latest changes in /workspace/repo/src.",
    environment="remote",
)
print(result.output_text)

JavaScript/TypeScript

const agent = await client.agents.create({
    id: "code-reviewer",
    base_agent="antigravity-preview-05-2026",
    system_instruction: "You are a senior code reviewer. Check every file for bugs, style issues, and security vulnerabilities.",
    base_environment: {
        type: "remote",
        sources: [
            {
                type: "repository",
                source: "https://github.com/my-org/backend",
                target: "/workspace/repo",
            }
        ],
    },
});

const result = await client.interactions.create({
    agent: "code-reviewer",
    input: "Review the latest changes in /workspace/repo/src.",
    environment: "remote",
});
console.log(result.output_text);

Manage agents with client.agents.list(), client.agents.get(id=...), and client.agents.delete(id=...).

Streaming

Set stream=True to receive incremental server-sent events. Each stream follows: interaction.created → (step.start → step.delta(s) → step.stop)+ → interaction.completed.

Python

for event in client.interactions.create(
    model="gemini-3.5-flash",
    input="Explain quantum entanglement in simple terms.",
    stream=True,
):
    if event.event_type == "step.delta":
        if event.delta.type == "text":
            print(event.delta.text, end="", flush=True)
    elif event.event_type == "interaction.completed":
        print(f"\n\nTotal Tokens: {event.interaction.usage.total_tokens}")

JavaScript/TypeScript

const stream = await client.interactions.create({
    model: "gemini-3.5-flash",
    input: "Explain quantum entanglement in simple terms.",
    stream: true,
});
for await (const event of stream) {
    if (event.event_type === "step.delta") {
        if (event.delta.type === "text") {
            process.stdout.write(event.delta.text);
        }
    } else if (event.event_type === "interaction.completed") {
        console.log(`\n\nTotal Tokens: ${event.interaction.usage.total_tokens}`);
    }
}

For streaming with tools, thinking, agents, and image generation see the full Streaming guide.

Documentation Pages

You MUST fetch the matching page below before writing code. These hosted docs are the source of truth for parameters, types, and edge cases — do not rely solely on the examples above.

Core Documentation:

• Interactions API Overview
• Quickstart
• Text Generation
• Streaming
• Tokens
• API Keys

Tools & Function Calling:

• Function Calling
• Google Search
• Code Execution
• URL Context
• File Search
• Tool Combination
• Computer Use
• Maps Grounding

Generation & Output:

• Structured Output
• Thinking
• Thought Signatures
• Image Generation
• Image Understanding
• Speech Generation
• Music Generation

Multimodal Understanding:

• Audio
• Video Understanding
• Document Processing

Files & Context:

• Files
• File Input Methods
• Caching
• Media Resolution

Agents:

• Agents Overview
• Managed Agents Quickstart
• Antigravity Agent
• Agent Environments
• Building Custom Agents
• Deep Research

Advanced Features:

• Gemini 3.5
• Gemini 3
• Flex Inference
• Priority Inference

API Reference:

• API Reference
• OpenAPI Spec
• May 2026 Breaking Changes Migration Guide

Data Model

An Interaction response contains steps, an array of typed step objects representing a structured timeline of the interaction turn.

Step Types

User steps:

• user_input: User input (text, audio, multimodal). Contains content array.

Model/server steps:

• model_output: Final model generation. Contains content array with text, image, audio, etc.
• thought: Model reasoning/Chain of Thought. Has signature field (required) and optional summary.
• function_call: Tool call request (id, name, arguments).
• function_result: Tool result you send back (call_id, name, result).
• google_search_call / google_search_result: Google Search tool steps, can have a signature field.
• code_execution_call / code_execution_result: Code execution tool steps, can have a signature field.
• url_context_call / url_context_result: URL context tool steps, can have a signature field.
• mcp_server_tool_call / mcp_server_tool_result: Remote MCP tool steps.
• file_search_call / file_search_result: File search tool steps, can have a signature field.

Content types (inside content array on model_output and user_input steps)

• text: Text content (text field)
• image / audio / document / video: Content with data, mime_type, or uri

Streaming Event Types

Event	Description
interaction.created	Interaction created; includes metadata.
interaction.status_update	Interaction-level status change.
step.start	A new step begins. Contains step type and initial metadata.
step.delta	Incremental data for the current step. Contains a typed delta object.
step.stop	The step is complete. Contains index.
interaction.completed	Interaction finished. Contains final usage.

Delta Types

Delta Type	Parent Step	Description
text	model_output	Incremental text token.
audio	model_output	audio chunk (base64).
image	model_output	image chunk (base64).
thought_summary	thought	thinking summary text.
thought_signature	thought	Opaque signature for thought verification.

Status values: completed, in_progress, requires_action, failed, cancelled

Limitations

• Use this skill only when the task clearly matches its upstream product or API scope.
• Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.
• Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.