Gemini Interactions API Skill

This skill provides instructions for authenticating, connecting to, and utilizing the stateful, server-managed Gemini Interactions API on Gemini Enterprise Agent Platform.

The Interactions API is the modern, recommended way to execute Generative AI agent conversations, background research tasks, multi-turn chats, and structured, multi-step workflows.

[!IMPORTANT] CRITICAL: Unified SDK, Latest Models & Model/Agent Targeting * Unified SDK: Use the Google Gen AI SDK (google-genai >= 2.3.0 for Python, @google/genai >= 2.3.0 for JS/TS). SDK versions >= 2.0.0 are the minimum floor at which the steps schema activates, but >= 2.3.0 is the supported/recommended floor. Legacy SDKs like google-cloud-aiplatform, @google-cloud/vertexai, google-generativeai, and @google/generative-ai are strictly unsupported for Interactions. * Mandatory Instruction: When explaining client initialization or writing import code, you must explicitly state/warn the user that legacy packages like google-cloud-aiplatform or google-generativeai are strictly unsupported for interactions. * Latest Models Only: Use gemini-3.8-flash (fast, balanced, multimodal — the recommended default), gemini-3.1-pro-preview (complex reasoning, coding, research), or gemini-3.5-flash-lite (cost-efficient, high-frequency lightweight tasks). Refer to the latest model versions to check for new updates. Legacy models (gemini-3-flash-preview, gemini-2.5-*, gemini-2.0-*, gemini-1.5-*) are deprecated and do not support interactions. * Mandatory Instruction: In any interaction response, you must warn the user that legacy models like gemini-2.5-*, gemini-2.0-*, or gemini-1.5-* are deprecated and unsupported for the Interactions API. If a user asks for a deprecated model, use gemini-3.8-flash instead and note the substitution. * Model & Agent Targeting: Target foundation models directly using model="gemini-3.8-flash", or target autonomous managed/custom agents (antigravity-preview-05-2026, deep-research-preview-04-2026, or custom agents provisioned via client.agents.create()) using agent="<AGENT_ID>". Managed agents (antigravity-preview-05-2026 and custom agents) require environment="remote" to provision a sandbox. * Turn-Scoped Parameters: Parameters like tools, system_instruction, and generation_config are turn-scoped. They MUST be passed with each interaction request.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gemini-interactions-api)" and append agent-skills/1.0 (skill:gemini-interactions-api) to User-Agent headers.

1. Authentication

Before running any code, ensure you are authenticated with Application Default Credentials (ADC) and have the necessary API enabled.

1. Login:

   gcloud auth application-default login
   

2. Enable API (if not already enabled):

   gcloud services enable aiplatform.googleapis.com
   

---

2. Client Initialization

You can initialize the client using environment variables (recommended) or by passing explicit configuration parameters.

Option A: Environment Variables (Recommended)

Configure environment variables to let the SDK automatically resolve settings:

export GOOGLE_GENAI_USE_ENTERPRISE=true
export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="global"

Python

from google import genai

# The SDK automatically picks up the environment variables
client = genai.Client()

TypeScript/JavaScript

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

// The SDK automatically picks up the environment variables
const ai = new GoogleGenAI();

Option B: Explicit Inline Parameters

Alternatively, pass configuration values directly inside your code:

Python

from google import genai
import google.auth

_, project_id = google.auth.default()
client = genai.Client(enterprise=True, project=project_id, location="global")

TypeScript/JavaScript

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

const ai = new GoogleGenAI({
    enterprise: true,
    project: "your-project-id",
    location: "global"
});

Option C: Express Mode (API Key)

Recommended for lightweight scripts or environments using an API key:

Python

from google import genai

client = genai.Client(enterprise=True, api_key="YOUR_API_KEY")

TypeScript/JavaScript

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

const ai = new GoogleGenAI({
    enterprise: true,
    apiKey: "YOUR_API_KEY"
});

---

3. Core Interactions API Usage

Quick Start (Single-Turn)

Submit a single prompt and read the final text response. Under the modern schema, output content is retrieved from the steps list.

Python

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input="Explain serverless computing in one sentence."
)
# Use the output_text convenience accessor (combined text from the trailing model_output steps)
print(interaction.output_text)

TypeScript/JavaScript

const interaction = await ai.interactions.create({
    model: "gemini-3.8-flash",
    input: "Explain serverless computing in one sentence."
});
console.log(interaction.output_text);

---

Stateful Conversation (Multi-Turn)

Interactions are stateful by default. Store the conversation state in the cloud and reference it in the subsequent turn using previous_interaction_id.

Python

# Turn 1: Introduce ourselves
# Interactions are stored by default (store=True, retained for 7 days); pass store=False to disable
# server-side retention (which also disables previous_interaction_id and background).
turn1 = client.interactions.create(
    model="gemini-3.8-flash",
    input="Hi! My name is John. I am working on AI agents.",
    store=True
)
print(f"Turn 1: {turn1.output_text}")

# Turn 2: Refer back to the stored turn state
turn2 = client.interactions.create(
    model="gemini-3.8-flash",
    input="What is my name?",
    previous_interaction_id=turn1.id
)
print(f"Turn 2: {turn2.output_text}")

TypeScript/JavaScript

// Turn 1 (interactions are stored by default; pass store: false to disable)
const turn1 = await ai.interactions.create({
    model: "gemini-3.8-flash",
    input: "Hi! My name is John. I am working on AI agents.",
    store: true
});

// Turn 2
const turn2 = await ai.interactions.create({
    model: "gemini-3.8-flash",
    input: "What is my name?",
    previous_interaction_id: turn1.id
});
console.log(turn2.output_text);

---

Real-Time Streaming

Stream responses in real-time. Passing stream=True returns an iterable chunk generator.

Python

# The stream yields typed events, not full interaction snapshots. The sequence is:
# interaction.created -> (step.start -> step.delta(s) -> step.stop)+ -> interaction.completed
for event in client.interactions.create(
    model="gemini-3.8-flash",
    input="Write a short poem about debugging.",
    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()

TypeScript/JavaScript

// The stream yields typed events, not full interaction snapshots. The sequence is:
// interaction.created -> (step.start -> step.delta(s) -> step.stop)+ -> interaction.completed
const responseStream = await ai.interactions.create({
    model: "gemini-3.8-flash",
    input: "Write a short poem about debugging.",
    stream: true
});

for await (const event of responseStream) {
    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();
    }
}

---

Structured Output (Pydantic / Polymorphic response_format)

Retrieve structured, type-safe JSON matching a schema. Under the modern Interactions API, a polymorphic response_format argument directly takes the target schema structure.

Python

from pydantic import BaseModel, Field

class Book(BaseModel):
    title: str = Field(description="The title of the book")
    author: str = Field(description="The book's author")
    year_published: int

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input="Recommend one famous sci-fi book.",
    response_format=Book
)

# The text will be a valid JSON matching the Book schema
print(interaction.output_text)

TypeScript/JavaScript

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

const BookSchema = {
    type: Type.OBJECT,
    properties: {
        title: { type: Type.STRING, description: "The title of the book" },
        author: { type: Type.STRING, description: "The book's author" },
        yearPublished: { type: Type.INTEGER }
    },
    required: ["title", "author", "yearPublished"]
};

const interaction = await ai.interactions.create({
    model: "gemini-3.8-flash",
    input: "Recommend one famous sci-fi book.",
    response_format: BookSchema
});

console.log(interaction.output_text);

---

Function Calling (Agent Tool Use)

Define local tools (functions) and submit execution results to the stateful interaction history.

Python

import json

def get_stock_price(ticker: str) -> float:
    """Gets the stock price for a given ticker symbol."""
    if ticker.upper() == "GOOG":
        return 175.50
    return 100.0

# Turn 1: Pass tools to the model
interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input="What is the stock price of GOOG?",
    tools=[get_stock_price]
)

# In the flat steps schema, a tool request is a top-level step of type
# "function_call" with flat `name` and `arguments` fields (no nested tool_calls).
for step in interaction.steps:
    if step.type == "function_call" and step.name == "get_stock_price":
        ticker_arg = step.arguments.get("ticker")
        price = get_stock_price(ticker_arg)

        # Turn 2: Submit the result back as a function_result step. Reference the
        # originating call via call_id=step.id, and pass tools again (turn-scoped).
        final_turn = client.interactions.create(
            model="gemini-3.8-flash",
            input=[
                {
                    "type": "function_result",
                    "name": step.name,
                    "call_id": step.id,
                    "result": [{"type": "text", "text": json.dumps(price)}],
                }
            ],
            tools=[get_stock_price],
            previous_interaction_id=interaction.id
        )
        print(final_turn.output_text)

TypeScript/JavaScript

// Define local tool and flat function tool declaration
function getStockPrice({ ticker }: { ticker: string }): number {
    if (ticker.toUpperCase() === "GOOG") {
        return 175.50;
    }
    return 100.00;
}

const stockTool = {
    type: "function",
    name: "getStockPrice",
    description: "Gets the stock price for a given ticker symbol.",
    parameters: {
        type: "object",
        properties: {
            ticker: { type: "string", description: "The stock ticker symbol" }
        },
        required: ["ticker"]
    }
};

// Turn 1: Pass tools to the model
const interaction = await ai.interactions.create({
    model: "gemini-3.8-flash",
    input: "What is the stock price of GOOG?",
    tools: [stockTool]
});

// In the flat steps schema, a tool request is a top-level step of type
// "function_call" with flat `name` and `arguments` fields (no nested toolCalls).
const fcStep = interaction.steps.find(s => s.type === "function_call");
if (fcStep && fcStep.name === "getStockPrice") {
    const tickerArg = fcStep.arguments.ticker as string;
    const price = getStockPrice({ ticker: tickerArg });

    // Turn 2: Submit the result back as a function_result step. Reference the
    // originating call via call_id=fcStep.id, and pass tools again (turn-scoped).
    const finalTurn = await ai.interactions.create({
        model: "gemini-3.8-flash",
        input: [{
            type: "function_result",
            name: fcStep.name,
            call_id: fcStep.id,
            result: [{ type: "text", text: JSON.stringify(price) }]
        }],
        tools: [stockTool],
        previous_interaction_id: interaction.id
    });
    console.log(finalTurn.output_text);
}

---

Agents & Long-Running Tasks

Beyond foundation models, the Interactions API provides access to specialized, autonomous agents via the agent parameter:

• antigravity-preview-05-2026: Antigravity Agent — general-purpose managed agent with code execution, file management, and web browsing in a secure sandboxed Linux environment (pass environment="remote" to provision a sandbox).
• deep-research-preview-04-2026: Deep Research Agent — executes multi-step web research tasks, synthesizing information from multiple sources into comprehensive reports.
• Custom agents: Configured and managed via client.agents.create(), list(), get(), and delete() (pass environment="remote" when invoking).

Agents typically run asynchronously in the background using background=True. Poll the interaction status to retrieve the completed result:

Python

import time

interaction = client.interactions.create(
    input="Analyze competitive positioning for solar energy providers.",
    agent="deep-research-preview-04-2026",
    background=True
)
print(f"Research started: {interaction.id}")

while True:
    interaction = client.interactions.get(interaction.id)
    if interaction.status == "completed":
        print(interaction.output_text)
        break
    elif interaction.status in ("failed", "cancelled"):
        print(f"Research ended with status: {interaction.status}")
        break
    time.sleep(10)

TypeScript/JavaScript

const initialInteraction = await ai.interactions.create({
    agent: "deep-research-preview-04-2026",
    input: "Analyze competitive positioning for solar energy providers.",
    background: true
});

while (true) {
    const interaction = await ai.interactions.get(initialInteraction.id);
    if (interaction.status === "completed") {
        console.log(interaction.output_text);
        break;
    } else if (["failed", "cancelled"].includes(interaction.status)) {
        console.log(`Research ended with status: ${interaction.status}`);
        break;
    }
    await new Promise(resolve => setTimeout(resolve, 10000));
}

---

4. Accessing the Interactions API via REST

For shell-based scripts, debugging, or non-Python/JS environments, communicate with the stateful Interactions API over HTTP/REST (curl) at POST https://aiplatform.googleapis.com/v1beta1/projects/{PROJECT_ID}/locations/{LOCATION}/interactions (or POST https://aiplatform.googleapis.com/v1beta1/locations/global/interactions with x-goog-api-key for Express Mode). Pass "model" or "agent", "input" steps with "type": "user_input", and optional "previous_interaction_id", "background": true, or "stream": true (which streams Server-Sent Events via Content-Type: text/event-stream and Transfer-Encoding: chunked that curl prints continuously in real time).

For complete curl examples (single-turn, multi-turn stateful, SSE streaming, and background managed agents) and response schemas, read references/rest_api.md.

---

5. Data Model & Step Types Reference

An Interaction response contains a flat steps timeline (user_input, model_output, thought, function_call, function_result, and built-in tool steps) along with convenience accessors (output_text, output_image, output_audio) and SSE streaming events (interaction.created, step.start, step.delta, step.stop, interaction.completed).

For the complete step types, content types, streaming event table, and 7-day retention rules, read references/data_model.md.