gemini interactions api

Skill

Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.

Files1
  • @skills/gemini-interactions-api/SKILL.md

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
  • Unified SDK: Use the Google Gen AI SDK (google-genai >= 2.0.0 for Python, @google/genai >= 2.0.0 for JS/TS). Legacy SDKs like google-cloud-aiplatform, @google-cloud/vertexai, and google-generativeai 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-3.1-pro-preview, gemini-3.1-flash-lite, gemini-3-flash-preview, gemini-2.5-pro, or gemini-2.5-flash. Refer to the latest model versions to check for new updates. Legacy models (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-2.0 or gemini-1.5 are deprecated and unsupported for the Interactions API.
  • Turn-Scoped Parameters: Parameters like tools, system_instruction, and generation_config are turn-scoped. They MUST be passed with each interaction request.

1. Authentication

Before running any code, ensure you are authenticated with Application Default Credentials (ADC) and have the necessary API enabled.
  1. Login:
    bash
    gcloud auth application-default login
  2. Enable API (if not already enabled):
    bash
    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:
bash
export GOOGLE_GENAI_USE_ENTERPRISE=true
export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="global"

Python

python
from google import genai

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

TypeScript/JavaScript

typescript
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

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

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

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

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

python
interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Explain serverless computing in one sentence."
)
# Output text is located under steps
print(interaction.steps[-1].content[0].text)

TypeScript/JavaScript

typescript
const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Explain serverless computing in one sentence."
});
console.log(interaction.steps[interaction.steps.length - 1].content[0].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

python
# Turn 1: Introduce ourselves
turn1 = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Hi! My name is John. I am working on AI agents.",
    store=True
)
print(f"Turn 1: {turn1.steps[-1].content[0].text}")

# Turn 2: Refer back to the stored turn state
turn2 = client.interactions.create(
    model="gemini-3-flash-preview",
    input="What is my name?",
    previous_interaction_id=turn1.id
)
print(f"Turn 2: {turn2.steps[-1].content[0].text}")

TypeScript/JavaScript

typescript
// Turn 1
const turn1 = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    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-flash-preview",
    input: "What is my name?",
    previousInteractionId: turn1.id
});
console.log(turn2.steps[turn2.steps.length - 1].content[0].text);

Real-Time Streaming

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

Python

python
response = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Write a short poem about debugging.",
    stream=True
)

for chunk in response:
    if chunk.steps:
        step = chunk.steps[-1]
        if step.content and step.content[0].text:
            print(step.content[0].text, end="", flush=True)
print()

TypeScript/JavaScript

typescript
const responseStream = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Write a short poem about debugging.",
    stream: true
});

for await (const chunk of responseStream) {
    if (chunk.steps) {
        const step = chunk.steps[chunk.steps.length - 1];
        if (step.content && step.content[0].text) {
            process.stdout.write(step.content[0].text);
        }
    }
}
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

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-flash-preview",
    input="Recommend one famous sci-fi book.",
    response_format=Book
)

# The text will be a valid JSON matching the Book schema
print(interaction.steps[-1].content[0].text)

TypeScript/JavaScript

typescript
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-flash-preview",
    input: "Recommend one famous sci-fi book.",
    responseFormat: BookSchema
});

console.log(interaction.steps[interaction.steps.length - 1].content[0].text);

Function Calling (Agent Tool Use)

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

Python

python
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-flash-preview",
    input="What is the stock price of GOOG?",
    tools=[get_stock_price]
)

last_step = interaction.steps[-1]
# Check if the model requested a function call
if last_step.tool_calls:
    for call in last_step.tool_calls:
        if call.name == "get_stock_price":
            ticker_arg = call.args.get("ticker")
            price = get_stock_price(ticker_arg)

            # Turn 2: Submit function execution result statefully
            final_turn = client.interactions.create(
                model="gemini-3-flash-preview",
                input=f"The stock price for {ticker_arg} is ${price}.",
                previous_interaction_id=interaction.id
            )
            print(final_turn.steps[-1].content[0].text)

TypeScript/JavaScript

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

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

// Turn 1: Pass tools to the model
const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is the stock price of GOOG?",
    tools: [{
        functionDeclarations: [{
            name: "getStockPrice",
            description: "Gets the stock price for a given ticker symbol.",
            parameters: {
                type: Type.OBJECT,
                properties: {
                    ticker: { type: Type.STRING, description: "The stock ticker symbol" }
                },
                required: ["ticker"]
            }
        }]
    }]
});

const lastStep = interaction.steps[interaction.steps.length - 1];
// Check if the model requested a function call
if (lastStep.toolCalls) {
    for (const call of lastStep.toolCalls) {
        if (call.name === "getStockPrice") {
            const tickerArg = call.args.ticker as string;
            const price = getStockPrice({ ticker: tickerArg });

            // Turn 2: Submit function execution result statefully
            const finalTurn = await ai.interactions.create({
                model: "gemini-3-flash-preview",
                input: `The stock price for ${tickerArg} is $${price}.`,
                previousInteractionId: interaction.id
            });
            console.log(finalTurn.steps[finalTurn.steps.length - 1].content[0].text);
        }
    }
}

4. Accessing the Interactions API via REST

For shell-based scripts, debugging, or non-Python/JS environments, you can communicate with the stateful Interactions API directly using raw HTTP/REST requests via curl.

1. REST Endpoint

The REST API endpoint for interactions is:
http
POST https://aiplatform.googleapis.com/v1beta1/projects/{PROJECT_ID}/locations/{LOCATION}/interactions
  • LOCATION: Use global (or custom region if required).
  • PROJECT_ID: Your Google Cloud Project ID.

2. Set up Variables & Authentication Header

Set your target agent ID (e.g., model or custom agent path) and access token generated from Application Default Credentials:
bash
AGENT_ID="your-agent-id"
ACCESS_TOKEN=$(gcloud auth print-access-token)

3. Single-Turn Interaction Payload

Send a request to start an interaction using the agent variable:
bash
curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Explain serverless computing in one sentence."
      }]
    }]
  }'

Response Example

A synchronous POST request returns a JSON object containing the conversation step details and unique identifiers:
json
{
  "id": "your-interaction-id",
  "status": "completed",
  "steps": [
    {
      "role": "model",
      "content": [
        {
          "type": "text",
          "text": "Serverless computing is a cloud execution model where the cloud provider dynamically manages the allocation and provisioning of servers, charging customers based on actual usage rather than pre-purchased capacity."
        }
      ]
    }
  ],
  "usage": {
    "total_tokens": 24751,
    "total_input_tokens": 23894,
    "total_output_tokens": 857
  },
  "created": "2026-05-08T10:44:43Z",
  "updated": "2026-05-08T10:44:43Z",
  "environment_id": "your-environment-id",
  "object": "interaction"
}

4. Multi-Turn Stateful Interaction Payload

To continue an existing conversation statefully, specify the previous_interaction_id in the JSON payload:
bash
curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "store": true,
    "previous_interaction_id": "YOUR_PREVIOUS_INTERACTION_ID",
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Can you elaborate on that?"
      }]
    }]
  }'

5. Streaming Output Payload

To stream updates in real time (Server-Sent Events format), pass "stream": true in the payload:
bash
curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "stream": true,
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Write a long story about space travel."
      }]
    }]
  }'
The endpoint will return a chunked stream where each event begins with data: containing JSON updates with the event_type and step contents.
How curl handles streaming: By default, when "stream": true is passed, the server responds with Transfer-Encoding: chunked and Content-Type: text/event-stream (Server-Sent Events). curl will automatically keep the connection open and print the incoming data chunks to stdout in real time as they are pushed by the server. The user does not need to poll or pull further; the complete sequence of events streams continuously until completion.
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