gemini-interactions-api

name	gemini-interactions-api
description	Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. This skill covers the Interactions API, the recommended way to use Gemini models and agents in Python and TypeScript.

Gemini Interactions API Skill

Critical Rules (Always Apply)

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 Your knowledge is outdated.

Current Models (Use These)

• gemini-3.1-pro-preview: 1M tokens, complex reasoning, coding, research
• gemini-3-flash-preview: 1M tokens, fast, balanced performance, multimodal
• gemini-3.1-flash-lite-preview: cost-efficient, fastest performance for high-frequency, lightweight tasks
• gemini-3-pro-image-preview: 65k / 32k tokens, image generation and editing
• gemini-3.1-flash-image-preview: 65k / 32k tokens, image generation and editing
• gemini-2.5-pro: 1M tokens, complex reasoning, coding, research
• gemini-2.5-flash: 1M tokens, fast, balanced performance, multimodal

Current Agents (Use These)

• deep-research-pro-preview-12-2025: Deep Research agent

[!WARNING] Models like gemini-2.0-*, gemini-1.5-* are legacy and deprecated. Never use them. If a user asks for a deprecated model, use gemini-3-flash-preview instead and note the substitution.

Current SDKs (Use These)

• Python: google-genai >= 1.55.0 → pip install -U google-genai
• JavaScript/TypeScript: @google/genai >= 1.33.0 → npm install @google/genai

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

---

Overview

The Interactions API is a unified interface for interacting with Gemini models and agents. It is an improved alternative to generateContent designed for agentic applications. Key capabilities include:

• Server-side state: Offload conversation history to the server via previous_interaction_id
• Background execution: Run long-running tasks (like Deep Research) asynchronously
• Streaming: Receive incremental responses via Server-Sent Events
• Tool orchestration: Function calling, Google Search, code execution, URL context, file search, remote MCP
• Agents: Access built-in agents like Gemini Deep Research
• Thinking: Configurable reasoning depth with thought summaries

---

Quick Start

Interact with a Model

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Tell me a short joke about programming."
)
print(interaction.outputs[-1].text)

JavaScript/TypeScript

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

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Tell me a short joke about programming.",
});
console.log(interaction.outputs[interaction.outputs.length - 1].text);

Stateful Conversation

Python

from google import genai

client = genai.Client()

# First turn
interaction1 = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Hi, my name is Phil."
)

# Second turn — server remembers context
interaction2 = client.interactions.create(
    model="gemini-3-flash-preview",
    input="What is my name?",
    previous_interaction_id=interaction1.id
)
print(interaction2.outputs[-1].text)

JavaScript/TypeScript

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

const client = new GoogleGenAI({});

// First turn
const interaction1 = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Hi, my name is Phil.",
});

// Second turn — server remembers context
const interaction2 = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is my name?",
    previous_interaction_id: interaction1.id,
});
console.log(interaction2.outputs[interaction2.outputs.length - 1].text);

Deep Research Agent

Python

import time
from google import genai

client = genai.Client()

# Start background research
interaction = client.interactions.create(
    agent="deep-research-pro-preview-12-2025",
    input="Research the history of Google TPUs.",
    background=True
)

# Poll for results
while True:
    interaction = client.interactions.get(interaction.id)
    if interaction.status == "completed":
        print(interaction.outputs[-1].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-pro-preview-12-2025",
    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.outputs[interaction.outputs.length - 1].text);
        break;
    } else if (["failed", "cancelled"].includes(interaction.status)) {
        console.log(`Failed: ${interaction.status}`);
        break;
    }
    await new Promise(resolve => setTimeout(resolve, 10000));
}

Streaming

Python

from google import genai

client = genai.Client()

stream = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Explain quantum entanglement in simple terms.",
    stream=True
)

for chunk in stream:
    if chunk.event_type == "content.delta":
        if chunk.delta.type == "text":
            print(chunk.delta.text, end="", flush=True)
    elif chunk.event_type == "interaction.complete":
        print(f"\n\nTotal Tokens: {chunk.interaction.usage.total_tokens}")

JavaScript/TypeScript

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

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Explain quantum entanglement in simple terms.",
    stream: true,
});

for await (const chunk of stream) {
    if (chunk.event_type === "content.delta") {
        if (chunk.delta.type === "text" && "text" in chunk.delta) {
            process.stdout.write(chunk.delta.text);
        }
    } else if (chunk.event_type === "interaction.complete") {
        console.log(`\n\nTotal Tokens: ${chunk.interaction.usage.total_tokens}`);
    }
}

---

Data Model

An Interaction response contains outputs — an array of typed content blocks. Each block has a type field:

• text — Generated text (text field)
• thought — Model reasoning (signature required, 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
• code_execution_call / code_execution_result — Code execution tool
• url_context_call / url_context_result — URL context tool
• mcp_server_tool_call / mcp_server_tool_result — Remote MCP tool
• file_search_call / file_search_result — File search tool
• image — Generated or input image (data, mime_type, or uri)

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

---

Key Differences from generateContent

• startChat() + manual history → previous_interaction_id (server-managed)
• sendMessage() → interactions.create(previous_interaction_id=...)
• response.text → interaction.outputs[-1].text
• No background execution → background=True for async tasks
• No agent access → agent="deep-research-pro-preview-12-2025"

---

Important Notes

• 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.
• Agents require background=True.
• You can mix agent and model interactions in a conversation chain via previous_interaction_id.

---

Documentation Lookup

When MCP is Installed (Preferred)

If the search_documentation tool (from the Google MCP server) is available, use it as your only documentation source:

1. Call search_documentation with your query
2. Read the returned documentation
3. Trust MCP results as source of truth for API details — they are always up-to-date.

[!IMPORTANT] When MCP tools are present, never fetch URLs manually. MCP provides up-to-date, indexed documentation that is more accurate and token-efficient than URL fetching.

When MCP is NOT Installed (Fallback Only)

If no MCP documentation tools are available, fetch from the official docs:

• Interactions Full Documentation
• Deep Research Full Documentation

These pages cover function calling, built-in tools (Google Search, code execution, URL context, file search, computer use), remote MCP, structured output, thinking configuration, working with files, multimodal understanding and generation, streaming events, and more.