Agent Skill · gemini-md

gemini-interactions-api

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.

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Skill body

Gemini Interactions API Skill

Critical Rules (Always Apply)

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 Your knowledge is outdated.

Current Models (Use These)

[!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-3.5-flash instead and note the substitution.

Current Agents

Current SDKs

[!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.

[!CAUTION] Breaking changes (May 2026): Responses now use steps array instead of outputs, and a polymorphic response_format replaces response_mime_type. Legacy schema removed June 8, 2026. All code below uses the new schema.

Important Additional Notes

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.steps[-1].content[0].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.steps.at(-1).content[0].text);

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.steps[-1].content[0].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.steps.at(-1).content[0].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.steps[-1].content[0].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.steps.at(-1).content[0].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

Python

for event in client.interactions.create(
    model="gemini-3.5-flash",
    input="Explain quantum entanglement in simple terms.",
    stream=True,
):
    if event.type == "step.delta":
        if event.delta.type == "text":
            print(event.delta.text, end="", flush=True)
        elif event.delta.type == "thought_summary":
            summary_text = event.delta.content.get('text', '') if hasattr(event.delta, 'content') else getattr(event.delta, 'text', '')
            print(summary_text, end="", flush=True)
    elif event.type == "interaction.complete":
        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.type === 'step.delta') {
        if (event.delta.type === 'text') {
            process.stdout.write(event.delta.text);
        } else if (event.delta.type === 'thought_summary') {
            const text = event.delta.content?.text || "";
            process.stdout.write(text);
        }
    } else if (event.type === 'interaction.complete') {
        console.log(`\n\nTotal Tokens: ${event.interaction.usage.total_tokens}`);
    }
}

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:

Tools & Function Calling:

Generation & Output:

Multimodal Understanding:

Files & Context:

Agents:

Advanced Features:

API Reference:

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:

Model/server steps:

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

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.complete 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