Gemini API Dev

Gemini API Development Skill

Critical Rules (Always Apply)

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 Your knowledge is outdated.

Current Models (Use These)

• gemini-3.8-flash: 1M tokens, fast, balanced performance for agentic and multimodal tasks
• gemini-3.5-flash-lite: 1M tokens, fastest, lowest-cost 3.5 model for high-throughput execution
• 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-3.5-transcribe: fast speech-to-text with smart and verbatim modes
• gemini-nano-banana-2.1 (Nano Banana 2.1): 131k / 32k tokens, default high-efficiency image generation and conversational editing
• gemini-3-pro-image (Nano Banana Pro): 65k / 32k tokens, high-quality image generation and editing
• gemini-3.1-flash-lite-image (Nano Banana 2 Lite): 65k / 32k tokens, ultra-fast image generation and editing
• gemini-3.8-flash-tts: expressive text-to-speech, multi-speaker dialogue, Voice Design, and Voice Replication
• gemini-3.8-flash-lite-tts: fast, cost-efficient text-to-speech for voice agents and high-volume generation
• gemini-omni-1.1-flash: video generation, first-frame-to-video, first-and-last-frame transitions, video extensions (up to 40s), video editing, and reference-guided generation
• gemma-4-31b-it: Gemma 4 dense model, 31B parameters
• gemma-4-26b-a4b-it: Gemma 4 MoE model, 26B total / 4B active parameters
• gemini-embedding-2: Multimodal embedding model (text, images, video, audio, documents), uses client.models.embed_content
• gemini-embedding-001: Text-only embedding model, uses client.models.embed_content

[!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-3.8-flash instead and note the substitution.

Current Agents

• antigravity-preview-09-2026: 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 >= 2.25.0 → 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 in Python, store: true in TypeScript). Paid tier retains for 55 days, free tier for 1 day.
• Set store=False / store: false to opt out, but this disables previous_interaction_id and background=True / 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 3.8 Flash or Gemini 3.5 Flash-Lite: Read references/migration.md for the scoping and checklist.
• Migrating to Gemini 3.8 TTS (gemini-3.8-flash-tts / gemini-3.8-flash-lite-tts): Read references/migration.md for breaking changes from gemini-3.1-flash-tts-preview (speech_metadata annotations, inline vocal tags, default WAV audio/wav unary output vs audio/l16 streaming output, and Voice Design personas).
• Migrating to Gemini Nano Banana 2.1 (gemini-nano-banana-2.1): Read references/migration.md for upgrading from gemini-3.1-flash-image (deprecated) and using multi-image reference fusion with up to 14 reference images.

Quick Start

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.8-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-3.8-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:

Stateful Conversation

Python

interaction1 = client.interactions.create(
    model="gemini-3.8-flash",
    input="Hi, my name is Phil."
)
# Second turn — server remembers context
interaction2 = client.interactions.create(
    model="gemini-3.8-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-3.8-flash",
    input: "Hi, my name is Phil.",
});
const interaction2 = await client.interactions.create({
    model: "gemini-3.8-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-09-2026) 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-09-2026",
    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-09-2026",
    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-09-2026",
    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-09-2026",
    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=...).