Gemini API Dev

Google (Gemini)OfficialPublished by Google (Gemini)

Skill
Tool integration
Software development
Apache-2.0
Low risk

From the author

Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, speech generation (TTS), voice design, voice replication, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. Covers SDK usage and best practices for Gemini models and agents in Python and TypeScript.

— the description by Google (Gemini)

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides the essential patterns, model references, and SDK usage guidelines for integrating the Gemini Interactions API into Python and TypeScript applications. It covers the modern Interactions API workflow, replacing legacy generateContent methods, and includes instructions for managing stateful conversations, background research agents, managed sandboxed environments, and advanced multimodal generation (image, video, and TTS).

What it does

Provides current model identifiers for text, image, video, and speech generation.
Outlines the Interactions API SDK patterns for Python and TypeScript.
Defines procedures for stateful multi-turn conversations and background agent execution.
Details the migration path from legacy generateContent APIs to the new Interactions structure.
Explains how to use managed agents with sandboxed Linux environments.
Provides streaming patterns for real-time interaction updates.

When to use it

When building or updating applications that utilize Gemini models for AI tasks.
When migrating legacy Gemini codebases to the current Interactions API.
When implementing complex agentic workflows, such as deep research or code execution.
When integrating multimodal capabilities like TTS, video generation, or image understanding.

Advantages

Ensures usage of the latest, non-deprecated Gemini models.
Standardizes interaction patterns across different AI tasks.
Provides clear migration paths for legacy codebases.
Supports advanced features like managed sandboxed environments and background research.

Usage examples

“How do I start a simple text generation interaction in Python?”: The agent provides a code snippet using client.interactions.create with the gemini-3.8-flash model.
“How can I run a background research task?”: The agent provides a polling loop example using deep-research-preview-04-2026 with background=True.
“How do I migrate my old generateContent code?”: The agent references the migration guide and explains the shift to client.interactions.create.
“How do I use the Antigravity agent to write a file?”: The agent provides a code example showing how to invoke the antigravity-preview-09-2026 agent with environment='remote'.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google-gemini/gemini-api-dev/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip gemini-api-dev.zip -d .agents/skills

Documentation by Google (Gemini)

Gemini API Development Skill

Critical Rules (Always Apply)

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

Current Models (Use These)

gemini-4-argon: 1M tokens, most powerful model for complex reasoning, coding, and research
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-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-1.2-flash: video generation (3s–15s, 10s default), first-frame-to-video, first-and-last-frame transitions (3s–10s), video extensions (3s–10s per turn, up to 30s total), 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 >= 2.30.1 → pip install -U google-genai
JavaScript/TypeScript: @google/genai >= 2.29.1 → 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.
Migrating to Gemini Omni 1.2 Flash (gemini-omni-1.2-flash): Read references/migration.md for upgrading from gemini-omni-1.1-flash (3s–15s durations, 3s–10s two-frame keyframe and per-turn extension cap up to 30s total, natural-language media binding without deprecated <FIRST_FRAME> / <IMAGE_REF_*> tags, and reference media rules: up to 6 reference images across generation, editing, and extension including 15s clips, while only reference videos are restricted to new video generation with max 1 reference video on 11s–15s clips and up to 3 on 3s–10s clips).

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:

Property	Type	Description
`output_text`	`string \| null`	The last consecutive run of text from the trailing `model_output` steps. Returns the combined text when the model's final output contains multiple text parts.
`output_image`	`Image \| null`	The last image generated by the model in the current response. Returns an object with `data` (base64) and `mime_type`.
`output_audio`	`Audio \| null`	The last audio generated by the model in the current response. Returns an object with `data` (base64) and `mime_type`.

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. It uses gemini-3.8-flash by default and also supports gemini-4-argon via agent_config={"type": "antigravity", "model": "gemini-4-argon"}. 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=...).

Streaming

Set stream=True to receive incremental server-sent events. Each stream follows: interaction.created → (step.start → step.delta(s) → step.stop)+ → interaction.completed.

Python

for event in client.interactions.create(
    model="gemini-3.8-flash",
    input="Explain quantum entanglement in simple terms.",
    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(f"\n\nTotal Tokens: {event.interaction.usage.total_tokens}")

JavaScript/TypeScript

const stream = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: "Explain quantum entanglement in simple terms.",
    stream: true,
});
for await (const event of stream) {
    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(`\n\nTotal Tokens: ${event.interaction?.usage?.total_tokens}`);
    }
}

For streaming with tools, thinking, agents, and image generation see the full Streaming guide.

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:

user_input: User input (text, audio, multimodal). Contains content array.

Model/server steps:

model_output: Final model generation. Contains content array with text, image, audio, etc.
thought: Model reasoning/Chain of Thought. Has signature field (required) and 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 steps, can have a signature field.
code_execution_call / code_execution_result: Code execution tool steps, can have a signature field.
url_context_call / url_context_result: URL context tool steps, can have a signature field.
mcp_server_tool_call / mcp_server_tool_result: Remote MCP tool steps.
file_search_call / file_search_result: File search tool steps, can have a signature field.

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

text: Text content (text field, plus optional annotations such as {"type": "speech_metadata", "speaker": "...", "style": "..."} for TTS)
image / audio / document / video: Content with data, mime_type, or uri

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.completed`	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, queued, requires_action, incomplete, failed, cancelled

Gemini Live API

For real-time, bidirectional audio/video/text streaming with the Gemini Live API (gemini-3.8-live, gemini-3.8-live-extended-thinking, and gemini-3.5-transcribe-live), install the google-gemini/gemini-live-api-dev skill. It covers WebSocket streaming, voice activity detection, background reasoning (extended thinking), asynchronous function calling, session management, ephemeral tokens, and more.

Files

ATTRIBUTION.mdAdded by AutoGineer900 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md20.5 KiB
references/migration.md16.2 KiB

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python, Needs Node.js

Security

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Attribution and licence

Published by

Google (Gemini)

Publisher

Google (Gemini) (Official)

Source

google-gemini/gemini-skills · skills/gemini-api-dev · commit 7cfc690

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

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2026-11-08

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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download gemini-api-dev.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Google (Gemini)

Gemini API Development Skill

Critical Rules (Always Apply)

Current Models (Use These)

Current Agents

Current SDKs

Important Additional Notes

Quick Start

Python

JavaScript/TypeScript

Response Helpers

Stateful Conversation

Python

JavaScript/TypeScript

Deep Research Agent

Python

JavaScript/TypeScript

Managed Agents

Antigravity Agent

Python

JavaScript/TypeScript

Custom Agents

Python

JavaScript/TypeScript

Streaming

Python

JavaScript/TypeScript

Documentation Pages

Data Model

Step Types

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

Streaming Event Types

Delta Types

Gemini Live API

Files

LICENSE

Requirements and compatibility

Security

Attribution and licence

Related skills

Implement Spec

MCP Builder

Schema

Postgresql Table Design

SQL Optimization Patterns

API Scaffolding Fastapi Pro

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