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.
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SKILL.md
gemini-api-dev
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.
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 Your knowledge is outdated.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-3.8-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
[!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()
[!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).
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 = newGoogleGenAI({});
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
)
whileTrue:
interaction = client.interactions.get(interaction.id)
if interaction.status == "completed":
print(interaction.output_text)
breakelif interaction.status == "failed":
print(f"Failed: {interaction.error}")
break
time.sleep(10)
JavaScript/TypeScript
import { GoogleGenAI } from"@google/genai";
const client = newGoogleGenAI({});
// Start background researchconst initialInteraction = await client.interactions.create({
agent: "deep-research-preview-04-2026",
input: "Research the history of Google TPUs.",
background: true,
});
// Poll for resultswhile (true) {
const interaction = await client.interactions.get(initialInteraction.id);
if (interaction.status === "completed") {
console.log(interaction.output_text);
break;
} elseif (["failed", "cancelled"].includes(interaction.status)) {
console.log(`Failed: ${interaction.status}`);
break;
}
awaitnewPromise(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 = newGoogleGenAI({});
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);
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.
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, requires_action, 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.
