gemini-api-dev

作者： google-gemini

使用Google的Gemini模型构建应用程序，支持多模态内容、函数调用和结构化输出，覆盖Python、JavaScript、Go和Java语言。可访问当前Gemini 3模型（Pro、Flash、Pro Image），具备100万token上下文；旧版Gemini 2.x和1.5模型已弃用。支持文本生成、图像/音频/视频理解、函数调用、结构化JSON输出、代码执行、上下文缓存和嵌入功能。提供官方SDK：google-genai（Python）...

npx skills add https://github.com/google-gemini/gemini-skills --skill gemini-api-dev

下载 ZIP GitHub

4.2k

试用此 Skill赞助

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 Your knowledge is outdated.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-3-pro-image (Nano Banana Pro): 65k / 32k tokens, high-quality image generation and editing
gemini-3.1-flash-image (Nano Banana 2): 65k / 32k tokens, fast, efficient image generation and editing
gemini-3.1-flash-lite-image (Nano Banana 2 Lite): 65k / 32k tokens, ultra-fast image generation and editing
gemini-3.1-flash-tts-preview: expressive text-to-speech with Director's Chair prompting
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-05-2026: 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 >= 2.3.0 → 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.

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-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

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)
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, install the google-gemini/gemini-live-api-dev skill. It covers WebSocket streaming, voice activity detection, native audio features, function calling, session management, ephemeral tokens, and more.