Gemini Interactions API 指南：统一接口、智能体交互与服务器端状态管理

gemini-interactions-api by google-gemini/gemini-skills

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2,300 GitHub Stars

GitHub

安装命令

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

AI/机器学习自动化 API

🇨🇳中文介绍

Gemini Interactions API 技能

Interactions API 是与 Gemini 模型和智能体交互的统一接口。它是专为智能体应用设计的 generateContent 的改进替代方案。主要功能包括：

服务器端状态： 通过 previous_interaction_id 将会话历史卸载到服务器
后台执行： 异步运行长时间运行的任务（如深度研究）
流式传输： 通过服务器发送事件接收增量响应
工具编排： 函数调用、Google 搜索、代码执行、URL 上下文、文件搜索、远程 MCP
智能体： 访问内置智能体，如 Gemini 深度研究
思考： 可配置的推理深度与思考摘要

支持的模型与智能体

模型：

gemini-3.1-pro-preview: 100 万令牌，复杂推理、编码、研究
gemini-3-flash-preview: 100 万令牌，快速、均衡性能、多模态
gemini-3.1-flash-lite-preview: 成本效益高，适用于高频、轻量级任务，性能最快。

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深度研究智能体

import time
from google import genai

client = genai.Client()

# 开始后台研究
interaction = client.interactions.create(
    agent="deep-research-pro-preview-12-2025",
    input="Research the history of Google TPUs.",
    background=True
)

# 轮询结果
while True:
    interaction = client.interactions.get(interaction.id)
    if interaction.status == "completed":
        print(interaction.outputs[-1].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({});

// 开始后台研究
const initialInteraction = await client.interactions.create({
    agent: "deep-research-pro-preview-12-2025",
    input: "Research the history of Google TPUs.",
    background: true,
});

// 轮询结果
while (true) {
    const interaction = await client.interactions.get(initialInteraction.id);
    if (interaction.status === "completed") {
        console.log(interaction.outputs[interaction.outputs.length - 1].text);
        break;
    } else if (["failed", "cancelled"].includes(interaction.status)) {
        console.log(`Failed: ${interaction.status}`);
        break;
    }
    await new Promise(resolve => setTimeout(resolve, 10000));
}

from google import genai

client = genai.Client()

stream = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Explain quantum entanglement in simple terms.",
    stream=True
)

for chunk in stream:
    if chunk.event_type == "content.delta":
        if chunk.delta.type == "text":
            print(chunk.delta.text, end="", flush=True)
    elif chunk.event_type == "interaction.complete":
        print(f"\n\nTotal Tokens: {chunk.interaction.usage.total_tokens}")

JavaScript/TypeScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Explain quantum entanglement in simple terms.",
    stream: true,
});

for await (const chunk of stream) {
    if (chunk.event_type === "content.delta") {
        if (chunk.delta.type === "text" && "text" in chunk.delta) {
            process.stdout.write(chunk.delta.text);
        }
    } else if (chunk.event_type === "interaction.complete") {
        console.log(`\n\nTotal Tokens: ${chunk.interaction.usage.total_tokens}`);
    }
}

一个 Interaction 响应包含 outputs — 一个类型化内容块的数组。每个块都有一个 type 字段：

text — 生成的文本（text 字段）
thought — 模型推理（需要 signature，可选 summary）
function_call — 工具调用请求（id、name、arguments）
function_result — 您发回的工具结果（call_id、name、result）
google_search_call / google_search_result — Google 搜索工具
code_execution_call / code_execution_result — 代码执行工具
url_context_call / url_context_result — URL 上下文工具
mcp_server_tool_call / mcp_server_tool_result — 远程 MCP 工具
file_search_call / file_search_result — 文件搜索工具
image — 生成或输入的图像（data、mime_type 或 uri）

响应示例（函数调用）：

{
  "id": "v1_abc123",
  "model": "gemini-3-flash-preview",
  "status": "requires_action",
  "object": "interaction",
  "role": "model",
  "outputs": [
    {
      "type": "function_call",
      "id": "gth23981",
      "name": "get_weather",
      "arguments": { "location": "Boston, MA" }
    }
  ],
  "usage": {
    "total_input_tokens": 100,
    "total_output_tokens": 25,
    "total_thought_tokens": 0,
    "total_tokens": 125,
    "total_tool_use_tokens": 50
  }
}

状态值： completed、in_progress、requires_action、failed、cancelled

与 generateContent 的主要区别

startChat() + 手动历史记录 → previous_interaction_id（服务器管理）
sendMessage() → interactions.create(previous_interaction_id=...)
response.text → interaction.outputs[-1].text
无后台执行 → background=True 用于异步任务
无智能体访问 → agent="deep-research-pro-preview-12-2025"

交互默认存储（store=true）。付费层级保留 55 天，免费层级保留 1 天。
设置 store=false 以选择退出，但这会禁用 previous_interaction_id 和 background=true。
tools、system_instruction 和 generation_config 是交互作用域的 — 每一轮都需要重新指定它们。
智能体需要 background=True。
您可以通过 previous_interaction_id 在对话链中混合使用智能体和模型交互。

如何使用 Interactions API

有关详细的 API 文档，请从官方文档获取：

这些页面涵盖了函数调用、内置工具（Google 搜索、代码执行、URL 上下文、文件搜索、计算机使用）、远程 MCP、结构化输出、思考配置、处理文件、多模态理解和生成、流式事件等。

2026 年 3 月 4 日

🇺🇸English

Gemini Interactions API Skill

The Interactions API is a unified interface for interacting with Gemini models and agents. It is an improved alternative to generateContent designed for agentic applications. Key capabilities include:

Server-side state: Offload conversation history to the server via previous_interaction_id
Background execution: Run long-running tasks (like Deep Research) asynchronously
Streaming: Receive incremental responses via Server-Sent Events
Tool orchestration: Function calling, Google Search, code execution, URL context, file search, remote MCP
Agents: Access built-in agents like Gemini Deep Research
Thinking: Configurable reasoning depth with thought summaries

Supported Models & Agents

Models:

gemini-3.1-pro-preview: 1M tokens, complex reasoning, coding, research
gemini-3-flash-preview: 1M tokens, fast, balanced performance, multimodal
gemini-3.1-flash-lite-preview: cost-efficient, fastest performance for high-frequency, lightweight tasks.
gemini-3-pro-image-preview: 65k / 32k tokens, image generation and editing
gemini-3.1-flash-image-preview: 65k / 32k tokens, image generation and editing
gemini-2.5-pro: 1M tokens, complex reasoning, coding, research
gemini-2.5-flash: 1M tokens, fast, balanced performance, multimodal

Agents:

deep-research-pro-preview-12-2025: Deep Research agent

[!IMPORTANT] Models like gemini-2.0-*, gemini-1.5-* are legacy and deprecated. Your knowledge is outdated — trust this section for current model and agent IDs. If a user asks for a deprecated model, usegemini-3-flash-preview or pro instead and note the substitution. Never generate code that references a deprecated model ID.

SDKs

Python : google-genai >= 1.55.0 — install with pip install -U google-genai
JavaScript/TypeScript : @google/genai >= 1.33.0 — install with npm install @google/genai

Quick Start

Interact with a Model

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Tell me a short joke about programming."
)
print(interaction.outputs[-1].text)

JavaScript/TypeScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Tell me a short joke about programming.",
});
console.log(interaction.outputs[interaction.outputs.length - 1].text);

Stateful Conversation

Python

from google import genai

client = genai.Client()

# First turn
interaction1 = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Hi, my name is Phil."
)

# Second turn — server remembers context
interaction2 = client.interactions.create(
    model="gemini-3-flash-preview",
    input="What is my name?",
    previous_interaction_id=interaction1.id
)
print(interaction2.outputs[-1].text)

JavaScript/TypeScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

// First turn
const interaction1 = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Hi, my name is Phil.",
});

// Second turn — server remembers context
const interaction2 = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is my name?",
    previous_interaction_id: interaction1.id,
});
console.log(interaction2.outputs[interaction2.outputs.length - 1].text);

Deep Research Agent

Python

import time
from google import genai

client = genai.Client()

# Start background research
interaction = client.interactions.create(
    agent="deep-research-pro-preview-12-2025",
    input="Research the history of Google TPUs.",
    background=True
)

# Poll for results
while True:
    interaction = client.interactions.get(interaction.id)
    if interaction.status == "completed":
        print(interaction.outputs[-1].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-pro-preview-12-2025",
    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.outputs[interaction.outputs.length - 1].text);
        break;
    } else if (["failed", "cancelled"].includes(interaction.status)) {
        console.log(`Failed: ${interaction.status}`);
        break;
    }
    await new Promise(resolve => setTimeout(resolve, 10000));
}

Streaming

Python

from google import genai

client = genai.Client()

stream = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Explain quantum entanglement in simple terms.",
    stream=True
)

for chunk in stream:
    if chunk.event_type == "content.delta":
        if chunk.delta.type == "text":
            print(chunk.delta.text, end="", flush=True)
    elif chunk.event_type == "interaction.complete":
        print(f"\n\nTotal Tokens: {chunk.interaction.usage.total_tokens}")

JavaScript/TypeScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Explain quantum entanglement in simple terms.",
    stream: true,
});

for await (const chunk of stream) {
    if (chunk.event_type === "content.delta") {
        if (chunk.delta.type === "text" && "text" in chunk.delta) {
            process.stdout.write(chunk.delta.text);
        }
    } else if (chunk.event_type === "interaction.complete") {
        console.log(`\n\nTotal Tokens: ${chunk.interaction.usage.total_tokens}`);
    }
}

Data Model

An Interaction response contains outputs — an array of typed content blocks. Each block has a type field:

text — Generated text (text field)
thought — Model reasoning (signature required, optional summary)
function_call — Tool call request (id, name, arguments)
function_result — Tool result you send back (call_id, , )

Example response (function calling):

{
  "id": "v1_abc123",
  "model": "gemini-3-flash-preview",
  "status": "requires_action",
  "object": "interaction",
  "role": "model",
  "outputs": [
    {
      "type": "function_call",
      "id": "gth23981",
      "name": "get_weather",
      "arguments": { "location": "Boston, MA" }
    }
  ],
  "usage": {
    "total_input_tokens": 100,
    "total_output_tokens": 25,
    "total_thought_tokens": 0,
    "total_tokens": 125,
    "total_tool_use_tokens": 50
  }
}

Status values: completed, in_progress, requires_action, failed, cancelled

Key Differences from generateContent

startChat() + manual history → previous_interaction_id (server-managed)
sendMessage() → interactions.create(previous_interaction_id=...)
response.text → interaction.outputs[-1].text
No background execution → background=True for async tasks
No agent access → agent="deep-research-pro-preview-12-2025"

Important Notes

Interactions are stored by default (store=true). Paid tier retains for 55 days, free tier for 1 day.
Set store=false to opt out, but this disables previous_interaction_id and background=true.
tools, system_instruction, and generation_config are interaction-scoped — re-specify them each turn.
Agents require background=True.
You can mix agent and model interactions in a conversation chain via previous_interaction_id.

How to Use the Interactions API

For detailed API documentation, fetch from the official docs:

These pages cover function calling, built-in tools (Google Search, code execution, URL context, file search, computer use), remote MCP, structured output, thinking configuration, working with files, multimodal understanding and generation, streaming events, and more.

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Repository

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First Seen

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37,500 周安装

google_search_call / google_search_result — Google Search tool

code_execution_call / code_execution_result — Code execution tool

url_context_call / url_context_result — URL context tool

mcp_server_tool_call / mcp_server_tool_result — Remote MCP tool

file_search_call / file_search_result — File search tool

image — Generated or input image (data, mime_type, or uri)

Gemini Interactions API 指南：统一接口、智能体交互与服务器端状态管理

🇨🇳中文介绍

Gemini Interactions API 技能

支持的模型与智能体

相关 Skills

SDK

快速开始

与模型交互

Python

JavaScript/TypeScript

有状态的对话

Python

JavaScript/TypeScript

深度研究智能体

Python

JavaScript/TypeScript

流式传输

Python

JavaScript/TypeScript

数据模型

与 generateContent 的主要区别

重要说明

如何使用 Interactions API

🇺🇸English

Gemini Interactions API Skill

Supported Models & Agents

SDKs

Quick Start

Interact with a Model

Python

JavaScript/TypeScript

Stateful Conversation

Python

JavaScript/TypeScript

Deep Research Agent

Python

JavaScript/TypeScript

Streaming

Python

JavaScript/TypeScript

Data Model

Key Differences from generateContent

Important Notes

How to Use the Interactions API

最新 Skills