受管代理快速入门

本指南将介绍如何使用 Gemini API 上的托管式智能体，并以 Antigravity 智能体为例进行说明。您将进行首次智能体调用、继续多轮对话、流式传输响应、从沙盒下载文件，以及使用 Antigravity 托管式智能体。

运行首次智能体互动

对 Interactions API 的单次调用会预配 Linux 沙盒、运行智能体循环并返回结果。您将定义三个参数：

  • 传入 agent 作为 "antigravity-preview-05-2026",，这是我们预定义和通用的托管式代理的当前版本。
  • 定义 environment="remote"，以预配新的沙盒环境。

  • 创建输入，定义您希望智能体执行的操作。

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 the agent's final output
print(f"Interaction ID: {interaction.id}")
print(f"Environment ID: {interaction.environment_id}")
print(f"Output: {interaction.output_text}")

JavaScript

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(`Interaction ID: ${interaction.id}`);
console.log(`Environment ID: ${interaction.environment_id}`);

console.log(`Output: ${interaction.output_text}`);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Api-Revision: 2026-05-20" \
-d '{
    "agent": "antigravity-preview-05-2026",
    "input": [{"type": "text", "text": "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": {"type": "remote"}
}'

响应会返回 Interaction 对象。存储 interaction.id 和 interaction.environment_id，以便在同一沙盒中继续对话。使用 interaction.output_text 访问智能体的最终响应。interaction.steps 列出了智能体执行的每个步骤（推理、工具调用、代码执行）。

继续对话（多轮）

该 API 会跟踪两个独立的状态维度：

  • 对话上下文 ：聊天记录、推理轨迹、工具使用情况，使用 previous_interaction_id。
  • **环境状态** ：文件、已安装的软件包和沙盒状态，使用 environment。

在各自的位置传入这两个参数以恢复：

Python

interaction_2 = client.interactions.create(
    agent="antigravity-preview-05-2026",
    previous_interaction_id=interaction.id,
    environment=interaction.environment_id,
    input="Now plot the Fibonacci sequence as a line chart and save it as chart.png.",
)

print(interaction_2.output_text)

JavaScript

const interaction2 = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    previous_interaction_id: interaction.id,
    environment: interaction.environment_id,
    input: "Now plot the Fibonacci sequence as a line chart and save it as chart.png.",
}, { timeout: 300_000 });

console.log(interaction2.output_text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Api-Revision: 2026-05-20" \
-d '{
    "agent": "antigravity-preview-05-2026",
    "previous_interaction_id": "interaction_id_from_step_1",
    "environment": "environment_id_from_step_1",
    "input": [{"type": "text", "text": "Now plot the Fibonacci sequence as a line chart and save it as chart.png."}]
}'

第 1 轮中的文件 (fibonacci.txt) 会保留在第 2 轮中。智能体还会保留对话上下文。

您可以独立混搭使用这些参数：

  • 清除对话，保留文件 ：省略 previous_interaction_id，仅使用 environment 传递环境 ID，以便在同一工作区中进行新的对话。
  • 保留对话，使用新工作区 ：传递 previous_interaction_id，设置 environment="remote" 以使用新的沙盒。

自动上下文压缩

在长时间运行的多轮对话中，推理步骤、工具调用和大型文件内容的原始历史记录可能会快速增长并占用大量上下文空间。为避免令牌限制错误并保持智能体的专注度（防止“上下文腐烂”），Managed Agents API 在大约 13.5 万个令牌处提供原生上下文压缩步骤。这个过程是自动进行的。

流式传输响应

对于长时间运行的任务，您可以流式传输响应，以便实时查看智能体的工作情况：

Python

from google import genai

client = genai.Client()

stream = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Read Hacker News, summarize the top 5 stories, and save the results as a PDF.",
    environment="remote",
    stream=True,
)

for event in stream:
    print(event)

JavaScript

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

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Read Hacker News, summarize the top 5 stories, and save the results as a PDF.",
    environment: "remote",
    stream: true,
});

for await (const event of stream) {
    console.log(event);
}

REST

curl -N -s -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Api-Revision: 2026-05-20" \
-d '{
    "agent": "antigravity-preview-05-2026",
    "input": "Read Hacker News, summarize the top 5 stories, and save the results as a PDF.",
    "environment": "remote",
    "stream": true
}'

流式传输会返回步骤增量的可迭代对象，这些增量是增量文本、推理令牌和工具调用更新。如需详细了解如何流式传输响应，请参阅流式传输指南。

从环境下载文件

当智能体在沙盒内创建文件时。使用 Files API 通过直接 HTTP 请求（尚无 SDK 方法）下载这些文件：

Python

import os
import requests
import tarfile

env_id = interaction.environment_id
api_key = os.environ["GEMINI_API_KEY"]

response = requests.get(
    f"https://generativelanguage.googleapis.com/v1beta/files/environment-{env_id}:download",
    params={"alt": "media"},
    headers={"x-goog-api-key": api_key},
    allow_redirects=True,
)

with open("snapshot.tar", "wb") as f:
    f.write(response.content)

with tarfile.open("snapshot.tar") as tar:
    tar.extractall(path="extracted_snapshot")

JavaScript

import fs from "fs";
import { execSync } from "child_process";

const envId = interaction.environment_id;
const apiKey = process.env.GEMINI_API_KEY || "";

const url = `https://generativelanguage.googleapis.com/v1beta/files/environment-${envId}:download?alt=media`;
const response = await fetch(url, {
    headers: {
        "x-goog-api-key": apiKey,
    },
});

if (!response.ok) {
    throw new Error(`Failed to download file: ${response.statusText}`);
}

const buffer = Buffer.from(await response.arrayBuffer());
fs.writeFileSync("snapshot.tar", buffer);

if (!fs.existsSync("extracted_snapshot")) {
    fs.mkdirSync("extracted_snapshot");
}
execSync("tar -xf snapshot.tar -C extracted_snapshot");

console.log(fs.readdirSync("extracted_snapshot"));

REST

curl -L -X GET "https://generativelanguage.googleapis.com/v1beta/files/environment-$ENV_ID:download?alt=media" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-o snapshot.tar

tar -xf snapshot.tar -C extracted_snapshot

保存托管式智能体

在前面的步骤中，我们使用了默认的 Antigravity 智能体，并对其进行了内嵌自定义。在对配置（说明、技能和环境）进行迭代后，您可以将其保存为托管式智能体。这样一来，您就可以通过 ID 调用它，而无需重复配置。

保存智能体时，您需要定义 base_environment（从来源定义或通过派生现有环境定义）。智能体将针对每次新的互动使用此环境。

从来源 ：内嵌定义来源，或从其他来源（例如 GitHub 或 Cloud Storage）定义来源。

Python

agent = client.agents.create(
    id="fibonacci-analyst",
    base_agent="antigravity-preview-05-2026",
    system_instruction="You are a math analysis agent. Generate sequences, visualize them, and export results as PDF reports.",
    base_environment={
        "type": "remote",
        "sources": [
            {
                "type": "inline",
                "target": ".agents/AGENTS.md",
                "content": "Always include a chart and a summary table in your reports.",
            },
            {
                "type": "repository",
                "source": "https://github.com/your-org/skills",
                "target": ".agents/skills"
            }
        ],
    },
)

print(f"Saved agent: {agent.id}")

JavaScript

const agent = await client.agents.create({
    id: "fibonacci-analyst",
    base_agent: "antigravity-preview-05-2026",
    system_instruction: "You are a math analysis agent. Generate sequences, visualize them, and export results as PDF reports.",
    base_environment: {
        type: "remote",
        sources: [
            {
                type: "inline",
                target: ".agents/AGENTS.md",
                content: "Always include a chart and a summary table in your reports.",
            },
            {
                type: "repository",
                source: "https://github.com/your-org/skills",
                target: ".agents/skills"
            }
        ],
    },
});

console.log(`Saved agent: ${agent.id}`);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/agents" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Api-Revision: 2026-05-20" \
-d '{
    "id": "fibonacci-analyst",
    "base_agent": "antigravity-preview-05-2026",
    "system_instruction": "You are a math analysis agent. Generate sequences, visualize them, and export results as PDF reports.",
    "base_environment": {
        "type": "remote",
        "sources": [
            {
                "type": "inline",
                "target": ".agents/AGENTS.md",
                "content": "Always include a chart and a summary table in your reports."
            },
            {
                "type": "repository",
                "source": "https://github.com/your-org/skills",
                "target": ".agents/skills"
            }
        ]
    }
}'

调用托管式智能体

保存托管式智能体后，您可以通过 ID 调用它。每次调用都会派生基本环境，因此每次运行都是从干净的状态开始：

Python

result = client.interactions.create(
    agent="fibonacci-analyst",
    input="Generate the first 50 prime numbers, plot their distribution, and save a PDF report.",
    environment="remote",
)

print(result.output_text)

JavaScript

const result = await client.interactions.create({
    agent: "fibonacci-analyst",
    input: "Generate the first 50 prime numbers, plot their distribution, and save a PDF report.",
    environment: "remote",
}, {
    timeout: 300_000,
});

console.log(result.output_text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Api-Revision: 2026-05-20" \
-d '{
    "agent": "fibonacci-analyst",
    "environment": "remote",
    "input": "Generate the first 50 prime numbers, plot their distribution, and save a PDF report."
}'

后续步骤