Panduan Memulai Agen Terkelola

Panduan ini akan memandu Anda membuat dan menggunakan Agen Terkelola di Gemini API, menggunakan agen Antigravitasi. Anda akan melakukan panggilan agen pertama, melanjutkan percakapan bolak-balik, melakukan streaming respons, mendownload file dari sandbox, dan menggunakan agen terkelola Antigravity.

Menjalankan interaksi agen pertama Anda

Satu panggilan ke Interactions API menyediakan sandbox Linux, menjalankan loop agen, dan menampilkan hasilnya. Anda akan menentukan tiga parameter:

  • Teruskan agent sebagai "antigravity-preview-05-2026", yang merupakan versi saat ini dari agen terkelola tujuan umum dan yang telah ditentukan sebelumnya.
  • Tentukan environment="remote", untuk menyediakan lingkungan sandbox baru yang bersih.

  • Buat input, yang menentukan tindakan yang Anda inginkan dari agen.

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"}
}'

Respons menampilkan objek Interaction. Simpan interaction.id dan interaction.environment_id untuk melanjutkan percakapan di sandbox yang sama. Gunakan interaction.output_text untuk mengakses respons akhir agen. interaction.steps mencantumkan setiap langkah yang dilakukan agen (penalaran, panggilan alat, eksekusi kode).

Lanjutkan percakapan (multi-giliran)

API melacak dua dimensi status independen:

  • Konteks percakapan: histori chat, jejak penalaran, penggunaan alat, menggunakan previous_interaction_id.
  • Status lingkungan: file, paket yang diinstal, dan status sandbox, menggunakan environment.

Teruskan keduanya di tempatnya masing-masing untuk melanjutkan:

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."}]
}'

File dari giliran 1 (fibonacci.txt) tetap ada di giliran 2. Agen juga mempertahankan konteks percakapan.

Anda dapat memadupadankan hal-hal ini secara terpisah:

  • Hapus percakapan, simpan file: Hilangkan previous_interaction_id, hanya teruskan ID lingkungan menggunakan environment untuk percakapan baru di ruang kerja yang sama.
  • Lanjutkan percakapan, ruang kerja baru: Teruskan previous_interaction_id, tetapkan environment="remote" untuk sandbox baru.

Pemadatan konteks otomatis

Dalam percakapan multi-turn yang berjalan lama, histori mentah langkah-langkah penalaran, panggilan alat, dan konten file besar dapat berkembang dengan cepat dan menggunakan ruang konteks yang signifikan. Untuk mencegah error batas token dan mempertahankan fokus agen (mencegah "context rot"), Managed Agents API menampilkan langkah pemadatan konteks native pada sekitar 135 ribu token. Hal ini terjadi secara otomatis.

Streaming respons

Untuk tugas yang berjalan lama, Anda dapat melakukan streaming respons untuk melihat pekerjaan agen secara real time:

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
}'

Streaming menampilkan iterable delta langkah, yang merupakan teks inkremental, token penalaran, dan update panggilan alat. Pelajari lebih lanjut cara melakukan streaming respons di panduan Streaming.

Mendownload file dari lingkungan

Saat agen membuat file di dalam sandbox. Download menggunakan Files API dengan permintaan HTTP langsung (belum ada metode SDK):

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

Menyimpan agen terkelola

Pada langkah sebelumnya, kita menggunakan agen Antigravity default dan menyesuaikannya secara inline. Setelah melakukan iterasi pada konfigurasi (petunjuk, kemampuan, dan lingkungan), Anda dapat menyimpannya sebagai agen terkelola. Dengan begitu, Anda dapat memanggilnya berdasarkan ID tanpa mengulangi konfigurasi.

Saat menyimpan agen, Anda menentukan base_environment (baik dari sumber atau dengan membuat cabang lingkungan yang ada). Agen akan menggunakan lingkungan ini untuk setiap interaksi baru.

Dari sumber: Tentukan sumber secara inline, atau dari sumber lain seperti GitHub atau Cloud Storage.

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"
            }
        ]
    }
}'

Memanggil agen terkelola

Setelah menyimpan agen terkelola, Anda dapat memanggilnya berdasarkan ID. Setiap pemanggilan membuat cabang lingkungan dasar, sehingga setiap eksekusi dimulai dengan bersih:

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."
}'

Langkah berikutnya