Agen Antigravitasi

Agen Antigravity adalah agen terkelola tujuan umum di Gemini API. Satu panggilan API memberi Anda agen yang dapat melakukan penalaran, mengeksekusi kode, mengelola file, dan menjelajahi web di dalam sandbox Linux aman Anda sendiri, yang dihosting oleh Google.

Gemini 3.5 Flash mendukungnya dan menggunakan harness yang sama dengan Antigravity IDE. Tersedia melalui Interactions API dan Google AI Studio.

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Read Hacker News, summarize the top 10 stories, and save the results as a PDF.",
    environment="remote",
)

print(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: "Read Hacker News, summarize the top 10 stories, and save the results as a PDF.",
    environment: "remote",
}, { timeout: 300000 });

console.log(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" \
-d '{
    "agent": "antigravity-preview-05-2026",
    "input": "Read Hacker News, summarize the top 10 stories, and save the results as a PDF.",
    "environment": "remote"
}'

Kemampuan

Setiap panggilan dapat menyediakan sandbox Linux dan memulai loop penggunaan alat. Agen merencanakan, bertindak, mengamati hasil, dan mengulangi hingga tugas selesai.

  • Eksekusi kode: Jalankan perintah Bash, Python, dan Node.js. Menginstal paket, menjalankan pengujian, membangun aplikasi.
  • Pengelolaan file: Membaca, menulis, mengedit, menelusuri, dan mencantumkan file di sandbox. File tetap ada di seluruh interaksi.
  • Akses web: Google Penelusuran dan pengambilan URL untuk data.
  • Pemadatan konteks: Pemadatan konteks otomatis (dipicu pada ~135 ribu token) untuk mendukung sesi multi-turn yang berjalan lama tanpa kehilangan konteks atau mencapai batas token.

Lihat Panduan memulai untuk penggunaan dan streaming multi-giliran.

Alat yang didukung

Secara default, agen memiliki akses ke code_execution, google_search, dan url_context. Alat sistem file diaktifkan secara otomatis saat Anda menentukan parameter environment. Anda juga dapat menentukan fungsi kustom untuk menghubungkan agen ke API dan alat Anda sendiri. Anda hanya perlu menentukan parameter tools saat menyesuaikan atau membatasi set default, atau saat menambahkan fungsi kustom.

Alat Nilai jenis Deskripsi
Eksekusi Kode code_execution Jalankan perintah shell (bash, Python, Node) dengan pengambilan stdout/stderr.
Google Penelusuran google_search Telusuri web publik.
Konteks URL url_context Mengambil dan membaca halaman web.
Filesystem (diaktifkan melalui environment) Membaca, menulis, mengedit, menelusuri, dan mencantumkan file di sandbox. Tidak ada jenis alat terpisah; diaktifkan secara otomatis saat environment disetel.
Fungsi Kustom function Tentukan fungsi kustom yang dapat diminta agen untuk dieksekusi. Lihat Panggilan fungsi.
Server MCP Jarak Jauh mcp_server Mendaftarkan server Model Context Protocol (MCP) eksternal sebagai alat. Lihat server MCP.

Untuk membatasi agen ke alat tertentu, teruskan hanya alat yang Anda butuhkan:

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Search for the latest AI research papers on reasoning and summarize them.",
    environment="remote",
    tools=[
        {"type": "google_search"},
        {"type": "url_context"},
    ],
)

print(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: "Search for the latest AI research papers on reasoning and summarize them.",
    environment: "remote",
    tools: [
        { type: "google_search" },
        { type: "url_context" },
    ],
}, { timeout: 300000 });

console.log(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" \
-d '{
    "agent": "antigravity-preview-05-2026",
    "input": "Search for the latest AI research papers on reasoning and summarize them.",
    "environment": "remote",
    "tools": [
        {"type": "google_search"},
        {"type": "url_context"}
    ]
}'

Input Multimodal

Agen Antigravity mendukung input multimodal. Saat ini, hanya input text dan image yang didukung. Gambar harus diberikan sebagai string berenkode base64 inline (data).

Python

import base64
from google import genai

client = genai.Client()

with open("path/to/chart.png", "rb") as f:
    image_bytes = f.read()

interaction_inline = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input=[
        {"type": "text", "text": "Analyze this chart and summarize the trends."},
        {
            "type": "image",
            "data": base64.b64encode(image_bytes).decode("utf-8"),
            "mime_type": "image/png",
        },
    ],
    environment="remote",
)

JavaScript


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

import * as fs from "node:fs";

const client = new GoogleGenAI({});
const base64Image = fs.readFileSync("path/to/chart.png", { encoding: "base64" });

const interactionInline = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: [
        { type: "text", text: "Analyze this chart and summarize the trends." },
        {
            type: "image",
            data: base64Image,
            mime_type: "image/png",
        },
    ],
    environment: "remote",
}, { timeout: 300000 });

REST

BASE64_IMAGE=$(base64 -w0 /path/to/chart.png)

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d "{
    \"agent\": \"antigravity-preview-05-2026\",
    \"input\": [
        {\"type\": \"text\", \"text\": \"Analyze this chart and summarize the trends.\"},
        {
            \"type\": \"image\",
            \"mime_type\": \"image/png\",
            \"data\": \"$BASE64_IMAGE\"
        }
    ],
    \"environment\": \"remote\"
}"

Panggilan fungsi

Panggilan fungsi memungkinkan Anda menghubungkan agen Antigravity ke API dan database eksternal dengan menentukan alat kustom yang dapat dipanggil agen. Untuk mengetahui konsep umumnya, lihat Panggilan fungsi dengan Gemini API.

Contoh berikut menunjukkan interaksi 2 putaran. Agen pertama-tama meminta panggilan fungsi get_weather kustom, dan klien mengeksekusinya serta menampilkan hasilnya pada giliran kedua.

Python

from google import genai

client = genai.Client()

# 1. Define the custom function
get_weather_tool = {
    "type": "function",
    "name": "get_weather",
    "description": "Gets the current weather for a given location.",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and country, e.g. San Francisco, USA",
            }
        },
        "required": ["location"],
    },
}

# 2. Call the agent with the custom tool (Turn 1)
interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="What is the weather in Tokyo?",
    environment="remote",
    tools=[
        {"type": "code_execution"},  # Enable default code execution
        get_weather_tool,            # Add custom function
    ],
)

# Check if the agent requested a function call
if interaction.status == "requires_action":
    # Find function calls that do not have a matching function result.
    # Filesystem tools (like write_file) are also represented as function calls
    # but are executed automatically by the environment.
    executed_calls = {step.call_id for step in interaction.steps if step.type == "function_result"}
    pending_calls = [step for step in interaction.steps if step.type == "function_call" and step.id not in executed_calls]

    if pending_calls:
        fc_step = pending_calls[0]
        print(f"Function to call: {fc_step.name} (ID: {fc_step.id})")
        print(f"Arguments: {fc_step.arguments}")

        # 3. Execute the function locally (simulated get_weather()) and send the result back (Turn 2)
        function_result = {
            "temperature": 23,
            "unit": "celsius"
        }

        final_interaction = client.interactions.create(
            agent="antigravity-preview-05-2026",
            previous_interaction_id=interaction.id,  # Reference the interaction ID
            environment=interaction.environment_id,
            input=[
                {
                    "type": "function_result",
                    "name": fc_step.name,
                    "call_id": fc_step.id,
                    "result": function_result,
                }
            ],
        )

        print(final_interaction.output_text)
        # Output: The current weather in Tokyo, Japan is 23°C (Celsius).
    else:
        print("No pending function calls.")
else:
    print(f"Interaction completed with status: {interaction.status}")

JavaScript

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

const client = new GoogleGenAI({});

// 1. Define the custom function
const get_weather_tool = {
  type: "function",
  name: "get_weather",
  description: "Gets the current weather for a given location.",
  parameters: {
    type: "object",
    properties: {
      location: {
        type: "string",
        description: "The city and country, e.g. San Francisco, USA",
      },
    },
    required: ["location"],
  },
};

// 2. Call the agent with the custom tool (Turn 1)
const interaction = await client.interactions.create({
  agent: "antigravity-preview-05-2026",
  input: "What is the weather in Tokyo?",
  environment: "remote",
  tools: [
    { type: "code_execution" },
    get_weather_tool,
  ],
}, { timeout: 300000 });

if (interaction.status === "requires_action") {
  // Find function calls that do not have a matching function result.
  // Filesystem tools (like write_file) are also represented as function calls
  // but are executed automatically by the environment.
  const executedCalls = new Set(
    interaction.steps
      .filter(s => s.type === "function_result")
      .map(s => s.call_id)
  );
  const pendingCalls = interaction.steps.filter(
    s => s.type === "function_call" && !executedCalls.has(s.id)
  );

  if (pendingCalls.length > 0) {
    const fcStep = pendingCalls[0];
    console.log(`Function to call: ${fcStep.name} (ID: ${fcStep.id})`);

    // 3. Execute the function locally (simulated get_weather()) and send the result back (Turn 2)
    const functionResult = {
      temperature: 23,
      unit: "celsius"
    };

    const finalInteraction = await client.interactions.create({
      agent: "antigravity-preview-05-2026",
      previous_interaction_id: interaction.id, // Reference the interaction ID
      environment: interaction.environment_id,
      input: [
        {
          type: "function_result",
          name: fcStep.name,
          call_id: fcStep.id,
          result: functionResult,
        }
      ],
    }, { timeout: 300000 });

    console.log(finalInteraction.output_text);
  } else {
    console.log("No pending function calls.");
  }
} else {
  console.log(`Interaction completed with status: ${interaction.status}`);
}

REST

# 1. Turn 1: Request function call
RESPONSE=$(curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "agent": "antigravity-preview-05-2026",
      "input": "What is the weather in Tokyo?",
      "environment": "remote",
      "tools": [
          {"type": "code_execution"},
          {
              "type": "function",
              "name": "get_weather",
              "description": "Gets the current weather for a given location.",
              "parameters": {
                  "type": "object",
                  "properties": {
                      "location": {"type": "string"}
                  },
                  "required": ["location"]
              }
          }
      ]
  }')

# Extract interaction ID, environment ID, and call ID (requires jq)
INTERACTION_ID=$(echo $RESPONSE | jq -r '.id')
ENVIRONMENT_ID=$(echo $RESPONSE | jq -r '.environment_id')
CALL_ID=$(echo $RESPONSE | jq -r '.steps[] | select(.type=="function_call") | .id')

# 2. Turn 2: Send function result back using variables
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d "{
      \"agent\": \"antigravity-preview-05-2026\",
      \"previous_interaction_id\": \"$INTERACTION_ID\",
      \"environment\": \"$ENVIRONMENT_ID\",
      \"input\": [
          {
              \"type\": \"function_result\",
              \"name\": \"get_weather\",
              \"call_id\": \"$CALL_ID\",
              \"result\": {
                  \"temperature\": 23,
                  \"unit\": \"celsius\"
              }
          }
      ]
  }"

Server MCP

Anda dapat menghubungkan agen Antigravity ke alat eksternal dengan mendaftarkan server Model Context Protocol (MCP) jarak jauh. Agen mendukung server MCP jarak jauh melalui HTTP yang dapat di-streaming.

Saat mendaftarkan server MCP, Anda harus menentukan kolom berikut dalam array tools:

Kolom Jenis Wajib diisi Deskripsi
type string Ya Harus berupa "mcp_server".
name string Ya ID unik untuk server. Harus huruf kecil dan alfanumerik (cocok dengan ^[a-z0-9_-]+$).
url string Ya URL endpoint server MCP jarak jauh.
headers objek Tidak Header kustom (misalnya, autentikasi) yang dikirim dengan permintaan.
allowed_tools array Tidak Daftar nama alat yang diizinkan untuk dieksekusi. Jika tidak disertakan, semua alat diizinkan.

Python

from google import genai

client = genai.Client()

# Register a remote HTTP MCP server
interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="What is the weather in Tokyo?",
    environment="remote",
    tools=[{
        "type": "mcp_server",
        "name": "weather", # Must be lowercase
        "url": "https://gemini-api-demos.uc.r.appspot.com/mcp"
    }]
)

print(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: "What is the weather in Tokyo?",
    environment: "remote",
    tools: [{
        type: "mcp_server",
        name: "weather", // Must be lowercase
        url: "https://gemini-api-demos.uc.r.appspot.com/mcp"
    }]
}, { timeout: 300000 });

console.log(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" \
  -d '{
      "agent": "antigravity-preview-05-2026",
      "input": "What is the weather in Tokyo?",
      "environment": "remote",
      "tools": [{
          "type": "mcp_server",
          "name": "weather",
          "url": "https://gemini-api-demos.uc.r.appspot.com/mcp"
      }]
  }'

Menyesuaikan agen

Anda dapat memperluas agen Antigravity dengan menyesuaikan petunjuk, alat, dan lingkungannya. Agen mendukung pendekatan native sistem file untuk penyesuaian: Anda dapat memuat file seperti AGENTS.md untuk petunjuk dan kemampuan di .agents/skills/ langsung ke sandbox, atau meneruskan konfigurasi inline pada waktu interaksi. Anda dapat melakukan iterasi pada konfigurasi secara inline, lalu menyimpannya sebagai agen terkelola saat sudah siap.

Untuk mengetahui detail lengkap tentang cara membuat agen kustom, lihat Membangun Agen Terkelola.

Eksekusi latar belakang

Tugas agen yang melibatkan penalaran multi-langkah, eksekusi kode, atau operasi file dapat memerlukan waktu beberapa menit untuk diselesaikan. Gunakan background=True untuk menjalankan interaksi secara asinkron. API akan langsung menampilkan ID interaksi yang Anda polling hingga statusnya completed atau failed.

Python

import time
from google import genai

client = genai.Client()

# 1. Start the interaction in the background
interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Run a complex analysis on the repository.",
    environment="remote",
    background=True,
)

print(f"Interaction started in background: {interaction.id}")

# 2. Poll for completion
while interaction.status == "in_progress":
    time.sleep(5)
    interaction = client.interactions.get(id=interaction.id)

if interaction.status == "completed":
    print(interaction.output_text)
else:
    print(f"Finished with status: {interaction.status}")

JavaScript

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

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Run a complex analysis on the repository.",
    environment: "remote",
    background: true,
});

console.log(`Interaction started in background: ${interaction.id}`);

let result = interaction;
while (result.status === "in_progress") {
    await new Promise(resolve => setTimeout(resolve, 5000));
    result = await client.interactions.get(interaction.id);
}

if (result.status === "completed") {
    console.log(result.output_text);
} else {
    console.log(`Finished with status: ${result.status}`);
}

REST

# 1. Start the interaction in the background
RESPONSE=$(curl -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": "Run a complex analysis on the repository.",
      "environment": "remote",
      "background": true
  }')

INTERACTION_ID=$(echo $RESPONSE | jq -r '.id')

# 2. Poll for results (repeat until status is "completed")
curl -s -X GET "https://generativelanguage.googleapis.com/v1beta/interactions/$INTERACTION_ID" \
  -H "x-goog-api-key: $GEMINI_API_KEY"

Eksekusi latar belakang memerlukan store=True, yang merupakan setelan default. Untuk update progres real-time selama eksekusi latar belakang, lihat Streaming interaksi latar belakang.

Anda dapat membatalkan interaksi latar belakang yang sedang berjalan menggunakan metode cancel.

Python

client.interactions.cancel(id="INTERACTION_ID")

JavaScript

await client.interactions.cancel({ id: "INTERACTION_ID" });

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions/INTERACTION_ID:cancel" \
  -H "x-goog-api-key: $GEMINI_API_KEY"

Multi-turn dengan eksekusi latar belakang

Jika interaksi latar belakang melibatkan alat stateful (seperti eksekusi kode di sandbox), gunakan environment_id dari interaksi yang telah selesai untuk melanjutkan di lingkungan yang sama. Tindakan ini memastikan agen melanjutkan dari tempat terakhir kali berhenti dengan semua file dan status tetap utuh.

Python

import time
from google import genai

client = genai.Client()

# First turn: run a task in the background
interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Clone https://github.com/google/generative-ai-python and run its tests.",
    environment="remote",
    background=True,
)

while interaction.status == "in_progress":
    time.sleep(5)
    interaction = client.interactions.get(id=interaction.id)

# Second turn: continue in the same environment
followup = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Fix any failing tests and re-run them.",
    previous_interaction_id=interaction.id,
    environment=interaction.environment_id,
    background=True,
)

while followup.status == "in_progress":
    time.sleep(5)
    followup = client.interactions.get(id=followup.id)

print(followup.output_text)

JavaScript

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

const client = new GoogleGenAI({});

// First turn: run a task in the background
let interaction = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Clone https://github.com/google/generative-ai-python and run its tests.",
    environment: "remote",
    background: true,
});

while (interaction.status === "in_progress") {
    await new Promise(resolve => setTimeout(resolve, 5000));
    interaction = await client.interactions.get(interaction.id);
}

// Second turn: continue in the same environment
let followup = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Fix any failing tests and re-run them.",
    previous_interaction_id: interaction.id,
    environment: interaction.environment_id,
    background: true,
});

while (followup.status === "in_progress") {
    await new Promise(resolve => setTimeout(resolve, 5000));
    followup = await client.interactions.get(followup.id);
}

console.log(followup.output_text);

REST

# 1. Start first interaction in the background
RESPONSE=$(curl -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": "Clone https://github.com/google/generative-ai-python and run its tests.",
      "environment": "remote",
      "background": true
  }')

INTERACTION_ID=$(echo $RESPONSE | jq -r '.id')

# 2. Poll until completed (repeat until status is "completed")
RESULT=$(curl -s -X GET "https://generativelanguage.googleapis.com/v1beta/interactions/$INTERACTION_ID" \
  -H "x-goog-api-key: $GEMINI_API_KEY")

ENVIRONMENT_ID=$(echo $RESULT | jq -r '.environment_id')

# 3. Continue in the same environment
curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Api-Revision: 2026-05-20" \
  -d "{
      \"agent\": \"antigravity-preview-05-2026\",
      \"input\": \"Fix any failing tests and re-run them.\",
      \"previous_interaction_id\": \"$INTERACTION_ID\",
      \"environment\": \"$ENVIRONMENT_ID\",
      \"background\": true
  }"

Lingkungan

Setiap panggilan membuat atau menggunakan kembali sandbox Linux. Parameter environment memiliki tiga bentuk:

Formulir Deskripsi
"remote" Sediakan sandbox baru dengan setelan default.
"env_abc123" Menggunakan kembali lingkungan yang ada berdasarkan ID, dengan mempertahankan semua file dan status.
{...} Penuh EnvironmentConfig dengan sumber dan aturan jaringan kustom.

Lihat Environments untuk mengetahui detail tentang sumber (Git, GCS, inline), jaringan, siklus proses, dan batas resource.

Ketersediaan dan harga

Agen Antigravity tersedia dalam pratinjau melalui Interactions API di Google AI Studio dan Gemini API.

Harga mengikuti model bayar sesuai penggunaan berdasarkan token model Gemini yang mendasarinya dan alat yang digunakan agen. Tidak seperti permintaan chat standar yang menghasilkan satu output, interaksi Antigravity adalah alur kerja agentik. Satu permintaan memicu loop penalaran, eksekusi alat, menjalankan kode, dan pengelolaan file yang otonom.

Perkiraan biaya

Biaya bervariasi berdasarkan kompleksitas tugas. Agen secara mandiri menentukan jumlah panggilan alat, eksekusi kode, dan operasi file yang diperlukan. Estimasi berikut didasarkan pada proses.

Kategori tugas Token input Token output Biaya umum
Riset & sintesis informasi 100 ribu–500 ribu 10.000–40.000 Rp4.500–Rp15.000
Pembuatan dokumen & konten 100 ribu–500 ribu 15 ribu–50 ribu Rp4.500–Rp19.500
Desain proses & sistem 100 ribu–400 ribu 10.000–30.000 $0,25–$0,80
Pemrosesan & analisis data 300 ribu–3 juta 30.000–150.000 $0,70–$3,25

50–70% token input biasanya di-cache. Alur kerja kompleks dengan banyak panggilan alat dapat mengakumulasi 3–5 juta token dalam satu interaksi, dengan biaya hingga sekitar$5.

Komputasi lingkungan (CPU, memori, eksekusi sandbox) tidak ditagih selama periode pratinjau.

Batasan

  • Status pratinjau: Agen Antigravity dan Interactions API. Fitur dan skema dapat berubah.
  • Konfigurasi pembuatan yang tidak didukung: Parameter berikut tidak didukung dan menampilkan error 400: temperature, top_p, top_k, stop_sequences, max_output_tokens.
  • Output terstruktur: Agen Antigravity tidak mendukung output terstruktur.
  • Alat yang tidak tersedia: file_search, computer_use, dan google_maps belum didukung.
  • Batasan MCP jarak jauh: Transpor Peristiwa yang Dikirim Server (SSE) tidak didukung (gunakan HTTP yang Dapat Di-stream). Selain itu, name server harus berupa huruf kecil dan alfanumerik (menggunakan huruf besar akan memicu error 400 Bad Request umum).
  • Alat sistem file: Saat ini tidak ada alat sistem file. Ini adalah bagian dari environment.
  • Persyaratan penyimpanan: Eksekusi agen menggunakan background=True memerlukan store=True.
  • Panggilan fungsi hanya stateful: Panggilan fungsi hanya didukung dalam mode stateful. Anda harus menggunakan previous_interaction_id untuk melanjutkan giliran; merekonstruksi histori secara manual (mode stateless) tidak didukung.
  • Jenis multimodal yang tidak didukung. Input audio, video, dan dokumen saat ini tidak didukung. Hanya teks dan gambar yang diizinkan.

Langkah berikutnya