Skip to main content

​
Overview

Antigravity Manager is compatible with official Python SDKs from OpenAI, Anthropic, and Google, allowing you to use familiar APIs with multi-account management and automatic quota rotation.

​
Installation

Install the SDK you want to use: 
pip install openai

​
OpenAI SDK

​
Basic chat completion

import openai

client = openai.OpenAI(
    api_key="sk-antigravity",
    base_url="http://127.0.0.1:8045/v1"
)

response = client.chat.completions.create(
    model="gemini-3-flash",
    messages=[
        {"role": "user", "content": "Hello, please introduce yourself"}
    ]
)

print(response.choices[0].message.content)

​
Streaming responses

response = client.chat.completions.create(
    model="claude-sonnet-4-6",
    messages=[
        {"role": "user", "content": "Write a short story"}
    ],
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

​
Image generation

import base64

# Generate image
response = client.images.generate(
    model="gemini-3-pro-image",
    prompt="A futuristic cityscape with neon lights and flying cars",
    size="1920x1080",
    quality="hd",
    n=1,
    response_format="b64_json"
)

# Save image
image_data = base64.b64decode(response.data[0].b64_json)
with open("output.png", "wb") as f:
    f.write(image_data)

​
Multi-modal input

import base64

# Read and encode image
with open("image.jpg", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="gemini-3-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{image_data}"
                    }
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

​
Anthropic SDK

​
Basic message

import anthropic

client = anthropic.Anthropic(
    api_key="sk-antigravity",
    base_url="http://127.0.0.1:8045"
)

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude!"}
    ]
)

print(message.content[0].text)

​
Streaming messages

with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=2048,
    messages=[
        {"role": "user", "content": "Tell me about Rust"}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="")

​
Extended thinking

message = client.messages.create(
    model="claude-sonnet-4-6-thinking",
    max_tokens=4096,
    thinking={
        "type": "adaptive",
        "budget_tokens": 24576
    },
    messages=[
        {"role": "user", "content": "Solve this complex problem..."}
    ]
)

# Access thinking and response
for block in message.content:
    if block.type == "thinking":
        print(f"Thinking: {block.thinking}")
    elif block.type == "text":
        print(f"Response: {block.text}")

​
Tool use

tools = [
    {
        "name": "get_weather",
        "description": "Get weather for a location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }
]

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=tools,
    messages=[
        {"role": "user", "content": "What's the weather in Tokyo?"}
    ]
)

# Handle tool use
for block in message.content:
    if block.type == "tool_use":
        print(f"Tool: {block.name}")
        print(f"Input: {block.input}")

​
Google Generative AI SDK

The Google SDK requires configuring a custom HTTP client to use Antigravity’s proxy.

​
Basic generation

import google.generativeai as genai
import requests

# Configure proxy
session = requests.Session()
session.proxies = {
    "http": "http://127.0.0.1:8045",
    "https": "http://127.0.0.1:8045"
}

genai.configure(
    api_key="sk-antigravity",
    transport="rest",
    client_options={"http_client": session}
)

model = genai.GenerativeModel("gemini-3-flash")
response = model.generate_content("Hello, Gemini!")

print(response.text)

​
Streaming

response = model.generate_content(
    "Write a poem about coding",
    stream=True
)

for chunk in response:
    print(chunk.text, end="")

​
Environment variables

For easier configuration, use environment variables: 
# OpenAI SDK
export OPENAI_API_KEY="sk-antigravity"
export OPENAI_BASE_URL="http://127.0.0.1:8045/v1"

# Anthropic SDK
export ANTHROPIC_API_KEY="sk-antigravity"
export ANTHROPIC_BASE_URL="http://127.0.0.1:8045"
Then in Python: 
import openai
import anthropic

# Automatically uses environment variables
openai_client = openai.OpenAI()
anthropic_client = anthropic.Anthropic()

​
Error handling

from openai import OpenAI, OpenAIError

client = OpenAI(
    api_key="sk-antigravity",
    base_url="http://127.0.0.1:8045/v1"
)

try:
    response = client.chat.completions.create(
        model="gemini-3-flash",
        messages=[{"role": "user", "content": "Hello"}]
    )
    print(response.choices[0].message.content)
except OpenAIError as e:
    print(f"Error: {e}")
    # Antigravity automatically retries on 429/401
    # If this fails, all accounts are likely exhausted

​
Best practices

Use streaming

Enable streaming for long responses to improve perceived performance

Handle errors gracefully

Implement retry logic and error handling for production use

Monitor quotas

Regularly check Antigravity dashboard for account status

Set appropriate limits

Configure max_tokens based on your use case to avoid unnecessary quota consumption

​
Complete examples

​
Chatbot with history

import openai

client = openai.OpenAI(
    api_key="sk-antigravity",
    base_url="http://127.0.0.1:8045/v1"
)

messages = []

while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        break
    
    messages.append({"role": "user", "content": user_input})
    
    response = client.chat.completions.create(
        model="gemini-3-flash",
        messages=messages
    )
    
    assistant_message = response.choices[0].message.content
    messages.append({"role": "assistant", "content": assistant_message})
    
    print(f"Assistant: {assistant_message}")

​
Batch image generation

import openai
import base64
import os

client = openai.OpenAI(
    api_key="sk-antigravity",
    base_url="http://127.0.0.1:8045/v1"
)

prompts = [
    "A serene mountain landscape",
    "A futuristic cityscape",
    "An underwater coral reef"
]

os.makedirs("images", exist_ok=True)

for i, prompt in enumerate(prompts):
    response = client.images.generate(
        model="gemini-3-pro-image",
        prompt=prompt,
        size="1024x1024",
        quality="hd"
    )
    
    image_data = base64.b64decode(response.data[0].b64_json)
    with open(f"images/image_{i+1}.png", "wb") as f:
        f.write(image_data)
    
    print(f"Generated: {prompt}")

​
Troubleshooting

Verify Antigravity proxy is running:
curl http://127.0.0.1:8045/health
Check authentication settings in Antigravity API Proxy tab. If enabled, use the actual API key.
Verify model name matches those available in Antigravity. Check the dashboard for active models.
All accounts may be exhausted. Check Antigravity dashboard and refresh account quotas.

Build docs developers (and LLMs) love

Get started for freeTalk to us