SDK Integration

Use Antigravity Gateway with popular AI SDKs.

OpenAI Python SDK

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="any-value"
)

# Non-streaming
response = client.chat.completions.create(
    model="gemini-3-flash",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

# Streaming
stream = client.chat.completions.create(
    model="gemini-3-flash",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

OpenAI JavaScript SDK

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'http://localhost:8080/v1',
  apiKey: 'any-value'
});

// Non-streaming
const response = await client.chat.completions.create({
  model: 'gemini-3-flash',
  messages: [{ role: 'user', content: 'Hello!' }]
});
console.log(response.choices[0].message.content);

// Streaming
const stream = await client.chat.completions.create({
  model: 'gemini-3-flash',
  messages: [{ role: 'user', content: 'Hello!' }],
  stream: true
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

Anthropic Python SDK

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080",
    api_key="any-value"
)

# Non-streaming
message = client.messages.create(
    model="claude-sonnet-4-5-thinking",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)
print(message.content[0].text)

# Streaming
with client.messages.stream(
    model="claude-sonnet-4-5-thinking",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="")

cURL

OpenAI Format

# Non-streaming
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer any-value" \
  -d '{
    "model": "gemini-3-flash",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

# Streaming
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer any-value" \
  -d '{
    "model": "gemini-3-flash",
    "messages": [{"role": "user", "content": "Hello!"}],
    "stream": true
  }'

Anthropic Format

# Non-streaming
curl http://localhost:8080/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: any-value" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-sonnet-4-5-thinking",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

# Streaming
curl http://localhost:8080/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: any-value" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-sonnet-4-5-thinking",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Hello!"}],
    "stream": true
  }'

LangChain

Python

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="http://localhost:8080/v1",
    api_key="any-value",
    model="gemini-3-flash"
)

response = llm.invoke("Hello!")
print(response.content)

JavaScript

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  configuration: {
    baseURL: "http://localhost:8080/v1",
  },
  apiKey: "any-value",
  modelName: "gemini-3-flash"
});

const response = await llm.invoke("Hello!");
console.log(response.content);

Vercel AI SDK

import { createOpenAI } from '@ai-sdk/openai';
import { generateText } from 'ai';

const gateway = createOpenAI({
  baseURL: 'http://localhost:8080/v1',
  apiKey: 'any-value',
});

const { text } = await generateText({
  model: gateway('gemini-3-flash'),
  prompt: 'Hello!',
});

console.log(text);