Quickstart

import litellm

# Set the API base and key
litellm.api_base = "https://api.savegate.ai/v1"
litellm.api_key = "your-savegate-api-key"

response = litellm.completion(
    model="gpt-5.1",
    messages=[
        {"role": "user", "content": "Hello! How are you?"}
    ]
)

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

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-savegate-api-key',
  baseURL: 'https://api.savegate.ai/v1'
});

async function main() {
  const response = await client.chat.completions.create({
    model: 'gpt-5.1',
    messages: [
      { role: 'user', content: 'Hello! How are you?' }
    ]
  });

  console.log(response.choices[0].message.content);
}

main();

curl https://api.savegate.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-savegate-api-key" \
  -d '{
    "model": "gpt-5.1",
    "messages": [
      {"role": "user", "content": "Hello! How are you?"}
    ]
  }'

# Claude Sonnet 4.5
response = client.chat.completions.create(
    model="claude-sonnet-4.5",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Claude Opus 4.5
response = client.chat.completions.create(
    model="claude-opus-4.5",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Claude 3.7 Sonnet
response = client.chat.completions.create(
    model="claude-3.7-sonnet",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Gemini Pro
response = client.chat.completions.create(
    model="gemini-pro",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Gemini 1.5 Pro
response = client.chat.completions.create(
    model="gemini-1.5-pro",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Llama 3 70B
response = client.chat.completions.create(
    model="meta-llama/llama-3-70b-instruct",
    messages=[{"role": "user", "content": "Hello!"}]
)