Getting Started

Welcome to LLMSaver! This guide will help you get started with our API in just a few minutes.

Quick Start

Create an account and get your API key
Make your first API call
Explore our models and features

Your First API Call

curl -X POST https://api.llmsaver.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [
      {
        "role": "user",
        "content": "Hello, world!"
      }
    ]
  }'

Authentication

LLMSaver uses API keys for authentication. Include your API key in the Authorization header of your requests.

API Key Format

Authorization: Bearer llms_1234567890abcdef

Keep your API key secure! Never expose it in client-side code or public repositories.

API Reference

Complete reference for all LLMSaver API endpoints.

Chat Completions

Generate text completions using various AI models.

Endpoint

POST /v1/chat/completions

Parameters

Parameter	Type	Required	Description
model	string	Yes	The model to use for completion
messages	array	Yes	Array of message objects
temperature	number	No	Sampling temperature (0-1)
max_tokens	integer	No	Maximum tokens to generate

Available Models

LLMSaver provides access to state-of-the-art AI models from leading providers.

GPT-4

OpenAI's most capable model with superior reasoning abilities.

Context: 128k tokens
Best for: Complex reasoning, analysis
Cost: $0.03/1k tokens

Claude 3

Anthropic's latest model with excellent safety and reasoning.

Context: 200k tokens
Best for: Long documents, safety-critical tasks
Cost: $0.025/1k tokens

Code Examples

Ready-to-use code examples in popular programming languages.

Python

import requests

url = "https://api.llmsaver.com/v1/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}
data = {
    "model": "gpt-4",
    "messages": [
        {"role": "user", "content": "Hello!"}
    ]
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

JavaScript

const response = await fetch('https://api.llmsaver.com/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'gpt-4',
    messages: [
      { role: 'user', content: 'Hello!' }
    ]
  })
});

const data = await response.json();
console.log(data);

Documentation