https://api.llmwate.com/v1

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

Authorization: Bearer YOUR_API_KEY

curl https://api.llmwate.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Write a story"}],
    "stream": true
  }'

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" world"},"finish_reason":null}]}

data: [DONE]

X-RateLimit-Limit: 60
X-RateLimit-Remaining: 45
X-RateLimit-Reset: 1716891660

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMWATE_API_KEY",
    base_url="https://api.llmwate.com/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain quantum computing in 2 sentences."}],
    temperature=0.7,
    max_tokens=512
)
print(response.choices[0].message.content)

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMWATE_API_KEY",
    base_url="https://api.llmwate.com/v1"
)

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a Python function to reverse a string."}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

import asyncio
from openai import AsyncOpenAI

async def main():
    client = AsyncOpenAI(
        api_key="YOUR_LLMWATE_API_KEY",
        base_url="https://api.llmwate.com/v1"
    )
    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "What is 2+2?"}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMWATE_API_KEY",
    base_url="https://api.llmwate.com/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful Python expert."},
        {"role": "user", "content": "How do I sort a list in Python?"}
    ],
    temperature=0.5
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMWATE_API_KEY,
  baseURL: "https://api.llmwate.com/v1"
});

const response = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Explain quantum computing in 2 sentences." }],
  temperature: 0.7,
  max_tokens: 512
});

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

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMWATE_API_KEY,
  baseURL: "https://api.llmwate.com/v1"
});

const stream = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Write a Python function to reverse a string." }],
  stream: true
});

for await (const chunk of stream) {
  if (chunk.choices[0].delta.content) {
    process.stdout.write(chunk.choices[0].delta.content);
  }
}

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

curl https://api.llmwate.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_LLMWATE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "Count to 5"}], "stream": true}'

curl https://api.llmwate.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_LLMWATE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of Japan?"}
    ],
    "temperature": 0.7,
    "max_tokens": 100
  }'

package main

import (
    "context"
    openai "github.com/sashabaranov/go-openai"
)

func main() {
    client := openai.NewClient("YOUR_LLMWATE_API_KEY")
    client.BaseURL = "https://api.llmwate.com/v1/"

    resp, err := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model: "gpt-4o",
            Messages: []openai.ChatCompletionMessage{
                {Role: "user", Content: "Explain quantum computing in 2 sentences."},
            },
        },
    )
    if err != nil {
        panic(err)
    }
    println(resp.Choices[0].Message.Content)
}

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

@RestController
public class LLMController {

    private final RestTemplate restTemplate;

    public LLMController() {
        this.restTemplate = new RestTemplate();
        this.restTemplate.getInterceptors().add((request, body, execution) -> {
            request.getHeaders().add("Authorization", "Bearer " + "YOUR_LLMWATE_API_KEY");
            request.getHeaders().add("Content-Type", "application/json");
            return execution.execute(request, body);
        });
    }

    @PostMapping("/chat")
    public String chat(@RequestBody Map<String, Object> request) {
        String url = "https://api.llmwate.com/v1/chat/completions";
        HttpEntity<Map<String, Object>> entity = new HttpEntity<>(request, new HttpHeaders());
        ResponseEntity<Map> response = restTemplate.postForEntity(url, entity, Map.class);
        Map body = response.getBody();
        List choices = (List) body.get("choices");
        Map message = (Map) ((Map) choices.get(0)).get("message");
        return (String) message.get("content");
    }
}

require "openai"

client = OpenAI::Client.new(
  api_key: ENV["LLMWATE_API_KEY"],
  uri_base: "https://api.llmwate.com/v1"
)

response = client.chat(
  parameters: {
    model: "gpt-4o",
    messages: [
      { role: "user", content: "Explain quantum computing in 2 sentences." }
    ],
    temperature: 0.7
  }
)

puts response.dig("choices", 0, "message", "content")

<?php
$api_key = "YOUR_LLMWATE_API_KEY";
$url = "https://api.llmwate.com/v1/chat/completions";

$data = [
    "model" => "gpt-4o",
    "messages" => [
        ["role" => "user", "content" => "Explain quantum computing in 2 sentences."]
    ],
    "temperature" => 0.7
];

$ch = curl_init($url);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($ch, CURLOPT_HTTPHEADER, [
    "Authorization: Bearer " . $api_key,
    "Content-Type: application/json"
]);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);

$response = curl_exec($ch);
curl_close($ch);

$result = json_decode($response, true);
echo $result["choices"][0]["message"]["content"];
?>

# .env file
LLMWATE_API_KEY=lmw_your_api_key_here
LLMWATE_BASE_URL=https://api.llmwate.com/v1

from dotenv import load_dotenv
from openai import OpenAI
import os

load_dotenv()

client = OpenAI(
    api_key=os.getenv("LLMWATE_API_KEY"),
    base_url=os.getenv("LLMWATE_BASE_URL", "https://api.llmwate.com/v1")
)

from openai import OpenAI

client = OpenAI(api_key="YOUR_KEY", base_url="https://api.llmwate.com/v1")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Return JSON with name and age fields."}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "age": {"type": "integer"}
                },
                "required": ["name", "age"]
            }
        }
    }
)
# Access: response.choices[0].message.content (JSON string)

from openai import OpenAI

client = OpenAI(api_key="YOUR_KEY", base_url="https://api.llmwate.com/v1")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is the weather in Tokyo?"}],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "parameters": {
                    "type": "object",
                    "properties": {"location": {"type": "string"}},
                    "required": ["location"]
                }
            }
        }
    ]
)
# Tool calls available in response.choices[0].message.tool_calls

from openai import OpenAI

client = OpenAI(api_key="YOUR_KEY", base_url="https://api.llmwate.com/v1")

response = client.chat.completions.create(
    model="gpt-4o",  # or claude-3.5-sonnet for vision
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
            ]
        }
    ]
)
print(response.choices[0].message.content)