Developer Guide

curl https://api.lexora.network/v1/chat/completions \
  -H "Authorization: Bearer sk-lexora-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"Qwen/Qwen3-8B","messages":[{"role":"user","content":"Hello"}]}'

from openai import OpenAI

client = OpenAI(
    base_url="https://api.lexora.network/v1",
    api_key="sk-lexora-YOUR_KEY",
)

# Streaming (recommended)
stream = client.chat.completions.create(
    model="Qwen/Qwen3-8B",
    messages=[{"role": "user", "content": "Explain quantum entanglement simply."}],
    stream=True,
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.lexora.network/v1",
  apiKey: process.env.LEXORA_API_KEY,
});

const stream = await client.chat.completions.create({
  model: "Qwen/Qwen3-8B",
  messages: [{ role: "user", content: "Hello!" }],
  stream: true,
});

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

response = client.chat.completions.create(
    model="Qwen/Qwen3-8B",
    messages=[{"role": "user", "content": "What is 2+2?"}],
    stream=False,
    max_tokens=512,
    temperature=0.7,
)

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

import base64, requests

resp = requests.post(
    "https://api.lexora.network/v1/images/generations",
    headers={"Authorization": "Bearer sk-lexora-YOUR_KEY"},
    json={
        "model": "black-forest-labs/FLUX.1-schnell",
        "prompt": "A futuristic cityscape at dawn, cinematic lighting",
        "width": 768,
        "height": 768,
        "num_inference_steps": 4,   # schnell default — do not increase beyond 4
        "guidance_scale": 0.0,      # schnell is guidance-free
        "n": 1,
    },
    timeout=120,
)

data = resp.json()["data"][0]["b64_json"]
with open("output.png", "wb") as f:
    f.write(base64.b64decode(data))

import requests

resp = requests.post(
    "https://api.lexora.network/v1/partner/chat/completions",
    headers={"Authorization": "Bearer sk-lexora-YOUR_KEY"},
    json={
        "model": "deepseek-v4-pro",   # swap for any partner model ID below
        "messages": [{"role": "user", "content": "Explain quicksort."}],
    },
    timeout=120,
)
print(resp.json()["choices"][0]["message"]["content"])

import requests

resp = requests.post(
    "https://api.lexora.network/v1/partner/chat/completions",
    headers={"Authorization": "Bearer sk-lexora-YOUR_KEY"},
    json={
        "model": "gemini-2.5-flash",  # or gpt-5.4-mini / gpt-4.1 / gpt-4.1-mini / gemini-2.5-pro
        "messages": [{
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}},
            ],
        }],
    },
    timeout=60,
)
print(resp.json()["choices"][0]["message"]["content"])

# Base64 data URI also works:
# "url": "data:image/jpeg;base64,/9j/4AAQ..."

// TypeScript / Node.js — same shape via the OpenAI SDK
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.lexora.network/v1/partner",
  apiKey: process.env.LEXORA_API_KEY,
});

const response = await client.chat.completions.create({
  model: "gemini-2.5-flash",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Describe this screenshot." },
      { type: "image_url", image_url: { url: "https://example.com/screenshot.png" } },
    ],
  }],
});

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