Developer Guide

curl https://api.lexora.network/v1/chat/completions \
  -H "Authorization: Bearer sk-lexora-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"meta-llama/Llama-3.2-3B-Instruct","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="meta-llama/Llama-3.2-3B-Instruct",
    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.DEPIN_API_KEY,
});

const stream = await client.chat.completions.create({
  model: "meta-llama/Llama-3.2-3B-Instruct",
  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="meta-llama/Llama-3.2-3B-Instruct",
    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))