Exomlapi backend sniffed, yep it is a scraper!

exomlapi.js

import axios from "axios";

const api = axios.create({
  baseURL: "https://exomlapi.com/api",
  timeout: 120_000,
  headers: {
    'Authority': 'exomlapi.com',
    'Accept': '*/*',
    'Content-Type': 'application/json,*/*',
    'Origin': 'https://exomlapi.com',
    'Referer': 'https://exomlapi.com/',
    'User-Agent': 'Zanixon/1.0.0'
  }
});

// completions
async function generateAntibot() {
  const res = await api.post('/genid');
  const data = res.data;
  if (!data?.antiBotId) throw 'failed fetch antibot id!';
  return data;
}

function parseRawtext(input) {
  const matches = [...input.matchAll(/\d+:"(.*?)"/g)];
  return matches.map(m => m[1]).join("");
}

async function completions(payload = {}) {
  try {
    return await new Promise(async (resolve, reject) => {
      const models = [
        "llama",
        "gemma",
        "qwen-3-235b",
        "gpt-4.1",
        "gpt-4o",
        "gpt-4o-mini",
        "llama-4-scout",
        "llama-4-maverick",
        "deepseek-r1",
        "qwq-32b"
      ];
      if (!payload?.model) return reject('missing model, please input the model first!');
      if (!models.includes(payload?.model)) return reject('invalid model, please input the model correctly!', models);
      if (!payload?.messages) return reject('missing messages input!');
      if (!Array.isArray(payload.messages)) return reject('invalid array messages, please input the payload correctly!');

      const id = Math.floor(Math.random() * 9999999999) + 1;
      const chatId = `chat-${Date.now()}-${Math.floor(Math.random() * 999999) + 1}`;
      const userId = `local-user-${Date.now()}-${Math.floor(Math.random() * 9999999) + 1}`;
      const antiBotId = (await generateAntibot().catch(reject))?.antiBotId;

      api.post('/chat', {
        id,
        chatId,
        userId,
        antiBotId,
        messages: payload.messages,
        model: payload.model,
        systemPrompt: payload?.systemPrompt || '',
        isAuthenticated: true
      }).then(res => {
        const answer = parseRawtext(res.data);
        if (!answer) return reject('failed get response.');
        return resolve({ success: true, answer });
      }).catch(reject);
    });
  } catch (e) {
    return {
      success: false,
      errors: e
    };
  }
}

async function llmSearch(query, count = 10) {
  try {
    return await new Promise(async(resolve, reject) => {
      if(!query) return reject('missing query input!');
      if(!count) return reject('missing count input!');
      if(!Number.isInteger(count)) return reject('invalid number inpur at count param!');

      axios.post('https://search.exomlapi.com/api/ml_search', {
        query,
        count
      }, {
        headers: {
          'Accept': '*/*',
          'Content-Type': 'application/json',
          'Origin': 'https://exomlapi.com',
          'Referer': 'https://exomlapi.com/',
          'User-Agent': 'Zanixon/1.0.0'
        }
      }).then(res => {
        const data = res.data;
        if(!data?.llm_response) return reject('failed generate response!');
        return resolve({
          success: true,
          result: {
            answer: data?.llm_response,
            searchResult: data?.results.map(d => ({
              url: d.url,
              title: d.title,
              description: d.text
            }))
          }
        })
      })
    })
  } catch (e) {
    return {
      success: false,
      errors: e
    }
  }
}

// image generation 
async function enhancePrompt(prompt) {
  try {
    return await new Promise(async(resolve, reject) => {
      if(!prompt) return reject('missing prompt input!');

      api.post('/prompts/enhance', {
        prompt
      }).then(res => {
        const data = res.data;
        if(!data?.enhancedPrompt) return reject('failed generate response!');
        return resolve({
          success: true,
          prompt: data?.enhancedPrompt
        })
      })
    })
  } catch (e) {
    return {
      success: false,
      errors: e
    }
  }
}

async function generateImage(prompt, model = "exo-image") {
  try {
    return await new Promise(async(resolve, reject) => {
      const models = [
        "exo-image",
        "flux.1-schnell",
        "flux.1-pro",
        "flux.1-dev"
      ]
      if(!prompt) return reject('missing prompt input!');
      if(!models.includes(model)) return reject('invalid model input!', models)

      api.post('/images/generate', {
        prompt,
        model,
        size: '1024x1024'
      }).then(res => {
        const data = res.data;
        if(data?.data.length < 1) return reject('failed generate response!');
        return resolve({
          success: true,
          result: data?.data.map(d => ({
            url: d.url,
            prompt: d.revised_prompt
          }))
        })
      })
    })
  } catch (e) {
    return {
      success: false,
      errors: e
    }
  }
}

🕷️ ExoML API Scraper

Unofficial wrapper/scraper untuk layanan ExoML API, yang menyediakan antarmuka untuk:

• Chat completions (berbagai model)
• Prompt enhancer untuk image gen
• Image generation
• LLM-powered web search

⚠️ Unofficial client – gunakan dengan bijak. Endpoint bisa berubah sewaktu-waktu.

---

📦 Instalasi

npm install axios

---

📁 Struktur Modul

├── api               # Axios instance dengan config khusus ExoML
├── generateAntibot   # Mengambil antiBotId (wajib sebelum request chat)
├── completions       # LLM chat completions
├── parseRawtext      # Utility parser hasil raw response ExoML
├── llmSearch         # Search + LLM summarizer
├── enhancePrompt     # Prompt enhancer untuk image generation
├── generateImage     # Image generator berbasis prompt

---

📘 API Docs

🔐 generateAntibot()

Ambil antiBotId yang diperlukan sebelum mengirim permintaan ke endpoint chat.

const id = await generateAntibot();
console.log(id.antiBotId); // string

---

🧠 completions(payload)

Mengirim permintaan completion berbasis chat. Butuh array messages dan model.

await completions({
  model: "gpt-4o",
  messages: [
    { role: "user", content: "Apa itu ExoML?" }
  ],
  systemPrompt: "Kamu adalah AI asisten."
});

✅ Payload

Key	Tipe	Wajib	Deskripsi
model	string	✅	Salah satu dari daftar model
messages	array	✅	Array pesan (role: user/system)
systemPrompt	string	❌	Prompt sistem opsional

🧪 Model yang didukung:

• "llama"
• "gemma"
• "qwen-3-235b"
• "gpt-4.1"
• "gpt-4o"
• "gpt-4o-mini"
• "llama-4-scout"
• "llama-4-maverick"
• "deepseek-r1"
• "qwq-32b"

---

🔍 llmSearch(query, count = 10)

Search berbasis query + LLM summarization dari hasil web.

await llmSearch("cara kerja AI");

✅ Hasil:

{
  success: true,
  result: {
    answer: "Penjelasan singkat...",
    searchResult: [
      {
        url: "https://example.com",
        title: "Artikel AI",
        description: "Isi ringkasan dari halaman..."
      }
    ]
  }
}

---

🪄 enhancePrompt(prompt)

Meningkatkan prompt sebelum digunakan untuk image generation.

await enhancePrompt("a castle in the sky");

---

🖼️ generateImage(prompt, model)

Generate gambar dari prompt. Default model: "exo-image"

await generateImage("A cyberpunk cityscape at night", "flux.1-pro");

🎨 Model yang tersedia:

• "exo-image"
• "flux.1-schnell"
• "flux.1-pro"
• "flux.1-dev"

---

🛠️ Utils

🔧 parseRawtext(input)

Utility internal untuk parsing raw response dari chat completion.

const text = parseRawtext(`1:"Hello world!" 2:"Another part"`);
console.log(text); // "Hello world!Another part"