MLflow Scraper

Extract documentation guides, API references, plugin listings, and release notes from the MLflow platform. Built on spider-browser .

Get started Docs

target
mlflow.org: success rate
99.9%: latency
~4ms

Quick start

Extract data in minutes.

mlflow-scraper.ts

import { SpiderBrowser } from "spider-browser";

const spider = new SpiderBrowser({
  apiKey: process.env.SPIDER_API_KEY!,
});

await spider.connect();
const page = spider.page!;
await page.goto("https://mlflow.org/docs/latest/index.html");

const data = await page.extractFields({
  title: "h1",
  sections: ".section h2",
  content: ".section p",
  codeExamples: ".highlight pre",
  navItems: ".toctree-l1 a",
  version: ".version",
});

console.log(data);
await spider.close();

ready to run · spider-browser · TypeScript

Fetch API

One endpoint for mlflow.org.

Structured JSON from mlflow.org with a single POST. AI-resolved selectors, cached on the first call.

POST /fetch/mlflow.org/

Page titleSectionContentCode examplesAPI methodsParameters

Try it Fetch docs

cURL

curl -X POST https://api.spider.cloud/fetch/mlflow.org/ \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"return_format": "json"}'

Python

import requests

resp = requests.post(
    "https://api.spider.cloud/fetch/mlflow.org/",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json",
    },
    json={"return_format": "json"},
)
print(resp.json())

Node.js

const resp = await fetch("https://api.spider.cloud/fetch/mlflow.org/", {
  method: "POST",
  headers: {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({ return_format: "json" }),
});
const data = await resp.json();
console.log(data);

Extraction