How to Scrape Martindale Data: Complete Guide for 2026

TL;DR

Use AlterLab's API to scrape Martindale directory pages with Python or Node.js. Start at tier T1 and let the service auto‑escalate if anti‑bot measures trigger. Extract public data such as lawyer names, firms, and practice areas, then structure the output with Cortex for JSON.

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

Why collect directory data from Martindale?

Martindale hosts a widely used legal directory. Teams scrape it for:

• Building competitor lists of law firms in specific regions
• Monitoring changes in attorney contact information for outreach
• Aggregating practice area data to feed market analysis models

These use cases rely on publicly listed profiles, not private client data.

Technical challenges

Directory sites like martindale.com employ standard anti‑bot protections: IP‑based rate limiting, header validation, and occasional JavaScript challenges that block simple HTTP GET requests. Raw requests often receive challenge pages or CAPTCHAs. AlterLab's Smart Rendering API automatically detects these responses and promotes the request to a higher tier that includes proxy rotation and headless browser rendering.

Quick start with AlterLab API

First install the SDK. See the Getting started guide for full setup.

Python example:

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://martindale.com/example-page")
print(response.text)

Node.js example (MUST include this):

import { AlterLab } from "alterlab";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape("https://martindale.com/example-page");
console.log(response.text);

cURL example:

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://martindale.com/example-page"}'

After a successful request you receive the raw HTML of the Martindale page. The service handles retries and tier promotion behind the scenes.

Extracting structured data

Once you have the HTML, parse it with a library like BeautifulSoup (Python) or cheerio (Node.js). Common selectors on Martindale profile pages:

• Lawyer name: h1.profile-name
• Firm: div.firm-name span
• Practice area: section.practice-areas li
• Phone: a[href^="tel:"]
• Address: div.office-address

Example Python snippet:

from bs4 import BeautifulSoup
soup = BeautifulSoup(response.text, "html.parser")
name = soup.select_one("h1.profile-name").get_text(strip=True)
firm = soup.select_one("div.firm-name span").get_text(strip=True)
areas = [li.get_text(strip=True) for li in soup.select("section.practice-areas li")]
print({"name": name, "firm": firm, "practice_areas": areas})

Node.js equivalent:

const cheerio = require("cheerio");
const $ = cheerio.load(response.text);
const name = $("h1.profile-name").text().trim();
const firm = $("div.firm-name span").text().trim();
const areas = [];
section.practice-areas li.each((_, el) => areas.push($(el).text().trim()));
console.log({ name, firm, practice_areas: areas });

Structured JSON extraction with Cortex

AlterLab's Cortex API lets you request typed JSON directly, avoiding manual parsing. Define a JSON schema that matches the data you need.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://martindale.com/example-page",
    schema={
        "type