DEV Community: Henk de Vries

Search Welcome to the Jungle and France Travail with one call: a France Jobs Scraper tutorial

Henk de Vries — Sat, 14 Nov 2026 09:29:24 +0000

If you build job aggregators, recruitment tooling or lead lists for French-speaking markets, you usually end up writing two integrations and then writing a deduplication step to clean up after them. France Jobs Scraper is an Apify Actor that does that work for you. This tutorial shows what it does and how to call it from code.

Actor page: https://apify.com/carabain-digital/france-jobs

What it does

The Actor searches Welcome to the Jungle and France Travail at once for offres d'emploi in France. You give it one keyword and it returns one dataset. The results are merged, deduplicated across both boards, and sorted newest first.

Feature summary:

Two sources, one run: Welcome to the Jungle and France Travail.
One keyword in, one dataset out.
Merged and deduplicated across both boards.
Newest first.
Contract types: CDI, CDD and alternance.
Optional full job descriptions.
No proxy needed.
Output as JSON, CSV or Excel.
Listed in the Apify Store under the JOBS and LEAD_GENERATION categories.

Pricing

Pricing is $0.75 per 1,000 job postings (the figure shown for the highest tier, FREE). Because you pay per job posting, you can estimate cost from the number of results you expect.

A note on input field names

The Store description tells us the Actor takes a keyword and has an option for full descriptions. It does not give the exact input field names. The examples below use keyword and includeDescription as placeholders. Open the Actor's Input tab on the page above and copy the real field names before you run anything.

Example 1: run it from Node.js

Install the Apify client:

npm install apify-client

Then call the Actor and read the dataset:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

// Field names are placeholders: check the Actor's Input tab.
const input = {
  keyword: 'développeur backend',
  includeDescription: false,
};

const run = await client.actor('carabain-digital/france-jobs').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log(`Got ${items.length} job postings`);
console.log(items[0]);

Example 2: run it from Python

pip install apify-client

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

# Field names are placeholders: check the Actor's Input tab.
run_input = {
    "keyword": "data analyst",
    "includeDescription": False,
}

run = client.actor("carabain-digital/france-jobs").call(run_input=run_input)

items = client.dataset(run["defaultDatasetId"]).list_items().items
for job in items[:5]:
    print(job)

Example 3: call the REST API with curl

This starts a run and waits for it to finish, then returns the dataset items. Replace the placeholder input as described above.

curl -X POST \
  "https://api.apify.com/v2/acts/carabain-digital~france-jobs/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keyword": "chef de projet", "includeDescription": false}'

Example 4: export to CSV or Excel

The Actor's output can be had as JSON, CSV or Excel. With the Apify dataset API you pick the format in the URL:

# CSV
curl -L "https://api.apify.com/v2/datasets/<DATASET_ID>/items?format=csv&token=$APIFY_TOKEN" -o jobs.csv

# Excel
curl -L "https://api.apify.com/v2/datasets/<DATASET_ID>/items?format=xlsx&token=$APIFY_TOKEN" -o jobs.xlsx

This is handy for agencies that hand results to people who work in spreadsheets rather than code.

Example 5: keep results only for a contract type

The Actor covers CDI, CDD and alternance. If you only want one of them and the output carries a contract field, you can filter after the run. The field name below is an assumption; print one item first and check what the dataset actually contains.

# Assumes each item has a "contractType" field. Verify with a sample item.
cdi_only = [j for j in items if j.get("contractType") == "CDI"]
print(len(cdi_only))

Where this fits

For developers: one Actor call replaces two source integrations plus a dedup step, and since no proxy is needed there is one less piece of infrastructure to configure. Skip full descriptions when you only need titles and links, and turn them on when you need the text.

For marketing and recruitment agencies: the categories include lead generation, so a keyword search can show which companies are hiring for a given role. Export to CSV or Excel and work from there.

Track record

As of the Apify Store data from 2026-11-14, the Actor has 8 runs in total and 2 users, one of them in the last 30 days. Runs in the last 30 days had a 100% success rate. That is a small sample, so test it on your own keywords before building on it.

Try it here: https://apify.com/carabain-digital/france-jobs

How to scrape doctor reviews from Zocdoc, Healthgrades, Vitals and WebMD in one run

Henk de Vries — Fri, 13 Nov 2026 13:47:32 +0000

Doctor reviews are spread over several sites. If you build reputation reports, lead lists or review monitoring for healthcare clients, you normally write one scraper per site and then reshape each output. The Healthcare Reviews Scraper on Apify (listed from $1.68/1K) puts that work into one run.

This tutorial covers what it does, why you might use it, and how to call it from code.

What it does

The Actor scrapes doctor reviews from four sources: Zocdoc, Healthgrades, Vitals and WebMD. You paste your links in any mix. Each link is detected and scraped, so you do not have to say which site a URL belongs to.

The output is one dataset. Each review has:

a rating (1-5)
the review text
the date
the reply
the source

The Actor is in the Apify Store categories Lead Generation and Marketing.

Page: https://apify.com/carabain-digital/healthcare-reviews

Why use it

One run instead of four. Mixed links go into a single input, so you do not manage separate tools per site.

One dataset. Every row carries the same kind of fields (rating, text, date, reply, source). The source field tells you which site a review came from, so you can filter or group by it afterwards.

Useful for agencies and developers. A marketing bureau can compare how a practice or doctor appears across sites. A developer can feed the dataset into a dashboard or a database without writing per-site parsers.

A note on maturity: at the time of writing (13 November 2026), the Store page shows 1 total run and 1 total user, with 0 users in the last 30 days. It is a new Actor. Test it on a small set of links before you build a client workflow on it.

How to use it

Step 1: Collect your links

Gather the doctor profile links you want reviews for. You can mix Zocdoc, Healthgrades, Vitals and WebMD URLs in the same list.

Step 2: Run it in the Apify Console

Open the Actor page, paste your links into the input, and start the run. When it finishes, open the dataset. You can view it in the Console or export it.

Step 3: Run it from code

The Actor ID follows from the page URL: carabain-digital/healthcare-reviews.

Important: the facts I have do not list the input field names. The field startUrls below is a placeholder. Open the Actor's Input tab and use the exact field name shown there.

JavaScript, using apify-client:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

// Replace the field name and URLs with the ones from the Actor's Input tab.
const input = {
  startUrls: [
    { url: 'https://www.zocdoc.com/...' },
    { url: 'https://www.healthgrades.com/...' },
    { url: 'https://www.vitals.com/...' },
    { url: 'https://doctor.webmd.com/...' },
  ],
};

const run = await client.actor('carabain-digital/healthcare-reviews').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log(`Got ${items.length} reviews`);

Python, using apify-client:

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

# Replace the field name and URLs with the ones from the Actor's Input tab.
run_input = {
    "startUrls": [
        {"url": "https://www.zocdoc.com/..."},
        {"url": "https://www.healthgrades.com/..."},
        {"url": "https://www.vitals.com/..."},
        {"url": "https://doctor.webmd.com/..."},
    ]
}

run = client.actor("carabain-digital/healthcare-reviews").call(run_input=run_input)
items = client.dataset(run["defaultDatasetId"]).list_items().items

print(f"Got {len(items)} reviews")

Step 4: Work with the dataset

Because every row has a rating, text, date, reply and source, simple analysis is short. The field names below (rating, source, reply) are assumed from the field list; check them against your first dataset.

from collections import defaultdict

by_source = defaultdict(list)
for item in items:
    by_source[item.get("source")].append(item.get("rating"))

for source, ratings in by_source.items():
    ratings = [r for r in ratings if r is not None]
    if ratings:
        print(source, round(sum(ratings) / len(ratings), 2), len(ratings))

# Reviews without a reply, e.g. for a response-gap report
unanswered = [i for i in items if not i.get("reply")]
print(f"{len(unanswered)} reviews have no reply")

Practical tips

Start with a few links from each site and check the dataset before scaling up.
Keep the source field in your exports so clients can see where each review came from.
Check the pricing section on the Actor page before large runs. The listed price starts from $1.68/1K.

Summary

The Healthcare Reviews Scraper takes mixed Zocdoc, Healthgrades, Vitals and WebMD links, detects each one, and returns one dataset with rating, text, date, reply and source. It is new and lightly used so far, so verify the output on a small sample first. Then wire it into your reporting with the Apify client.