Medium Scraper [Just 💰$1.79]

What does Medium Scraper do?

Medium Scraper extracts structured record data from medium.com — including author, publish dates, full article text, and tags. It supports keyword search and controllable result limits, so you can run the same query consistently over time. The actor also offers detail enrichment (extra per-record fields the search results omit).

New to Apify? Sign up free and use the included $5 monthly platform credit to test this actor.

Key features

• 🖼️ Images: imageUrl on every record, ready for a catalog or a feed.
• 🗂️ Category path: categories place each record in medium.com's own category tree.
• 🗓️ Posting dates: publishedAt tell you how fresh each record is.
• 🔍 Keyword search: search medium.com by keyword with the query input.
• 🔗 Paste-mode: paste medium.com URLs into startUrls to scrape exactly the pages you picked in the browser.
• 📋 Detail enrichment: toggle two-stage scraping: first collect listings, then enrich each with full description + detail-page-only fields. Off by default to keep runs fast; flip on when you need the deep payload.
• ♻️ Incremental mode: recurring runs emit only directory entries that are new or updated — keep your supplier-outreach lists fresh without re-fetching the full directory. Saves 80–95% on weekly syncs.
• 🔔 Notifications: Telegram, Slack, Discord, WhatsApp Cloud API, and generic webhook out of the box. Pair with incremental for daily new-listing alerts without pipeline glue.
• 📦 Compact mode: compact directory entries — name, contact, location, industry — ready to import into your CRM or outreach tool.
• 🔌 MCP connectors: push your leads into Notion via Apify's MCP connectors — a tidy run-summary page, no export step, no glue code. Opt-in, deterministic field-mapping (no AI). More destinations as Apify's connector catalog grows.
• 📌 Change classification: each record carries a changeType of NEW / UPDATED / UNCHANGED / REAPPEARED / EXPIRED. Default emits NEW + UPDATED + REAPPEARED; opt into the others with emitUnchanged / emitExpired.

What data can you extract from medium.com?

Each result includes Core record fields (id, title, url, portalUrl, author, publication, publishedAt, and updatedAt, and more) and detail fields when enrichment is enabled (detailFetched). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Enable detail enrichment in the input to fetch each record's detail page — extra fields the search results omit.

Input

The main inputs are a search keyword and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• query — What to collect articles from. A topic tag (python, machine learning), an author handle with the @ (@quincylarson), or a publication slug (towards-data-science). Paste a JSON array to run several in one go — ["python", "rust", "@quincylarson"] — which is also how you get more than one surface's worth of articles.
• startUrls — Paste tag, author or publication pages instead of typing names — e.g. https://medium.com/tag/python, https://medium.com/@quincylarson, https://medium.com/towards-data-science. Feed URLs (/feed/tag/python) work too. Each URL becomes its own search and results are merged and deduplicated across all of them. When supplied, these replace the search field. (default: [])
• maxResults — Maximum total articles to return (0 = no limit). Medium publishes only the newest posts per tag, author or publication, so a single search returns roughly ten articles — raise the count AND pass several searches to collect more. (default: 100)
• includeDetails — Fetch each article's complete body where the author publishes it in full. Off = title, byline, tags, image and teaser only, which is faster. (default: true)
• descriptionMaxLength — Truncate the plain-text and Markdown body to N characters. 0 = no truncation. The HTML body is left intact — cutting markup mid-tag breaks whatever parses it downstream. (default: 0)
• compact — Headline fields only — title, byline, publication, tags, timestamps, image and teaser, without the article body. Keeps records small for AI-agent/MCP workflows. (default: false)
• excludeEmptyFields — Drop null / empty-string / empty-array fields (and fully-null nested objects like socialProfiles) from each record. Off by default to preserve dataset schema compatibility; turn on to slim output for LLM / agent consumption. (default: false)
• incrementalMode — Compare against the previous run and return only new or edited articles — the point of a scheduled run against a stream of posts. stateKey is optional; it defaults to a value derived from the searches so different search sets never share state. (default: false)
• stateKey — Optional. Stable identifier for the tracked set of searches. Leave empty to auto-generate from the search inputs.
• emitUnchanged — Also return articles that have not changed since the last run (incremental mode only). (default: false)
• notifyOnlyChanges — When Incremental Mode is on, only send notifications for new and edited articles. Has no effect outside incremental mode. (default: false)
• notificationLimit — Maximum number of articles included in each notification message (1–20). (default: 5)
• ...and 12 more parameters

Input examples

Basic search — Keyword-driven search with a result cap.

→ Full payload per result — all standard fields populated where the source provides them.

{
  "query": "python",
  "maxResults": 50
}

Incremental tracking — Only emit records that changed since the previous run with this stateKey.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed. Set emitUnchanged: true to include unchanged records as well.

{
  "query": "python",
  "maxResults": 200,
  "incrementalMode": true,
  "stateKey": "python-tracker"
}

Compact output for AI agents — Return only core fields for AI-agent and MCP workflows.

→ Small payload with the most important fields — ideal for piping into LLMs without token overhead.

{
  "query": "python",
  "maxResults": 50,
  "compact": true
}

Output

Each run produces a dataset of structured record records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example record record

{
  "id": "428ee98273fb",
  "title": "Introduction to Computer Vision with Python",
  "url": "https://medium.com/@kaivalya25comp/introduction-to-computer-vision-with-python-428ee98273fb",
  "portalUrl": "https://medium.com/@kaivalya25comp/introduction-to-computer-vision-with-python-428ee98273fb",
  "author": "Kaivalya Thombare",
  "publishedAt": "2026-10-04T10:40:26.000Z",
  "updatedAt": "2026-10-04T10:40:26.941Z",
  "categories": [
    "python",
    "machine-learning",
    "programming",
    "education",
    "usp"
  ],
  "tags": [
    "python",
    "machine-learning",
    "programming",
    "education",
    "usp"
  ],
  "excerpt": "Introduction to Computer Vision with Python",
  "contentHtml": "<div class=\"medium-feed-item\"><p class=\"medium-feed-image\"><a href=\"https://medium.com/@kaivalya25comp/introduction-to-computer-vision-with-python-428ee98273fb?source=rss------python-5\"><img src=\"http...",
  "contentText": "Introduction to Computer Vision with Python",
  "contentMarkdown": "Introduction to Computer Vision with Python\n\n[Continue reading on Medium »](https://medium.com/@kaivalya25comp/introduction-to-computer-vision-with-python-428ee98273fb?source=rss------python-5)",
  "imageUrl": "https://cdn-images-1.medium.com/max/1672/1*3TLyfI4MsjK8u5NNLQBGGQ.png",
  "feedUrl": "https://medium.com/feed/tag/python",
  "searchQuery": "python",
  "contentQuality": "thin",
  "detailFetched": false,
  "scrapedAt": "2026-10-04T11:01:41.087Z",
  "source": "medium.com",
  "contentHash": "722805c5086bcc2b41d3bb1c1a1a7566b6dd8eb70eebf04f62847ffbed8e8797"
}

Incremental fields

When incremental mode is on, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED, REAPPEARED. Default output covers NEW / UPDATED / REAPPEARED; set emitUnchanged: true to opt into the others.

How to scrape medium.com

1. Go to Medium Scraper in Apify Console.
2. Enter a search keyword.
3. Set maxResults to control how many results you need.
4. Enable includeDetails if you need the extra detail-page fields.
5. Click Start and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

Use cases

• Extract record data from medium.com for market research and competitive analysis.
• Monitor new and changed records on scheduled runs without processing the full dataset every time.
• Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
• Export clean, structured data to dashboards, spreadsheets, or data warehouses.

How much does it cost to scrape medium.com?

Medium Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.01 per run
• Per result: $0.00179 per record record

Example costs:

• 10 results: $0.028
• 25 results: $0.055
• 100 results: $0.19
• 200 results: $0.37
• 500 results: $0.91

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of records that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 250 records per run, daily polling (30 runs/month). Costs scale linearly with the number of records.

Churn rate	Full re-scrape run cost	Incremental run cost	Savings vs full re-scrape	Monthly cost after baseline
5% — stable niche query	$0.46	$0.03	$0.43 (93%)	$0.97
15% — moderate broad query	$0.46	$0.08	$0.38 (83%)	$2.31
30% — high-volume aggregator	$0.46	$0.14	$0.31 (68%)	$4.33

Full re-scrape monthly cost at the same cadence: $13.73. First month with incremental costs $1.40 / $2.70 / $4.64 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

Platform usage is included in the per-result fee shown above.

FAQ

How many results can I get from medium.com?

The number of results depends on the search query and available records on medium.com. Use the maxResults parameter to control how many results are returned per run.

Does Medium Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed records on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Medium Scraper with other apps?

Yes. Medium Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Medium Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Medium Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode, descriptionMaxLength, a single descriptionFormat, and excludeEmptyFields to keep payloads manageable for LLM context windows.

Is it legal to scrape medium.com?

This actor extracts publicly available data from medium.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

New to Apify? Create a free account with $5 credit — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.

Disclaimer

This actor accesses only publicly available data on medium.com. You are responsible for how you use the extracted data — in particular any personal information such as names, phone numbers, or email addresses — and for complying with Medium's terms of use, applicable data-protection law (including the GDPR where it applies), and the anti-spam rules of your jurisdiction.

This actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Medium or any of its subsidiaries. All trademarks mentioned are the property of their respective owners.

Search keywords

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