Google Scholar Profiles Scraper

Export public Google Scholar author profiles from profile URLs or user IDs. Get citation metrics, annual citation history, interests, visible publications and optional coauthor cards in one dataset row per author, ready for CSV, Excel, JSON or API access.

This Google Scholar profiles scraper works with known public profiles, not keyword searches or full paper text. Google Scholar may challenge requests or withhold fields, so results are snapshots rather than guaranteed complete publication histories.

Quick start

1. Paste a public profile URL into Google Scholar profile URLs, or enter its user= value in Google Scholar user IDs.
2. Choose how many visible publications to include. Use 0 for metrics without publication rows.
3. Review the connection settings, run the Actor and open its dataset to export results.

{
  "userIds": ["qc6CJjYAAAAJ"],
  "maxPublications": 10,
  "includeCoauthors": true
}

The form defaults to Residential proxy. Review its help and platform charges before running. If both profile selectors are omitted or empty, the Actor uses its example profile.

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

Choose a public example to see its settings before starting a run:

• Collect Google Scholar Coauthor Cards
• Scrape Google Scholar Publication Rows
• Export Google Scholar Author Metrics

What you get

• Researcher shortlists: compare public affiliations, interests and citation metrics for known profile IDs.
• Citation snapshots: save total and recent metrics with the cutoff year actually displayed by Scholar.
• Publication samples: collect titles, author lines, venues, years, citation counts and detail links from visible rows.
• Research-network context: include public coauthor cards, homepage and profile image links when available.

Repeat snapshots and comparisons are downstream workflows. The Actor does not classify changes or create a monitoring schedule for you.

Who is it for?

• Research administrators reviewing public metrics for a known author list.
• Academic analysts assembling citation snapshots with the cutoff years Scholar actually displays.
• Research-development teams checking visible interests and publication samples before further source review.
• Scholarly-data engineers retaining profile identifiers and nested records for their own comparisons or integrations.

How you can use the data

• Collect a known-profile sample. Use the existing qc6CJjYAAAAJ identifier with ten publications and visible coauthors enabled. Keep the profile URL and user ID with the metrics and publication sample rather than treating an author name as a unique identifier.
• Prepare a metrics-only sheet. Set the publication cap to zero for a known profile. Keep all-time metrics, recent metrics and metricsSinceYear together: legacy Since2019 aliases do not guarantee a 2019 cutoff.
• Review visible publications and coauthor context. Keep each publication's title, authors, venue, year, citation count and URL beside the profile. Coauthor cards provide visible names, profile URLs and affiliations rather than a complete collaboration network. A verified email domain is not an email address.
• Compare your own snapshots. Retain user IDs, collection times and displayed cutoff years, then compare later files yourself. The Actor does not classify changes, create schedules, build a spreadsheet or create a RAG index. Check pending profiles and summary issues before calling missing data a researcher change.

Input settings

Setting	JSON key	Summary
Profile URLs	profileUrls	Public Scholar profile URLs or bare user IDs. Up to 1,000 entries.
User IDs	userIds	Alternative or additional IDs. Up to 1,000 entries. Duplicate IDs are combined.
Publications per profile	maxPublications	0–100. API default 20, form prefill 10.
Visible coauthors	includeCoauthors	Include visible cards. Default true.
Request retries	maxRequestRetries	Additional attempts. Default 2, maximum 5.
Request timeout	requestTimeoutSecs	Per-response limit. Default 30 seconds, range 5–120.
Processing safety limit	runTimeSecs	Stop admitting profiles. Default 270 seconds, range 30–290.
Proxy configuration	proxyConfiguration	Existing managed, custom or direct settings. See field help for defaults and limitations.

URLs and IDs can be supplied together. Invalid entries can be skipped when usable profiles remain. An entirely invalid selection fails validation. Results follow profile input order and visible publication order, not a configurable ranking or sort.

Platform timeout guidance

Keep the default 300-second platform timeout. API callers should omit the timeout override or use Python timeout_secs=300; wait_secs controls only how long the client waits, not the run deadline. The Actor cannot extend a caller's platform timeout.

Allow at least 60 seconds for useful work; 300 seconds is recommended. If too little time remains after initialization, the Actor finishes successfully without fetching profiles or manually charging startup/results, with a warning, status message and RUN_SUMMARY reason. It cannot extend your timeout or guarantee completion if the platform stops the container before initialization finishes. During longer runs it stops admitting requests before the deadline, saves collected profiles and records remaining profiles in the summary/checkpoint. A small max-cost limit is handled similarly, using the run's effective event prices; platform usage charges can still apply. Genuine extraction, storage and billing failures are not treated as customer-limit stops.

Example inputs

Use the Quick start input as a small first run. Adjust the fields described above for your own data.

Output data

Each saved row contains one profile. Missing scalar fields are generally null. Lists can be empty. coauthors is omitted when disabled.

Synthetic shape-only example (not a measured researcher or live citation snapshot. Shortened):

{
  "profileUrl": "https://scholar.google.com/citations?user=ExampleId1&hl=en",
  "userId": "ExampleId1",
  "name": "Example profile",
  "affiliation": null,
  "verifiedEmailDomain": null,
  "homepageUrl": null,
  "profileImageUrl": null,
  "interests": [],
  "citations": 0,
  "metricsSinceYear": 2021,
  "citationsSinceYear": 0,
  "hIndex": 0,
  "hIndexSinceYear": 0,
  "i10Index": 0,
  "i10IndexSinceYear": 0,
  "citationHistory": [],
  "publications": [],
  "publicationCount": 0,
  "coauthors": [],
  "scrapedAt": "2026-10-24T00:00:00.000Z"
}

Fields	Meaning
profileUrl, userId, name	Public profile identity.
affiliation, verifiedEmailDomain	Visible affiliation and verified email domain, not an email address.
homepageUrl, profileImageUrl, interests	Public links and research-interest labels.
citations, hIndex, i10Index	All-time metrics shown on the profile.
metricsSinceYear	Cutoff year displayed for recent metrics.
citationsSinceYear, hIndexSinceYear, i10IndexSinceYear	Recent metrics for that cutoff.
citationsSince2019, hIndexSince2019, i10IndexSince2019	Retained aliases of the recent metrics. Despite their names, these do not guarantee a 2019 cutoff.
citationHistory	Visible annual chart entries with year and citations.
publications, publicationCount	Saved rows and their count. Each row has title, authors, venue, year, citations and url.
coauthors	Visible cards with name, profileUrl and affiliation, when enabled.
scrapedAt	Snapshot timestamp.

The run's key-value store can also contain RUN_SUMMARY, with counts, per-input issues and pending profiles. PENDING_PROFILES preserves remaining URLs when processing stops early and is cleared on completion. Resume an interrupted run using the same storage to skip already-saved profile IDs. This is not automatic continuation in a new run.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

Pay-per-event pricing uses a one-time run-start start event and a result charge for each saved profile. Publication rows and coauthor cards are nested within a profile rather than separate result events. Skipped profiles and summary records are not saved profile results, and resuming a stored pending-profile checkpoint does not add another start event.

Start with a small profile list and only the publication rows needed. See the live Pricing tab for current rates and discounts. Platform usage, including configured proxy usage, may also be charged.

Tips for best results

• Supply known public IDs or full profile URLs. This Actor does not discover authors through name or keyword searches.
• Choose a small publication cap for the initial export. Zero requests metrics without publication rows, while the maximum does not promise a full history.
• Keep the displayed cutoff year with recent metrics. Do not interpret legacy field names as fixed-year definitions.
• Inspect summary issues and pending profiles before comparing authors. Saved profiles can survive another profile's failure or an early time stop.
• Resume interrupted work with the same storage when appropriate. Starting a new run does not automatically continue the old pending-profile list.

Limits and caveats

• Empty results: confirmed HTTP 404 responses from the requested profile succeed without dataset rows, with not_found reasons in RUN_SUMMARY. A Google sign-in redirect explicitly bound to the requested profile succeeds without a row, with a login_required reason; the Actor does not log in. Mixed batches retain useful profiles. Neither response proves deletion or privacy. Challenges, rate limits and unrecognized pages remain actionable failures.

• Blocking: Scholar may challenge requests even with proxy settings. Runs with no useful profiles still fail for challenges, unexpected HTML, missing profile-name markup or request errors. These are not treated as confirmed absence. Essential summary, dataset and billing failures also fail.

Only known public profiles are supported. There is no private or login-only access, keyword search, email-address discovery, full paper text or PDF collection. Visible publication rows are capped at one hundred per profile and do not guarantee full histories. Counts reflect what Scholar displays at collection time.

Profiles follow input order and publications follow visible order rather than a configurable ranking. Processing can stop before all profiles are attempted. Inspect pending profiles and per-input summary issues, and retain unknown scalar fields or empty lists rather than inferring missing values.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~google-scholar-profiles-scraper/runs' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data @- <<'JSON'
{
  "userIds": [
    "qc6CJjYAAAAJ"
  ],
  "maxPublications": 10,
  "includeCoauthors": true
}
JSON

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/google-scholar-profiles-scraper').call({
  "userIds": [
    "qc6CJjYAAAAJ"
  ],
  "maxPublications": 10,
  "includeCoauthors": true
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/google-scholar-profiles-scraper').call(run_input={'userIds': ['qc6CJjYAAAAJ'], 'maxPublications': 10, 'includeCoauthors': True})
print(client.dataset(run['defaultDatasetId']).list_items().items)

API usage notes

The REST call starts a run asynchronously. Wait for completion, then use its defaultDatasetId to retrieve results with the dataset API.

MCP and AI agents

You can ask your AI assistant to collect data with Google Scholar Profiles Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's Google Scholar Profiles Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/google-scholar-profiles-scraper"
    }
  }
}

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use Google Scholar Profiles Scraper by Fetch Cat, or give it the identifier fetch_cat/google-scholar-profiles-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Use Fetch Cat’s Google Scholar Profiles Scraper to collect the public profile qc6CJjYAAAAJ with up to 10 visible publications and coauthor cards enabled. Keep author IDs, profile links and available citation metrics without claiming a complete publication history.”
• “Retrieve a metrics-only snapshot for qc6CJjYAAAAJ with zero publication rows and coauthors disabled. Show all-time and recent metrics with the displayed cutoff year, and do not interpret the legacy Since2019 fields as a fixed 2019 window.”
• “Collect the public profile qc6CJjYAAAAJ with at most 5 publications and visible coauthors enabled. Make a source-linked review sheet of publication titles and available coauthor affiliations, leaving missing fields unknown and distinguishing an email domain from an email address.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

FAQ

Can I use this as a Google Scholar profiles API?

You can call it through the Apify API or MCP. It is not an official Google API and cannot search authors by name.

Where do I find a profile ID?

Copy the user= value from a public Google Scholar profile URL, or supply the full URL.

Does it need a Google login?

No. Only public profile pages are supported.

Can I export to Sheets or build a research dataset?

Download CSV, Excel or JSON from the dataset, or connect it to your own integration. The Actor itself does not create a spreadsheet or a RAG index.

Why are recent metrics not “since 2019”?

Scholar changes the displayed cutoff over time. Read metricsSinceYear. The older Since2019 fields retain the same values as the current recent-metric fields.

Why is a profile missing?

Check RUN_SUMMARY: a missing public profile, unavailable response, challenge, request error or time limit can prevent a saved row. Do not infer privacy or deletion solely from a failed request.

Related Actors

• Bing Search Results Scraper — Collect ranked public web results for adjacent research. This uses Bing rather than Google.
• Google News Scraper — news context for institutions and topics.
• Google Play Apps Scraper — public app metadata for product research.
• Google Play Reviews Scraper — app-review research.
• Google Ads Transparency Center Scraper — public advertiser research.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥