Quora Search Scraper With Author Lead Enrichment

Quora Search Scraper — Extract Answers, Questions and Author Leads

Quora Search Scraper With Author Lead Enrichment searches Quora by keyword or direct URL and returns questions, answers, profiles, topics and spaces — then opens every distinct answer author's public profile and turns them into a deduplicated author_lead row with bio, credentials, follower/answer reach and a computed lead score. Every response is typed, normalized JSON — no HTML, no selectors, no login required. Read on for the exact input parameters, every output field the Actor pushes, and how the author-enrichment pass is billed.

What is Quora Search Scraper With Author Lead Enrichment?

Quora Search Scraper With Author Lead Enrichment is an Apify Actor that turns a Quora keyword search or a list of direct Quora URLs into structured rows for six content types: questions, answers, profiles, topics, spaces, and a sixth derived type — author_lead. No Quora account or login is required; every field returned is already visible to an anonymous visitor. The Actor is not reachable through an MCP server — it runs as a standard Apify Actor, callable through the Apify Console, API, or SDK.

• Scrape Quora answers and questions discovered from a keyword search (via DuckDuckGo, with a Bing fallback)
• Scrape profiles, topics and spaces from pasted Quora URLs
• Turn every distinct answer author into a lead: bio, credentials, follower/following/answer/question counts, total views, a computed lead_score, and links pulled from the bio
• Export as JSON, CSV, Excel or XML from the Apify dataset
• No proxy management required — the Actor climbs an automatic proxy ladder on its own

What data does Quora Search Scraper With Author Lead Enrichment collect?

The Actor returns six distinct row shapes in one dataset, distinguished by a content_type field: question, answer, profile, topic, space, and author_lead.

Data Type	Key Fields	JSON Field Names
Answers	Answer text, upvotes, author, comment/share counts	answer_text, upvotes, author_name, comments_count, shares_count
Questions	Title, answer count, followers, topics	title, answer_count, follow_count, topics
Profiles	Name, bio, credentials, reach	name, bio, credentials, follower_count, total_views
Author leads	Bio, credentials, reach, computed lead score, bio links	bio, credentials, lead_score, extracted_links, answers_seen_in_run
Topics	Name, description, follower/question counts	name, description, follower_count, question_count
Spaces	Name, description, follower/post/contributor counts	name, description, post_count, contributor_count

🧲 Need more Quora data?

If you need Quora content from non-English portals, the same account publishes Quora Scraper By Language Portal, which scrapes questions, answers, profiles, topics and spaces from 24 localized Quora portals (es/fr/de/pt/hi/ar and more) with optional AI translation to English. This Actor stays keyword- and English-portal-focused, with its differentiator being the author lead enrichment pass.

Why not build this yourself?

Quora does not publish a general-purpose public API that returns search results or question/answer content to anonymous callers, so scraping it yourself means solving three problems before you extract a single row.

First, Quora sits behind Cloudflare — a plain HTTP client gets an interstitial challenge page, not content. You need a real, stealth-patched Chromium browser that renders JavaScript and waits out the challenge. Second, Quora's data isn't in clean HTML — it's embedded as GraphQL payloads inside window.ansFrontendGlobals.data.inlineQueryResults, and more answers arrive via a streaming gql_para_POST endpoint as the page scrolls. You have to capture both the inline payload and the network responses, then walk an unstable nested structure to find Answer, Question and User nodes by __typename. Third, blocked or datacenter-flagged requests need a retry and proxy-escalation strategy, or your run returns zero rows the moment Quora pushes back.

This Actor does all three: stealth browser rendering, dual-source GraphQL extraction (inline script tags plus captured network responses), and an automatic DIRECT → DATACENTER → RESIDENTIAL proxy ladder that only climbs when needed and sticks once escalated.

🧲 Why do developers and teams scrape Quora and enrich its authors?

🤖 For AI engineers and agent builders

Quora's Q&A pairs are dense, topical training and retrieval data. An AI engineer building a RAG pipeline can index answer_text alongside question_title and topics per row, then use author_credentials as a lightweight authority signal when ranking retrieved passages. Because every row is typed JSON with stable field names, it drops straight into a vector store or an agent's tool-call response — no HTML stripping or answer-length truncation to work around first.

📈 For sales and lead-generation teams

The author-lead pass is built for this: every distinct expert who answered a question in your niche becomes one deduplicated author_lead row with bio, credentials, follower_count, total_views, a computed lead_score, and extracted_links pulled from their bio for outreach. A team selling into "SaaS pricing" or "supply chain software" can search those keywords, skip building a scraper, and get a ranked list of people who publicly demonstrated expertise — with no email field, since Quora exposes none.

🔬 For researchers and analysts

Public sentiment and expertise distribution on a topic are visible in the answer set itself: upvotes, comments_count, answer_timestamp and is_ai_answer let a researcher separate high-signal human answers from AI-generated ones and track how a topic's public discourse ages. Because authorFilter can pin the run to one expert, a researcher can also track a single public figure's answer history across many keyword searches in one run.

🛠️ For developers building data products

maxResults scales up to 50,000 content rows in a single run, and directUrls accepts bulk pasted Quora links, so a developer can build a scheduled Quora-monitoring pipeline (via an Apify schedule) that feeds a product — a competitor-intelligence dashboard, an expert-directory tool, or a content-gap tracker — without re-solving Quora's anti-bot layer on every deploy.

How to scrape Quora and enrich author leads (step by step)

1. Open Quora Search Scraper With Author Lead Enrichment on the Apify Store and click Try for free
2. Provide at least one of searchQueries (keywords) or directUrls (Quora question/profile/topic/space links) — both are optional individually, but the Actor does nothing if both are empty
3. Set maxResults to the total number of content rows you want (default 10), and leave scrapeAuthorProfiles on (default) if you want the author-lead pass
4. Click Start — no Apify token setup is required beyond having an Apify account, since the Actor uses your account's proxy allowance
5. Open the Output tab, switch the view dropdown between All Results, Author Leads, Answers, Questions, Profiles, Topics and Spaces, and export as JSON, CSV, Excel or XML

What to do when Quora changes its structure

The Actor reads Quora's own embedded GraphQL payloads rather than CSS selectors, so a visual redesign generally does not break it — a selector-based scraper would need updating on every layout change, this one only needs updating if Quora renames the underlying GraphQL fields. The scraper is maintained and the output schema (field names and types) stays stable on your end. No specific turnaround time is promised.

⬇️ Input

None of the parameters are required — but the Actor logs a warning and exits with zero rows if both searchQueries and directUrls are empty.

Parameter	Required	Type	Description	Example Value
searchQueries	No	array	Keywords to search on Quora. Each keyword discovers relevant Quora question URLs (via DuckDuckGo, Bing fallback) and scrapes them. No Quora login needed.	["what is python used for"]
directUrls	No	array	Paste any Quora URLs directly — questions, profiles, topics or spaces. Supports bulk input (one per line).	["https://www.quora.com/profile/Jane-Doe-123"]
maxResults	No	integer	Total number of CONTENT rows (answers/questions/profiles/topics/spaces) to collect across all keywords and URLs combined. Default 10. Minimum 1, maximum 50000. Author-lead rows are added on top of this cap — one per distinct answer author.	10
scrapeAuthorProfiles	No	boolean	On: for every distinct answer author found, open their public profile and emit an enriched author_lead row. Off: only the author name stays on each answer. Default true.	true
authorFilter	No	string	Only keep answers and leads written by this author (Quora display name or profile slug). Matched client-side against author name/profile URL. Leave empty for all authors.	"Jane Doe"
location	No	string	Biases keyword discovery toward a location (e.g. "London") by appending it to the search query text. Does NOT filter authors by geography — Quora exposes no reliable author location. Leave empty for global search.	"New York"
aiEnhancement	No	boolean	OFF by default. When on AND an API key is supplied, each author lead also gets ai_expertise and ai_intent inferred from its public bio and credentials. On failure or without a key these fields stay null.	false
aiModel	No	string	AI provider/model for aiEnhancement. Provider is auto-detected from the name prefix. Default "claude-haiku-4-5". Enum: claude-haiku-4-5, claude-sonnet-5, claude-opus-4-8, gpt-4o-mini, gpt-4o, gpt-4.1-mini, o3-mini, gemini-2.0-flash-lite, gemini-2.0-flash, gemini-2.5-flash, grok-3-mini, deepseek-chat, sonar, mistral-small-latest.	"claude-haiku-4-5"
aiApiKey	No	string (secret)	Provider API key for AI enrichment. Kept secret by the input editor. Falls back to the matching provider environment variable if left empty (ANTHROPIC_API_KEY / OPENAI_API_KEY / GEMINI_API_KEY / XAI_API_KEY / DEEPSEEK_API_KEY / PERPLEXITY_API_KEY / MISTRAL_API_KEY).	""
proxyConfiguration	No	object	Apify Proxy configuration used as the mid-tier (datacenter) rung. Residential is applied automatically as the final fallback. Default {"useApifyProxy": true}.	{"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]}

Example input

{
  "searchQueries": ["what is python used for", "best CRM for startups"],
  "directUrls": ["https://www.quora.com/topic/Python-programming-language"],
  "maxResults": 200,
  "scrapeAuthorProfiles": true,
  "authorFilter": "",
  "location": "",
  "aiEnhancement": false,
  "aiModel": "claude-haiku-4-5",
  "aiApiKey": "",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

⚠️ Common pitfall: authorFilter only filters answer and author_lead rows — question, profile, topic and space rows from your search or direct URLs are never filtered by author, since those content types don't carry an author field to match against.

⬆️ Output

Every row lands in the default dataset with a content_type discriminator, plus common bookkeeping fields (source_url, scrape_timestamp, and for search-driven rows, source_query). Alongside the default dataset, each content type is mirrored into its own named dataset view (answers, questions, profiles, topics, spaces, author_leads) so you can pull one type cleanly without filtering. Export as JSON, CSV, Excel, XML, RSS or HTML from any dataset view.

Scraped author leads

The core output of this variant — one row per distinct answer author, deduplicated across every keyword and URL in the run.

{
  "content_type": "author_lead",
  "is_lead": true,
  "title": "Jane Doe",
  "name": "Jane Doe",
  "url": "https://www.quora.com/profile/Jane-Doe-123",
  "bio": "Data engineer, 8 years building ETL pipelines. Writing about Python and dbt.",
  "credentials": "Data Engineer at Acme Corp",
  "profile_image_url": "https://qph.cf2.quoracdn.net/...jpg",
  "follower_count": 4210,
  "following_count": 88,
  "answer_count": 312,
  "question_count": 9,
  "total_views": 1850000,
  "lead_score": 71.4,
  "answers_seen_in_run": 3,
  "extracted_links": ["https://janedoe.dev", "@janedoedata"],
  "matched_keyword": "what is python used for",
  "ai_expertise": null,
  "ai_intent": null,
  "source_url": "https://www.quora.com/profile/Jane-Doe-123",
  "scrape_timestamp": "2026-07-25T14:02:11.483210+00:00"
}

ai_expertise and ai_intent stay null unless aiEnhancement is true and a usable API key was supplied — they are never fabricated on failure. lead_score is a computed 0–100 value derived from log-scaled follower count, total views and answer count (not a Quora field). follower_count, following_count, answer_count, question_count and total_views are null — not zero — when the author's profile page could not be opened or returned no usable node, so a failed enrichment is visibly distinct from a real zero.

Scraped answers

{
  "content_type": "answer",
  "title": "What is Python used for?",
  "url": "https://www.quora.com/What-is-Python-used-for/answer/Jane-Doe-123",
  "answer_text": "Python is used for web backends, data analysis, automation scripting...",
  "answer_url": "https://www.quora.com/What-is-Python-used-for/answer/Jane-Doe-123",
  "author_name": "Jane Doe",
  "author_url": "https://www.quora.com/profile/Jane-Doe-123",
  "author_credentials": "Data Engineer at Acme Corp",
  "upvotes": 128,
  "comments_count": 6,
  "shares_count": 14,
  "answer_timestamp": "2y",
  "is_ai_answer": false,
  "question_title": "What is Python used for?",
  "question_url": "https://www.quora.com/What-is-Python-used-for",
  "source_url": "https://www.quora.com/What-is-Python-used-for",
  "source_query": "what is python used for",
  "scrape_timestamp": "2026-07-25T14:01:47.100022+00:00"
}

Scraped questions

{
  "content_type": "question",
  "title": "What is Python used for?",
  "url": "https://www.quora.com/What-is-Python-used-for",
  "answer_count": 214,
  "follow_count": 3021,
  "topics": ["Python (programming language)", "Computer Programming"],
  "source_url": "https://www.quora.com/What-is-Python-used-for",
  "source_query": "what is python used for",
  "scrape_timestamp": "2026-07-25T14:01:46.902110+00:00"
}

Scraped profiles, topics and spaces

Direct profile/topic/space URLs pasted into directUrls return one row each, structurally distinct from an author_lead — a profile row is a plain snapshot of whatever page you pointed at, while an author_lead row only ever comes from the enrichment pass over answer authors and additionally carries lead_score, answers_seen_in_run, extracted_links and matched_keyword.

{
  "content_type": "profile",
  "title": "Jane Doe",
  "name": "Jane Doe",
  "url": "https://www.quora.com/profile/Jane-Doe-123",
  "bio": "Data engineer, 8 years building ETL pipelines.",
  "credentials": "Data Engineer at Acme Corp",
  "profile_image_url": "https://qph.cf2.quoracdn.net/...jpg",
  "follower_count": 4210,
  "following_count": 88,
  "answer_count": 312,
  "question_count": 9,
  "total_views": 1850000,
  "source_url": "https://www.quora.com/profile/Jane-Doe-123",
  "scrape_timestamp": "2026-07-25T14:03:02.771003+00:00"
}

{
  "content_type": "topic",
  "title": "Python (programming language)",
  "name": "Python (programming language)",
  "url": "https://www.quora.com/topic/Python-programming-language",
  "description": "Python is a general-purpose, high-level programming language.",
  "follower_count": 89210,
  "question_count": 41200,
  "source_url": "https://www.quora.com/topic/Python-programming-language",
  "scrape_timestamp": "2026-07-25T14:03:19.552017+00:00"
}

{
  "content_type": "space",
  "title": "Data Engineering Weekly",
  "name": "Data Engineering Weekly",
  "url": "https://www.quora.com/q/data-engineering-weekly",
  "description": "Curated posts on pipelines, warehousing and dbt.",
  "follower_count": 3105,
  "post_count": 412,
  "contributor_count": 18,
  "source_url": "https://www.quora.com/q/data-engineering-weekly",
  "scrape_timestamp": "2026-07-25T14:03:34.118820+00:00"
}

Field groups across the six content types

Every row starts with content_type (one of question, answer, profile, topic, space, author_lead) so a single dataset export can be split back into its parts with one filter.

Identity fields — title/name, url — appear on every row and are always absolute https://www.quora.com/... links, never relative paths, since the Actor resolves every URL it reads out of Quora's payloads through the same abs_url() helper.

Engagement fields — upvotes, comments_count, shares_count on answers; answer_count, follow_count on questions; follower_count, question_count, post_count, contributor_count on profiles/topics/spaces — are coerced to plain integers even though Quora itself serializes many of them as strings ("1,240") in its GraphQL payloads.

Reach fields on author leads — follower_count, following_count, answer_count, question_count, total_views — are the raw signals lead_score is computed from. Unlike the engagement fields above, these stay null rather than 0 when the author's profile page couldn't be read, so a genuinely inactive author and a failed fetch never look identical in the data.

Computed fields — lead_score on author leads is derived, not a Quora field: min(40, log10(followers+1)*8) + min(35, log10(views+1)*6) + min(25, log10(answers+1)*8), rounded to one decimal, capped at 100. It rewards the presence of all three reach signals rather than one dominant metric — a highly-followed author with few visible answers scores lower than one strong in all three. It returns null, not 0, when the profile carried no usable reach data at all.

Bookkeeping fields — source_url, scrape_timestamp on every row, plus source_query on rows that came from a keyword search (empty string for rows sourced from directUrls) and matched_keyword on author leads (the first keyword that surfaced that author, not every keyword that did).

Content-type quick reference

content_type value	Comes from	Distinguishing fields
question	Keyword search or a question URL in directUrls	answer_count, follow_count, topics
answer	Same question pages, one row per answer	answer_text, upvotes, is_ai_answer, author_credentials
profile	A profile URL pasted into directUrls	bio, credentials, total_views — a static snapshot, not a lead
topic	A topic URL pasted into directUrls	description, follower_count, question_count
space	A space (/q/...) URL pasted into directUrls	post_count, contributor_count
author_lead	The enrichment pass over every distinct answer author	lead_score, answers_seen_in_run, extracted_links, is_lead: true

How does Quora Search Scraper With Author Lead Enrichment compare to other Quora lead enrichment scrapers?

Feature	This Actor	Generic alternative
Output format	Typed JSON, six discriminated content types in one schema	Often HTML or a single flat content type
Author-to-lead pipeline	Built in — dedupes answer authors and enriches each into a lead row automatically	Usually requires chaining a second scraper or manual profile lookups
Entity coverage	Questions, answers, profiles, topics, spaces and author leads	Typically one or two entity types
Proxy/anti-bot handling	Automatic DIRECT → DATACENTER → RESIDENTIAL ladder, sticky once escalated	Varies; often requires the caller to configure a proxy manually
Schema stability	Reads GraphQL payloads by field name, not CSS selectors	Selector-based scrapers break on layout changes

If you're building an AI agent or RAG pipeline, the output format row is the decision-maker — parsing HTML inside an agent loop is a reliability failure mode, not a feature. constructive_wainscot/companyscope-mcp, checked on the Apify Store 2026-07-25, is a general company-research MCP server that aggregates 12 public data sources (not Quora-specific), and anchor/linkedin-profile-enrichment, checked the same date, advertises "+100 000 profile successfully enriched" on its listing as a LinkedIn-specific enrichment tool — not measured here — neither targets Quora's question/answer content the way this Actor does.

How many results can you scrape with Quora Search Scraper With Author Lead Enrichment?

maxResults accepts up to 50,000 and is a total across all searchQueries and directUrls combined, not a per-query or per-URL figure — the Actor tracks a shared running budget and stops issuing new content rows once it's spent. Author-lead rows from the enrichment pass are added on top of that budget, one per distinct answer author, so a maxResults of 200 with scrapeAuthorProfiles on can still produce more than 200 total dataset rows once leads are counted. Pagination for large result sets works by scrolling the rendered page and capturing each streamed GraphQL response until the requested count is reached or the page stops yielding new answers. No benchmark run time is published — actual throughput depends on how many proxy escalations a given run needs.

Billing follows row count directly: every row pushed to the dataset — whether it's a question, answer, profile, topic, space or author_lead — fires the same row_result pay-per-event charge, once per row, with no separate event name for the enrichment pass and no uncharged accounting or error rows mixed into the dataset. That means an author_lead row with every reach field left null (profile fetch failed) is billed the same as one with a full reach profile — there's no partial-credit or refund path for a failed enrichment, so a very large maxResults combined with scrapeAuthorProfiles on a niche keyword with few real answers is the scenario most likely to surprise a first-time user with row count.

Integrate Quora Search Scraper With Author Lead Enrichment and automate your workflow

Quora Search Scraper With Author Lead Enrichment works with any language or tool that can send an HTTP request through the Apify API.

🔗 REST API integration

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run = client.actor("<YOUR_USERNAME>/quora-search-scraper-with-author-lead-enrichment").call(run_input={
    "searchQueries": ["what is python used for"],
    "maxResults": 50,
    "scrapeAuthorProfiles": True,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("content_type") == "author_lead":
        print(item["name"], item["lead_score"], item["extracted_links"])

Works in Python, Node.js, Go, Ruby, cURL — any language that can make an HTTP request.

Automation platforms (n8n, Make, LangChain)

In n8n, use the Apify node (or an HTTP Request node against the Apify run endpoint with your token) with the same JSON input shown above, then filter the output items node on content_type == "author_lead" to route only enriched leads into a CRM step. In Make, use the Apify module to start a run and the "Get Dataset Items" action to pull results straight into a Google Sheets or Airtable module — schedule it weekly for ongoing lead discovery on a keyword set. In LangChain or any custom agent framework, wrap the client.actor(...).call() pattern above as a tool function; the returned JSON needs no parsing step before it's passed into a prompt or a retriever.

Is it legal to scrape Quora and build author lead lists?

Scraping publicly accessible Quora content is generally permissible when no login is used and no access controls are bypassed, and this Actor returns only what an anonymous visitor already sees — no Quora login, cookie or session is ever used.

Author-lead rows do include personal identifiers (name, bio, follower reach, profile URL) drawn from public profile pages, so GDPR/CCPA-style personal-data considerations apply if you store or process leads on EU or California residents — the Actor itself never returns an email address, since Quora's public profile pages carry none. Quora's own Terms of Service govern automated access to the site, separately from any personal-data regime. Consult legal counsel before using scraped author data for commercial outreach at scale, particularly across jurisdictions with distinct consent requirements for unsolicited contact.

❓ Frequently asked questions

Does Quora Search Scraper With Author Lead Enrichment work without a Quora account?

Yes. No Quora login, cookie or account is used anywhere in the Actor — searches are discovered via DuckDuckGo/Bing, and content pages are read as an anonymous visitor would see them.

How often is the scraped data updated?

Every run performs a live fetch — the Actor opens a real browser session against Quora (or the discovery search engines) on each execution. There is no caching layer; results reflect what Quora was serving at run time.

What happens if a search returns zero questions or an author's profile can't be opened?

If a keyword search yields no discoverable question URLs after climbing the full proxy ladder and retrying on residential, that keyword contributes zero rows and the run continues with the next keyword or URL — it is logged, not treated as a fatal error. If an individual author's profile page can't be opened during the enrichment pass, the Actor still emits an author_lead row for that author (name and URL carried over from the answer that surfaced them) with follower_count, total_views and the other reach fields left null rather than fabricated — and that row is still charged, since billing is per row pushed, not per successfully enriched field.

What's the difference between a profile row and an author_lead row?

A profile row is a plain snapshot of a Quora profile URL you pasted directly into directUrls — it carries bio, credentials and reach counts, nothing more. An author_lead row only comes from the enrichment pass over the authors who wrote answers your search or URLs surfaced, and additionally carries lead_score, answers_seen_in_run (how many of the run's answers that author wrote), extracted_links (bio links pulled for outreach) and matched_keyword. If you paste the same person's profile URL into directUrls and their answer gets surfaced by a search in the same run, you'll get both a profile row and an author_lead row for them — they are not deduplicated against each other, only leads are deduplicated among themselves.

Can I scrape private or restricted Quora content?

No. Only publicly accessible questions, answers and profile pages are returned — content behind a Quora login wall (some newer questions require sign-in to view answers) is not accessible to this Actor, since it never authenticates.

Is there a free trial before I pay for results?

The Actor is billed per row_result event on the Apify platform's pay-per-event model — check the Actor's pricing tab on its Apify Store listing for the current per-event rate and any free-tier usage included with your Apify plan.

Does it work for AI agent workflows and LLM pipelines?

Yes, as a standard HTTP endpoint callable through the Apify API — it is not currently reachable through an MCP server. Any agent framework that can call client.actor(...).call() (LangChain, CrewAI, a custom tool definition, n8n) receives typed JSON rows directly, with no HTML or selector-based parsing step required before passing a row to an LLM.

How does it handle Quora's anti-bot system?

Quora sits behind Cloudflare. The Actor renders every page with a stealth-patched Chromium browser (via playwright-stealth), waits out "Just a moment" / "Attention Required" interstitials with retries, and escalates through a DIRECT → DATACENTER → RESIDENTIAL proxy ladder — climbing only when a block is detected, and sticking on the higher rung once escalated rather than reverting. Once on residential, it grants extra retries (three, by default) before giving up on a URL.

How does it compare to other Quora scrapers?

Most Actors on the Apify Store positioned near "lead enrichment," checked 2026-07-25, target company or LinkedIn data rather than Quora specifically — fortunate_favorite/company-intelligence analyzes a company domain into an AI-written business summary, and constructive_wainscot/companyscope-mcp aggregates 12 public company-data sources behind an MCP server. Neither returns Quora questions, answers or author-derived leads; this Actor's differentiator is turning Quora's own answer authors into a scored, deduplicated lead list without a second tool.

Does it return data in a format LLMs can use directly?

Yes. Every row is typed, normalized JSON with stable field names across runs. No HTML, no selectors, no parsing step. Pass a row directly into an LLM context window, index it into a vector store, or route it through an agent tool.

Can I use it without managing proxies?

Yes. Leave proxyConfiguration at its default ({"useApifyProxy": true}) and the Actor handles the DIRECT → DATACENTER → RESIDENTIAL escalation, retry backoff and sticky-session behavior itself. You never rotate an IP or manage a proxy account directly.

What happens when Quora changes its structure or blocks the scraper?

The scraper is maintained, and because it reads Quora's embedded GraphQL data by field name rather than CSS selectors, a visual redesign typically doesn't affect it — only a change to the underlying GraphQL schema would. Field names and types on your end do not change. No numeric turnaround time is promised.

💬 Your feedback

Found a bug, or a Quora field that should be in the output but isn't? Open an issue on the Actor's Issues tab on Apify — a field request backed by a sample question, profile or search keyword is the fastest to act on.