Retriever Enrich appends external data to your extracted results and sheets by matching them against Bright Data's dataset marketplace — LinkedIn people and companies, Crunchbase, Instagram, Amazon, Zillow, Indeed, and Google Maps. It runs inside a code plan via the rtrvr.enrich(...) helper, is billed in credits at cost, and every paid call asks for your approval first.
Available Datasets
Reference a dataset in a plan with the rtrvr.datasets constants (e.g. rtrvr.datasets.linkedinPeople) so generated code never retypes a raw gd_ id. The links below open each dataset's page in the Bright Data marketplace.
| Dataset | rtrvr.datasets key | Bright Data ID | Modes |
|---|---|---|---|
| LinkedIn people profiles | linkedinPeople | gd_l1viktl72bvl7bjuj0 | Instant lookup + Live scrape |
| LinkedIn people + business contact info (emails) | linkedinPeopleContact | gd_me5ppxjr2ge6icjuh0 | Instant lookup + Live scrape |
| LinkedIn company information | linkedinCompanies | gd_l1vikfnt1wgvvqz95w | Instant lookup + Live scrape |
| Crunchbase companies | crunchbaseCompanies | gd_l1vijqt9jfj7olije | Live scrape only |
| Instagram profiles | instagramProfiles | gd_l1vikfch901nx3by4 | Live scrape only |
| Amazon products | amazonProducts | gd_l7q7dkf244hwjntr0 | Live scrape only |
| Zillow properties | zillowProperties | gd_lfqkr8wm13ixtbd8f5 | Live scrape only |
| Indeed job listings | indeedJobs | gd_l4dx9j9sscpvs7no2 | Live scrape only |
| Google Maps businesses | googleMaps | gd_m8ebnr0q2qlklc02fz | Live scrape only |
Fields
Each dataset exposes dozens of fields. The most commonly used ones are below — call field discovery (rtrvr.enrich({ dataset, fields: true }), free) to get the full, live list before you filter, since wrong field names fail the job.
| Dataset | Key fields |
|---|---|
linkedinPeople / linkedinPeopleContact | id (profile slug — the lookup key), url, name, position (role/title), current_company_name, city, about (bio), experience. The contact variant adds email and cellphone_number — partial coverage, most profiles return null for both. |
linkedinCompanies | url, name, website, industries, company_size, headquarters |
crunchbaseCompanies | Company profile fields — name, about, industries, founded date, size, region, website, socials — use field discovery for the full list |
instagramProfiles | Profile fields — profile name/id, followers, posts, website, bio, verification — use field discovery for the full list |
amazonProducts | asin, url, title, brand, initial_price, currency, availability, reviews_count, categories — 113 fields in total, use field discovery |
zillowProperties | zpid, url, address, price, bedrooms, bathrooms, homeStatus, yearBuilt — 140 fields in total, use field discovery |
indeedJobs | jobid, url, job_title, company_name, location, salary_formatted, job_type, date_posted_parsed, description_text |
googleMaps | place_id, url, name, address, category, reviews_count, open_hours, business_details |
Two Modes: Instant Lookup vs Live Scrape
Enrichment runs one of two ways. Instant lookup queries Bright Data's pre-collected index and is the default for people and company enrichment; live scrape fetches fresh data on demand for exact URLs or datasets the index doesn't serve.
| Instant Lookup | Live Scrape | |
|---|---|---|
| API | Bright Data Search API | Web Scraper API (/scrape, sync) |
| Speed | Sub-second | ~10–30s per input |
| Cost | 0.25 credits / returned record | 0.15 credits / successful record |
| Datasets | The 3 LinkedIn datasets only | Any scraper-backed dataset (Amazon, Zillow, Indeed, Google Maps, Crunchbase, Instagram) |
| How to call | { dataset, filter, fields } | { dataset, scrapeUrls, fields } |
| Best for | Bulk lookups by field (e.g. profile id) | Exact URLs, non-LinkedIn data, or lookup misses |
| Limits | Pass all keys in one in filter — batching is server-side | Max 20 inputs per call — chunk larger lists with rtrvr.mapLimit |
Slow Scrapes: Pending Jobs
Bright Data serves a scrape inline only while it fits their one-minute window. A heavier job (20 Zillow listings, say) hands back a job handle instead, and the call returns { pending: true, snapshotId } having billed nothing. Do other work, then collect the records later in the same plan:
let { records, pending, snapshotId } = await rtrvr.enrich({
dataset: rtrvr.datasets.zillowProperties,
scrapeUrls: listingUrls, // max 20 per call
fields: ['zpid', 'address', 'price', 'bedrooms'],
});
// ...do other free work here while Bright Data finishes...
if (pending) {
({ records, pending } = await rtrvr.enrich({ collect: snapshotId, fields: ['zpid', 'address', 'price', 'bedrooms'] }));
}
// Never re-scrape a pending job — collecting it is how you avoid paying twice.Cost & Credits
- Instant lookup — 0.25 credits per returned record; zero matches cost nothing.
- Live scrape — 0.15 credits per successful record; failed inputs are free.
- Discovery — listing datasets and inspecting fields is always free.
- Pending jobs — a scrape that exceeds the sync window bills nothing until you collect it.
- Credits are billed at cost (1 credit = $0.01), and every paid call is estimated and approved before it runs.
Approval Flow
Retriever Enrich never spends credits without your explicit go-ahead. For every paid run, the agent:
- Builds the entity list locally for free (from your sheet rows or extracted data).
- Estimates the credit cost (records × the per-record rate).
- Asks you to approve the spend via a prompt inside the task.
- Runs the paid lookup or scrape only after you approve, then joins the results back onto your rows.
Example: Enrich LinkedIn Profiles
You do not have to write this yourself — ask in plain English (e.g. "enrich these LinkedIn profile URLs with name, title, and company") and the agent generates and runs the plan. The code below is what it runs under the hood.
// Build lookup keys from your sheet rows — match on the lowercase LinkedIn
// profile slug (the segment after /in/), NOT the full URL.
const ids = rows
.map(r => r.linkedinUrl?.split('/in/')[1]?.split(/[/?#]/)[0].toLowerCase())
.filter(Boolean);
// Estimate cost and get explicit approval before any paid call.
const estCredits = Math.ceil(ids.length * 0.25);
const { answers } = await rtrvr.askUser({
questions: [{
key: 'enrichApproval',
query: `Enrich ${ids.length} LinkedIn profiles for ~${estCredits} credits?`,
choices: ['Yes', 'No'],
}],
});
if (answers.enrichApproval !== 'Yes') return { summary: 'Enrichment declined.' };
// Instant lookup — pass ALL ids in ONE `in` filter (batching is server-side).
const { records } = await rtrvr.enrich({
dataset: rtrvr.datasets.linkedinPeople,
filter: { name: 'id', operator: 'in', value: ids },
fields: ['id', 'name', 'position', 'current_company_name', 'city'],
});
// records → join back onto your rows on `id`, then write the enriched columns.id slug, not the full profile URL — stored URLs vary by country subdomain and trailing slash, so URL equality matches nothing. Always pass fields to keep records small (full records are tens of KB each); the join keys id, url, and input_url are always returned so you can match records back to your rows.Enriching LinkedIn URLs with Emails
To add emails and phone numbers, use the linkedinPeopleContact dataset — same instant lookup by id slug, but request the email and cellphone_number fields explicitly (they are dropped unless named). Coverage is partial: this is partner-sourced B2B contact data, and most profiles return null for both, so treat any email you get as a bonus rather than a guarantee.
const { records } = await rtrvr.enrich({
dataset: rtrvr.datasets.linkedinPeopleContact,
filter: { name: 'id', operator: 'in', value: slugs },
fields: ['id', 'name', 'position', 'current_company_name', 'email', 'cellphone_number'],
});
const filled = records.filter(r => r.email).length;
// e.g. "12 of 60 profiles had an email" — a blank email on a matched row is
// normal, not a failed lookup.headline: position is the role/title and about is the bio.Example: Enrich Companies by Domain
The linkedinCompanies dataset is instant-searchable and carries a website field, so a column of company domains can be enriched with firmographics in a sub-second lookup — no scraping required.
const { records } = await rtrvr.enrich({
dataset: rtrvr.datasets.linkedinCompanies,
filter: { name: 'website', operator: 'in', value: domains },
fields: ['name', 'website', 'industries', 'company_size', 'headquarters'],
});
// Domains are stored as full URLs, so `in` can miss on scheme or a leading
// "www.". If a domain returns nothing, retry it with the `includes` operator.Example: Enrich Amazon Products by URL
Non-LinkedIn datasets are live-scrape only: you supply each product's URL and get a fresh record back. Chunk lists larger than 20 with rtrvr.mapLimit.
const chunks = [];
for (let i = 0; i < productUrls.length; i += 20) chunks.push(productUrls.slice(i, i + 20));
const results = await rtrvr.mapLimit(chunks, 2, async chunk =>
rtrvr.enrich({
dataset: rtrvr.datasets.amazonProducts,
scrapeUrls: chunk,
fields: ['asin', 'title', 'brand', 'initial_price', 'availability'],
}),
);
// Each result may come back `pending` — collect it with { collect: snapshotId }.
// Join back onto your rows on `url` (or `input_url`, the URL you passed in).Web Search
Plans can also search the web programmatically with rtrvr.webSearch({ query, count?, gl?, hl? }) — one Google query per call, returning parsed organic results ({ title, url, snippet }) and, for place-flavored queries, localResults (Google's local pack: name, address, rating, reviews, category, and a canonical maps link). Phone numbers and websites are not in the pack — they come from each business's own site or listing. It costs a flat 0.15 credits per query with no approval prompt, and replaces navigating a browser tab to Google. Localize with gl (country) and hl (language).
Recipe: Local-Business Lead Generation
The pieces compose into a lead-generation pipeline — ask in plain English (e.g. "find 100 businesses in Casablanca and Rabat that likely need a website") and the agent runs this shape:
- Fan
rtrvr.webSearchover each industry × city (localized, e.g.gl: 'ma', hl: 'fr') and harvest the local pack — business name, phone, website (or its absence), rating, maps link. - Construct a Google Maps link per business:
https://www.google.com/maps/search/?api=1&query=Name+Cityresolves to the real place page. - Enrich businesses that have a website with a free fetch of their contact/about page (email, socials), or instantly via the
linkedinCompaniesdataset filtered onwebsite. - Qualify with one LLM pass (
rtrvr.inferSheetData) — e.g. a "why they need a website" column and a priority score. - Rows append to a Google Sheet as they are found, so partial progress always survives; export the sheet as CSV when done.