A101 Product Scraper

Slug: fatihtahta/a101-product-scraper

Overview

A101 Product Scraper collects structured product records from A101 search queries, A101 search result URLs, category URLs, and individual product URLs. Records include product identity, title, brand, category hierarchy, public product URL, pricing, availability, images, badges, variants, collection context, and source context when available. A101 is a major Turkish retail and e-commerce destination, making its public catalog useful for assortment analysis, price monitoring, product discovery, and reporting. The actor helps teams turn repeatable catalog collection into structured records that can be reviewed, exported, merged into existing systems, or monitored over time. It supports both A101 Ekstra and A101 Kapında catalog scopes, optional TRY price boundaries, bounded output volume, and richer product detail collection. Results are delivered as structured Apify dataset records suitable for review, export, analytics, AI-agent workflows, and downstream processing. The actor is designed for dependable recurring public catalog acquisition without making unsupported claims about full-market coverage, completeness, or service-level guarantees.

Who Should Use This Actor

• Market research and analytics teams: monitor A101 assortment, price points, category movement, availability signals, and campaign positioning in a repeatable dataset.
• Product and catalog teams: enrich internal product catalogs with public product names, brand labels, category paths, image URLs, availability fields, and merchandising context.
• E-commerce operations teams: track selected categories, product families, price bands, and recurring catalog segments for operational reporting.
• Developers and data engineering teams: feed normalized product records into ETL jobs, warehouses, search indexes, dashboards, and internal data products.
• AI agents and workflow automations: collect scoped product datasets that can be summarized, compared, routed, or checked against downstream business rules.
• Competitive intelligence teams: compare public product availability, visible price changes, product groupings, badges, and category coverage across repeated runs.

Common Use Cases

• Assortment monitoring: track which A101 products appear for selected searches, category URLs, or product groups.
• Price tracking: collect current, original, discounted, and display price fields for products in a chosen TRY price band.
• Availability reporting: monitor stock flags, enabled status, quantity limits, sales channels, and detail availability when those fields are present.
• Catalog enrichment: append product IDs, barcodes, brands, descriptions, category paths, image URLs, and merchandising attributes to internal item records.
• Campaign and collection analysis: review public collection labels, badges, variants, and category placement for merchandising workflows.
• Recurring reporting: schedule the same query, URL, category, or price-band inputs to compare product records over time.
• Agentic research workflows: let an internal agent collect a scoped product dataset, validate the record shape, summarize changes, and route records to the next workflow step.

Real-World Questions This Data Can Answer

• Which A101 products match a keyword, category URL, product URL list, catalog mode, or TRY price range?
• Which products are currently visible for a recurring product family or campaign search?
• Which products changed price, availability, quantity limits, badges, or collection membership since the previous run?
• Which product records include usable identifiers such as product.product_id, product.barcode, or product.url for catalog matching?
• Which categories, brands, and collection labels appear most often for a selected discovery term or source URL?
• Which records are strong candidates for enrichment, review, monitoring, or downstream catalog updates?
• Which products should be deduplicated or upserted when repeated runs produce the same public product identity?

Quick Start

1. Choose whether to start from A101 URLs in startUrls, keyword searches in queries, or both.
2. Select the A101 catalog scope with search_mode: A101 Ekstra or A101 Kapında.
3. Add optional TRY price boundaries with min_price and max_price when you need a narrower product set.
4. Set a small limit for the first validation run and choose whether enrich_data should collect richer product details.
5. Run the actor in Apify Console, inspect the first dataset records, then increase coverage or schedule recurring runs once the output shape is confirmed.

Input Parameters

The actor accepts A101 URLs, product search queries, catalog mode, optional TRY price filters, enrichment preference, and a per-input record limit.

Parameter	Type	Description	Default
startUrls	array of strings	A101 search result URLs, category URLs, or individual product URLs. Use this when the exact source scope is already known. Homepage URLs are ignored.	-
queries	array of strings	Product search phrases such as a brand, product family, category, or campaign keyword. Each query is collected separately so records can be traced back to the discovery term.	-
search_mode	string	A101 catalog scope for all discovery inputs in the run. Allowed values: A101 Ekstra, A101 Kapında.	A101 Ekstra
min_price	number	Minimum product price in TRY. Only products priced at or above this value are included when the source provides pricing.	-
max_price	number	Maximum product price in TRY. Only products priced at or below this value are included when the source provides pricing.	-
enrich_data	boolean	When enabled, the actor collects richer product details when A101 provides them. Leave disabled for lighter validation or monitoring runs.	true
limit	integer	Maximum number of records to save per URL or query. Use lower values for validation and higher values for scheduled exports or ETL batches. Minimum: 1.	- (form prefill: 100)

Choosing Inputs

Use queries when you want A101 to discover products from keywords such as a brand, product family, category, or campaign term. Use startUrls when you already have a defined A101 search page, category page, or product URL and want a repeatable collection scope. Use individual product URLs for enrichment backfills or catalog checks against known items. Use category or search URLs for recurring assortment monitoring where the same source scope should be revisited over time.

search_mode controls whether the run targets A101 Ekstra or A101 Kapında; choose one mode per run so downstream comparisons stay clean. min_price and max_price narrow the dataset to a TRY price band, while leaving them empty improves discovery across a broader price range. When comparing segments, keep separate runs for different categories, price bands, catalog modes, or query groups so exports remain easier to audit. Start with a small limit to validate the record shape, then increase it after confirming the dataset matches your workflow.

Input Recipes

• Validation run: use one simple queries value, the default search_mode, enrich_data enabled, and a small limit to inspect the product, pricing, availability, media, and merchandising fields.
• Targeted category collection: provide one A101 category URL in startUrls, set a practical limit, and use the same input repeatedly for category-level assortment monitoring.
• Known product enrichment: provide individual A101 product URLs in startUrls and keep enrich_data enabled when you need more complete product context for internal catalog matching.
• Price-band monitoring: combine queries with min_price and max_price to collect products inside a defined TRY range for pricing reports or review queues.
• Kapında catalog check: set search_mode to A101 Kapında, provide focused queries, and keep the run separate from A101 Ekstra exports.
• Segmented analysis: run separate inputs by brand, product family, category URL, catalog mode, or price band so downstream dashboards can compare segments without extra cleanup.

Example Inputs

Example: query validation run

{
  "search_mode": "A101 Ekstra",
  "queries": ["laptop"],
  "min_price": 10000,
  "max_price": 60000,
  "enrich_data": true,
  "limit": 10
}

Example: direct category URL run

{
  "search_mode": "A101 Ekstra",
  "startUrls": ["https://www.a101.com.tr/elektronik/elektrikli-supurge/"],
  "enrich_data": true,
  "limit": 25
}

Example: Kapında price-band run

{
  "search_mode": "A101 Kapında",
  "queries": ["kahve"],
  "min_price": 50,
  "max_price": 500,
  "enrich_data": false,
  "limit": 20
}

Output

Output destination

The actor writes results to an Apify dataset as JSON records. The dataset is designed for direct consumption by analytics tools, ETL pipelines, AI agents, and downstream APIs with minimal post-processing.

The provided output contract contains one product record shape. If future exports include multiple entity types or record shapes, each shape should be documented separately based on the records returned by the actor.

Record envelope and stable identifiers

Each dataset item is a structured product record with grouped objects for source_context, product, pricing, availability, media, and merchandising. The recommended idempotency key is the composite key source_context.source_id + product.product_id; use source_context.source_url or product.url as a practical fallback when a URL-based upsert is easier for your system. Use the recommended key to deduplicate repeated runs, merge records into existing product tables, and update matching items over time. Stable identifiers make records easier to compare, sync, deduplicate, and route across recurring collection, warehouse loading, review queues, and monitoring workflows.

source_context provides provenance for the record, including the source identifier, public source URL, discovery query, page number, result position, reported item count, selected catalog mode, seed type, and enrichment status when present.

Example: enriched product record

{
  "source_context": {
    "source_id": "a101_product_scraper",
    "source_url": "https://www.a101.com.tr/elektronik/lenovo-loq-83gs00l7tr-512-gb-8-gb-ram-i5-12600hx-15-6-full-hd-gaming-notebook_p-26063395",
    "query": "laptop",
    "page_number": 1,
    "position": 1,
    "total_item_count": 8,
    "platform_name": "ecom",
    "seed_type": "query",
    "enrichment_enabled": true,
    "enrichment_status": "enriched"
  },
  "product": {
    "product_id": "26063395",
    "barcode": "198155399961",
    "title": "Lenovo LOQ 83GS00L7TR 512 GB 8 GB RAM i5-12600HX 15.6\" Full HD Gaming Notebook",
    "brand": "Lenovo",
    "category": "Laptop & Notebook",
    "category_path": [
      "Elektronik",
      "Bilgisayar & Tablet",
      "Laptop & Notebook"
    ],
    "url": "https://www.a101.com.tr/elektronik/lenovo-loq-83gs00l7tr-512-gb-8-gb-ram-i5-12600hx-15-6-full-hd-gaming-notebook_p-26063395",
    "country": "Çin",
    "description": "Lenovo Loq 83Gs00l7tr 512 Gb 8 Gb Ram I5-12600hx 15.6\" Full Hd Gaming Notebook ürününü A101 güvencesiyle en uygun fiyata satın almak için hemen tıklayın!"
  },
  "pricing": {
    "currency": "TRY",
    "current_price": 42999,
    "original_price": 42999,
    "discounted_price": 42999,
    "current_price_text": "₺42.999,00",
    "original_price_text": "₺42.999,00",
    "detail_price": 42999
  },
  "availability": {
    "in_stock": true,
    "is_enabled": true,
    "min_quantity": 1,
    "max_quantity": 5,
    "daily_max_quantity": 5,
    "channels": [
      "Online Satışa Özel"
    ],
    "detail_availability": "in stock"
  },
  "media": {
    "images": [
      {
        "id": "1.jpg",
        "url": "https://cdn2.a101.com.tr/dbmk89vnr/CALL/Image/getcl/livephotos/8/26063395default/1.jpg",
        "type": "product",
        "is_external": true
      }
    ],
    "primary_image_url": "https://cdn2.a101.com.tr/dbmk89vnr/CALL/Image/getcl/livephotos/8/26063395default/1.jpg"
  },
  "merchandising": {
    "collections": [
      {
        "id": "S10011001",
        "name": "Babalar Günü-Elektronik"
      },
      {
        "id": "S1001",
        "name": "Babalar Günü - A101 Ekstra"
      },
      {
        "id": "S10861015",
        "name": "Hadi Alışveriş Kredisi ile Taksitli Öde"
      },
      {
        "id": "S10291005",
        "name": "A101 Ekstra Özel Banka Kampanyası"
      },
      {
        "id": "S10291004",
        "name": "Bonus Kampanyası"
      },
      {
        "id": "S10911025",
        "name": "hadi veresiye"
      },
      {
        "id": "S10911026",
        "name": "Hadi Kredi Kartınla Her Ay 1000 TL"
      },
      {
        "id": "S10531296",
        "name": "Peşin Fiyatına 6 Taksitli Ürünler"
      },
      {
        "id": "S1032",
        "name": "Ekstraya Özel Ürünler"
      },
      {
        "id": "S10291001",
        "name": "A101’de 1.750 TL’ye varan fırsat!"
      },
      {
        "id": "S10911052",
        "name": "Hadi ile 18 Aya Varan Taksit"
      },
      {
        "id": "S10911056",
        "name": "Banka Kampanyaları - A101 Ekstra"
      },
      {
        "id": "S10911046",
        "name": "Peşin fiyatına 6 taksit ürünler"
      },
      {
        "id": "S10911014",
        "name": "A101-Garanti Bankası"
      },
      {
        "id": "S10911059",
        "name": "A101 Ekstra - Garanti  Bankası Kampanyası"
      },
      {
        "id": "S10291003",
        "name": "Garanti Bonus Kampanyası"
      },
      {
        "id": "S10081014",
        "name": "Elektronik Ürünler"
      },
      {
        "id": "S10911068",
        "name": "Hadi Black Kredi Kartı Özel Okul Ödemeleri"
      },
      {
        "id": "S10861020",
        "name": "Hadi Black Kredi Kartı ile Ramaza Kampanyası"
      },
      {
        "id": "S10911070",
        "name": "Hadi Ramazan Kampanyası"
      },
      {
        "id": "S10411272",
        "name": "Aldın Aldın - Bilgisayar & Tablet"
      }
    ],
    "variants": [
      {
        "id": "26063395"
      }
    ],
    "variant_count": 1,
    "badges": [
      {
        "bgColor": "#EA242A",
        "frameColor": "#EA242A",
        "id": "6taksit",
        "text": "Peşin Fiyatına 6 Taksit",
        "textColor": "#FFFFFF"
      }
    ]
  }
}

Field Reference

source_context

• source_context.source_id (string, required): source identifier for the A101 product record family.
• source_context.source_url (string, required): public A101 URL associated with the saved product record.
• source_context.query (string, optional): search query that produced the record when the run started from a keyword.
• source_context.page_number (integer, optional): result page number within the collected scope.
• source_context.position (integer, required): product position within the collected result scope.
• source_context.total_item_count (integer, optional): reported count of matching items when available.
• source_context.platform_name (string, optional): catalog surface label returned with the record.
• source_context.seed_type (string, optional): input source type, such as a query or URL-derived seed.
• source_context.enrichment_enabled (boolean, required): whether richer product detail collection was requested.
• source_context.enrichment_status (string, optional): detail collection status when enrichment information is available.

product

• product.product_id (string, required): stable A101 product identifier.
• product.barcode (string, optional): product barcode when A101 provides it.
• product.title (string, required): product title.
• product.brand (string, optional): product brand label.
• product.category (string, optional): most specific category label available in the record.
• product.category_path (array of strings, optional): category hierarchy from broader to more specific labels.
• product.url (string, required): public A101 product URL.
• product.country (string, optional): source-provided country label when available.
• product.description (string, optional): product description or detail text when available.

pricing

• pricing.currency (string, required): currency code for numeric price fields.
• pricing.current_price (number, required): normalized current price in the listed currency.
• pricing.original_price (number, optional): original or normal price when available.
• pricing.discounted_price (number, optional): discounted price when available.
• pricing.current_price_text (string, optional): source display text for the current price.
• pricing.original_price_text (string, optional): source display text for the original price.
• pricing.discount_rate (number or string, optional): source-provided discount rate when available.
• pricing.detail_price (number, optional): price captured from richer product detail context when available.

availability

• availability.in_stock (boolean, optional): normalized stock availability flag.
• availability.is_enabled (boolean, optional): whether the product appears enabled in the public product data.
• availability.min_quantity (integer, optional): minimum purchase quantity when available.
• availability.max_quantity (integer, optional): maximum purchase quantity when available.
• availability.daily_max_quantity (integer, optional): daily purchase quantity limit when available.
• availability.channels (array of strings, optional): public sales channel labels associated with the product.
• availability.detail_availability (string, optional): availability text from richer product detail context when available.

media

• media.images (array of objects, optional): product image records in source order.
• media.images.id (string, optional): source image identifier.
• media.images.url (string, optional): public product image URL.
• media.images.type (string, optional): image type label.
• media.images.is_external (boolean, optional): whether the image is marked as external.
• media.primary_image_url (string, optional): primary product image URL.

merchandising

• merchandising.collections (array of objects, optional): public collection or campaign groupings attached to the product.
• merchandising.collections.id (string, optional): collection identifier when available.
• merchandising.collections.name (string, optional): collection or campaign name.
• merchandising.variants (array of objects, optional): product variant records when available.
• merchandising.variants.id (string, optional): variant identifier.
• merchandising.variant_count (integer, optional): number of variants reported for the product.
• merchandising.badges (array of objects, optional): merchandising badges attached to the product.
• merchandising.badges.bgColor (string, optional): badge background color value when provided.
• merchandising.badges.frameColor (string, optional): badge frame color value when provided.
• merchandising.badges.id (string, optional): badge identifier.
• merchandising.badges.text (string, optional): visible badge text.
• merchandising.badges.textColor (string, optional): badge text color value when provided.

Data Model Notes

• Identity fields: use source_context.source_id + product.product_id for product-level matching, deduplication, and upserts; use product.url or source_context.source_url as a URL-based fallback.
• Source and provenance fields: source_context explains how the record was discovered and where it can be reviewed publicly.
• Business attributes: product, pricing, availability, media, and merchandising contain the main user-facing product attributes for analysis and reporting.
• Point-in-time values: prices, availability flags, quantity limits, result positions, and collection membership should be treated as values observed at run time.
• Nested objects: related attributes are grouped to keep JSON exports easier to inspect, validate, and load into downstream systems.
• Optional fields: null-check or presence-check fields that depend on the product page, catalog mode, availability, enrichment setting, or source-side data quality.
• Repeated runs: compare records across runs using stable identifiers plus Apify run metadata, stored input configuration, and dataset export timestamps.

Data Quality, Guarantees, And Handling

• Structured records: results are normalized into predictable JSON objects for downstream use.
• Best-effort extraction: fields may vary by catalog mode, product availability, public page contents, UI experiments, or source-side changes.
• Optional fields: null-check optional fields in downstream code, dashboards, and review tools.
• Deduplication: use source_context.source_id + product.product_id as the strongest stable key, with product.url or source_context.source_url as fallback identifiers.
• Freshness: results reflect publicly available A101 product information at run time.
• Repeated runs: use the recommended idempotency key when syncing records into warehouses, CRMs, search indexes, vector stores, or monitoring systems.
• Schema awareness: downstream systems should rely on documented fields and handle newly missing optional fields gracefully.

Tips For Best Results

• Start with a small limit to validate the output shape before scaling up.
• Use one catalog mode, category, query group, or price segment per run when you need cleaner comparisons.
• Leave min_price and max_price empty when the goal is broad discovery.
• Add price filters gradually to understand how each boundary changes coverage.
• Use startUrls for known category or product scopes and queries for discovery.
• Keep enrich_data enabled for catalog backfills that need richer product context.
• Schedule recurring runs for monitoring workflows instead of relying only on manual one-off exports.
• Store the input configuration used for each recurring workflow alongside the exported dataset.

How to Run on Apify

1. Open the actor in Apify Console.
2. Configure the available input fields for the desired A101 catalog scope.
3. Set limit to control the maximum number of records saved per query or URL.
4. Click Start and wait for the run to finish.
5. Open the dataset and inspect the first records.
6. Download results in JSON, CSV, Excel, or another supported format.

Agentic And API-First Usage

A101 Product Scraper can be used as a structured data acquisition step inside larger automated workflows. It gives agents, applications, and data pipelines a predictable product record shape that can be validated, deduplicated, summarized, and routed without asking downstream systems to infer the meaning of raw page content.

Agent workflow pattern:

1. Generate or select a scoped input from the supported schema.
2. Run the actor manually, on a schedule, or through Apify platform automation.
3. Wait for completion and read the dataset records.
4. Validate records against the field reference.
5. Upsert records into the downstream system using source_context.source_id + product.product_id.
6. Trigger analysis, enrichment, alerts, or human review.

Practical notes for agentic use:

• Keep prompts and automations grounded in startUrls, queries, search_mode, min_price, max_price, enrich_data, and limit.
• Start with small validation runs before allowing broad automated collection.
• Feed the Field Reference and a small output sample to downstream AI steps.
• Treat optional fields as nullable instead of asking agents to infer missing values.
• Store run ID, input configuration, and dataset export metadata outside the record when building audit trails.

Scheduling & Automation

Scheduling

Automated Data Collection

Schedule runs to keep A101 product datasets current for monitoring, reporting, catalog enrichment, or alerting workflows. Use the same saved input for consistent comparisons across time.

• Navigate to Schedules in Apify Console.
• Create a new schedule, such as daily, weekly, or a custom cron interval.
• Configure input parameters for the catalog mode, URLs, queries, price filters, enrichment preference, and limit.
• Enable notifications for run completion.
• Add webhooks for automated processing.

Integration Options

• BI dashboards: monitor prices, availability, category movement, badges, and assortment coverage over time.
• Data warehouses: store recurring product snapshots for historical reporting, price trend analysis, and campaign review.
• Scheduled exports: deliver repeatable JSON, CSV, or Excel files to analysts and operations teams.
• Webhooks: trigger ingestion, validation, alerts, or downstream processing after each completed run.
• Alerts and monitoring: notify teams when selected products change availability, price, quantity limits, or merchandising status.
• Search and vector indexes: make product titles, descriptions, categories, images, and merchandising context searchable for internal tools or AI retrieval workflows.
• Google Sheets or Airtable: review smaller validation runs, category samples, or manual QA queues in lightweight operational tables.

Export Formats And Downstream Use

• JSON: for APIs, applications, AI agents, and data pipelines that need nested records with full fidelity.
• CSV or Excel: for spreadsheet workflows, stakeholder review, sampling, and lightweight analysis.
• API access: for automated ingestion into internal systems after each completed run.
• BI and warehouses: for reporting, dashboards, historical analysis, price monitoring, and assortment tracking.
• Search or vector indexes: for discovery, semantic search, retrieval workflows, or agent context using product titles, descriptions, categories, and merchandising fields.

Downstream Pipeline Guide

• Idempotency: use source_context.source_id + product.product_id for upserts, with product.url or source_context.source_url as fallback identifiers.
• Null handling: treat optional product, pricing, availability, media, and merchandising fields as nullable.
• Type handling: preserve numbers, booleans, arrays, and nested objects when exporting to JSON-first systems.
• Flattening: if exporting to CSV or Excel, flatten nested objects deliberately and keep the original JSON export for full fidelity.
• Partitioning: store run date, input segment, catalog mode, query, category URL, price band, or workflow name alongside records for easier analysis.
• Change detection: compare repeated runs by stable key and selected business fields such as pricing.current_price, availability.in_stock, availability.max_quantity, merchandising.badges, and merchandising.collections.
• Quality checks: monitor record count, duplicate rate, required identifiers, price field fill rates, and important optional field coverage.
• Human review: route records with missing critical fields, unusual prices, changed availability, or changed merchandising status into a review queue when needed.
• Retention: decide how long to keep raw exports versus normalized warehouse tables based on monitoring, audit, and reporting requirements.

Performance

Estimated run times:

• Small runs (< 1,000 outputs): ~3-5 minutes.
• Medium runs (1,000-5,000 outputs): ~5-15 minutes.
• Large runs (5,000+ outputs): ~15-30 minutes.

Execution time varies based on filters, result volume, target availability, and how much information is returned per record. Highly filtered runs can finish faster, while broad discovery or detail-rich records may take longer.

Limitations

• Availability depends on what A101 publicly exposes at run time.
• Some optional fields may be missing on sparse products, category-only records, unavailable products, or records collected without richer detail.
• Very broad searches may take longer or require a higher limit to collect enough records for analysis.
• Target-side changes can affect field availability, labels, naming, or product visibility.
• A101 Ekstra and A101 Kapında may expose different product coverage or catalog context.
• The actor provides structured public product data, not business advice, legal advice, valuation advice, or guaranteed completeness.

Troubleshooting

• No results returned: check query spelling, direct URLs, catalog mode, price filters, and whether A101 has matching public products.
• Fewer results than expected: broaden price filters, raise limit, try a broader query, or verify that the selected catalog contains enough matching records.
• Some fields are empty: optional fields depend on what each public product record provides and whether richer detail is available.
• Duplicate-looking records: compare source_context.source_id + product.product_id and decide whether records represent the same product, variants, different discovery scopes, or repeated observations.
• Run takes longer than expected: reduce scope, lower limit for validation, or split broad collection into smaller query, URL, catalog, or price-band segments.
• Output changed: compare the current output with the Field Reference and include a small sample when reporting the issue.
• Downstream import failed: check JSON validity, nullable fields, nested objects, arrays, and whether your destination expects flattened columns.

FAQ

What data does this actor collect?

It collects structured public A101 product records, including product identity, title, brand, category path, public URL, pricing, availability, media, merchandising context, and source context when available.

Can I filter by location, category, date, price, status, keyword, or other criteria?

The supported input filters are keywords through queries, A101 URLs through startUrls, catalog scope through search_mode, TRY price range through min_price and max_price, enrichment preference through enrich_data, and per-input volume through limit. The schema does not include location, date, status, radius, or sort parameters.

Why did I receive fewer results than my limit?

limit is a maximum per URL or query, not a guarantee. The selected catalog scope, query, category, price range, product availability, and public result count can all produce fewer records.

How should I choose a limit for my first run?

Start with a small limit, review the first dataset records, confirm the fields are useful, then increase the limit for scheduled exports or larger monitoring workflows.

Can I schedule recurring runs?

Yes. Use Apify schedules to run the same query, URL, catalog mode, price filter, and limit configuration on a recurring cadence.

How do I avoid duplicates across runs?

Use source_context.source_id + product.product_id as the main upsert key. If your workflow is URL-based, also compare product.url or source_context.source_url.

What is the best field to use as a unique key?

The strongest documented key is the composite of source_context.source_id and product.product_id. This identifies the product within the A101 product record family.

Can I use the output with AI agents or automated workflows?

Yes. The actor returns predictable JSON records that agents can validate against the Field Reference, summarize, compare, route, or upsert into downstream systems.

Can I export the data to CSV, Excel, or JSON?

Yes. Apify datasets support common export formats including JSON, CSV, Excel, and API-based access for downstream ingestion.

Does this actor collect private data?

No. The actor is intended for publicly available A101 product catalog information.

What should I include when reporting an issue?

Include the redacted input, run ID, expected versus actual behavior, a small output sample if useful, and the downstream destination or export format if the issue is pipeline-related.

Compliance & Ethics

Responsible Data Collection

This actor collects publicly available product catalog information from A101 for legitimate business purposes, including:

• Retail and e-commerce research and market analysis.
• Assortment, pricing, and availability monitoring.
• Catalog enrichment, reporting, and operational review workflows.

This section is informational and not legal advice. Users are responsible for ensuring that their use of collected data complies with applicable laws, regulations, contractual obligations, and platform rules.

Best Practices

• Use collected data in accordance with applicable laws, regulations, and the target site's terms.
• Respect individual privacy and personal information.
• Use data responsibly and avoid disruptive or excessive collection.
• Do not use this actor for spamming, harassment, discrimination, or other harmful purposes.
• Follow relevant data protection requirements where applicable, such as GDPR, CCPA, or sector-specific rules.
• Review your own retention, access control, and data-sharing policies before operationalizing the dataset.

Support

For help, use the actor page or Issues. Include the input used with sensitive values redacted, the run ID, expected versus actual behavior, a small output sample when relevant, and the downstream destination or export format if the issue is related to an import, export, or pipeline workflow.