CPSC Product Recall Intelligence

Search public CPSC recall data for marketplace compliance, supplier screening, retail risk, insurance research, product monitoring, and agent workflows. Run it from the Apify Console, the Apify API, or a scheduled automation. Each run starts with a summary and then returns normalized recall records with the official recall link retained.

What does CPSC Product Recall Intelligence return?

The actor reads the official CPSC Recall JSON endpoint and turns its nested response into predictable dataset rows. It is designed for a team that needs recall numbers, dates, products, hazards, remedies, companies, countries, images, and provenance in a form that can move into a queue, spreadsheet, or agent without HTML parsing.

The first row is a summary, which reports the applied search, recall number, optional date range, returned count, source status, hazardCount, recallsWithImages, recallsWithInjuries, and a warning that the official endpoint is filtered locally. Each following recall row contains a stable id, recall number and dates, title, description, product names, hazards, remedies, manufacturers, retailers, countries, images, injuries, contact text, sales text, unit strings, source URL, retrieval timestamp, completeness score, and agentMarkdown.

The live E2E check queried official recall number 26621 with limit: 1. It returned exactly one result with products and confirmed these record keys: schemaVersion, recordType, id, sourceUrl, retrievedAt, fieldCompletenessScore, recallNumber, recallDate, products, hazards, remedies, manufacturers, images, and agentMarkdown. The public source was a non-empty JSON array, with nested official product, hazard, remedy, company, country, injury, and image entries normalized to arrays.

If this actor saved review time, please leave a short review after the run: CPSC Product Recall Intelligence reviews.

Why use this for product compliance and monitoring?

The official endpoint currently returns a full JSON feed rather than a server-side search result. This actor makes the result set actionable: it downloads the official feed, applies the search, exact recall number, date filters, and limit locally, and makes that processing visible in the summary warning. A repeatable input then produces a bounded set of normalized records for your workflow.

Use it to identify records that deserve review. It does not decide whether an item in your inventory is affected, whether a listing must be removed, or whether a customer remedy is owed. Those decisions require matching official product identifiers, dates, model details, and the controlling recall notice.

Input reference

Field	Type	Default	What it controls
search	string, up to 160 characters	""	Case-insensitive text match across title, description, product names, hazards, and manufacturers.
recallNumber	string, up to 40 characters	""	Exact official recall-number match.
startDate	YYYY-MM-DD	omitted	Include recalls on or after this official recall date.
endDate	YYYY-MM-DD	omitted	Include recalls on or before this official recall date.
limit	integer, 1 to 100	25	Maximum number of matching recall records returned.

The date range is validated: startDate cannot be after endDate. A concise category-monitoring input looks like this:

{
  "search": "chandelier",
  "recallNumber": "",
  "startDate": "2026-01-01",
  "endDate": "2026-12-31",
  "limit": 25
}

For the most deterministic lookup, use the exact public recall number:

{"recallNumber":"26621","limit":1}

Output reference

All rows carry schemaVersion: "1.0", a recordType, a stable actor id, sourceUrl, retrievedAt, fieldCompletenessScore, and agent-ready markdown. The following shows the verified shape, using illustrative values rather than asserting that a particular product currently has these facts:

{
  "schemaVersion": "1.0",
  "recordType": "recall",
  "id": "cpsc-recall-26621",
  "sourceUrl": "https://www.cpsc.gov/Recalls/...",
  "retrievedAt": "2026-07-18T15:00:00.000Z",
  "fieldCompletenessScore": 91,
  "recallNumber": "26621",
  "recallDate": "2026-01-01T00:00:00.000Z",
  "title": "Example product recall title",
  "products": ["Example product"],
  "hazards": ["Example hazard"],
  "remedies": ["Refund"],
  "manufacturers": ["Example manufacturer"],
  "retailers": [],
  "countries": [],
  "images": [],
  "injuries": []
}

The summary row tells an automation how the run behaved before it processes recalls:

{
  "recordType": "summary",
  "query": "chandelier",
  "recallNumber": null,
  "startDate": "2026-01-01",
  "endDate": "2026-12-31",
  "count": 0,
  "sourceStatus": "ok",
  "recallsWithImages": 0,
  "recallsWithInjuries": 0,
  "hazardCount": 0,
  "warnings": [
    "CPSC Recall API filtering is performed locally because this endpoint currently returns its full JSON feed."
  ]
}

Counts in the examples are deliberately illustrative. Read the current summary for the real returned count and coverage indicators. Empty arrays and null values preserve what is available from the official feed, they do not mean a product is safe or unaffected.

Tutorial: monitor a product category safely

1. Start with an exact recall number when you have one. Otherwise choose one meaningful product, hazard, or manufacturer phrase for search.
2. Add a date range when your workflow is concerned with a period. Keep the range valid and begin with a low limit while reviewing relevance.
3. Run the actor and inspect the first summary row. Confirm sourceStatus: "ok", note the returned count, and retain the local-filtering warning in your audit record.
4. For each recall, send recallNumber, title, recallDate, products, hazards, remedies, manufacturers, and sourceUrl to a review queue.
5. Open the official source URL and compare model numbers, product names, manufacturing dates, remedy instructions, and any sales details before removing a listing or contacting a customer.
6. Save id or recallNumber as the deduplication key, and keep retrievedAt so your team can distinguish a new observation from a previously reviewed record.

The actor writes AGENT_BRIEFING.md to the key-value store. It is a compact operational summary with the query, recall number, returned count, total captured hazards, and source status.

Worked examples from the verified output contract

1. Look up a specific official recall

Input:

{"recallNumber":"26621","limit":1}

The live E2E test used this input and returned one recall row. It verified an exact recallNumber match and a non-empty products array. The resulting row carries the official source URL, normalized arrays for hazards and remedies, and a stable ID of the form cpsc-recall-<recall number>.

2. Watch a product category

Input:

{"search":"chandelier","startDate":"2026-01-01","endDate":"2026-12-31","limit":25}

The actor checks the search term against the official title, description, product names, hazards, and manufacturers, then applies the date boundaries to RecallDate. Use summary.hazardCount as a quick signal of returned hazard entries, then route every individual recall for a product-identifier review.

3. Screen a supplier name

Input:

{"search":"Example Manufacturer","limit":50}

The local match includes manufacturer names. Store manufacturers, products, recallDate, remedies, and sourceUrl with the supplier record. A text match is a research lead, not proof that a corporate affiliate or every product line is affected.

4. Build a marketplace compliance queue

Input:

{"search":"battery","limit":100}

For each returned record, use recallNumber as the queue's external reference and include products, hazards, remedies, soldAt, consumerContact, and the official link. Add a manual comparison step for SKU, model number, batch, serial number, and sale dates before an enforcement action.

5. Monitor recent recall activity

Input:

{"startDate":"2026-01-01","endDate":"2026-12-31","limit":100}

This returns a bounded current-period set after local filtering. Preserve the summary warning because the official source is fetched as a full feed. Use recallDate, lastPublishDate, and retrievedAt to understand the difference between publication timing and your collection timing.

6. Handle an expected no-match

Input:

{"search":"unlikely exact phrase","limit":25}

An expected no-match succeeds with a zero-count summary. A malformed response, non-OK HTTP response, empty upstream feed, or invalid row shape fails instead of appearing as a reassuring empty result. Your monitor should treat those two outcomes differently.

Agent workflows and monitoring

For ongoing marketplace or supplier monitoring, run one fixed query per product family or supplier at a defined cadence. Store the summary separately from recall rows. Alert when sourceStatus is not ok, when a normally populated query unexpectedly returns zero, or when a new recallNumber appears.

A practical review path is:

1. Schedule an actor run daily or weekly with a fixed input and a small enough limit to review.
2. Deduplicate returned records on id or recallNumber.
3. Create a review item for new records, carrying the official URL, products, hazards, remedies, and retrieval timestamp.
4. Ask a human or a product-matching service to compare the official notice against the actual SKU, model, date, and market.
5. Record the decision outside this actor, including the official source and reviewer rationale.

An AI agent can summarize agentMarkdown, classify the listed hazards, or prepare a checklist. It must not treat a text match as authorization to remove products, issue notices, or give safety or legal advice without a controlled verification step.

Limitations and data handling

• The official endpoint is currently retrieved as a full JSON feed. Filtering is local, so broad searches may take longer than a server-filtered API request.
• Search is text matching, not SKU, barcode, manufacturer-entity, or model-number resolution. Matching words can produce false positives or miss differently worded notices.
• The data reflects the public CPSC feed at collection time. It does not replace the full official recall notice, manufacturer communications, retailer records, or a product-specific inspection.
• Nested source arrays can be empty. Empty images, injuries, or retailers do not establish that no imagery, incident, or seller information exists elsewhere.
• Date filtering uses the recall date normalized from the source. lastPublishDate remains an output field and can differ from the initial recall date.
• The actor provides public-data research support only. It is not legal, product-safety, insurance, or compliance advice.

Buyer FAQ

Is this actor affiliated with the CPSC?

No. This independent actor reads public CPSC data. It is not affiliated with, endorsed by, sponsored by, or operated by the U.S. Consumer Product Safety Commission.

Why does a run download a large response?

The official Recall endpoint currently provides a full JSON feed rather than documented server-side search parameters. The actor filters that official response locally and states this in the summary warning so the behavior is transparent.

Does a result prove my SKU is recalled?

No. A result identifies a recall record that may warrant review. Compare model numbers, products, manufacturing dates, locations, remedy terms, and the official notice before any inventory, customer, or compliance action.

Can I search an exact recall number?

Yes. Provide recallNumber with the exact public value. The verified E2E input {"recallNumber":"26621","limit":1} returned one matching record and confirmed its product array was populated.

What does a zero-result run mean?

For a valid query, it means no records matched the local filters and the summary has count: 0. A source or parser problem fails the run, so it is not silently represented as a no-match.

Can I use the image and official URLs in my workflow?

They are normalized from the public response for reference. Follow the relevant source terms, keep the official notice as the controlling reference, and do not imply that CPSC assets or links endorse your product or service.

What should I use as a deduplication key?

Use id or recallNumber. Preserve retrievedAt as a separate collection timestamp, because official source content can change after your first review.

Can an AI agent act on the output automatically?

An agent can triage, summarize, or create a human-review task. Product removals, safety notices, refunds, and legal conclusions should require verification against the official notice and your own product records.

Pricing

Live Store pricing is $0.01 per Actor start plus $0.003 per dataset result, displayed as $3.00 per 1,000 results. Apify platform usage is included rather than billed separately. Use limit and your Apify run-charge controls to keep recall monitoring within budget.

Legal and non-affiliation

CPSC Product Recall Intelligence is an independent data-processing tool. CPSC names, data, images, and links belong to their respective owners. Use public sources according to their applicable terms and policies. You are responsible for validating data for your situation and for meeting the legal, product-safety, privacy, retention, and internal-control requirements that apply to your organization.

Related actors

• FDIC Bank Intelligence: enrich public bank-institution research with certificates, status, financial fields, regulators, and official source URLs.
• NHTSA Vehicle Safety Intelligence: add vehicle-recall and complaint research when a product, fleet, or marketplace workflow includes automotive goods.

Support and feedback

For a reproducible source-shape or output issue, open the actor's Issues tab with the input, sourceUrl, timestamp, actor run ID, and error text. Do not include confidential inventory, customer, or credential data. For a product request or a successful workflow, use the feedback section or leave a review. Specific examples of the expected recall field and the public source make feedback actionable.