# FDA Data Scraper (openFDA) (`trendlab/openfda-scraper`) Actor Scrape FDA data via the official openFDA API: drug, device, and food recalls, adverse events, and drug labels. One unified, AI-ready JSON output. No API key needed. - **URL**: https://apify.com/trendlab/openfda-scraper.md - **Developed by:** [Cheoljae Lee](https://apify.com/trendlab) (community) - **Categories:** Developer tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 record scrapeds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## FDA Data Scraper (openFDA) Pull **FDA data from the official openFDA API** — drug, device, and food **recalls**, **adverse events**, and **drug labels** — into one unified, clean, AI-ready JSON dataset. No API key required. Most FDA scrapers cover a single dataset; this one covers six, with consistent fields and a simple keyword search across all of them. ### What does this Actor do? Select one or more FDA datasets and get normalized records back: - 💊 **Drug recalls** (enforcement reports) — firm, product, reason, class, date - 🍎 **Food recalls** (enforcement reports) - 🩺 **Device recalls** - ⚠️ **Drug adverse events** (FAERS) — product, reaction, seriousness, death flag - 🔬 **Device adverse events** (MAUDE) - 🏷️ **Drug labels** (SPL) — brand, generic, manufacturer, purpose - 🔎 **Unified search** — one keyword or a raw openFDA query applied across every dataset, plus date filtering - 🤖 **AI-ready JSON** — normalized top-level fields plus the full raw record, built for dashboards, monitoring, and AI agents (via the Apify MCP server) ### Why scrape openFDA? FDA data powers real decisions across industries, but the raw API is spread across many endpoints with different schemas. Pharma companies, health-tech, medical-device makers, product-liability lawyers, insurers, supply-chain teams, and journalists use this Actor to: - **Monitor recalls** for their products or competitors in real time - Track **drug/device safety signals** (adverse events) for pharmacovigilance - Build **compliance and risk dashboards** - Feed **regulatory data** into models and AI agents ### Input example ```json { "datasets": ["drugRecalls", "foodRecalls", "deviceRecalls"], "searchTerm": "insulin", "dateFrom": "2025-01-01", "maxRecordsPerDataset": 500 } ```` - **searchTerm** — simple keyword matched across firm/product/reason (easiest). - **searchQuery** — raw openFDA expression for power users, e.g. `classification:"Class I"` or `recalling_firm:Pfizer`. - **datasets** — mix and match any of the six. ### Output example ```json { "dataset": "drugRecalls", "id": "D-0626-2026", "date": "2026-07-01", "firm": "Annora Pharma Private Limited", "product": "Metformin HCl Tablets, 500 mg", "reason": "Presence of foreign tablets: possible product mix-up", "classification": "Class II", "status": "Ongoing", "raw": { "…": "full openFDA record" } } ``` Every record keeps the complete original openFDA payload under `raw`, so no data is lost. ### How much does it cost? Pay per record returned — a small fee each. Pulling 1,000 records costs a fraction of a dollar. No subscription. ### FAQ **Do I need an API key?** No. It works key-free (1,000 requests/day). For large jobs, add a free openFDA key (`apiKey` input) to raise the limit to 120,000/day — get one at open.fda.gov/apis/authentication. **Is this affiliated with the FDA?** No. It uses the FDA's official public openFDA API. Not endorsed by the FDA. openFDA data has usage caveats (do not use to make clinical decisions) — see the FDA's terms. **Can AI agents use it?** Yes — works with the Apify MCP server, so Claude, ChatGPT, and other agents can call it and pay per use. ### Support Need another FDA dataset (NDC directory, drug shortages, 510k clearances) or a feature? Open an issue in the **Issues tab** — actively maintained, answered within one business day. # Actor input Schema ## `datasets` (type: `array`): Which openFDA datasets to pull. Each is queried and returned in one unified dataset. ## `searchQuery` (type: `string`): Optional openFDA search expression applied to every dataset, e.g. 'classification:"Class I"' or 'recalling\_firm:Pfizer'. Leave empty to return the most recent records. See openFDA docs for field names. ## `searchTerm` (type: `string`): Simple keyword searched across each dataset's main text fields (firm, product, reason). Easier alternative to the raw query above. ## `dateFrom` (type: `string`): Only records on or after this date (based on each dataset's primary date field). ## `dateTo` (type: `string`): Only records on or before this date. ## `maxRecordsPerDataset` (type: `integer`): Maximum records to return per selected dataset (paginated automatically, 1000 per request). ## `apiKey` (type: `string`): Optional free openFDA API key for higher rate limits (120,000/day vs 1,000/day). Get one at open.fda.gov/apis/authentication. Works without a key for small runs. ## Actor input object example ```json { "datasets": [ "drugRecalls", "foodRecalls", "deviceRecalls" ], "maxRecordsPerDataset": 100 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("trendlab/openfda-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("trendlab/openfda-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call trendlab/openfda-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=trendlab/openfda-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FDA Data Scraper (openFDA)", "description": "Scrape FDA data via the official openFDA API: drug, device, and food recalls, adverse events, and drug labels. One unified, AI-ready JSON output. No API key needed.", "version": "0.1", "x-build-id": "8tUFPaVz9q7GZ1DKH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/trendlab~openfda-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-trendlab-openfda-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/trendlab~openfda-scraper/runs": { "post": { "operationId": "runs-sync-trendlab-openfda-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/trendlab~openfda-scraper/run-sync": { "post": { "operationId": "run-sync-trendlab-openfda-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "datasets" ], "properties": { "datasets": { "title": "Datasets to fetch", "uniqueItems": true, "type": "array", "description": "Which openFDA datasets to pull. Each is queried and returned in one unified dataset.", "items": { "type": "string", "enum": [ "drugRecalls", "foodRecalls", "deviceRecalls", "drugAdverseEvents", "deviceAdverseEvents", "drugLabels" ], "enumTitles": [ "Drug recalls (enforcement)", "Food recalls (enforcement)", "Device recalls", "Drug adverse events (FAERS)", "Device adverse events (MAUDE)", "Drug labels (SPL)" ] }, "default": [ "drugRecalls", "foodRecalls", "deviceRecalls" ] }, "searchQuery": { "title": "Search query (openFDA syntax)", "type": "string", "description": "Optional openFDA search expression applied to every dataset, e.g. 'classification:\"Class I\"' or 'recalling_firm:Pfizer'. Leave empty to return the most recent records. See openFDA docs for field names." }, "searchTerm": { "title": "Keyword (simple)", "type": "string", "description": "Simple keyword searched across each dataset's main text fields (firm, product, reason). Easier alternative to the raw query above." }, "dateFrom": { "title": "Date from (YYYY-MM-DD)", "type": "string", "description": "Only records on or after this date (based on each dataset's primary date field)." }, "dateTo": { "title": "Date to (YYYY-MM-DD)", "type": "string", "description": "Only records on or before this date." }, "maxRecordsPerDataset": { "title": "Max records per dataset", "minimum": 1, "maximum": 25000, "type": "integer", "description": "Maximum records to return per selected dataset (paginated automatically, 1000 per request).", "default": 100 }, "apiKey": { "title": "openFDA API key (optional)", "type": "string", "description": "Optional free openFDA API key for higher rate limits (120,000/day vs 1,000/day). Get one at open.fda.gov/apis/authentication. Works without a key for small runs." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```