# PubMed Scraper (`bgfc97/pubmed-scraper`) Actor

Scrape biomedical literature from PubMed — title, abstract, authors, journal, publication date, DOI, PMC ID, MeSH terms, keywords and publication types — by search query or PMID. Official NCBI E-utilities API, no key, no proxy.

- **URL**: https://apify.com/bgfc97/pubmed-scraper.md
- **Developed by:** [Bruno](https://apify.com/bgfc97) (community)
- **Categories:** AI, Business, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 article 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## PubMed Scraper 🩺

Scrape biomedical literature from **PubMed** (the NCBI/NIH database of 37M+ citations). Search by keyword or fetch exact papers by PMID, and get clean structured records — **including full abstracts**.

Perfect for **systematic / literature reviews, pharma & biotech intelligence, RAG / AI training datasets, meta-analyses, and researcher / KOL mapping.**

### What it extracts (one row per article)

- `pmid`, `title`, `abstract` (with section labels)
- `authors`, `journal`, `published`
- `doi`, `doi_url`, `pmc_id`
- `mesh_terms`, `keywords`, `publication_types`, `language`
- `url`

### Input

```json
{
  "queries": ["CRISPR cancer therapy", "semaglutide[Title] AND 2024[PDAT]"],
  "pmids": ["38000000"],
  "maxItemsPerQuery": 300,
  "apiKey": ""
}
```

- **queries** — full PubMed query syntax supported (`[Title]`, `[Author]`, `[MeSH]`, `[PDAT]`, boolean AND/OR/NOT).
- **pmids** — fetch exact records by PubMed ID.
- **apiKey** — optional NCBI key to raise the rate limit (3 → 10 req/s). Works fine without one.

### Output example

```json
{
  "type": "article",
  "pmid": "38010000",
  "title": "CRISPR-based approaches in solid tumors",
  "abstract": "BACKGROUND: ...\nMETHODS: ...\nRESULTS: ...",
  "authors": ["Jane A Smith", "Wei Chen"],
  "journal": "Nature Reviews Cancer",
  "published": "2024-Jan-15",
  "doi": "10.1038/s41568-024-00000-0",
  "doi_url": "https://doi.org/10.1038/s41568-024-00000-0",
  "pmc_id": "PMC10800000",
  "mesh_terms": ["CRISPR-Cas Systems", "Neoplasms"],
  "publication_types": ["Journal Article", "Review"],
  "url": "https://pubmed.ncbi.nlm.nih.gov/38010000/"
}
```

### Notes

- Uses the **official NCBI E-utilities API** (esearch + efetch) — free, stable.
- Respects NCBI rate limits automatically; add your API key for higher throughput.
- No proxy required.

# Actor input Schema

## `queries` (type: `array`):

PubMed search queries. Supports PubMed syntax, e.g. 'CRISPR cancer', 'semaglutide\[Title]', 'Smith J\[Author] AND 2024\[PDAT]'.

## `pmids` (type: `array`):

Specific PubMed IDs to fetch full records for.

## `apiKey` (type: `string`):

Optional NCBI API key to raise the rate limit from 3 to 10 requests/sec. Leave blank to run without a key.

## `maxItemsPerQuery` (type: `integer`):

Stop after this many articles per query.

## `proxyConfiguration` (type: `object`):

Optional. NCBI E-utilities is an open API; proxy usually not needed.

## Actor input object example

```json
{
  "queries": [
    "CRISPR cancer therapy"
  ],
  "pmids": [],
  "maxItemsPerQuery": 100,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "queries": [
        "CRISPR cancer therapy"
    ],
    "pmids": [],
    "proxyConfiguration": {
        "useApifyProxy": false
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("bgfc97/pubmed-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "queries": ["CRISPR cancer therapy"],
    "pmids": [],
    "proxyConfiguration": { "useApifyProxy": False },
}

# Run the Actor and wait for it to finish
run = client.actor("bgfc97/pubmed-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 '{
  "queries": [
    "CRISPR cancer therapy"
  ],
  "pmids": [],
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}' |
apify call bgfc97/pubmed-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=bgfc97/pubmed-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/QQTKlWnE7hTHO7fYz/builds/Pqxt8zT0U2JqKiIgX/openapi.json