PubMed Scraper

Search 35 million biomedical research papers and medical articles from PubMed/MEDLINE. Batch queries, MeSH filtering, full abstracts, author affiliations, and structured metadata, ready for literature reviews, systematic reviews, AI and RAG pipelines, and drug research.

Extract structured article data from PubMed via the NCBI E-utilities API. No API key required. Clean JSON output with consistent fields, ready for downstream analysis, RAG pipelines, or LLM tool use.

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🚀 What's New (v1.1.0)

Batch Query Search

Run multiple search queries in a single job. Pass queriesList: ["cancer immunotherapy", "CAR-T therapy", "checkpoint inhibitors"] and get merged, deduplicated results across all queries. One paid run replaces 10+ separate searches, built for systematic reviews and broad topic mapping.

Emoji Input Labels

All input fields now have emoji labels for faster navigation in the Apify console.

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🎯 Use Cases

🤖 AI Agents & LLM Pipelines

PubMed is the gold standard for biomedical knowledge. Wire this actor into your AI pipeline as a callable tool:

• RAG knowledge bases: Index 10,000+ abstracts for semantic search over medical literature
• MCP tool use: AI agents call this actor in real time to look up evidence, find citations, or answer clinical questions
• Literature-grounded generation: Pull structured abstracts as context before generating summaries, reports, or clinical content
• Evidence retrieval: Agent receives a medical question → queries PubMed → returns cited sources

One run can return thousands of structured, citable records ready for embedding. The consistent schema (pmid, abstract, mesh_terms, doi_url) means no cleaning required.

Example (AI agent topic scan):

{
    "mode": "search_articles",
    "queriesList": ["GLP-1 receptor agonist", "semaglutide weight loss", "tirzepatide clinical trial"],
    "publicationType": "Clinical Trial",
    "dateFrom": "2022",
    "maxResults": 500
}

💊 Pharma & Biotech Research

Track the scientific landscape around drug targets, mechanisms, and therapeutic areas, without manual PubMed sessions:

• Competitive intelligence: Monitor competitor publications by author, institution, or research area
• Target validation: Pull all publications on a gene or protein target to assess research depth
• Clinical trial landscape: Filter by Clinical Trial or RCT publication type to see what's been run
• Drug repurposing: Cross-reference MeSH terms across therapeutic areas to find unexplored connections
• Pipeline monitoring: Track publications on specific drugs or mechanisms as they emerge

Example (drug target landscape):

{
    "mode": "search_articles",
    "meshTerm": "PCSK9",
    "publicationType": "Review",
    "sort": "pub_date",
    "maxResults": 200
}

📑 Systematic Reviews & Meta-Analyses

Systematic reviews require comprehensive literature searches across multiple query formulations. This is where batch search pays off:

• Multi-query search: Cover all synonyms and related terms in one job, no manual re-running
• Deduplication: Results automatically deduplicated by PMID, no overlap in your dataset
• Date-bounded searches: Filter to a specific review window with dateFrom/dateTo
• Study design filtering: Filter to RCTs, meta-analyses, or systematic reviews only
• Export ready: Download as CSV or Excel directly from the Apify dataset UI

Example (systematic review search):

{
    "mode": "search_articles",
    "queriesList": [
        "cognitive behavioral therapy depression",
        "CBT major depressive disorder",
        "psychotherapy depressive symptoms randomized"
    ],
    "publicationType": "Randomized Controlled Trial",
    "dateFrom": "2015",
    "dateTo": "2024",
    "maxResults": 1000
}

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📖 Usage Examples

Example 1: Batch Query Search (Systematic Review)

{
    "mode": "search_articles",
    "queriesList": ["cancer immunotherapy", "CAR-T cell therapy", "PD-1 inhibitor"],
    "publicationType": "Clinical Trial",
    "maxResults": 500
}

Example 2: Single Keyword Search

{
    "mode": "search_articles",
    "query": "CRISPR gene editing",
    "publicationType": "Review",
    "maxResults": 100
}

Example 3: MeSH Term Search

{
    "mode": "search_articles",
    "meshTerm": "Alzheimer Disease",
    "publicationType": "Meta-Analysis",
    "dateFrom": "2020",
    "maxResults": 200
}

Example 4: Author Search

{
    "mode": "search_by_author",
    "author": "Zhang F",
    "query": "CRISPR",
    "maxResults": 50
}

Example 5: Journal Search

{
    "mode": "search_by_journal",
    "journal": "Nature Medicine",
    "query": "immunotherapy",
    "sort": "pub_date",
    "maxResults": 100
}

Example 6: Single Article Lookup

{
    "mode": "get_article",
    "pmid": "33243215"
}

Example 7: Date-Bounded Search

{
    "mode": "search_articles",
    "query": "COVID-19 long COVID",
    "dateFrom": "2023/01/01",
    "dateTo": "2024/12/31",
    "sort": "pub_date",
    "maxResults": 300
}

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👥 Who Uses This

🔬 Systematic Reviewers & Meta-Analysts

Running a systematic review means covering all synonyms and related terms, one query isn't enough. Batch search handles this in a single run.

• Cover all query variants (queriesList) and get a deduplicated PMID set across all of them
• Bound by date range to match your review window
• Filter to the exact study design you need (RCT, Meta-Analysis, Systematic Review)
• Download as CSV/Excel directly from the Apify dataset UI, no cleaning needed

{
    "mode": "search_articles",
    "queriesList": [
        "cognitive behavioral therapy depression",
        "CBT major depressive disorder",
        "psychotherapy depressive symptoms"
    ],
    "publicationType": "Randomized Controlled Trial",
    "dateFrom": "2015",
    "dateTo": "2024",
    "maxResults": 1000
}

💊 Pharma & Biotech Teams

Track the publication landscape around a drug target, mechanism, or therapeutic area without manual PubMed sessions.

• Map all publications on a gene or protein target to assess research depth before investing in it
• Monitor competitor publications by author, institution, or research area over time
• Filter to Review or Clinical Trial types to see what stage the evidence is at
• Track emerging publications with sort: pub_date + date range

{
    "mode": "search_articles",
    "meshTerm": "PCSK9",
    "publicationType": "Clinical Trial",
    "sort": "pub_date",
    "dateFrom": "2022",
    "maxResults": 500
}

🧪 Clinical Researchers

Find evidence for a specific intervention, track a researcher's publication record, or monitor a journal's output.

• Pull all papers by a specific author to build their publication profile
• Search a journal combined with a keyword to scope relevant output
• Use MeSH terms for precise clinical concept matching (avoids terminology variation)
• Combine author + query to track a researcher's work on a specific topic

{
    "mode": "search_by_author",
    "author": "Bhatt DL",
    "query": "cardiovascular outcomes trial",
    "maxResults": 100
}

🤖 AI/LLM Engineers

PubMed is the gold-standard source for biomedical knowledge. Wire this into your pipeline as a callable data source.

• Index thousands of abstracts into a vector store for semantic retrieval over medical literature
• Use via MCP so AI agents can look up live evidence during a conversation
• Pull literature as structured context before generating summaries, clinical notes, or reports
• The consistent output schema (pmid, abstract, mesh_terms, doi_url) means no field normalization needed downstream

{
    "mode": "search_articles",
    "queriesList": ["GLP-1 receptor agonist", "semaglutide weight loss", "tirzepatide efficacy"],
    "publicationType": "Clinical Trial",
    "dateFrom": "2020",
    "maxResults": 500
}

📝 Medical Writers & Journalists

Find citations fast, scope coverage on a topic, or pull an author's full bibliography without spending hours on PubMed.

• Single article lookup by PMID to get structured metadata for citation formatting
• Author search to build a bibliography or verify publication history
• Topic search with date range to understand how coverage of a story has evolved
• pmc_url field points directly to free full text when available

{
    "mode": "get_article",
    "pmid": "36352213"
}

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🔗 Related Actors

Other actors from the same portfolio that pair well with PubMed Scraper:

Actor	What It Does
NPI Provider Contact Finder	Finds doctor emails, practice websites, and LinkedIn profiles from the NPPES NPI Registry
FDA Drug Labels Scraper	Pulls structured drug label data from FDA DailyMed, pair with PubMed for full drug research coverage
Academic Paper Scraper	Research papers from ArXiv, Semantic Scholar, and CrossRef, broader coverage beyond biomedical

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🔍 Input Parameters

Parameter	Type	Default	Description
mode	string	search_articles	search_articles, get_article, search_by_author, search_by_journal
query	string	""	Single keyword search. Ignored when queriesList is provided
queriesList	array	[]	Batch search multiple queries, results merged and deduplicated. meshTerm, author, journal, publicationType and the date range apply to every query
pmid	string	""	PubMed ID for get_article mode
author	string	""	Author name (e.g. "Smith J")
journal	string	""	Journal name (e.g. "Nature", "Cell")
meshTerm	string	""	MeSH subject heading (e.g. "Neoplasms")
publicationType	string	""	Filter by study design: Clinical Trial, Review, Meta-Analysis, Randomized Controlled Trial, Systematic Review
dateFrom	string	""	Publication start date: YYYY, YYYY/MM, or YYYY/MM/DD
dateTo	string	""	Publication end date: YYYY, YYYY/MM, or YYYY/MM/DD
sort	string	relevance	relevance, pub_date, Author, JournalName
maxResults	integer	100	Max articles to return (1 to 10,000). Free Apify plan: 10 per run
requestIntervalSecs	number	0.34	Seconds between requests to NCBI (0.2 to 5)
timeoutSecs	integer	30	Timeout for each HTTP request (5 to 120)
maxRetries	integer	5	Retries on failed or rate-limited requests (0 to 10)

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📊 Output Format

Each article is pushed as a structured JSON record. Download as JSON, CSV, or Excel from the Apify dataset UI.

{
    "schema_version": "1.0",
    "type": "article",
    "pmid": "38786024",
    "doi": "10.3390/cells13100800",
    "pmc_id": "PMC11119143",
    "title": "CRISPR-Based Gene Therapies: From Preclinical to Clinical Treatments.",
    "abstract": "In recent years, clustered regularly interspaced short palindromic repeats (CRISPR)...",
    "authors": [
        {
            "last_name": "Laurent",
            "fore_name": "Marine",
            "initials": "M",
            "affiliation": "INTEGRARE, UMR_S951, Genethon, Inserm, Univ Evry, Université Paris-Saclay, 91190 Evry, France."
        }
    ],
    "journal": "Cells",
    "journal_abbreviation": "Cells",
    "issn": "2073-4409",
    "volume": "13",
    "issue": "10",
    "pages": "",
    "publication_date": "2024 May 08",
    "pub_year": "2024",
    "keywords": ["CRISPR/Cas9", "Duchenne muscular dystrophy", "gene editing"],
    "mesh_terms": ["Humans", "Genetic Therapy", "CRISPR-Cas Systems", "Gene Editing"],
    "publication_types": ["Journal Article", "Review"],
    "language": "eng",
    "country": "Switzerland",
    "grant_list": [],
    "reference_count": 207,
    "pubmed_url": "https://pubmed.ncbi.nlm.nih.gov/38786024/",
    "doi_url": "https://doi.org/10.3390/cells13100800",
    "pmc_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11119143/"
}

Output Fields

Field	Description
pmid	PubMed ID
doi / pmc_id	DOI and PubMed Central ID
title	Article title
abstract	Full abstract text
authors	List with last name, first name, initials, affiliation
journal / journal_abbreviation / issn	Full and abbreviated journal name, and journal ISSN
volume / issue / pages	Publication details
publication_date / pub_year	Date and year
keywords	Author-provided keywords
mesh_terms	MeSH subject headings
publication_types	Article types (Review, Clinical Trial, etc.)
language / country	Article language (ISO 639-2 code) and the journal's country
grant_list	Funding sources
reference_count	Number of references
pubmed_url / doi_url / pmc_url	Direct links to PubMed, DOI, and free full text

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🤖 MCP Integration

Wire this actor as a tool in your AI agent pipeline via the Apify MCP server. No custom server needed.

• Endpoint: https://mcp.apify.com?tools=labrat011/pubmed-scraper
• Auth: Authorization: Bearer <APIFY_TOKEN>
• Works with: Claude Desktop, Cursor, VS Code, Windsurf, Warp, Gemini CLI

MCP config:

{
    "mcpServers": {
        "pubmed-scraper": {
            "url": "https://mcp.apify.com?tools=labrat011/pubmed-scraper",
            "headers": {
                "Authorization": "Bearer <APIFY_TOKEN>"
            }
        }
    }
}

Ask your AI: "Find all clinical trials on semaglutide published in the last 2 years and summarize the findings"

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💰 Pricing

Pay-per-result pricing: $0.80 per 1,000 articles, plus a small one-time start fee of $0.005 per run. You only pay for the articles you scrape.

Tier	Limit	Use case
Free Apify plan	10 results/run	Testing and integration verification
Paid Apify plan	Up to 10,000 results/run	Systematic reviews, RAG pipelines, bulk research

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❓ FAQ

Can I get full text?

This actor returns abstracts and metadata. Check the pmc_url field, if present, the full text is freely available on PubMed Central. Otherwise use doi_url to reach the publisher.

What are MeSH terms?

Medical Subject Headings (MeSH) are controlled vocabulary terms assigned by NLM indexers. They help find articles on specific topics even when authors use different terminology. Use the MeSH Browser to find the right term.

How current is the data?

PubMed is updated daily. This actor queries NCBI directly, so results reflect the current state of the database.

Is there a rate limit?

NCBI allows 3 requests/second without an API key. Rate limiting is built in, you don't need to configure anything.

What's the difference between query and queriesList?

query runs a single search. queriesList runs multiple searches in one job and merges the results, deduplicating by PMID. Use queriesList for systematic reviews where you need to cover multiple synonyms or related terms.

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📚 Resources

• PubMed: Web interface
• MeSH Browser: Search MeSH terms
• NCBI E-utilities: API documentation
• PubMed Help: Search syntax guide

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Built for researchers, AI engineers, and data scientists who need biomedical literature at scale.