TL;DR
- You can integrate PubMed content into your AI app in 3 lines of code using Valyu’s Search API
- Valyu offers daily-updated, structured access to millions of peer-reviewed biomedical studies with natural language search
- Native support for LangChain, Vercel AI SDK, or LlamaIndex
Why PubMed Matters for AI Builders
PubMed is the primary gateway to peer-reviewed biomedical research. It’s used to power:
- AI Medical research assistants
- AI Drug discovery tools
- AI Mechanistic explainers and hypothesis generators
- AI Clinical decision support systems
By grounding outputs in the scientific literature, your AI application becomes more explainable, defensible, and useful in real-world settings.
The Problem With Native PubMed Access
- Keyword-only search: no semantic or natural language understanding
- HTML/XML formats: scraping required
- No integrated pipeline with clinical trials or drug data
- No filters for date, journal, or study type via API
- Hard to plug into RAG workflows
The Easy Way: Use Valyu’s PubMed Search API
Valyu transforms PubMed into a clean, developer-ready experience:
- Semantic search across millions of papers
- Clean JSON with title, authors, abstract, and metadata
- Near real-time updates
- Plug-and-play with AI frameworks
3-Line Setup
1import { Valyu } from 'valyu-js';23const valyu = new Valyu({ apiKey: 'your-valyu-api-key' });45const response = await valyu.search(6 "Phase 3 melanoma immunotherapy trials currently recruiting"7);89console.log(response);
Example Use Cases
AI Research Assistant
“Summarise recent studies on checkpoint inhibitor resistance.”
Drug Mechanism Explorer
“Find PubMed articles explaining CRISPR off-target effects.”
Evidence Synthesis Copilot
“Combine PubMed papers and clinical trial data on melanoma treatments.”
Benchmark
MedAgent benchmark: Valyu achieved 48% accuracy, Google 45%, Exa 44%, and Parallel 42% on complex medical queries.
Valyu makes PubMed natively usable in AI workflows without the need to scrape, glue code, or worry about latency.
Check out our Health Benchmark blog post or our public benchmarks on GitHub to understand our benchmarking methodology to learn more.
Advanced Search Example
Query PubMed for papers published in the last 6 months on resistance mechanisms in melanoma:
1const response = await valyu.search(2 "checkpoint inhibitor resistance mechanisms melanoma",3 {4 included_sources: ["valyu/valyu-pubmed"],5 start_date: "2025-05-01",6 max_num_results: 107 }8);
Combine this with valyu/valyu-clinical-trials to enrich answers with real-world data.
Live Demo: PubMed + AI
Try the PubMed demo
Search biomedical literature in natural language, apply filters, and stream results into your LLM context window ideal for RAG, agents, and summarisation tasks.
Best Practices for AI-Powered PubMed Search
- Too many results? Use max_num_results (e.g., 5–10) to save tokens
- Low quality hits? Raise relevance_threshold to 0.75+
- Want recency? Add date_range: "last_12_months" or similar
- For summaries? Chunk abstracts or use response_length: "summary"
FAQ (Schema-Enabled)
Q: How often is PubMed data updated?
A: Valyu syncs with PubMed daily for the latest papers and metadata.
Q: Can I search by journal name or author?
A: Yes- simply include the name or author in your query string.
Q: Do you return full-text articles?
A: Abstracts are always included. Full-text is available for open access papers.
Q: Can I combine PubMed with other datasets?
A: Yes- Valyu supports multi-source queries (e.g., PubMed + clinical trials + FDA labels).
Build Smarter Medical AI Apps
Use PubMed data the way it was meant to be used — structured, searchable, and optimized for AI applications.