Bio-Research Agent
Accelerating scientific discovery with a semantic search engine that finds relevant papers and generates experimental protocols instantly — cutting literature review from hours to seconds.
The Challenge
A researcher starting a new experimental line faces two bottlenecks before they can run a single assay. First, literature review: PubMed alone indexes over 35 million papers, and keyword search returns hundreds of loosely relevant results that take hours to triage manually. Second, protocol translation: even after finding the right paper, converting a methods section into a step-by-step bench protocol requires deep domain familiarity — a task that is time-consuming for veterans and genuinely difficult for junior lab members who may misinterpret critical steps. Both bottlenecks slow science down at the exact moment when speed matters most.
The Solution
This agent acts as a tireless research assistant with two specialized skills working in sequence. A semantic search engine built on TF-IDF and Cosine Similarity surfaces the most contextually relevant papers for any scientific query — understanding intent rather than just matching keywords. The top results are passed to the Researcher Skill, which identifies consensus findings across sources and summarizes them, then hands off to the Lab Assistant Skill, which synthesizes that knowledge into a structured, step-by-step experimental protocol complete with materials, controls, and expected outcomes.
Key Capabilities
- 🧬 Semantic Search TF-IDF vectorization plus Cosine Similarity ranks papers by conceptual relevance to the query — so a search for "CRISPR off-target editing" surfaces papers discussing guide RNA specificity even if those exact words don't appear in the title.
- 📖 Multi-Paper Synthesis The Researcher Skill reads across the top-ranked abstracts, identifies where findings converge or conflict, and produces a consensus summary — the equivalent of a focused mini-review written in seconds.
- 🧪 Protocol Generation The Lab Assistant Skill operationalizes the research into a structured bench protocol: reagent list, numbered procedural steps, positive/negative controls, and troubleshooting notes — ready to hand directly to a technician.
- 🔗 Source Attribution Every protocol step is traceable back to the specific papers that informed it — maintaining scientific rigor and giving reviewers a clear audit trail from literature to bench decision.
- ⚡ Instant Results Search, synthesis, and protocol generation complete in under 10 seconds — versus the 2–4 hours a researcher would spend manually reviewing literature and drafting the same output.
Tech Stack
- Scikit-Learn (TF-IDF)
- NLTK (NLP)
- Streamlit
- Anthropic Claude API
- Docker / Cloud Run
Patterns Used:
Business Value
Literature review and protocol preparation can consume 20–30% of a researcher's working hours — time not spent at the bench. By compressing that work from hours to seconds, this agent effectively multiplies a lab's experimental throughput without adding headcount or budget.
Who this is built for
Research teams and life sciences organizations where literature review and protocol prep are eating into actual bench time.
Try it live
Type a research query — try "CRISPR off-target editing", "mRNA vaccine immunogenicity", or "Western blot optimization". The agent finds relevant papers, synthesizes findings, and generates a step-by-step experimental protocol. No login required.
Launch Live Demo →Cut literature review from hours to seconds
The demo above is the full working agent. If you want a version trained on your organization's internal research corpus, connected to a specific literature database, or integrated into your lab management workflow — let's talk.