BM Librarian¶

BM Librarian is a sophisticated Python system for AI-powered biomedical literature research. It combines multiple specialized AI agents with robust database infrastructure to convert research questions into comprehensive, evidence-based medical reports with proper citations.

What is BM Librarian?¶

BM Librarian provides an intelligent platform for biomedical research that goes far beyond traditional literature search:

Multi-Agent AI Architecture: Specialized agents work together to query, score, cite, and synthesize biomedical literature
Comprehensive Database: Indexed PubMed abstracts, MedRxiv publications, and open access full texts with semantic search capabilities
Evidence-Based Reports: Generates publication-style reports with validated citations, preventing AI hallucination
Counterfactual Analysis: Identifies and integrates contradictory evidence for balanced conclusions

Key Features¶

Fact Checker System¶

Evaluates biomedical statements as yes/no/maybe based on literature evidence. Includes both CLI and desktop GUI with blind mode for unbiased human annotation.

PaperChecker System¶

Validates research abstracts through multi-strategy searches (semantic, HyDE, keyword) and generates counter-reports with evidence-based verdicts.

Multi-Agent Architecture¶

Agent	Role
QueryAgent	Converts natural language to database queries
DocumentScoringAgent	Rates document relevance (1-5 scale)
CitationFinderAgent	Extracts relevant passages from documents
ReportingAgent	Synthesizes citations into publication-style reports
CounterfactualAgent	Identifies contradictory evidence
EditorAgent	Creates balanced reports integrating all evidence
FactCheckerAgent	Evaluates biomedical statements with literature evidence
PaperCheckerAgent	Validates abstract claims against contradictory literature

Advanced Analytics¶

Multi-model query generation using up to 3 AI models simultaneously for 20-40% improved document retrieval
Query performance tracking showing which models find most relevant documents
Counterfactual analysis with progressive audit trails
Citation validation preventing AI fabrication

Infrastructure¶

PostgreSQL with pgvector for semantic search
SQLite-based task queue for memory-efficient processing
Automated database migrations at startup
PostgreSQL audit trail tracking complete research sessions
Local LLM integration via Ollama

Getting Started¶

Requirements¶

Python 3.12+
PostgreSQL 12+ with pgvector extension
Ollama service for local LLM

Installation¶

git clone https://github.com/hherb/bmlibrarian
cd bmlibrarian
uv sync

Usage¶

Research CLI:

uv run python bmlibrarian_cli.py

GUI Research Application:

uv run python bmlibrarian_research_gui.py

Fact-Checker CLI:

uv run python fact_checker_cli.py input.json -o results.json

Fact-Checker Review GUI:

uv run python fact_checker_review_gui.py --input-file results.db --blind

Configuration GUI:

uv run python bmlibrarian_qt.py

Stay Updated¶

Check out our Blog for the latest development updates, release announcements, and tutorials.

Open Source¶

BM Librarian is free software released under the GNU General Public License v3.0. Visit our GitHub repository to contribute.