Tools & libraries for neuroscience research.
BrainKB provides a set of tools and libraries that facilitate knowledge extraction, structured representation, provenance tracking, and advanced analytics. While integrated into the BrainKB platform, each is also designed for independent use in your own research.
Knowledge Extraction
Extract structured data from text, PDFs, and other unstructured sources.
Provenance Tracking
Track data lineage and changes across the knowledge graph over time.
Advanced Analytics
Compare, analyze, and reason over structured neuroscience knowledge.
Tools & Libraries
End-to-end applications and open-source Python libraries built on BrainKB — usable within the platform and standalone in your own research.
A task-agnostic agentic framework for structured information extraction from text and documents, with human-in-the-loop evaluation and benchmarking.
Run end-to-end PRISMA literature reviews — from search strategy to screening, critical appraisal, and synthesis — orchestrated across multiple LLMs.
Turn meeting audio and notes into structured, queryable knowledge graphs — capturing decisions, action items, and key concepts automatically.
Map terms and concepts to standardized ontologies for consistent, interoperable, and machine-readable knowledge representation.
EviSense
A Python library to extract evidence and rationales for specific terms within documents, including scientific publications. It supports multiple LLM providers (e.g., Ollama and OpenRouter) and allows the use of multiple models for greater flexibility.
SchemaExtractor
A multi-agent based Python library for extracting and analyzing schemas from knowledge graphs.
GrobidArticleExtractor
A Python library that extracts content from PDF files using GROBID and organizes it by sections, providing a structured way to extract both metadata and content from academic papers.
ProvSense
A Python library that allows comparing the changes in the knowledge graphs.