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Research agents for Lobster AI - literature discovery and data management

Project description

lobster-research

Literature discovery and data acquisition agents for scientific research workflows.

Installation

pip install lobster-research

Agents

Agent Description
research_agent Literature discovery specialist. PubMed/bioRxiv search, GEO/SRA dataset discovery, metadata extraction, publication queue management.
data_expert_agent Data operations specialist. Queue-based downloads, modality management, local file loading, workspace orchestration.

Services

Service Purpose
ModalityDetectionService Auto-detect data modality type from file characteristics

Features

Research Agent (Online Operations)

  • PubMed literature search with filters and related paper discovery
  • bioRxiv and medRxiv preprint search with full-text access
  • GEO dataset discovery with organism and platform filtering
  • SRA run metadata extraction and download URL generation
  • PRIDE proteomics repository integration
  • Full-text content extraction from PMC articles
  • Methods section parsing for computational parameter discovery
  • Publication queue for batch processing of research papers
  • Automatic extraction of associated dataset identifiers

Data Expert Agent (Offline Operations)

  • Execute downloads from pre-validated queue entries
  • Zero online access boundary for security and reproducibility
  • Multi-format file loading (CSV, TSV, H5AD, Excel)
  • Modality listing, inspection, and validation
  • Download strategy selection (AUTO, H5_FIRST, MATRIX_FIRST)
  • Sample concatenation with union or intersection logic
  • Failed download retry with exponential backoff
  • Custom Python code execution for edge cases

Platform Support

  • 10x Genomics MTX format (matrix, barcodes, features)
  • H5AD pre-processed AnnData files
  • Kallisto and Salmon bulk RNA-seq quantification
  • CSV and TSV generic delimited matrices
  • MaxQuant, Olink, and generic proteomics formats

Architecture

The research and data_expert agents implement a clean boundary pattern:

research_agent (ONLINE)              data_expert (OFFLINE)
-- Search literature                 -- Execute downloads
-- Discover datasets                 -- Load local files
-- Extract metadata/URLs             -- Manage modalities
-- Validate metadata                 -- Retry failed downloads
-- Create queue entries              -- Concatenate samples
         |                                    |
         ----------- Queue Entry -------------
                   (PENDING -> IN_PROGRESS -> COMPLETED)

Requirements

  • Python 3.12+
  • lobster-ai >= 1.0.0

Documentation

Full documentation: docs.omics-os.com/docs/agents/research

License

MIT

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