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
Project details
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