biolit 0.1.3

LLM-assisted biomedical literature screening and structured extraction for PubMed and GEO.

biolit

LLM-assisted biomedical literature screening and structured extraction. Accepts PubMed alert emails, plain PMID lists, or GEO accession lists. Supports multiple LLM providers and optional full-text retrieval.

Setup

Requirements: Python 3.8+

Install the package (creates the biolit command):

pip install -e .

Copy .env.example to .env and add your API key:

cp .env.example .env
# edit .env and set ANTHROPIC_API_KEY (or OPENAI_API_KEY)

Usage

The tool accepts several input formats, auto-detected by file extension or content:

Input	How to pass	Example
PubMed alert email	positional .eml file	alert.eml
PMID list (file)	positional plain-text file, one PMID per line	pmids.txt
GEO accession list (file)	positional plain-text file, one accession per line	geo_accessions.txt
PMIDs (inline)	--pmids flag, comma-separated	--pmids 41795042,41792186
GEO accessions (inline)	--accessions flag, comma-separated	--accessions GSE53987,GSE12345

Use --default to run with schizophrenia genomics defaults (no prompts):

biolit alert.eml --default
biolit pmids.txt --default
biolit geo_accessions.txt --default
biolit --pmids 41795042,41792186 --default
biolit --accessions GSE53987 --default

Or specify criterion and fields as flags:

biolit pmids.txt \
  --criterion "Is this about treatment-resistant schizophrenia?" \
  --fields "methodology, sample_size, treatment, outcomes"

Or interactively (prompted if not provided):

biolit alert.eml

Single-record screening

Use biolit screen to quickly check one paper or GEO record for relevance without running the full extraction pipeline:

biolit screen --pmid 41627908 --default
biolit screen --accession GSE53987 --default
biolit screen --pmid 41627908 --criterion "Is this about treatment-resistant schizophrenia?"
biolit screen --pmid 41627908 --fulltext --default

Output is a single line to stdout:

RELEVANT [abstract] — Paper uses GWAS to investigate schizophrenia risk loci.

GEO accession input

Pass a file of GEO series accessions (GSE, GDS, GSM, or GPL prefixes) to screen GEO records directly. The tool fetches each record's MINiML XML, extracts the summary, overall design, experiment type, and organism, then runs the same LLM screening and extraction pipeline.

biolit geo_accessions.txt \
  --criterion "Does this study perturb a transcription factor?" \
  --fields "organism, experiment_type, tf_perturbed, perturbation_method, summary"

GEO results include geo_accession and pmids (linked PubMed IDs) columns in place of pmid.

Full-text retrieval (PubMed inputs only)

Use --fulltext to screen and extract from full text instead of just the abstract. The pipeline tries each source in order:

1. PMC JATS XML (open access)
2. Preprint XML (bioRxiv / medRxiv)
3. Unpaywall PDF (requires --unpaywall-email)
4. Abstract fallback

biolit alert.eml --default --fulltext --unpaywall-email [e-mail]

Limit which sections are sent to the LLM:

biolit alert.eml --default --fulltext --sections methods,results

LLM providers

The tool supports Anthropic (default), OpenAI, and local Ollama models:

# OpenAI
biolit pmids.txt --default --provider openai --model gpt-4o

# Ollama (local)
biolit pmids.txt --default --provider ollama --model llama3

You can also set LLM_PROVIDER and LLM_MODEL as environment variables.

Output

Each run creates a timestamped directory (e.g. run_20260313_142000/) containing:

• results.csv — one row per relevant record
• artifacts/<id>/ — per-record folder with the text sent to the LLM, metadata, and any retrieved full-text files

With --default on PubMed inputs, the CSV columns are:

Column	Description
title	Paper title
url	PubMed link
pmid	PubMed ID
doi	DOI
text_source	Where the text came from (abstract, pmc_fulltext, preprint_fulltext, unpaywall_pdf)
methodology	General method (e.g. GWAS, scRNA-seq, proteomics)
sample_type	Tissue/sample type and origin
causal_claims	Statements about causes of schizophrenia inferred from the data
genetics_claims	Claims about specific genes, loci, or pathways
summary	2-3 sentence plain-language summary for triage

For GEO inputs, pmid is replaced by geo_accession and pmids.

The CSV can be imported directly into Google Sheets (File → Import).

MCP server

biolit ships an MCP server that exposes the pipeline as tools for any MCP-compatible client (Claude Desktop, Claude CLI, OpenAI Agents SDK, etc.).

Start the server:

biolit-mcp

Or test interactively with the MCP inspector:

mcp dev biolit/mcp_server.py

Configure Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "biolit": {
      "command": "biolit-mcp"
    }
  }
}

Restart Claude Desktop. The tools will appear in the tool picker.

Configure Claude CLI

Add a .mcp.json in your project root:

{
  "mcpServers": {
    "biolit": {
      "command": "biolit-mcp"
    }
  }
}

Available tools

Batch pipelines (equivalent to the biolit CLI):

Tool	Description
run_pipeline	Screen + extract a list of PMIDs, write results CSV
run_geo_pipeline	Screen + extract a list of GEO accessions, write results CSV

Single-record (equivalent to biolit screen):

Tool	Description
screen_by_pmid	Fetch + screen a PubMed paper in one call
screen_by_geo	Fetch + screen a GEO record in one call

Low-level (for custom workflows):

Tool	Description
search_pubmed	Fetch PubMed metadata by PMID
fetch_geo_record	Fetch and parse a GEO record by accession
fetch_fulltext	Retrieve full text for a PMID
screen_paper	LLM relevance screen given pre-fetched text
extract_fields	Structured field extraction given pre-fetched text
read_pmids_from_eml	Parse PMIDs from a PubMed alert .eml file

Use as a Python library

The pipeline functions are importable directly:

from biolit.pipeline import screen_by_pmid, screen_by_geo, run, run_geo
from biolit.llm import get_llm_client

client = get_llm_client("anthropic")

# Single-record screen
result = screen_by_pmid(client, "41627908", "Is this about schizophrenia genomics?")
# {"relevant": True, "reason": "...", "text_source": "abstract"}

# Batch pipeline
run(client, pmids=["41627908", "33741721"], criterion="...", fields_description="methodology, summary", output_path="results.csv")

Known Limitations

• Papers without abstracts or accessible full text are skipped silently.
• Full-text retrieval (--fulltext) applies to PubMed inputs only; GEO records use the record metadata directly.