biolit 0.1.18

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 and mixed lists of PMIDs, DOIs, and GEO accessions in any combination. Retrieves full text from PMC, Europe PMC, bioRxiv/medRxiv, Unpaywall, and Semantic Scholar. Supports multiple LLM providers and exposes all functionality as an MCP server.

Setup

Requirements: Python 3.8+

Install from PyPI:

pip install biolit

Or install from source for development:

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 a PubMed alert email (.eml) or a plain-text file of identifiers, as well as inline identifiers via --ids. Identifiers can be PMIDs, DOIs, or GEO accessions — mixed lists are supported in a single run.

Input	How to pass	Example
PubMed alert email	positional .eml file	alert.eml
Identifier file (mixed)	positional plain-text file, one per line	identifiers.txt
Inline identifiers	--ids flag, comma-separated	--ids 41795042,GSE53987,10.1101/2025.03.17.25324098

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

biolit docs/alert.eml --default
biolit docs/pmids.txt --default
biolit docs/geo_accessions.txt --default
biolit --ids 41795042,41792186,GSE53987 --default
biolit --ids 10.1101/2025.03.17.25324098 --default

Or specify criterion and fields as flags:

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

Or use a JSON config file to store reusable parameters (CLI flags take precedence). The config can include ids or input_file (path to an .eml or identifier list), so no positional argument or --ids flag is needed:

biolit alert.eml --config my_config.json
biolit --config my_config.json   # ids or input_file supplied by config

The fields key in a config file can be a comma-separated string or a JSON object mapping field names to extraction descriptions. Using a dict skips the schema-building LLM call and gives the model precise instructions:

{
  "fields": {
    "tf_name": "HGNC symbol of the transcription factor perturbed in this experiment",
    "organism": "scientific name of the organism used",
    "platform": "GPL accession of the microarray platform"
  }
}

Omit --criterion to skip screening (all records are extracted). Omit --fields to use the default fields (methodology, sample_type, causal_claims, summary):

# fetch + extract with defaults (no screening)
biolit alert.eml

# fetch + screen only, then extract with defaults
biolit alert.eml --criterion "Is this about treatment-resistant schizophrenia?"

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 --doi 10.64898/2026.02.16.706214 --default
biolit screen --pmid 41627908 --criterion "Is this about treatment-resistant schizophrenia?"

Output is a single line to stdout:

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

Mixed identifier lists

PMIDs, DOIs, and GEO accessions can be freely mixed in a file or via --ids. Each identifier is auto-detected by format:

• 41795042 → PMID (all digits)
• 10.1101/2025.03.17.25324098 → DOI (starts with 10.)
• GSE53987 → GEO accession (starts with GSE, GDS, GSM, or GPL)

biolit --ids 41795042,GSE53987,10.1101/2025.03.17.25324098 --default

GEO records additionally include a linked_pmids column. All record types share pmid, doi, and geo_accession columns (null when not applicable).

Full-text retrieval

Full-text retrieval runs automatically for every PMID and DOI (including preprints). For GEO records, the pipeline attempts full-text retrieval via each linked PMID in order, falling back to the GEO record metadata if no linked paper has accessible full text. The pipeline tries each source in order:

1. PMC JATS XML (open access)
2. Europe PMC JATS XML (broader open-access coverage)
3. Preprint XML (bioRxiv / medRxiv)
4. Unpaywall PDF (requires --unpaywall-email)
5. Semantic Scholar open-access PDF
6. Abstract fallback

To enable Unpaywall (step 4), pass your email:

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

Limit which sections are sent to the LLM:

biolit alert.eml --default --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 fields, the CSV columns are:

Column	Description
title	Paper title
url	Link to PubMed, GEO, or DOI
pmid	PubMed ID (null for unindexed preprints)
doi	DOI (null for GEO records)
geo_accession	GEO accession (null for non-GEO records)
text_source	Where the text came from (abstract, pmc_fulltext, europepmc_fulltext, preprint_fulltext, unpaywall_pdf, s2_pdf, geo_linked_fulltext, geo_linked_abstract, geo_record)
citation_count	Citation count from Semantic Scholar (null if not found)
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
summary	2-3 sentence plain-language summary for triage

GEO records additionally include a linked_pmids column listing all associated PubMed IDs.

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 pipeline (equivalent to the biolit CLI):

Tool	Description
run_pipeline	Fetch, optionally screen, and optionally extract a mixed list of PMIDs, DOIs, and/or GEO accessions; write results CSV. All parameters optional — pass only config_path to drive the entire run from a JSON file.

Low-level (for custom workflows):

Tool	Description
fetch_pubmed_metadata	Fetch PubMed metadata by PMID
fetch_geo_record	Fetch and parse a GEO record by accession
fetch_fulltext	Retrieve full text for a PMID (6-step chain)
fetch_geo_fulltext	Retrieve full text for a GEO accession via its linked PMIDs
screen_paper	LLM relevance screen given pre-fetched text
extract_fields	Structured field extraction given pre-fetched text
resolve_doi	Resolve a DOI to PMID + PMCID via the NCBI ID Converter
lookup_s2_pdf	Check whether Semantic Scholar has an open-access PDF for a DOI
read_pmids_from_eml	Parse PMIDs from a PubMed alert .eml file
get_version	Return the installed biolit package version

Use as a Python library

The pipeline functions are importable directly:

from biolit.pipeline import run, screen_paper, fetch_record
from biolit.llm import get_llm_client

client = get_llm_client("anthropic")

# Batch pipeline — PMIDs, DOIs, and GEO accessions can be mixed freely
# criterion and fields_description are optional; omit either to skip that step
# Returns (csv_path, record_count)
csv_path, count = run(client, ids=["41627908", "GSE53987", "10.1101/2025.03.17.25324098"],
    criterion="...", fields_description="methodology, summary", output_path="results.csv")

# Fetch + write metadata only (no LLM calls)
csv_path, count = run(client, ids=["41627908", "GSE53987"])

# Fetch a single record (auto-detects PMID / DOI / GEO)
paper = fetch_record("10.1101/2025.03.17.25324098")

# Screen pre-fetched text
result = screen_paper(client, paper, "Is this about schizophrenia genomics?", paper["abstract"])
# {"relevant": True, "reason": "..."}

Known Limitations

• Papers without abstracts or accessible full text are skipped silently.
• GEO records attempt full-text retrieval via linked PMIDs. text_source will be geo_linked_fulltext, geo_linked_abstract, or geo_record depending on what was accessible.
• bioRxiv/medRxiv JATS XML is frequently blocked by Cloudflare regardless of headers. The pipeline falls back to the title and abstract from the bioRxiv API (text_source: preprint_abstract).
• The Semantic Scholar API allows roughly 100 unauthenticated requests per day. Set SEMANTIC_SCHOLAR_API_KEY in .env for higher limits.