A simple implementation of the knowledge-updating research agent from the DOE Agent Requirements document.

1. Searches arXiv for recent papers on protein-ligand binding affinity
2. Triages relevance using Claude to determine if papers are relevant
3. Extracts binding data (protein, ligand, affinity values) from abstracts
4. Stores in SQLite for querying and analysis
5. Alerts on high-impact findings (logs to console; extend for email/Slack)
6. Tracks retraining threshold (stubs out the actual NERSC job submission)

cd examples/knowledge-agent
pip install -r requirements.txt
export ANTHROPIC_API_KEY=your-key-here

python agent.py

# Run daily at 2am
0 2 * * * cd /path/to/knowledge-agent && python agent.py >> agent.log 2>&1

• knowledge_base.db — SQLite database with papers and extracted binding data
• agent_state.json — Agent state (last run, record counts, model version)
• agent.log — Log file

sqlite3 knowledge_base.db

-- Recent relevant papers
SELECT title, published FROM papers WHERE is_relevant = 1 ORDER BY published DESC LIMIT 10;

-- Extracted binding data
SELECT protein, ligand, affinity_value, affinity_unit, method
FROM binding_data
ORDER BY created_at DESC LIMIT 20;

-- Count by protein
SELECT protein, COUNT(*) as count FROM binding_data GROUP BY protein ORDER BY count DESC;

Edit the CONFIG dict in agent.py:

CONFIG = {
    "search_query": "protein ligand binding affinity",  # arXiv search terms
    "max_papers_per_run": 20,                           # papers to process per run
    "lookback_days": 7,                                 # initial lookback window
    "records_before_retrain": 200,                      # trigger retrain threshold
    ...
}

Add more sources (PubMed, bioRxiv):

• PubMed: Use biopython with Entrez
• bioRxiv: Use their RSS feed or API

Real alerts:

• Email: Use smtplib or SendGrid
• Slack: Use slack_sdk

Real retraining:

• Use Globus Compute to submit jobs to NERSC
• Add human approval flow (could be a simple prompt or web interface)

This is a demonstration. A production version would need:

• Better error handling and retries
• Rate limiting for APIs
• More sophisticated extraction (full-text, not just abstracts)
• Actual model retraining integration
• Human approval workflow
• Proper secrets management