docs/pending/03_modal_integration.md

Modal Integration

Priority: P1 - HIGH VALUE ($2,500 Modal Innovation Award)

---

What Modal Is For

Modal provides serverless GPU/CPU compute. For DeepCritical:

Current Use Case (Mario's Code)

• src/tools/code_execution.py - Run LLM-generated analysis code in sandboxes
• Scientific computing (pandas, scipy, numpy) in isolated containers

Potential Additional Use Cases

Use Case	Benefit	Complexity
Code Execution Sandbox	Run statistical analysis safely	✅ Already built
LLM Inference	Run local models (no API costs)	Medium
Batch Processing	Process many papers in parallel	Medium
Embedding Generation	GPU-accelerated embeddings	Low

---

Current State

Mario implemented src/tools/code_execution.py:

# Already exists - ModalCodeExecutor
executor = get_code_executor()
result = executor.execute("""
import pandas as pd
import numpy as np
# LLM-generated statistical analysis
""")

What's Missing

1. Not wired into the main pipeline - The executor exists but isn't used
2. No Modal tokens configured - Needs MODAL_TOKEN_ID/MODAL_TOKEN_SECRET
3. No demo showing it works - Judges need to see it

---

Integration Plan

Step 1: Wire Into Agent Pipeline

Add a StatisticalAnalysisAgent that uses Modal:

# src/agents/analysis_agent.py
from src.tools.code_execution import get_code_executor

class AnalysisAgent:
    """Run statistical analysis on evidence using Modal sandbox."""

    async def analyze(self, evidence: list[Evidence], query: str) -> str:
        # 1. LLM generates analysis code
        code = await self._generate_analysis_code(evidence, query)

        # 2. Execute in Modal sandbox
        executor = get_code_executor()
        result = executor.execute(code)

        # 3. Return results
        return result["stdout"]

Step 2: Add to Orchestrator

# In orchestrator, after gathering evidence:
if settings.enable_modal_analysis:
    analysis_agent = AnalysisAgent()
    stats_results = await analysis_agent.analyze(evidence, query)

Step 3: Create Demo

# examples/modal_demo/run_analysis.py
"""Demo: Modal-powered statistical analysis of drug evidence."""

# Show:
# 1. Gather evidence from PubMed
# 2. Generate analysis code with LLM
# 3. Execute in Modal sandbox
# 4. Return statistical insights

---

Modal Setup

1. Install Modal CLI

pip install modal
modal setup  # Authenticates with Modal

2. Set Environment Variables

# In .env
MODAL_TOKEN_ID=your-token-id
MODAL_TOKEN_SECRET=your-token-secret

3. Deploy (Optional)

modal deploy src/tools/code_execution.py

---

What to Show Judges

For the Modal Innovation Award ($2,500):

1. Sandbox Isolation - Code runs in container, not local
2. Scientific Computing - Real pandas/scipy analysis
3. Safety - Can't access local filesystem
4. Speed - Modal's fast cold starts

Demo Script

# Run the Modal verification script
uv run python examples/modal_demo/verify_sandbox.py

This proves code runs in Modal, not locally.

---

Files to Update

• Wire code_execution.py into pipeline
• Create src/agents/analysis_agent.py
• Update examples/modal_demo/ with working demo
• Add Modal setup to README
• Test with real Modal account

---

Cost Estimate

Modal pricing for our use case:

• CPU sandbox: ~$0.0001 per execution
• For demo/judging: < $1 total
• Free tier: 30 hours/month

Not a cost concern.