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Joseph Pollack
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DeepCritical

AI-Native Drug Repurposing Research Agent

DeepCritical is a deep research agent system that uses iterative search-and-judge loops to comprehensively answer research questions. The system supports multiple orchestration patterns, graph-based execution, parallel research workflows, and long-running task management with real-time streaming.

Features

  • Multi-Source Search: PubMed, ClinicalTrials.gov, Europe PMC (includes bioRxiv/medRxiv)
  • MCP Integration: Use our tools from Claude Desktop or any MCP client
  • HuggingFace OAuth: Sign in with your HuggingFace account to automatically use your API token
  • Modal Sandbox: Secure execution of AI-generated statistical code
  • LlamaIndex RAG: Semantic search and evidence synthesis
  • HuggingFace Inference: Free tier support with automatic fallback
  • Strongly Typed Composable Graphs: Graph-based orchestration with Pydantic AI
  • Specialized Research Teams of Agents: Multi-agent coordination for complex research tasks

Quick Start

# Install uv if you haven't already
pip install uv

# Sync dependencies
uv sync

# Start the Gradio app
uv run gradio run src/app.py

Open your browser to http://localhost:7860.

For detailed installation and setup instructions, see the Getting Started Guide.

Architecture

DeepCritical uses a Vertical Slice Architecture:

  1. Search Slice: Retrieving evidence from PubMed, ClinicalTrials.gov, and Europe PMC
  2. Judge Slice: Evaluating evidence quality using LLMs
  3. Orchestrator Slice: Managing the research loop and UI

The system supports three main research patterns:

  • Iterative Research: Single research loop with search-judge-synthesize cycles
  • Deep Research: Multi-section parallel research with planning and synthesis
  • Research Team: Multi-agent coordination using Magentic framework

Learn more about the Architecture.

Documentation

Links