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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:
- Search Slice: Retrieving evidence from PubMed, ClinicalTrials.gov, and Europe PMC
- Judge Slice: Evaluating evidence quality using LLMs
- 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
- Overview - System architecture and design
- Getting Started - Installation and setup
- Configuration - Configuration guide
- API Reference - API documentation
- Contributing - Development guidelines