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| title: OncoAgent |
| emoji: 🧬 |
| colorFrom: red |
| colorTo: blue |
| sdk: gradio |
| sdk_version: 5.31.0 |
| app_file: app.py |
| pinned: false |
| license: apache-2.0 |
| short_description: Multi-Agent Oncology Triage powered by AMD MI300X |
| --- |
| |
| # 🧬 OncoAgent — Multi-Agent Oncology Triage System |
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| > **AMD Developer Hackathon 2026** · Powered by AMD Instinct™ MI300X · ROCm 7.2 |
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| ## 🌍 100% Open-Source: Democratizing Oncology |
| OncoAgent is proudly 100% open-source. We believe that life-saving clinical intelligence should not be locked behind proprietary APIs. Our solution is designed to: |
| - **Guarantee Patient Privacy:** Run locally on AMD MI300X hardware or private clouds, ensuring zero patient data leaves the hospital. |
| - **Foster Global Contribution:** Allow medical communities worldwide to easily audit, modify, and contribute to the RAG knowledge base. |
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| OncoAgent is a state-of-the-art multi-agent clinical triage system designed to combat **unstructured data blindness** in primary care oncology. It leverages a tier-adaptive architecture featuring **Qwen 3.5-9B** (Speed Triage) and **Qwen 3.6-27B** (Deep Reasoning) models. Orchestrated via a sophisticated LangGraph state machine, it provides evidence-based oncological reasoning strictly grounded in NCCN/ESMO clinical guidelines, with built-in human-in-the-loop (HITL) safety gates and a Reflexion-based critic loop. |
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| ## 🏗️ Architecture |
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| ``` |
| ┌────────┐ ┌─────────┐ ┌─────────┐ ┌────────────┐ ┌────────────┐ ┌─────────┐ |
| │ Router │──▶│Ingestion│──▶│Corrective│──▶│ Specialist │◀────│ Critic │ │ Formatter│ |
| │(Triage)│ │ (PHI) │ │ RAG │ │ (Qwen 9B/ │ │(Reflexion │ │(Output) │ |
| └────────┘ └─────────┘ └─────────┘ │ 27B) │────▶│ Validation)│ └─────────┘ |
| │ │ │ └────────────┘ └────────────┘ ▲ |
| │ │ │ │ │ │ |
| ▼ ▼ ▼ ▼ ▼ │ |
| ┌───────────────────────────────────────────────────────────────────┐ ┌────────────┐ |
| │ Fallback Node │ │ HITL Gate │ |
| └───────────────────────────────────────────────────────────────────┘ │(Acuity Chk)│ |
| └────────────┘ |
| ``` |
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| **Key Components:** |
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| | Module | Description | |
| |--------|-------------| |
| | `data_prep/` | Dataset builder: PMC-Patients/OncoCoT → Strict JSONL (Llama 3 chat template) | |
| | `rag_engine/` | The "Brain": PyMuPDF extraction, Adaptive Semantic Chunking of NCCN/ESMO PDFs, & ChromaDB + PubMedBERT vectorization. | |
| | `agents/` | The "Reasoning": LangGraph multi-agent orchestration (Router → Corrective RAG → Specialist ↔ Critic → HITL Gate). | |
| | `ui/` | The "Face": Gradio 6 UI with Glassmorphism for clinical note input, real-time source citations, and reasoning output. | |
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| --- |
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| ## 🧠 Dual-Tier Model Strategy (Qwen) |
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| To maximize the compute capabilities of the **AMD MI300X**, OncoAgent implements a dynamic **Dual-Tier** routing strategy using the Qwen model family. **Both tiers have been fine-tuned on +200,000 real-world oncological cases covering all major cancer types** (derived from PMC-Patients and OncoCoT datasets) to ensure hyper-specialized medical reasoning: |
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| - **Tier 1: Qwen 3.5-9B (Speed Triage):** A lightweight, extremely fast model used by the `Router` to assess initial complexity, perform simple triage, and handle low-risk queries. |
| - **Tier 2: Qwen 3.6-27B (Deep Reasoning):** The heavy-lifter. Activated for high-complexity clinical cases (e.g., metastasis, multi-mutations). It performs deep reasoning and entailment checks, avoiding confirmation bias through rigorous Reflexion loops. |
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| ## ⚡ Hardware Target |
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| - **GPU:** AMD Instinct™ MI300X (192GB HBM3) |
| - **Software Stack:** ROCm 7.2.x, PyTorch (HIP), vLLM with PagedAttention |
| - **Models:** `Qwen/Qwen3.5-9B` (Speed Triage) & `Qwen/Qwen3.6-27B-Instruct` (Deep Reasoning) |
| - **Precision:** QLoRA 4-bit NormalFloat4 via `bitsandbytes` (ROCm compatible) |
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| ## 🚀 Quick Start |
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| ```bash |
| # 1. Clone and setup |
| git clone <repo-url> |
| cd OncoAgent |
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| # 2. Install dependencies |
| python -m venv .venv |
| source .venv/bin/activate |
| pip install -r requirements.txt |
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| # 3. Start Inference Server (vLLM on Docker) |
| # This spins up the Qwen models optimized for AMD MI300X via ROCm PagedAttention |
| docker run --device /dev/kfd --device /dev/dri -p 8000:8000 rocm/vllm:latest \ |
| --model Qwen/Qwen3.6-27B-Instruct --tensor-parallel-size 1 |
| |
| # 4. Configure environment & Run UI |
| cp .env.example .env |
| # Set VLLM_API_BASE=http://localhost:8000/v1 in .env |
| python -m ui.app |
| ``` |
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| --- |
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| ## 📁 Project Structure |
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| ``` |
| ├── docs/ # Documentation & research |
| │ ├── research/ # Deep Research analysis documents |
| │ ├── ADR/ # Architectural Decision Records |
| │ ├── oncoagent_master_directive.md |
| │ └── antigravity_rules.md |
| ├── data_prep/ # Dataset preparation (Fase 0) |
| ├── rag_engine/ # RAG ingestion & retrieval (Fase 0-3) |
| ├── agents/ # LangGraph orchestration (Fase 3) |
| ├── ui/ # Gradio frontend (Fase 4) |
| ├── tests/ # Unit & integration tests |
| ├── scripts/ # Utility scripts |
| ├── logs/ # Paper log & social media log |
| ├── requirements.txt # Pinned dependencies |
| └── Dockerfile # HF Spaces deployment |
| ``` |
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| --- |
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| ## 🩺 Safety Guarantees |
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| - **Reflexion-based Critic Loop:** A dedicated safety node audits the Specialist's output against the RAG context (entailment verification). It forces the Specialist to regenerate its output if it detects ungrounded claims or invented dosages. |
| - **Human-In-The-Loop (HITL) Gate:** An acuity-based checkpoint that stops the pipeline for human clinician approval on high-risk cases (e.g., Stage IV + complex mutations). |
| - **Corrective RAG:** The system grades retrieved context relevance. If insufficient evidence is found, it safely falls back instead of guessing. |
| - **Zero-PHI:** Regex-based PII redaction before any processing |
| - **Reproducibility:** Fixed seeds (`torch.manual_seed(42)`) across all ML scripts |
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| ## 📄 License |
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| This project was built for the AMD Developer Hackathon 2026. |
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| ## 👥 Team |
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| Built with ❤️ and AMD Instinct MI300X. |
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