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🏥 MedKit: A Unified Platform for Medical Data APIs

CI Status Test Coverage Strict Mypy Python 3.9+ License: MIT Version

MedKit is a high-performance, unified SDK that transforms fragmented medical APIs into a single, programmable platform. It provides a clean interface for OpenFDA, PubMed, and ClinicalTrials.gov, augmented with a clinical intelligence layer and relationship mapping.

Important

v3.0.0 Release: This major update transforms MedKit into a production-grade SDK. It introduces robust connection pooling, dynamic rate limiting, circuit breakers, exponential jitter retries, strict strict MyPy types, and completely formalized Pydantic V2 validations spanning a 39/39 passing test suite.

MedKit CLI Demo

✨ Async Example (v3.0.0)

import asyncio
from medkit import AsyncMedKit

async def main():
    async with AsyncMedKit() as med:
        # Unified search across all providers in parallel
        results = await med.search("pembrolizumab")
        
        print(f"Drugs found: {len(results.drugs)}")
        print(f"Clinical Trials: {len(results.trials)}")
        
        # Get a synthesized conclusion
        conclusion = await med.ask("What is the clinical status of Pembrolizumab for NSCLC?")
        print(f"Summary: {conclusion.summary}")
        print(f"Confidence: {conclusion.confidence_score}")

asyncio.run(main())

🤔 Why MedKit?

Feature Without MedKit With MedKit
Integrations 3 separate APIs / SDKs Unified Sync/Async Client
Resilience 403 blocks from gov APIs Auto-Fallback (Curl/v2 API)
Synthesis Alphabetical/Noisy lists Frequency-Ranked Intervals
Logic Manual data correlation Native knowledge graphs
Speed Sequential network calls Parallel Async Orchestration

Note: This is still a Work in Progress, meaning there might be missing functionaliy, placeholders, etc. If you find something that you would like to be fixed/implemented soon, please open an issue. Also, this SDK is not FDA-Approved, and has no official medical licensing. Use at your own discretion!

🏗️ Architecture

MedKit abstracts complexity through a high-performance orchestration layer:

      Developer / User
             │
             ▼
    ┌───────────────────┐
    │  MedKit / Async   │ (Unified Interface)
    └─────────┬─────────┘
              │
    ┌─────────┴─────────────────────┐
    │       Intelligence Layer      │
    │  ├─ Ask Engine (Extraction)   │
    │  ├─ Graph Engine (Context)    │
    │  ├─ Interaction Engine        │
    │  └─ Synthesis Engine (Ranked) │
    └─────────┬─────────────────────┘
              │
    ┌─────────┴─────────────────────┐
    │       Providers Layer         │
    │  ├─ OpenFDA     (Drug Label)  │
    │  ├─ PubMed      (Research)    │
    │  └─ ClinTrials  (v2 + Fallback)│
    └───────────────────────────────┘

🚀 Core Platform Features

  • Robust Connectivity (NEW): Automatic curl fallback for ClinicalTrials.gov bypasses TLS fingerprinting blocks, ensuring 100% data availability.
  • Enterprise Reliability: Embedded exponential backoff retries with full jitter, circuit breakers preventing upstream cascades, and sliding-window rate limiters.
  • Strictly Typed Ecosystem: Zero Any leakage. 100% strictly typed medkit/py.typed interface enforcing strict Pydantic V2 extra="forbid" models natively.
  • Async-First Orchestration: Parallel health checks and search execution eliminate latency bottlenecks and perceived "hangs."
  • Precision Evidence Synthesis: Automated clinical verdicts with frequency-ranked interventions and filtered therapeutic agents (Drugs/Biologicals).
  • High-Performance CLI: Interactive, list-based visualization for trials and research papers, optimized for all terminal sizes.
  • Unified Caching: Enhanced Disk and Memory caching for high-performance repeat queries.

🛠️ Testing

MedKit ships with a production-grade, isolated mock testing infrastructure that achieves comprehensive validation without relying on live API stability.

pytest tests/ -v

📦 Installation

pip install medkit-sdk

🖥️ CLI Power Tools

Clinical Ask (Synthesized)

$ medkit ask "pembrolizumab for lung cancer"

 Clinical Conclusion 

Summary: Highly-validated therapeutic landscape with multi-modal evidence.
Evidence Confidence: [████████████████████] 1.00

Top Interventions: Pembrolizumab, Bevacizumab, Carboplatin, Cisplatin

Trials Search

$ medkit trials "melanoma" --limit 5

Clinical Trials for 'melanoma'
- NCT01234567: RECRUITING - Study of Pembrolizumab in Advanced Melanoma
- NCT07654321: COMPLETED - Comparison of B-Raf Inhibitors

Knowledge Graph

$ medkit graph "lung cancer"

Knowledge Graph: lung cancer
Nodes: 26 | Edges: 8

 Lung Cancer 
├── Drugs
│   └── None found
├── Trials
│   ├── A Study of QL1706 Combined Wit...
│   ├── Circulating Tumor DNA Detectio...
│   └── Trial of Single Protein Encaps...
└── Papers
    ├── Phase III placebo-controlled o...
    └── Therapeutic strategies for eld...

🤝 Contributing

We welcome contributions! As an open-source project, community feedback and improvements can be the backbone of thi.

  1. Check the Code: Feel free to dive into the codebase and identify any bugs or areas for improvement.
  2. Open an Issue: If you find a fault, no matter how small, please open an issue or start a discussion.
  3. Submit a Pull Request: Direct improvements and new provider integrations are highly encouraged.

I'd much rather have a brutal code review that helps me improve the engine than silence!

🗺️ Roadmap

  • v1.0.0: Foundation medical mesh and provider integration.
  • v2.0.0: Async architecture, v2 API support.
  • v3.0.0: Major revamp: Large-scale readiness (Circuit Breakers, Retries, Coverage, Pydantic V2, CLI UI).
  • v4.0.0: Local GraphQL medical mesh endpoint.

📄 License

MIT License - see LICENSE for details.