aicliniq

AICliniq

The open-source Python framework for building healthcare AI applications.
Like Django is to web apps, like HuggingFace is to ML models, AICliniq is to clinical AI.

Created and maintained by Satheesh Kola.

Install the framework. Use pre-built clinical agents. Build your healthcare AI product in days.

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What is AICliniq?

AICliniq is a 5-layer framework for healthcare AI:

Layer 1: Core Framework      → pip install aicliniq
Layer 2: Domain Packs         → pip install aicliniq[clinical]
Layer 3: Server               → aicliniq serve
Layer 4: Dashboard            → aicliniq ui
Layer 5: CLI Scaffolding      → aicliniq new my-app

17 clinical domains · 39 model providers · LLM Council (multi-model consensus) · MCP Server · HIPAA audit trail · FHIR R4 · 4 memory types · 6 reasoning patterns · 7 safety layers

New to AICliniq? Read ARCHITECTURE.md to understand the 5-layer framework model.

Quick Start — 5 Levels

Level 1: Beginner (3 lines, 5 minutes)

pip install aicliniq[groq]
export GROQ_API_KEY=gsk_...

from aicliniq import quick
import asyncio

# Clinical scribe — transcript to SOAP note
note = asyncio.run(quick.scribe("Patient is a 45yo male with chest pain..."))
print(note.soap)

# Triage — symptoms to urgency classification
result = asyncio.run(quick.triage("chest pain, diaphoresis", age=62))
print(f"{result.urgency} ({result.score}/10)")

# Clinical Q&A — evidence-based answers
answer = asyncio.run(quick.ask("First-line treatment for HFrEF?"))
print(answer.answer)

Level 2: Builder (use pre-built domain agents)

pip install aicliniq[clinical-radiology]

from aicliniq.domains.clinical.radiology import RadiologyReportAgent
from aicliniq.providers.vlm.medgemma import MedGemmaProvider

agent = RadiologyReportAgent(vlm=MedGemmaProvider())
report = await agent.run(input=dicom_image)
print(report.findings)

Level 3: Customizer (extend for your hospital)

from aicliniq.domains.clinical.radiology import RadiologyReportAgent

class CityGeneralRadiologyAgent(RadiologyReportAgent):
    """Customized for City General Hospital"""
    REPORT_TEMPLATE = CITY_GENERAL_TEMPLATE
    
    async def on_critical_finding(self, finding):
        await notify_slack(finding)

Level 4: Product Builder (scaffold full app)

aicliniq new my-radiology-saas --template radiology
cd my-radiology-saas
docker compose up -d
# Full FastAPI + React + Postgres app running

Level 5: Enterprise (self-hosted platform)

git clone https://github.com/kolasatheesh/aicliniq
cd aicliniq
docker compose up -d
# Postgres (pgvector) + FastAPI server + React console, all on your own infrastructure

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I Want To...

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What's Inside

17 Clinical Domains (34+ Agents)

39 Model Providers

LLM (24): Groq, OpenAI, Anthropic, Gemini, DeepSeek, xAI/Grok, Cohere, Mistral, Fireworks, Together, HuggingFace, LiteLLM, Ollama, + 11 more VLM (3): MedGemma 1.5, PathChat+, BiomedCLIP Genomics (2): ESM-2 (proteins), Enformer (gene expression) Chemistry (2): ChemBERTa, RDKit Speech (6): Whisper, AssemblyAI, ElevenLabs, Google STT, Azure Speech, Kokoro

Production Platform

Full-Stack Application

• FastAPI server — 11 API routers (50+ endpoints), JWT auth, multi-tenant, SSE streaming
• React console — 28 pages covering every clinical domain + LLM Council
• PostgreSQL + pgvector — semantic search and long-term memory
• Docker Compose — one-command deployment

CLI

aicliniq serve          # Start API server
aicliniq ui             # Start React console
aicliniq agents         # List all 34+ agents
aicliniq domains        # List all 17 domains
aicliniq init           # Generate starter project
aicliniq sandbox        # Explore synthetic clinical data
aicliniq learn          # Interactive tutorials
aicliniq run scribe -i '{"transcript": "..."}'  # Run agent from CLI
aicliniq validate       # Check configuration

v0.1.0-alpha Status

Covered by Tests (1,800+ automated tests, LLM calls mocked)

• All template agents (Scribe, Triage, RAG-QA, Diagnostic Dialogue)
• LLM Council (25 tests: consensus, PHI routing, agreement scoring)
• Groq and OpenAI LLM providers
• PHI Masker, AES-256 encryption, sandbox, hallucination guard
• FHIR R4 resources (all 11 types), HL7 v2 parser, terminology service
• Observability (tracing, cost tracking, drift detection)
• Full server test suite (auth, agents, audit, compliance, multitenancy, FHIR)

Scaffolded (Contributions Welcome)

• 22 of 39 providers (stubs with clear error messages — see PROVIDERS.md)
• Federated learning, voice AI, fine-tuning (architecture ready)

Documentation

Full documentation at docs.aicliniq.org: concepts, platform layers, guides, deployment, and persona-specific onboarding.

For the complete usage guide including how to run, build, package, and deploy: HOW_TO_USE.md

Contributing

See CONTRIBUTING.md. We especially welcome:

• New clinical domain packs
• Provider implementations
• Evaluation benchmarks
• Translations (10 languages supported)

License

Copyright 2026 Satheesh Kola. Licensed under the Apache License 2.0; see NOTICE.