Tula is an open-source collection of OpenClaw skills, configurations, and patterns designed to transform a general-purpose AI agent into a personal health intelligence assistant.
Named after a brilliant, strong woman - a Mensa member and mother of five - Tula embodies sharp intelligence, warmth, and directness in service of one goal: helping individuals take an active, informed role in their health.
Tula is also designed to be deployed anywhere. It is open source, self-hosted, model-agnostic, and accessible through Telegram, which operates on low-bandwidth connections and basic smartphones worldwide. A community health center in rural Rwanda, a patient advocacy group in Brazil, or an individual managing a chronic condition in India can deploy the same platform used in a US academic medical center. Health equity requires not just better tools, but tools that are free, private, and available to everyone.
Tula exists because health is not abstract. Behind every biomarker is a person. Behind every caregiver is someone they love.
The people building this project are not doing so as a technical exercise. One of us is building Tula because he lost a parent to cancer and carries hereditary risk factors he is determined to monitor proactively. Another is building it because his wife is undergoing cancer treatment, and the demands of caregiving alongside daily life require better tools for tracking medications, understanding test results, and staying organized across multiple providers. Both need the same thing: an AI that consolidates health data, provides context, and highlights what matters, without selling it, sharing it, or placing it behind a subscription.
That is the core insight. The architecture that supports a healthy individual in tracking wellness metrics is the same architecture that supports a patient in managing treatment adherence, or a caregiver in coordinating complex care. It is not three different products. It is one platform that adapts to the user's needs.
Whether you are here to support long-term health, manage a condition, or help someone you love navigate a difficult diagnosis, Tula is designed to serve your needs.
Caregivers deserve dedicated support. Medication adherence, appointment coordination, treatment journaling, and caregiver wellbeing tracking are primary use cases for Tula, not secondary features.
Tula is not a standalone application. It is a health-focused skill layer built on top of OpenClaw, providing the following capabilities:
- 📧 Intelligent Email Ingestion - Forward any health-related correspondence to Tula for automatic classification and routing. Tula identifies the content type (laboratory results, imaging studies, explanation of benefits, appointment confirmations, prescription notifications, provider messages) and routes each item to the appropriate skill for processing and structured storage. Email security is enforced at the Exchange transport layer with sender and recipient allowlists. See the security model.
- 📸 Universal Photo Capture - Photograph any health document with your phone and email it to Tula. Printed lab reports, patient portal screens, prescription bottles, hospital whiteboards, insurance EOBs, discharge instructions, imaging reports. If you can see it, you can photograph it, and Tula can extract structured data from it using multimodal AI. No patient portal integration required. No FHIR API. No IT department involvement. Your phone camera becomes the universal health data connector.
- 🧪 Laboratory Result Parsing - Automated extraction of biomarker values, units, and reference ranges from laboratory report PDFs using purpose-built medical text models (MedGemma) or general-purpose reasoning models (Claude). Longitudinal trend tracking with out-of-range flagging.
- 🩻 Medical Image Interpretation - Support for DICOM imaging studies including MRI, CT, radiograph, mammography, and ultrasound. Powered by purpose-built healthcare imaging models (Google MedGemma multimodal or Microsoft MedImageInsight/CXRReportGen, depending on deployment context). Provides plain-language annotation of key findings, medical terminology translation, and longitudinal comparison across sequential studies.
- 🧬 Genomic Health Reports - Import and analysis of consumer genomic data (e.g., 23andMe) to identify clinically relevant genetic variants and correlate predispositions with current biomarker profiles and care protocols.
- 🏥 Electronic Health Record Integration - Retrieval of medical history, visit summaries, and provider documentation from patient portals via FHIR R4 and patient access APIs.
- ⌚ Wearable Device Integration - Synchronization of daily physiological metrics from compatible wearable devices (e.g., Garmin), including heart rate variability (HRV), resting heart rate, sleep architecture, and stress indicators.
- 🩺 Home Health Device Integration - Connectivity with Bluetooth and Wi-Fi enabled home monitoring devices including blood pressure monitors, body composition scales, pulse oximeters, and glucose meters for continuous, passive data collection.
- 📓 Patient Health Journal - Structured daily check-ins via Telegram for tracking sleep quality, energy levels, mood, symptom burden, and treatment protocol adherence.
- 💼 Professional Journal - Business-focused note capture with automated daily summaries, weekly synthesis, and searchable history.
- 🔬 Research Synthesis - Scheduled retrieval and summarization of current peer-reviewed literature and clinical evidence relevant to the user's health profile and active protocols.
- 🗣️ Voice Input - Speech-to-text transcription of Telegram voice messages. Medical voice input uses MedASR (5x more accurate than general-purpose transcription on clinical terminology). General voice uses Whisper or Azure Speech Services.
- 🔒 De-Identification - Removal of protected health information (PHI) from health documents prior to sharing, export, or use in research contexts. Designed to support HIPAA Safe Harbor de-identification principles.
- 🧠 Intelligent Model Routing - Each task is directed to the most capable, cost-effective, and privacy-appropriate model available. Purpose-built healthcare models handle medical imaging and text extraction. General-purpose reasoning models handle clinical synthesis and trend analysis. Lightweight models handle routine interactions. See the model routing reference for details.
Tula supports patients navigating complex illness, caregivers, individuals managing chronic conditions, those with hereditary risk factors, community health programs in low-resource settings, and anyone focused on preventive health and wellness. See the detailed use cases for more information.
User Interface (Telegram / Email / Voice)
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Data Sources
|-- Email Inbox (automated classification and routing)
|-- Phone Camera (photograph any health document, email to Tula)
|-- Laboratory PDFs (Quest Diagnostics, LabCorp, institutional labs)
|-- Genomic Reports (23andMe, AncestryDNA, clinical panels)
|-- EHR / Patient Portal (FHIR R4 API)
|-- Wearable Devices (Garmin, compatible devices)
|-- Home Health Devices (BP monitors, scales, pulse oximeters, glucose meters)
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OpenClaw Gateway (Azure B2s, Ubuntu 24.04 LTS)
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Tula Skills (this repository)
|-- Email Router (classification and skill routing)
|-- Laboratory Parser
|-- Medical Image Interpreter (DICOM)
|-- Genomic Analyzer
|-- EHR Connector (FHIR R4)
|-- Biomarker Tracker
|-- Patient Health Journal
|-- Professional Journal
|-- Wearable Sync
|-- Home Device Sync
|-- De-Identification Engine
|-- Research Synthesis
|
AI Model Routing Layer (deployment-context-aware)
|-- Medical Imaging: MedGemma 4B / MedImageInsight / CXRReportGen
|-- Medical Text: MedGemma 27B / Claude in Foundry
|-- Medical Speech: MedASR / Azure Speech Services
|-- Clinical Reasoning: Claude Sonnet / Opus
|-- General Tasks: Gemini Flash / GPT-4o mini / Qwen / Llama
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FHIR R4 Storage (local JSON files, user-controlled)
- An OpenClaw instance (see our Deployment Guide)
- An Azure VM (B2s is sufficient, approximately $30/month) or any server running Ubuntu 24.04 LTS
- API keys for Anthropic (Claude) and Google AI Studio (Gemini)
- A Telegram account
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Deploy OpenClaw - Follow the step-by-step deployment guide. The guide covers the complete process from Azure VM creation to Telegram integration. It is written to be accessible to administrators without prior Linux experience.
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Install Tula Skills - Coming soon. Skills will be installable via ClawHub or by copying skill directories into the OpenClaw workspace.
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Configure Data Sources - Set up email forwarding for laboratory results, connect wearable and home health devices, and configure check-in schedules.
This project is in early development. Current status:
| Component | Status |
|---|---|
| Deployment Guide | ✅ Complete |
| OpenClaw Setup | ✅ Complete |
| Telegram Integration | ✅ Complete |
| Email Security Model | ✅ Complete |
| Intelligent Email Ingestion and Router | 🔨 In Progress |
| Laboratory Parser Skill | 🔨 In Progress |
| Medical Image Interpretation (DICOM) | 📋 Planned |
| Patient Health Journal Skill | 🔨 In Progress |
| Professional Journal Skill | 🔨 In Progress |
| Wearable Device Integration | 📋 Planned |
| Genomic Report Import | 📋 Planned |
| EHR / Patient Portal Connector (FHIR R4) | 📋 Planned |
| Home Device Sync (BP, Scale, Pulse Ox) | 📋 Planned |
| De-Identification Engine | 📋 Planned |
| Research Synthesis | 📋 Planned |
| Healthcare Model Routing | 📋 Planned |
| MedGemma Integration | 📋 Planned |
| Microsoft Healthcare AI Integration | 📋 Planned |
| MedASR Medical Speech | 📋 Planned |
| Voice Transcription (Whisper/MedASR) | 📋 Planned |
| Medication Adherence (IoT) | 💡 Community Idea |
| Caregiver Dashboard | 💡 Community Idea |
Contributions are welcome. Tula is built as a set of standard OpenClaw skills. Contributors familiar with OpenClaw can begin contributing immediately.
Ways to contribute:
- Report issues - If something does not work as expected, open an issue. Detailed bug reports are among the most valuable contributions at this stage.
- Propose a health skill - We are tracking community ideas in Discussions.
- Build a skill - Review the skill template for the expected structure. Submit a pull request.
- Improve documentation - The deployment guide was written during a real setup session. If any section is unclear or outdated, improvements are appreciated.
See CONTRIBUTING.md for detailed guidelines. See the community skill ideas for a full list of skills we would like to build.
- Patient empowerment through health literacy. Tula translates clinical information into language that supports informed decision-making.
- Data sovereignty. All data is stored locally on the user's own server. No cloud health platforms. No third-party data sharing.
- Intelligent model routing. Each task is directed to the most capable model for that specific job. Purpose-built healthcare models for medical imaging and text. General-purpose reasoning models for clinical synthesis. The right model for the right task in the right deployment context.
- Caregiver recognition. Caregiver support is a core use case, not a secondary consideration.
- Global health equity. Open source, self-hosted, model-agnostic, and accessible on low-bandwidth networks. Designed so that a clinic in a low-resource setting has access to the same tools as a patient in a high-income country.
- Defense in depth. Email ingestion is locked to authorized senders at the Exchange transport layer. Outbound email is restricted to authorized recipients. Prompt injection risks are analyzed honestly and mitigated at multiple layers. See the security model.
See the full principles for our complete set of values and commitments.
Running Tula costs approximately $35 - $115/month depending on usage, from text-based journaling and laboratory parsing at the low end to medical image interpretation and genomic analysis at the high end. No subscription fees. No platform lock-in. Users provide their own API keys. See the cost guide for a detailed breakdown.
This project originated as a personal build by a Windows Server administrator of 25 years deploying his first native Linux server to run an AI health agent. The deployment guide was written in real time as issues were encountered and resolved. It documents the actual experience, including common errors and their solutions.
Tula is a RealActivity initiative.
- Paul Swider - Creator. Health data integration, laboratory parsing, wearable integration, infrastructure.
- Sal Rosales - Medical adherence, caregiver tools, IoT integration.
MIT - see LICENSE. The code is free to use, modify, and distribute.
"Tula" and "RealActivity" are trademarks of RealActivity. The MIT license covers the code, not the name. See TRADEMARK.md for details.
Tula is an open-source software tool intended to support personal health data organization and health literacy. It is not a medical device, not FDA-cleared or approved, and not intended to diagnose, treat, cure, or prevent any disease or medical condition. Tula does not provide clinical decision support and should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always seek the guidance of qualified healthcare providers with any questions regarding a medical condition. If you are experiencing a medical emergency, contact your local emergency services immediately.
Your health. Your data. Your AI. Whatever your journey, Tula is here to help. 🧬
