openhealthforall/open-health

OpenHealth, AI Health Assistant | Powered by Your Data

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Summary Information

Updated 29 minutes ago
Added to GitGenius on September 17th, 2026
Created on January 23rd, 2025
Open Issues & Pull Requests: 58 (+0)
GitHub issues: Enabled
Number of forks: 429
Total Stargazers: 3,952 (+0)
Total Subscribers: 50 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 13.9 hours
Mean response time: 25.8 days
90th percentile: 18.0 days
Tracked items: 44

Most active contributors

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How this project is maintained

100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Three people close 84% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 46
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 550 days
Stale 30+ days: 45
Stale 90+ days: 45

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • good first issue (7)
  • llm (4)
  • source (2)

Most active issues this week

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Detailed Description

OpenHealth is an AI health assistant that helps users consolidate and manage their personal health data through conversational AI interactions.

The tool addresses the challenge of fragmented health information by providing a centralized platform where users can input various health data sources including blood test results, checkup data, physical information, family history, and symptoms. It automatically parses this data into structured formats and uses that context to power personalized conversations with AI language models, enabling users to better understand and manage their health. The system can run entirely locally for maximum privacy, with data parsing handled through a separate component and language model interactions supported through multiple providers.

OpenHealth suits individuals who want to take an active role in their health management and value privacy in handling sensitive medical information. The platform offers two deployment options: a lightweight clinic version for quick health consultations and a full platform for comprehensive health management. Users should be aware that the data parsing functionality currently runs on a separate Python server, though migration to TypeScript is planned. For local execution without external dependencies, the tool supports Ollama for the language model component and docling for parsing.

The project maintains active development with support for multiple language models including LLaMA, DeepSeek-V3, GPT, Claude, and Gemini. Documentation is available in multiple languages, reflecting an international development focus. The codebase is built with TypeScript and Next.js, indicating a modern web-first architecture approach.