📘 Journiv - Private Journal

Note

Active development on this project is currently paused during June–August due to summer travel and family commitments.

Development will resume in August.

For any urgent issues, please contact: contact@journiv.com.

⚠️ Beta Software

Journiv is currently in beta and under active development. While the developers aims to keep data backward-compatible, breaking changes may still occur. Please keep regular backups of your data to avoid loss during updates.

Journiv is a self-hosted private journal. It features comprehensive journaling capabilities including mood tracking, prompt-based journaling, media uploads, analytics, and advanced search with a clean and minimal UI.

        

👉 Watch Demo Videos          

Quick Start

Give Journiv a quick try with one docker command.

Note

This docker run command starts a minimal version of Journiv. It lack components needed for various features of Journiv like import/export etc. For a complete docker compose file use this.

Docker Run

docker run -d \
  --name journiv \
  -p 8000:8000 \
  -e SECRET_KEY=your-secret-key-here \
  -e DOMAIN_NAME=192.168.1.1 \
  -v journiv_data:/data \
  --restart unless-stopped \
  swalabtech/journiv-app:latest

Access Journiv: Open http://192.168.1.1:8000 (replace with your server IP) in your browser to try it out.

For complete installation guide see installation guide.

Demo

Want to just try a demo? (Thanks to JasonFieldz for hosting a demo instance).

• Username: demo@test.com
• Password: Demo1234

Documentation

Read the docs to learn more about Journiv and configuring it.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md and LICENSE for guidelines.

License

This project is licensed under the terms specified in the LICENSE file.

Support

Need help or want to report an issue?

• GitHub Issues: Report bugs or request features
• Discussions: Ask questions and share ideas
• Email: journiv@protonmail.com
• Discord: Join our community server

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Disclaimer

AI/LLM Usage

This repository contains portions of code, documentation, or text generated with the assistance of AI/LLM tools. All outputs have been reviewed and adapted by the author to the best of their ability before inclusion.