Building a persistent-memory AI desktop app with Tauri (architecture notes + lessons learned)

[belkadimehdi98-commits]

Over the past few months, I’ve been building a Windows desktop AI application using Rust + Tauri focused on long-session performance and persistent memory.

One of the problems I kept running into with AI tools was:

• Long conversations becoming slow
• Context resets between sessions
• No clear separation between runtime state and stored memory

So I decided to experiment with a different architecture approach.

🧠 Core Ideas

• Local-first persistent memory (SQLite-based)
• Explicit session management instead of implicit context stacking
• Memory prefetching + selective injection instead of full transcript replay
• Separation between runtime state, encrypted storage, and sync layer
• Hybrid secure storage model (OS keychain when available + encrypted vault fallback)

⚙️ Technical Challenges Faced

• Managing long-session token growth without freezing the UI
• Designing local-first memory with optional cloud sync
• Handling async Rust orchestration without blocking Tauri
• Preventing image/file state loss across reloads
• Windows packaging & WebView2 runtime edge cases

📦 Stack

• Rust backend (Tauri commands + async orchestration)
• React / TypeScript frontend
• SQLite for local persistence
• Optional Supabase sync layer
• Windows MSI distribution

This project pushed me to think differently about how AI apps should handle memory and state over time.

I’d love feedback from other Tauri builders on:

• Best practices for cross-platform secure storage
• Plugin patterns for memory-heavy desktop apps
• Performance profiling strategies in Tauri for long-running sessions

If anyone is interested in the deeper architectural breakdown, I documented it here:

https://github.com/belkadimehdi98-commits/mymate-architecture

[belkadimehdi98-commits]

If helpful, I can also share a short breakdown of how I structured session isolation and memory prefetching in more detail.

Curious how others are handling long-running context in desktop AI tools.