Show HN: SuperLocalMemory – AI memory that stays on your machine, forever free
github.com*First open-source project. Built after getting tired of Mem0's $50/month bills.*
For the past year, I've been using Claude for everything—debugging, architecture reviews, code generation. The problem? Every new session starts from zero. "Remember that auth bug we fixed?" → "I don't have access to previous conversations."
I tried Mem0. Great product, but $50/month for cloud storage felt wrong when my machine has 1TB of disk sitting idle. I tried building my own with simple key-value storage. Worked okay for 100 memories, broke down at 500+.
So I spent 6 months implementing 2026 research: PageIndex (Meta), GraphRAG (Microsoft), xMemory (Stanford). The result is SuperLocalMemory V2.
*Architecture (what makes it different):*
- *Layer 1:* SQLite + FTS5 for raw storage (<10ms writes) - *Layer 2:* Tree hierarchy for context (parent-child relationships) - *Layer 3:* Knowledge graph with auto-clustering (TF-IDF + Leiden algorithm) - *Layer 4:* Pattern learning (learns your coding style: "You prefer React over Vue - 73% confidence")
*It works everywhere:* - MCP integration → Auto-configures Claude Desktop, Cursor, Windsurf, Continue.dev - Universal skills → Works in Claude Code, Cody, VS Code extensions - CLI → `slm remember "content"` from any terminal
*What I learned building this:*
1. *Graph clustering is expensive* - Leiden algorithm is O(n²) worst case. Solution: profile splitting for >1000 memories.
2. *Backward compatibility is hard* - V1 users upgrading to V2 hit schema changes. Solution: Try-catch on every ALTER TABLE with existence checks.
3. *MCP auto-configuration is fragile* - 11 different IDEs, 11 different JSON formats. Solution: Template system with jq fallback.
*Current status:* 2.2.0 release. 100% test coverage (35/35 passing). Works on Mac/Linux/Windows. Single `./install.sh` script.
*Performance (tested up to 10K memories):* - Add memory: <10ms - Hybrid search: 80ms (combines semantic + exact + graph) - Graph build (100 memories): <2s - Storage: 60-96% compression via tiering
*When NOT to use this:*
- If you need cloud sync across devices → Use Mem0 or Zep - If you need enterprise SSO/audit logs → Use Zep Enterprise - If you don't code → This is developer-focused
*GitHub:* https://github.com/varun369/SuperLocalMemoryV2 *Install:* `git clone https://github.com/varun369/SuperLocalMemoryV2.git && cd SuperLocalMemoryV2 && ./install.sh`
*Honest ask:* This is my first open-source project. I'd love feedback on: - Architecture decisions (especially the 4-layer approach) - Performance bottlenecks (graph build feels slow) - Integration priorities (what tools to support next) - Documentation clarity
I'm a senior manager at Accenture (25+ years) with basic coding skills. Built this because I needed it, not because I wanted to compete with Mem0/Zep (they're great products).
*What's next:* V3 release with `npm install -g superlocalmemory` for easier installation. Same features, better distribution.