I built a persistent memory layer for AI agents in Rust

1 points by architsingh15 122 days ago | 2 comments

Every Claude Code session starts from zero. It doesn't remember the bug you debugged yesterday, the architecture decision you made last week, or that you prefer Tailwind over Bootstrap. I built Memori to fix this.

It's a Rust core with a Python CLI. One SQLite file stores everything -- text, 384-dim vector embeddings, JSON metadata, access tracking. No API keys, no cloud, no external vector DB.

What makes it different from Mem0/Engram/agent-recall:

- Hybrid search: FTS5 full-text + cosine vector search, fused with Reciprocal Rank Fusion. Text queries auto-vectorize -- no manual --vector flag needed.

- Auto-dedup: cosine similarity > 0.92 between same-type memories triggers an update instead of a new insert. Your agent can store aggressively without worrying about duplicates.

- Decay scoring: logarithmic access boost + exponential time decay (~69 day half-life). Frequently-used memories surface first; stale ones fade.

- Built-in embeddings: fastembed AllMiniLM-L6-V2 ships with the binary. No OpenAI calls.

- One-step setup: `memori setup` injects a behavioral snippet into ~/.claude/CLAUDE.md that teaches the agent when to store, search, and self-maintain its own memory.

Performance (Apple M4 Pro): - UUID get: 43µs - FTS5 text search: 65µs (1K memories) to 7.5ms (500K) - Hybrid search: 1.1ms (1K) to 913ms (500K) - Storage: 4.3 KB/memory, 8,100 writes/sec - Insert + auto-embed: 18ms end-to-end

The vector search is brute-force (adequate to ~100K), deliberately isolated in one function for drop-in HNSW replacement when someone needs it.

After setup, Claude Code autonomously:

- Recalls relevant debugging lessons before investigating bugs

- Stores architecture insights that save the next session 10+ minutes of reading

- Remembers your tool preferences and workflow choices

- Cleans up stale memories and backfills embeddings ~195 tests (Rust integration + Python API + CLI subprocess), all real SQLite, no mocking.

MIT licensed.

GitHub: https://github.com/archit15singh/memori

Blog post on the design principles: https://archit15singh.github.io/posts/2026-02-28-designing-cli-tools-for-ai-agents/

AlexCalderAI 122 days ago |

This is exactly the problem most agent builders hit around turn 10-15. Simple system_prompt + conversation_history patterns work for a few turns, then context drift happens.

The 5-layer approach (chunk → embed → retrieve → rank → synthesize) fixes this: your agent needs to forget smartly, not remember everything.

Key insight from building agents daily: the hard part isn't storage - it's knowing WHEN to chunk, expire, or summarize. Session boundaries matter more than raw persistence.

If you're building for multi-agent workflows, think about concurrent write conflicts early. Much cheaper to design around than retrofit.

Terretta 122 days ago |

Something something session boundaries mumble think much cheaper to design around.

Project sounds cool. Would have tried it except for this weird engagement.