nan-forget: Local AI coding memory in a single SQLite file

nan-forget is a local memory system for AI coding agents that addresses context loss across sessions. Instead of re-explaining your stack repeatedly, it maintains persistent memory in a single SQLite file without requiring background processes or consuming significant RAM.

Key Details

The tool was built with Claude Code over several weeks. Claude helped design the 3-stage retrieval pipeline (recognition → recall → spreading activation), wrote most of the SQLite migration from Qdrant, and caught edge cases in the vector search scoring.

Setup is straightforward: npx nan-forget setup and you're done. The entire database fits in one SQLite file (~3MB) with no background services required.

Four hooks automatically save context as you work—you never need to manually call save. The system includes an "auth system" example that can find specific implementation details like "We chose JWT with Clerk." Search works by meaning rather than keywords.

Memories are structured with problem/solution/concepts fields, allowing bug fixes from months ago to surface when you encounter similar errors later. Old memories decay on a 30-day half-life, with stale ones consolidating into summaries while active memories sharpen.

The same database works across multiple tools: Claude Code (via MCP), Codex, Cursor (via REST API), and terminal (via CLI). All memory operations run locally without LLM calls, and the project is free and open source.