Show HN: Hippo, biologically inspired memory for AI agents

The secret to good memory isn't remembering more. It's knowing what to forget.

Works with: Claude Code, Codex, Cursor, OpenClaw, any CLI agent
Imports from: ChatGPT, Claude (CLAUDE.md), Cursor (.cursorrules), any markdown
Storage: SQLite backbone + markdown/YAML mirrors. Git-trackable and human-readable.
Dependencies: Zero runtime deps. Requires Node.js 22.5+. Optional embeddings via @xenova/transformers.

AI agents forget everything between sessions. Existing solutions just save everything and search later. That's a filing cabinet, not a brain.

Your memories are also trapped. ChatGPT knows things Claude doesn't. Cursor rules don't travel to Codex. Switch tools and you start from zero.

Multi-tool developers. You use Claude Code on Monday, Cursor on Tuesday, Codex on Wednesday. Context doesn't carry over. Hippo is the shared memory layer across all of them.

Teams where agents repeat mistakes. The agent hit the same deployment bug last week. And the week before. Hippo's error memories and decay mechanics mean hard lessons stick and noise fades.

Anyone whose CLAUDE.md is a mess. Your instruction file grew to 400 lines of mixed rules, preferences, and stale workarounds. Hippo gives that structure: tags, confidence levels, automatic decay of outdated info.

People who want portable AI memory. No vendor lock-in. Markdown files in your repo. Import from ChatGPT, Claude, Cursor. Export by copying a folder.

npm install -g hippo-memory
hippo init
hippo remember "FRED cache silently dropped the tips_10y series" --tag error
hippo recall "data pipeline issues" --budget 2000

That's it. You have a memory system.

Auto-sleep on session exit. hippo hook install claude-code now installs a Stop hook in ~/.claude/settings.json so hippo sleep runs automatically when Claude Code exits. hippo init does this too when Claude Code is detected.

Working memory layer (hippo wm push/read/clear/flush). Bounded buffer (max 20 per scope) with importance-based eviction. Current-state notes live separately from long-term memory.

Session handoffs (hippo handoff create/latest/show). Persist session summaries, next actions, and artifacts so successor sessions can resume without transcript archaeology.

Hybrid search blends BM25 keywords with cosine embedding similarity. Install @xenova/transformers, run hippo embed, recall quality jumps. Falls back to BM25 otherwise.

Agent eval benchmark validates the learning hypothesis: hippo agents drop from 78% trap rate to 14% over a 50-task sequence.

hippo init auto-detects your agent framework and wires itself in. It also sets up a daily cron job that runs hippo learn --git and hippo sleep automatically. Memories get captured from your commits and consolidated every day without you thinking about it.