Hipocampus

hipocampus

Drop-in proactive memory harness for AI agents. Zero infrastructure — just files.

One command to set up. Works immediately with Claude Code, OpenCode, and OpenClaw.

Benchmark

Evaluated on MemAware — 900 implicit context questions across 3 months of conversation history. The agent must proactively surface relevant past context that the user never explicitly asks about.

Hipocampus + Vector is 21.6x better than no memory and 5.1x better than search alone. On hard questions (cross-domain, zero keyword overlap), Hipocampus scores 8.0% vs 0.7% for vector search — 11.4x better. Search structurally cannot find these connections; the compaction tree can.

Increasing the ROOT.md budget from 3K to 10K tokens (120 topics vs 39) improves Easy from 26% to 34% and overall from 17.3% to 21.0% — more topic coverage means more connections found. Hard tier remains at 8.0%, indicating cross-domain reasoning is bottlenecked by the answer model, not the index size.

Install

Claude Code Plugin

Then run npx hipocampus init for full setup.

Standalone (npm)

Options

The Problem: You Can't Search for What You Don't Know You Know

AI agents forget everything between sessions. The obvious solutions — RAG, long context windows, memory files — each solve part of the problem. But they all miss the hardest part: knowing that relevant context exists when nobody asked about it.

A concrete example

You ask your agent: "Refactor this API endpoint for the new payment flow."

Three weeks ago, you and the agent had a long discussion about API rate limiting and decided on a token bucket strategy. That decision is recorded in the session logs. But the agent doesn't know it exists — so it refactors the endpoint without considering rate limits. The payment flow starts dropping requests under…