Know what to
forget.
A memory layer for AI agents, modeled on the hippocampus. Decay by default, strength through use, provenance on every memory.
npm install -g hippo-memory Works with
- Claude Code
- Codex
- Cursor
- OpenClaw
- OpenCode
- Pi
- any MCP client
an example session
The problem
Most AI memory saves everything and searches later.
That's storage with semantic search bolted on. It's why your agent kept hitting the same deploy bug last week. And the week before.
The system saw the failure four times. It had no way to know it should remember.
How it works
Memories decay. Retrieval makes them stronger.
The thing brains have been getting right for 500 million years. Hard lessons stick because you used them. Trivia fades because you didn't.
strength = decay over time, re-strengthened on every recall
Decay by default
Every memory fades on a 7-day half-life. Persistence is earned, not assumed.
Retrieval strengthens
Use it or lose it. Each recall extends the half-life. Memories you reach for learn to survive.
Errors stick
Tag a failure once. It decays slower and resurfaces every time you walk back into that code.
Sleep consolidates
On `hippo sleep`, three or more related episodes merge into one semantic pattern. The originals decay; the pattern survives.
Receipts
Numbers, not adjectives.
every claim links to its source
Compare
Forget by default. Earn persistence through use.
The AI-memory category matured fast in 2026. Hippo's take - bio-decay, strengthen-on-use, outcome-weighted half-lives - is one stance among several. The matrix below is a feature snapshot, not a verdict.
| Feature | Hippo | MemPalace | Mem0 | Basic Memory | gbrain | Zep | Letta | Cognee | Memoria | EverMind |
|---|---|---|---|---|---|---|---|---|---|---|
| Decay by default | Yes | No | No | No | No | No | No | No | No | No |
| Retrieval strengthening | Yes | No | No | No | No | No | No | Partial (recall tuning) | No | Partial (Skill Memory distills patterns) |
| Reward-proportional decay | Yes | No | No | No | No | No | No | No | No | No |
| Hybrid search (BM25 + embeddings) | Yes | Embeddings + spatial | Embeddings only | No | Yes (vec + rerank + graph) | Yes (graph + vec) | ? | Yes (GraphRAG) | Yes (vector + full-text) | Yes (mRAG, multi-modal) |
| Schema acceleration / knowledge graph | Yes (schema) | No | No | No | Yes (typed KG, self-wiring) | Yes (temporal KG) | No | Yes (auto-ontologies) | No (typed claims) | Yes (hierarchical: user/group/agent) |
| Conflict detection + resolution | Yes | No | No | No | Yes (eval-surfaced) | Yes (auto-invalidate stale facts) | No | No | Yes (auto-detect + quarantine) | Partial (temporal tracking) |
| Multi-agent shared memory | Yes | No | No | No | Yes (brain repo, team mounts) | Yes | No (single-agent state) | Yes | Yes (branch/merge across sessions) | Yes (multi-agent coordination) |
| Transfer scoring | Yes | No | No | No | No | No | No | No | No | No |
| Outcome tracking | Yes | No | No | No | No | No | No | No | No | Partial (Cases: agent trajectories) |
| Confidence tiers | Yes | No | No | No | No (typed facts) | No | No | No | No | No |
| Spatial organization | No | Yes (wings/halls/rooms) | No | No | No | No | No | No | No | No |
| Lossless compression | No | Yes (AAAK, 30x) | No | No | No | No | No | No | No | No |
| Cross-tool import (ChatGPT/Claude/Cursor) | Yes | No | No | No | Partial (data sources) | ? | No | Partial (28 data sources) | No (Git ops) | Partial (mRAG: PDFs/images/URLs) |
| Auto-hook install | Yes | No | No | No | No | No | No | No | No | No |
| MCP server | Yes | Yes | No | No | Yes (stdio + HTTP/OAuth) | Partial (managed) | Yes (via Letta Code) | Yes (first-party Claude/LangGraph) | Yes | ? |
| Zero runtime deps | Yes | No (ChromaDB) | No | No | No (PGLite or PG+pgvector) | No (managed service) | No (Python deps) | No (Python deps) | Yes (single Rust binary) | No (managed + OSS) |
| LongMemEval (best published) | 86.8% R@5 (F13+F9, oracle*) | 96.6% raw / 100% reranked R@5 | ~49-85% R@5 | N/A | 97.6-97.9% R@5 (s_cleaned*) | N/A (LoCoMo 80.3%) | N/A | N/A | 88.78% overall accuracy w/ reader** | 83.00% overall** (LoCoMo 93.05%, HaluMem 93.04%) |
| Git-friendly | Yes | No | No | Yes | Yes | No | No | No | Yes (Git is the model) | ? |
| Framework agnostic | Yes | Yes | Partial | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| License | MIT | (open) | Apache-2.0 | (open) | MIT | Apache-2.0 (community) | Apache-2.0 | MIT (core) | Apache-2.0 | Apache-2.0 (OSS) + cloud |
* Split-mismatched: Hippo's 86.8% is on longmemeval_oracle (3 sessions per haystack); gbrain's 97.6% is on longmemeval_s_cleaned (~40 sessions per haystack). Different splits, different difficulty. Not directly comparable.
** Different metric: Memoria's 88.78% and EverMind's 83% are reported as overall accuracy with a reader LLM, not retrieval R@5. Higher denominator + LLM helps. Not directly comparable to retrieval-only R@5 numbers above.
Different tools answer different questions. Mem0 and Basic Memory implement "save everything, search later." MemPalace organizes spatially. gbrain, Zep, and Cognee extract typed entities into a knowledge graph. Letta lets the agent edit its own memory blocks. Memoria is Git-style version control over memory. EverMind is self-evolving Skill Memory. Hippo implements "forget by default, earn persistence through use." Complementary takes, not a single-axis ranking.
One command. Every repo gets memory.
Zero config. SQLite under the hood, zero runtime deps, works with every CLI agent you have.
npm install -g hippo-memory hippo init --scan ~