Comparison
hippo vs mem0
Both are open-source memory for AI agents. The difference is philosophy. mem0 saves and searches: it extracts facts and retrieves them by similarity. hippo runs a memory lifecycle: it forgets by default and earns persistence through use, so the store stays small and current instead of growing without bound.
LongMemEval
R@5
R@5
hippo 98.6% per-question haystack, local MiniLM default
mem0 ~49-85% self-reported, own conditions
hippo measured 2026-06-09 on longmemeval_s_cleaned. mem0 reports its own LongMemEval figures under its own conditions, so this is not a controlled head-to-head. Full methodology and per-haystack results are on the benchmarks page.
Feature comparison
| Feature | hippo | mem0 |
|---|---|---|
| Core model | Memory lifecycle: forget by default | Save everything, search later |
| Decay by default | Yes | No |
| Retrieval strengthening | Yes | No |
| Reward-proportional decay | Yes | No |
| Conflict detection + resolution | Yes | No |
| Search | BM25 + optional embeddings | Embeddings only |
| Zero runtime dependencies | Yes | No |
| Storage | Local SQLite + markdown | Vector store |
| MCP server | Yes | No |
| License | MIT | Apache-2.0 |
Feature rows are from hippo's comparison table (opens in new tab); the qualifier behind each Yes/No is in the full matrix. Benchmark figures and the comparability caveat are in the strip above.
When to choose which
Choose hippo You want the store to stay small and current over time, local-first with no cloud or account, zero runtime dependencies, and a memory that forgets the noise and keeps what gets used.
Choose mem0 You mainly want to extract, store, and search facts, and a hosted or vector-store-backed setup fits your stack.