Skip to content

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: a memory marked wrong stops coming back, and a newer fact replaces the old one, so what your agent recalls stays current.

LongMemEval
R@5
hippo 98.0% per-question haystack, free local MiniLM
mem0 ~49-85% self-reported, own conditions

hippo re-measured 2026-09-23 on longmemeval_s_cleaned, the best of five settings in its benchmark scripts. 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: learn what is wrong 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 memory that stays current over time, local-first with no cloud or account, zero runtime dependencies, and a memory that drops what turned out wrong 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.
Get started with hippo Benchmarks