hindsight vs Memoria
Agent memory that learns, not just recalls: retain/recall/reflect API over Postgres, SOTA on LongMemEval. Self-host via Docker with UI; Python/TS clients, any LLM provider. — versus — Rust memory layer for AI agents with Git-style version control — snapshot, branch, merge and rollback over MatrixOne's copy-on-write engine, plus hybrid vector + full-text retrieval.
Both add governance to agent memory; hindsight is retain/recall/reflect over Postgres, Memoria is version-controlled 'Git for memory' with auditable snapshots.
| hindsight | Memoria | |
|---|---|---|
| Stars | 19k | 422 |
| Forks | 1.2k | 53 |
| Language | Python | Rust |
| License | MIT | Apache-2.0 |
| Last activity | 3 days ago | 27 days ago |
| Topics | memory, agents | memory |
| Curated connections | 4 | 4 |
hindsight — the curator's take
Pick it when you want a deployable memory *service* whose pitch is learning — agents that get better over time, not a transcript search. The LongMemEval lead was independently reproduced (Virginia Tech, Washington Post), which is more than most memory vendors offer, and the LLM side is pluggable down to Ollama/LM Studio for fully-local stacks. NOT an embedded library: you run a Docker service with Postgres and talk to it over HTTP — overkill for a single coding agent wanting session notes. The ™ and Hindsight Cloud signal a commercial trajectory; watch where the open/paid line lands.
Memoria — the curator's take
Pick it when memory auditability and reversibility matter — you want to branch an agent's memory, roll back a bad write, or trace the provenance of every fact, and you're fine running MatrixOne underneath. Overkill if you just need a simple recall store (memmachine, memmolt) or fully-local files (memanto) — the versioning engine and DB dependency are the cost. Young project, Apache-2.0.