hindsight vs memory-os
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 — A 7-layer memory operating system for Hermes Agent: Qdrant vectors, structured facts, fabric recall, an auto-curated wiki, and surgical context injection.
Same layered-memory idea, different scope: hindsight is a harness-agnostic API benchmarked on LongMemEval, memory-os is a 7-layer stack wired specifically into Hermes Agent.
| hindsight | memory-os | |
|---|---|---|
| Stars | 24k | 1.4k |
| Forks | 1.8k | 128 |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | 2 days ago | 3 months ago |
| Topics | memory, agents | memory |
| Curated connections | 12 | 5 |
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.
memory-os — the curator's take
The most architecturally ambitious take on agent memory we've mapped: seven distinct layers from raw vectors to an auto-curated wiki, each with its own recall path, so the agent gets the RIGHT kind of memory injected rather than a similarity dump. The catch is coupling: it's built FOR Hermes Agent — adopting the architecture elsewhere means porting, not installing; and 7 layers is real operational surface for a ~1.3k-star project.