MemMachine vs memory-os
Long-term memory layer for AI agents — episodic (graph), profile (SQL) and working memory behind Python/TS SDKs, REST and MCP; ships LangChain, LangGraph, CrewAI and LlamaIndex integrations. — versus — A 7-layer memory operating system for Hermes Agent: Qdrant vectors, structured facts, fabric recall, an auto-curated wiki, and surgical context injection.
Both give agents durable, structured memory; MemMachine is the framework-agnostic layer with SDKs and MCP, memory-os the deeper 7-layer architecture wedded to Hermes Agent.
| MemMachine | memory-os | |
|---|---|---|
| Stars | 3.3k | 1.3k |
| Forks | 196 | 119 |
| Language | Python | Python |
| License | Apache-2.0 | MIT |
| Last activity | today | 1 months ago |
| Topics | memory | memory |
| Curated connections | 5 | 1 |
MemMachine — the curator's take
Pick it when memory is a product requirement, not a cache: separating episodic (graph) from profile (SQL) from working memory maps to how assistants actually personalize, and the documented LangGraph/CrewAI/LlamaIndex integrations mean you don't write the glue. NOT worth the footprint for a single-user tool — it wants a server plus Neo4j and SQL; a vector store or a JSON file gets a prototype further. Watch the open-core boundary: the managed platform is the business model.
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.