hindsight vs MemMachine
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 — 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.
Both self-hostable long-term memory services for agents behind Python/TS SDKs and REST. MemMachine splits episodic/profile/working memory with framework integrations; Hindsight bets on retain/recall/reflect and benchmark-topping learned memory.
| hindsight | MemMachine | |
|---|---|---|
| Stars | 19k | 3.3k |
| Forks | 1.2k | 197 |
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Last activity | today | today |
| Topics | memory, agents | memory |
| Curated connections | 2 | 8 |
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.
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.