Chroma vs MemMachine
Open-source embedding database for building AI apps with retrieval. — 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.
Same slot — 'what my agent remembers' — different bets: Chroma is a general embedding store you shape into memory; MemMachine is purpose-built memory with episodic/profile/working tiers, at the cost of running Neo4j + SQL.
| Chroma | MemMachine | |
|---|---|---|
| Stars | 29k | 3.3k |
| Forks | 2.4k | 196 |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | yesterday | today |
| Topics | rag, memory | memory |
| Curated connections | 8 | 5 |
Chroma — the curator's take
Open-source embedding database for building AI apps with retrieval.
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.