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MemMachine vs supermemory

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 — Memory and context engine for AI: fact extraction, user profiles, contradiction handling and forgetting, hybrid RAG + memory search, connectors, agent plugins and a one-binary local mode.

The curated verdict

Same job, long-term memory behind SDKs, REST and MCP: memmachine separates episodic, profile and working memory with framework integrations, Supermemory fuses memory with RAG and connectors.

MemMachinesupermemory
Stars3.2k31k
Forks2152.7k
LanguagePythonTypeScript
LicenseApache-2.0MIT
Last activitytodaytoday
Topicsmemorymemory, rag
Curated connections164

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.

supermemory — the curator's take

The most complete memory product on the map: fact extraction with temporal updates and forgetting, ~50ms user profiles, RAG and memory in one query, connectors for Drive, Gmail, Notion and GitHub, and plugins for Claude Code, Codex, Cursor, OpenCode and Hermes. `npx supermemory local` runs the same Memory API on your machine with local embeddings, so prototyping does not need their cloud; connectors are not in the local feature list, so check before planning around them. Treat the '#1 on every benchmark' banner as vendor-reported. Skip it if you want memory you can read and edit as files (acontext, okf-agent-memory) or only need session recall for one coding agent.