agent-memory alternatives
Curated alternatives to agent-memory — and why you'd switch.
MemMachine
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.
Why switchBoth are drop-in long-term memory layers with framework adapters; MemMachine splits an episodic graph from a SQL profile store, while this keeps conversations, entities and reasoning in one Neo4j graph you can query in Cypher.
Full comparison →hindsight
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.
Why switchSame reflect-and-learn premise, different substrate: Hindsight runs on Postgres and benchmarks on LongMemEval; this trades that for a real graph with entity resolution and audit edges. Choose by whether you'd rather write Cypher or keep one Postgres dependency.
Full comparison →omnigraph
Lakehouse graph database for agent context — graph, vector and full-text retrieval fused in one runtime on branchable Lance/S3 storage; agent fleets write on isolated branches and merge Git-style.
Why switchBoth sell graph-native agent context. OmniGraph is a lakehouse engine with branchable Lance storage for agent fleets; Neo4j Agent Memory is an opinionated short-term/long-term/reasoning memory API on a database most teams already know.
Full comparison →