agent-memoryNeo4j Labs' graph-native agent memory: conversations, a POLE+O entity knowledge graph and reasoning traces in one store, with a 16-tool MCP server and hosted or self-hosted backends.
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 → hindsightAgent 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 switchBoth 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.
Full comparison → MemoriaRust memory layer for AI agents with Git-style version control — snapshot, branch, merge and rollback over MatrixOne's copy-on-write engine, plus hybrid vector + full-text retrieval.
Why switchBoth are long-term memory layers for agents; memmachine splits episodic-graph/profile-SQL/working tiers, Memoria adds Git-style snapshot/branch/merge/rollback and contradiction quarantine on MatrixOne's copy-on-write engine.
Full comparison → EverOSOne portable memory layer for every agent: conversations, files and trajectories kept as canonical Markdown, indexed locally by SQLite and LanceDB, with offline reflection that refines them.
Why switchSame job, opposite substrate: MemMachine puts episodic memory in a graph and profiles in SQL behind SDKs and REST; EverOS puts everything in Markdown you can edit by hand. Choose by whether memory should be a service or a folder.
Full comparison → LightMemICLR 2026 memory framework for LLMs/agents: LLMLingua pre-compression, topic segmentation and offline memory updates — leading LoCoMo/LongMemEval results at lower token cost.
Why switchBoth are agent memory layers with MCP servers; MemMachine ships product-shaped SDKs and framework integrations, LightMem ships a compression-first pipeline with published benchmark wins.
Full comparison → OpenVikingVolcengine's context database: memories, resources and skills as one `viking://` filesystem agents ls, tree and grep — L0/L1/L2 tiers, traceable retrieval, sessions distilled into memory.
Why switchBoth are long-term memory backends for agents; MemMachine exposes episodic, profile and working memory through SDKs, OpenViking makes the entire context a browsable namespace with tiered summaries. Pick by whether you want an API or a filesystem agents explore.
Full comparison → ChromaOpen-source embedding database for building AI apps with retrieval.
Why switchSame 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.
Full comparison → memory-osA 7-layer memory operating system for Hermes Agent: Qdrant vectors, structured facts, fabric recall, an auto-curated wiki, and surgical context injection.
Why switchBoth 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.
Full comparison → MemMoltStructured long-term memory over MCP: an enforced bucket-thread-memo hierarchy in one SQLite file, hybrid FTS5 + vector search fused with RRF, local embeddings.
Why switchBoth agent memory layers over MCP: MemMachine ships episodic/profile/working memory with framework SDKs; MemMolt bets on one enforced bucket-thread-memo hierarchy in a single SQLite file.
Full comparison →