metronix-memorySelf-hosted agent memory stack in Docker: Postgres + Qdrant + Neo4j hybrid retrieval, a temporal knowledge graph, ontology layer and freshness checks behind one MCP-native API.
Why switchBoth put agent memory on Neo4j and expose it over MCP. Neo4j Labs' version is graph-purist (POLE+O entities, reasoning traces); Metronix wraps the graph in a hybrid retrieval service with vector and sparse tiers alongside it.
Full comparison → MemMachineLong-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 → 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 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 → omnigraphLakehouse 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 → 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 switchTwo substrates for agent context: Neo4j Labs' POLE+O entity graph behind a 16-tool MCP server, versus Volcengine's viking:// filesystem an agent can ls, tree and grep. Graph semantics vs shell-native familiarity.
Full comparison → linearmemoryLegible persistent agent memory: records observable execution events, consolidates only validated knowledge and explains every relation — MCP server on PostgreSQL 17 with a web explorer.
Why switchBoth are graph-shaped agent memories with reasoning traces. Neo4j Labs' agent-memory is graph-native on Neo4j with a POLE+O entity model; LinearMemory keeps Postgres authoritative and derives the graph, with an explanation on every edge.
Full comparison →