Hebbrix MCP Server
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Alternatives to Hebbrix MCP Server
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AlicenseAqualityDmaintenanceProvides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.148 npmMIT- AlicenseNot gradedqualityDmaintenanceProvides persistent knowledge graph memory for AI agents, enabling them to store, recall, and query facts about people, projects, and relationships across sessions.MIT
- FlicenseNot gradedqualityDmaintenanceProvides AI agents with persistent, searchable memory that survives across conversations using semantic search, temporal versioning, and smart organization. Enables long-term context retention and cross-session continuity for AI assistants.14-
- AlicenseNot gradedqualityBmaintenanceEnables persistent, graph-based memory for AI agents, allowing them to store, traverse, and recall relationships between facts, decisions, and context across sessions for efficient reasoning and reduced token usage.MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI assistants to build and query temporally-aware knowledge graphs from conversations and data, maintaining persistent memory of entities, relationships, and facts across interactions.-
- FlicenseNot gradedqualityNot gradedmaintenanceGives AI agents persistent memory with bi-temporal tracking, automatically extracting entities from natural language and enabling time-travel queries to understand facts as they existed at any point in history.2-
TDQS
Scored across 33 tools
Each tool has a clear primary resource — memory, graph entity, procedure, decision, or account — and the detailed descriptions separate them well. A few boundaries could still trip up an agent (hebbrix_search vs hebbrix_ask vs hebbrix_graph_query; hebbrix_remember vs hebbrix_remember_many vs hebbrix_import), but these are not duplicates.
All tools share a clean hebbrix_ snake_case prefix and most use recognizable actions like create, get, list, update, delete, search, import, and export. However, verb/noun order is not uniform — e.g., claim_start vs account_status, graph_query vs search_entities — so the naming pattern is predictable only within clusters.
33 tools is well into the 'too many' range for a single MCP server and forces agents to hold a large tool surface in context. The tools are organized into clear subdomains, so the count is not chaotic, but several niche capabilities (claim workflow, extraction_status, learning_insights) could be consolidated or nested.
The memory lifecycle is complete — write, batch write, read, update, delete, search, history, export, and import — and knowledge-graph, procedure, decision-learning, and account surfaces are all represented. Minor gaps remain, such as no procedure-execution history/audit tool and no collection-level management beyond listing and exporting.