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Alternatives to MCP Memory SQLite

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    • A
      license
      Not graded
      quality
      D
      maintenance
      Provides LLMs with persistent memory across conversations using a knowledge graph that stores entities, relationships, and observations with support for PostgreSQL or SQLite backends.
      55 npm
      26
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    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to store and retrieve memories with semantic search capabilities using vector embeddings. Provides persistent memory storage with SQLite backend for context retention across conversations.
      55 npm
      1
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    • -
      license
      Not graded
      quality
      Not graded
      maintenance
      Provides persistent local memory functionality for AI assistants, enabling them to store, retrieve, and search contextual information across conversations with SQLite-based full-text search. All data stays private on your machine while dramatically improving context retention and personalized assistance.
      3
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    • A
      license
      Not graded
      quality
      F
      maintenance
      Provides persistent memory functionality for AI conversations by creating, managing and querying entities and relationships in a knowledge graph. Features SQLite storage, advanced search capabilities, and Windows auto-start integration for long-term AI memory retention.
      21 npm
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    • A
      license
      Not graded
      quality
      C
      maintenance
      Provides AI assistants with persistent memory across sessions using local SQLite and keyword search, allowing storage and retrieval of user preferences, project context, and decisions.
      16 npm
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    TDQS

    C2.7/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: create_entities and create_relations handle creation of different graph components, delete_entity and delete_relation handle deletion of different components, get_entity_with_relations focuses on a specific entity's context, read_graph provides a general overview, and search_nodes enables text-based discovery. There is no overlap in functionality that would cause confusion.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with snake_case: create_entities, create_relations, delete_entity, delete_relation, get_entity_with_relations, read_graph, and search_nodes. The naming is predictable and readable throughout the set.

    Tool Count5/5

    With 7 tools, this is well-scoped for a knowledge graph management server. Each tool earns its place by covering essential operations like creation, deletion, retrieval, and search, without being overly sparse or bloated.

    Completeness5/5

    The tool set provides complete CRUD/lifecycle coverage for entities and relations in a knowledge graph domain: create, read (via get_entity_with_relations, read_graph, search_nodes), update (implied via create_entities for updates), and delete. There are no obvious gaps that would hinder agent workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues