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Alternatives to hive-memory

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    Related Servers

    • F
      license
      A
      quality
      B
      maintenance
      Shared fleet memory for AI agents — what one agent learns, the whole fleet knows. Nightly self-correction: stale facts rewritten in place, duplicates merged. Recall pushed into every prompt in supported coding agents. Self-hosted.
      11
      3
      -
    • A
      license
      Not graded
      quality
      A
      maintenance
      Provides persistent memory for AI agents via MCP, enabling agents to store and access notes, files, and session context across sessions and clients while under user control.
      4
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to store, search, assemble, and manage local-first memories through seven MCP tools, including conversation turns, feedback, status, and dashboard access without cloud dependencies.
      AGPL 3.0
    • A
      license
      C
      quality
      A
      maintenance
      Provides AI agents with a human-inspired memory layer via MCP, enabling episodic and semantic memory recall, forgetting curves, consolidation, and contradiction detection. It integrates with MCP clients to offer local-first, dependency-free memory management.
      98
      1
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables MCP-compatible coding tools to share a portable, local-first AI work identity and memory layer, so preferences, decisions, lessons, and project context persist across sessions and clients. Users can inspect, edit, approve, and override what the AI receives, keeping the store on their own machine without a cloud account.
      -
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables multiple AI agents and clients to share a self-hosted memory store over MCP or REST, supporting note creation, search, change tracking, and optional semantic retrieval from desktop, web, or mobile.
      MIT

    TDQS

    A4.4/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool targets a distinct resource and action: recall searches confirmed memory, get_context seeds a session, propose queues new memory, manage handles lifecycle by id/queue state, and say/board write and read the shared board. Descriptions explicitly cross-reference each other to resolve the few tempting confusions (recall vs get_context vs manage list_pending), so an agent can select reliably.

    Naming Consistency3/5

    Names mix bare verbs (recall, propose, say, manage), a bare noun (board), and a verb_noun form (get_context), so there is no single predictable pattern. Every name is still readable and memorable, so the inconsistency is stylistic rather than harmful.

    Tool Count5/5

    Six tools is well matched to a memory server with a shared board: one each for search, session seeding, proposal, lifecycle management, board write, and board read. No tool looks redundant and none of the needed operations are crammed together beyond manage's clearly enumerated actions.

    Completeness4/5

    The memory lifecycle is well covered end to end: propose, discover via recall/get_context, review pending, read by id, soft-delete with restore, and discard. The main gaps are the absence of any way to edit a pending or confirmed entry's content and no richer board operations (e.g., replying to or addressing a specific message), which agents can mostly work around.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues