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    • F
      license
      Not graded
      quality
      C
      maintenance
      Local-first cross-agent memory for AI coding agents. Persistent, shared memory over MCP — what you tell one agent can be recalled by another — with all data stored in a single local SQLite file, no cloud and no API keys.
      -
    • A
      license
      B
      quality
      A
      maintenance
      Provides local-first durable memory and session continuity for AI coding agents over MCP, enabling context across restarts without cloud services or telemetry.
      2
      1
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Local-first persistent memory for coding agents and MCP clients. It keeps important project context across sessions and reduces wasted tokens by retrieving only relevant memories instead of replaying unnecessary history.
      8
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      A shared, local-first memory layer for AI CLIs, providing persistent, layered memory across Claude Code, Gemini CLI, and other MCP-aware clients.
      1
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Local-first, source-traceable memory for AI agents — no LLM at ingest, $0 per message, zero data egress. Gives Claude Code, Cursor, and any MCP client one shared persistent memory with semantic recall, belief revision, selective forgetting, and a provenance guard that blocks acting on stale or unconfirmed memories.
      23
      50 PyPI
      14
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Local-first, file-based memory layer for AI agents — one shared Markdown vault across Claude, Codex, Gemini, Cursor and any MCP client. Provides read/write memory tools with an audit trail, per-agent trust levels, and Git sync; no cloud and no lock-in.
      2
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    TDQS

    B3.2/5.0

    Scored across 25 tools

    Disambiguation2/5

    chat and ask_memory are nearly identical in purpose—both answer using approved conclusions first with raw fallback and return provenance plus a recallId—creating clear ambiguity. Additionally, search_memory, get_context, recall_memory, and ask_memory overlap in retrieval behavior, though their descriptions partially clarify output differences. The raw-evidence tools (remember, create_conclusion, consolidate_memory) are more distinct but still require careful reading.

    Naming Consistency4/5

    Most tools follow a consistent verb_noun snake_case pattern such as create_conclusion, approve_conclusion, delete_memory, and export_memory. A few bare-verb or noun-like names like remember, chat, and session_trace deviate slightly, but the overall convention is recognizable and predictable.

    Tool Count3/5

    At 25 tools, this sits at the heavy end of the borderline range and feels like more surface than most agents will need. The count is defensible for a full memory lifecycle system covering capture, recall, conclusions, audit, and maintenance, but it risks overwhelming users.

    Completeness4/5

    The tool set covers the core memory lifecycle well: capture raw evidence, propose and approve conclusions, search and recall, update/delete/supersede, audit, compact, backup, and export. Minor gaps exist—there is no import tool to complement export, and no direct get-by-id retrieval—but agents can work around these via search and export/backup workflows.

    Maintenance

    ActivityActive
    ResponsivenessNo issues