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crewmemory

by GIGAParviz
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    • 0f

      zanarellidev
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      quality
      B
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      Enables AI coding agents to share a persistent local-first memory hub, storing and recalling architectural decisions and context across different tools via MCP, so users can switch assistants without losing context.
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    • Kage

      kage-core
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      license
      A
      quality
      A
      maintenance
      Shared, code-grounded memory for developers and their coding agents. Capture a learning once and the whole team plus every agent recalls it; memory is grounded in your code and stored as git-tracked JSON reviewed in PRs, with citations validated on write and stale memory withheld from recall. Works with any MCP client.
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    • Tages

      ryantlee25-droid
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to maintain persistent, cross-session memory of codebase architecture, naming conventions, and decisions through MCP tools. Eliminates repetitive project re-explanation by automatically injecting stored context into every session with local-first SQLite storage and optional team sharing capabilities.
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    • Waystone

      hb407033
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      quality
      B
      maintenance
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    • teamhub-mcp

      imortis
      A
      license
      A
      quality
      B
      maintenance
      Enables teams to share structured, git-synced context among AI coding agents working on the same repository, including living plans, task declarations, handoff briefs, file-provenance history, and conflict detection.
      12
      909 npm
      2
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.5/5.0

    Scored across 30 tools

    Disambiguation5/5

    Each tool targets a distinct action or query type. Even the lookup tools (recall, search_memory, list_recent) have clear differentiators: recall is smart ranking, search_memory is raw faceted filtering, and list_recent is chronological listing. The save tools are clearly separated by memory kind (note, decision, solution, gotcha, pattern, handoff), and lifecycle/status/profile tools have no overlap.

    Naming Consistency4/5

    Names follow snake_case with a strong verb-first pattern (save_note, log_decision, get_memory, verify_memory) but there are minor deviations such as 'why_code', 'memory_at', 'team_context', and 'latest_handoff' (non-verb leading). These are still readable and do not disrupt the overall consistency significantly.

    Tool Count2/5

    30 tools is heavy and above the 25+ threshold. While the breadth covers many memory operations, it feels over-scoped; some tools (e.g., remember_commit_digest, git_blame_context) could be merged into broader workflows. The count likely introduces agent confusion and selection overhead.

    Completeness5/5

    The surface fully covers the memory lifecycle: create (save_*, log_decision), retrieve (recall, search_memory, get_memory), update (mark_superseded, verify_memory), delete (delete_memory), discovery (find_duplicates, memory_stats), provenance (entry_history, memory_at), collaboration (status, profiles, handoffs), and integration (sync_memory, pr_memory_review, git_blame_context). No critical gaps are apparent.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources