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GIGAParviz
by GIGAParviz

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Alternatives to crewmemory

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    Related Servers

    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI agents to maintain Git-backed long-term project memory over MCP, supporting on-demand retrieval, curated writes, conflict-safe versioning, idempotency, and crash recovery so new sessions or machines can recover context and continue working.
      6
      Apache 2.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables teams of AI coding agents (Claude Code, Cursor, Codex) to share persistent, time-aware graph memory and coordinate their work via MCP, so agents recall verified fixes at the moment of failure, learn procedures automatically, avoid file conflicts, claim tasks race-free, and hand off facts and decisions between sessions.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      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.
      Apache 2.0
    • A
      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.
      11
      42
      GPL 3.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Gives AI coding agents a persistent memory that carries across sessions and projects, letting them save and search decisions, bug fixes, and discoveries through MCP tools. Each new session loads the recent memories of the current project so agents resume with the same context instead of re-explaining it.
      5
      MIT

    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

    ActivitySlowing
    ResponsivenessNo issues