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    • A
      license
      A
      quality
      A
      maintenance
      Local-first memory layer for AI coding agents — captures issues, attempts, fixes, and decisions, and warns at git commit before you repeat a mistake.
      17
      822 PyPI
      876
      MIT
    • A
      license
      A
      quality
      F
      maintenance
      Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.
      8
      2
      Apache 2.0
    • A
      license
      Not graded
      quality
      D
      maintenance
      Provides AI coding assistants with persistent project memory to retain architectural decisions, code patterns, and domain knowledge across sessions. It stores data locally in a SQLite database, allowing agents to remember, recall, and manage project-specific context using full-text search.
      4 npm
      Apache 2.0
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to read and write persistent, local-first memory so project decisions, preferences, and cross-project guidelines survive across sessions and can be retrieved by semantic search. Optionally also exposes a task queue for autonomous execution that draws on the same stored context.
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Provides AI coding agents with a local-first, persistent memory layer so they can store and semantically recall project-specific and cross-project knowledge, conventions, and error patterns during sessions. It also optionally enables autonomous task queue execution and issue resolution driven by that same memory.
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Provides AI coding agents with persistent, local-first memory across sessions — storing and semantically searching project decisions, preferences, error patterns, and cross-project guidelines via SQLite — and optionally exposes a task queue for autonomous execution.
      -

    TDQS

    A3.8/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: checking before editing, recalling context, recording decisions, failures, fragile files, and session summary. No overlap in functionality.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern in snake_case (e.g., recall_project_context, remember_decision). The pattern is predictable and clear.

    Tool Count5/5

    With 6 tools, the set is well-scoped for managing agent memory and sessions. Each tool covers a specific necessary action without redundancy.

    Completeness4/5

    The set covers core memory operations (load, record decisions/failures/fragile files, session summary, pre-edit check). Minor gaps exist, such as no explicit tool to list or forget memories, but the surface is largely adequate.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues