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Alternatives to ZenBrain

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    Related Servers

    • A
      license
      A
      quality
      C
      maintenance
      Enables AI agents and MCP clients to store, retrieve, and manage bi-temporal, graph-aware facts in an embedded SQLite memory engine with token-budgeted recall and contradiction tracking.
      6
      1
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Persistent long-term memory for MCP clients. Store and recall semantic facts, episodic events, and task state with vector-based relevance search. Write what matters, retrieve what's needed — memory that persists across sessions and agents, backed by a local SQLite store with optional embeddings for meaning-based lookup. Simple file-based storage, no external services required.
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      A local memory engine for AI agents. Stores conversation episodes, consolidates knowledge through a neuroscience-inspired lifecycle, and builds a personal knowledge graph — all in a local SQLite database.
      17
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Local-first persistent memory layer for AI agents. Provides hybrid search (FTS5 keyword + vector embeddings) over 223K+ knowledge chunks via MCP. Tools: brain_search, brain_store, brain_entity, brain_subscribe. Features pub/sub with stable agent identity, delivery tracking, and Claude --channels integration. SQLite + BrainBar Swift daemon on Unix socket.
      2,361 PyPI
      9
      Apache 2.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Provides persistent, cooperative memory for LLMs via MCP, with SQLite storage and tools for capturing, recalling, consolidating, crystallizing, and forgetting memories across sessions.
      3
      MIT

    TDQS

    A4.6/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool has a clearly distinct role: recall reads, store writes, consolidate maintains, and health reports stats. Descriptions explicitly cross-reference to avoid confusion (e.g., 'to see how much is stored rather than what, use zenbrain_health'), and no overlap exists among the four operations.

    Naming Consistency4/5

    All tools share the zenbrain_ prefix and use snake_case, forming a predictable pattern. However, the action words mix verbs (recall, store, consolidate) with a noun (health), a minor deviation from a pure verb-based convention.

    Tool Count5/5

    Four tools cover the core memory lifecycle: write, read, maintain, and monitor. The count is well within the 3-15 well-scoped range, and each tool clearly earns its place without redundancy.

    Completeness4/5

    The surface covers storing, recalling, consolidating, and monitoring memory layers. Minor gaps exist: no explicit update or delete for non-core memories, though storing anew works around this and deletion is intentionally absent.

    Maintenance

    ActivityActive
    ResponsivenessSlow