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dsh-mneme 与其他记忆工具的能力对比 / Capability Comparison

更新于 2026-10-16。对比基于各项目公开文档与源码(出处见文末),只比较能力结构,不回答「谁更准」——效果类问题请以各系统在公开基准上的实测为准。dsh-mneme 是为 DSH 设计的记忆插件(SQLite + Markdown 镜像、完全离线),同时自带独立服务:stdio MCP server(npm 包 mneme-memory,任意 MCP 客户端可挂载)与不开 DSH 的常驻 daemon(dsh-mneme-serve),第三方集成读写同一份记忆库;其余为通用记忆服务/库,定位不同,读表时请带着这个前提。

能力 dsh-mneme Zep / Graphiti Mem0 Letta (MemGPT) Hindsight
存储底座 SQLite 主库 + 人类可编辑 Markdown 镜像 时序知识图谱(Neo4j / FalkorDB / Neptune) 向量 + 图混合的抽取式记忆 自编辑记忆块(核心内存) 四层仿生结构(事实/经历/观察/心智模型)
检索 BM25 + 图谱 + 会话热记忆,v0.8.4 起实体图第四路(RRF 融合) 时序图遍历 + 语义 语义 + 关键词 + 图 递归检索 + 自编辑 四路并行(语义/BM25/图/时间)+ RRF + 重排
写入时 LLM 抽取 否(原文直存,巩固期才用 LLM) 是(抽实体/关系) 是(抽事实) 部分 是(retain 抽实体/时间)
离线巩固 autoDream(快写慢巩固) — — sleep-time compute(学术同源) 观察沉淀
冲突处理 矛盾冻结 → 人工裁决队列 时序失效(边失效标注) ADD/UPDATE 等操作 无专门机制 证据回传更新观察
遗忘 heat 热度衰减降权 + 显式 archive/delete,无静默淘汰 边失效 ADD/UPDATE/DELETE 操作 无 —
作用域隔离 agent / workspace 双维 + opt-in 硬过滤 用户/会话分 graph user/agent/run 多层 多 agent memory bank 按 bank
可审计性 sha256 审计链 + 幂等重放 — — — —
多宿主接入 DSH 内直用 + stdio MCP(mneme-memory,任意 MCP 客户端)+ HTTP daemon(dsh-mneme-serve)+ 网页端桥接(Mneme-Bridge) 托管 API + SDK SDK / API API / server SDK / API / MCP
完全离线 ✅(可配本地嵌入模型) 需 LLM/嵌入服务 需 LLM 需 LLM 需 LLM
许可证 MIT Apache-2.0 Apache-2.0 Apache-2.0 MIT

如何读这张表

  • 「—」不等于「差」:通用服务面向多租户在线场景,把裁决权交给调用方也是一种合理设计;dsh-mneme 的差异点在于把记忆管理做成有账本、可人工复核、可回放的流水线。
  • 效果类问题(检索准不准、巩固有没有用)看各系统的公开基准与本项目自己的评测,本表不回答跑分。
  • 许可证为 2026-10-16 各仓库 LICENSE 文件实文核对结果。

Capability Comparison (English)

Updated 2026-10-16. Based on public docs and source code (sources below). This compares capability structure, not accuracy — effect questions belong to public benchmarks and our own evals. dsh-mneme is a memory plugin designed for DSH (SQLite + human-editable Markdown mirror, fully offline) that also ships standalone: a zero-dependency stdio MCP server (npm package mneme-memory, mountable by any MCP client) and a DSH-free daemon (dsh-mneme-serve), so third-party integrations read and write the same library; the others are general-purpose memory services/libraries.

Capability dsh-mneme Zep / Graphiti Mem0 Letta (MemGPT) Hindsight
Storage SQLite + human-editable Markdown mirror Temporal knowledge graph (Neo4j / FalkorDB / Neptune) Vector + graph, extraction-based Self-editing core-memory blocks 4-layer biomimetic (facts/experiences/observations/mental models)
Retrieval BM25 + graph + hot session memory; entity-graph 4th path since v0.8.4 (RRF) Temporal graph traversal + semantic Semantic + keyword + graph Recursive search + edits 4 parallel strategies + RRF + rerank
LLM extraction at write No (raw text stored; LLM only during consolidation) Yes (entities/relations) Yes (facts) Partial Yes (retain extracts entities/time)
Offline consolidation autoDream (fast write, slow consolidate) — — sleep-time compute (same academic thread) Observation layer
Conflict handling Freeze → human review queue Temporal edge invalidation ADD/UPDATE ops None specific Evidence-fed observation updates
Forgetting Heat decay (down-weight) + explicit archive/delete; no silent eviction Edge invalidation ADD/UPDATE/DELETE ops None —
Scoping agent + workspace dual-dim, opt-in hard filter per-user/session graphs user/agent/run layers multi-agent memory banks
Auditability sha256 audit chain + idempotent replay — — — —
Multi-host access in-DSH + stdio MCP (mneme-memory, any MCP client) + HTTP daemon (dsh-mneme-serve) + web-client bridge (Mneme-Bridge) hosted API + SDKs SDK / API API / server SDK / API / MCP
Fully offline ✅ (local embedding model supported) needs LLM/embedding service needs LLM needs LLM needs LLM
License MIT Apache-2.0 Apache-2.0 Apache-2.0 MIT

How to read this table

  • "—" does not mean "worse": general services target online multi-tenant settings where the caller owns adjudication; dsh-mneme's differentiator is turning memory management into an accounted, human-reviewable, replayable pipeline.
  • Accuracy questions belong to public benchmarks and this project's own evals; this table does not answer them.
  • Licenses verified against each repository's LICENSE file on 2026-10-16.

Sources