@deepseek-ai/dsh-memory-connect

Cross-session memory plugin for DeepSeek Harness (DSH)
跨会话记忆插件 — 让 AI Agent 拥有持久记忆

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English | 中文

Overview

dsh-memory-connect is a cross-session memory sharing plugin for DeepSeek Harness. It automatically extracts, stores, and recalls memories across sessions, giving your AI agent persistent, intelligent memory.

Zero-config — works out of the box with SQLite FTS5 and DSH's built-in LLM.

Features

Feature	Description
🔍 Auto Extraction	Extracts facts, preferences, decisions, and context from conversations
🧠 Semantic Recall	RRF (Reciprocal Rank Fusion) combines keyword and semantic search
⏰ Scheduled Maintenance	Automatic periodic decay and consolidation via built-in scheduler
🤖 LLM Consolidation	Automatic intelligent memory merging using DSH's built-in ctx.llm
📉 Memory Decay	Old, unused memories naturally fade; frequently accessed ones persist
🔧 Zero Config	Works with SQLite FTS5, no additional dependencies needed

Architecture

┌──────────────────────────────────────────────────────────────────┐
│                    Memory Plugin Data Flow                        │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│  ┌──────────┐    session/event    ┌──────────────────┐           │
│  │ Session  │ ──────────────────→ │ MemoryExtractor  │           │
│  │ Engine   │                     │ (Rule-based)     │           │
│  └──────────┘                     └────────┬─────────┘           │
│                                            │                     │
│                                            ▼                     │
│                                   ┌──────────────────┐           │
│                                   │  SQLite FTS5     │           │
│                                   │  memories        │           │
│                                   │  + memory_fts    │           │
│                                   └────────┬─────────┘           │
│                                            │                     │
│  ┌──────────┐   session/created   ┌────────▼─────────┐           │
│  │ New      │ ←────────────────── │ ContextInjector  │           │
│  │ Session  │                     │ (FTS5 + RRF)     │           │
│  └──────────┘                     └──────────────────┘           │
│                                                                   │
│  ┌──────────────────────┐  ┌─────────────────────┐              │
│  │   MemoryScheduler    │  │ SemanticConsolidator │              │
│  │  (setInterval)       │→ │ (Tag+Word+Temporal)  │              │
│  │  • Decay (1h)        │  │ + ctx.llm merge     │              │
│  │  • Consolidate (6h)  │  └─────────────────────┘              │
│  └──────────────────────┘                                        │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

Installation

npm install @deepseek-ai/dsh-memory-connect

Add to your DSH composition:

# agent.cordis.yml
- id: memory
  name: '@deepseek-ai/dsh-memory-connect'
  config:
    path: ~/.dsh/memory.db

Configuration

Option	Default	Description
path	(required)	Path to SQLite memory database
openAt	startup	When to open: startup, first-query, never
maxRecallCount	10	Max memories to recall per session
decayRate	0.02	Decay constant (higher = faster decay)
minRelevanceThreshold	0.3	Min relevance score for recall
journalMode	wal	SQLite journal mode
schedulerEnabled	true	Enable periodic maintenance
schedulerDecayIntervalMs	3600000	Decay interval (ms), default 1h
schedulerConsolidateIntervalMs	21600000	Consolidation interval (ms), default 6h
similarityThreshold	0.5	Semantic similarity threshold (0-1)

Full configuration example:

- id: memory
  name: '@deepseek-ai/dsh-memory-connect'
  config:
    path: ~/.dsh/memory.db
    openAt: startup
    maxRecallCount: 10
    decayRate: 0.02
    journalMode: wal
    schedulerEnabled: true
    schedulerDecayIntervalMs: 3600000
    schedulerConsolidateIntervalMs: 21600000
    similarityThreshold: 0.5

API

Search Memories

const memories = await ctx.crossSessionMemory.searchMemories({
  query: 'TypeScript configuration',
  types: ['fact', 'decision'],
  limit: 5,
})

Recall for Session

const memories = await ctx.crossSessionMemory.recallForSession(
  'session-123',
  'Setting up a new React project',
  10
)

Store Memory

await ctx.crossSessionMemory.storeMemory({
  type: 'preference',
  content: 'User prefers functional programming style',
  sessionId: 'session-123',
  tags: ['coding-style', 'preference'],
})

Manual Maintenance

// Trigger a full maintenance cycle (decay + consolidation)
const result = await ctx.crossSessionMemory.triggerMaintenance()

// Or run individually
await ctx.crossSessionMemory.runDecay()
await ctx.crossSessionMemory.consolidate()

Inspect & Monitor

// Scheduler status
const status = ctx.crossSessionMemory.getSchedulerStatus()

// Find similar memories (without merging)
const pairs = await ctx.crossSessionMemory.findSimilarMemories(0.6)

// Consolidation history
const log = await ctx.crossSessionMemory.getConsolidationLog(10)

// Statistics
const stats = await ctx.crossSessionMemory.getStats()

Memory Types

Type	Description	Example
fact	Objective information	"Project uses TypeScript 5.3"
preference	User preferences	"Prefers functional components"
context	Project context	"E-commerce platform migration"
decision	Decisions made	"Chose PostgreSQL over MySQL"
skill	Learned patterns	"How to configure ESLint"

Decay Formula

score = importance × e^(-λ × days) × log(access_count + 1)

Semantic Consolidation

Multi-signal similarity scoring:

Signal	Weight	Description
Tag overlap	35%	Jaccard similarity of extracted tags
Content word overlap	35%	Jaccard similarity of tokenized words
Type match	10%	Same memory type
Temporal proximity	20%	Decays over 90 days

When similarity ≥ threshold (default 0.5), memories are automatically merged using LLM.

Development

git clone https://github.com/Asher-2000/dsh-memory-connect.git
cd dsh-memory-connect
npm install
npm test

License

MIT

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中文

概述

dsh-memory-connect 是 DeepSeek Harness 的跨会话记忆共享插件。它自动从对话中提取、存储和检索记忆，让 AI Agent 拥有持久化的智能记忆能力。

零配置 — 基于 SQLite FTS5 和 DSH 内置 LLM，开箱即用。

核心功能

功能	说明
🔍 自动提取	从对话中提取事实、偏好、决策和上下文
🧠 语义召回	RRF 融合关键词和语义搜索
⏰ 定时维护	内置调度器自动执行衰减和整合
🤖 LLM 整合	使用 DSH 内置 LLM 智能合并相似记忆
📉 记忆衰减	旧的、不常用的记忆自然消退
🔧 零配置	基于 SQLite FTS5，无需额外依赖

工作原理

1. 监听 — 订阅 session/event 事件，实时捕获对话信息
2. 提取 — 基于规则的模式匹配，识别事实、偏好、决策等
3. 存储 — 写入 SQLite 数据库，建立 FTS5 全文索引
4. 召回 — 新会话创建时，自动检索相关历史记忆并注入
5. 维护 — 定期衰减旧记忆、整合重复记忆

快速开始

npm install @deepseek-ai/dsh-memory-connect

添加到 DSH 配置：

# agent.cordis.yml
- id: memory
  name: '@deepseek-ai/dsh-memory-connect'
  config:
    path: ~/.dsh/memory.db

API 示例

// 搜索记忆
const memories = await ctx.crossSessionMemory.searchMemories({
  query: 'TypeScript 配置',
  types: ['fact'],
})

// 为新会话召回相关记忆
await ctx.crossSessionMemory.recallForSession('session-123', '搭建 React 项目')

// 手动触发维护
await ctx.crossSessionMemory.triggerMaintenance()

// 查看统计
const stats = await ctx.crossSessionMemory.getStats()

许可证

MIT

Roadmap

• Add embedding-based semantic search
• Support for multi-user memory isolation
• Web UI for memory management
• Memory export/import functionality