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MCP Memory Server

by teddashh

Overview Schema Related Servers Score Discussions

Python

Hybrid

MCP Memory Server

Every interaction leaves a trace. Every trace becomes knowledge.

凡走過必留下痕跡。每個痕跡都將成為知識。

License: MIT Python 3.11+ MCP Compatible Tools CJK Ready

A unified long-term memory, knowledge base, and audit trail for AI agents.

統一的 AI Agent 長期記憶、知識庫與稽核軌跡系統。

Quick Start | Tools | Architecture | Salience | 繁體中文

The Problem / 問題

AI agents are stateless. They forget everything between sessions. Your decisions, your solved problems, your hard-won knowledge — gone. Every new conversation starts from zero.

Existing memory solutions are either too simple (flat key-value stores) or too opinionated (locked to one vendor, one database, one language).

AI 代理是無狀態的。它們在對話之間會遺忘所有事情。現有的記憶方案不是太簡單（扁平的 key-value），就是太限制（綁定單一廠商、資料庫、語言）。

Related MCP server: Memsolus MCP Server

The Solution / 解決方案

MCP Memory Server gives every AI agent a persistent brain with:

Any AI Agent ──→ MCP Tool Call ──→ Memory Server ──→ Local DB + Cloud DB
                 (native)          (auto-detect       (structured +
                                    workspace)         vector search)

What makes it different	獨特之處
Not just memory — a knowledge base. Decisions, questions, and knowledge are fundamentally different things. We store them differently.	不只是記憶——是知識庫。決策、問題、知識本質不同，分別儲存。
Not just storage — intelligent recall. Salience tracking, access reinforcement, and natural decay surface what matters.	不只是儲存——是智慧回想。 Salience 追蹤、存取強化、自然衰減，讓重要的浮上來。
Not just local — cross-workspace. 18-table cloud schema with vector search across all your projects.	不只是本地——跨專案。 18 張表的雲端 schema，跨所有專案的向量搜尋。
Not just English — CJK-native. Token estimation built for Chinese, Japanese, Korean from day one.	不只是英文——原生 CJK。 Token 估算從第一天就為中日韓文設計。

Quick Start / 快速開始

git clone https://github.com/teddashh/mcp-memory-server.git
cd mcp-memory-server
pip install -e .

# Interactive setup (optional — works with SQLite defaults)
python setup_config.py

Claude Code

Add to ~/.claude/.mcp.json:

{
  "mcpServers": {
    "memory": {
      "command": "python",
      "args": ["-X", "utf8", "-m", "mcp_memory"],
      "cwd": "/path/to/mcp-memory-server"
    }
  }
}

Gemini CLI / Codex CLI

python -m mcp_memory --http  # → http://127.0.0.1:8787/mcp

Architecture / 系統架構

┌─ Claude Code ────────────┐
├─ Gemini CLI ─────────────┤
├─ Codex CLI ──────────────┼──→  MCP Memory Server (19 tools)
├─ Custom MCP Client ──────┤          │
└─ Any Future AI Tool ─────┘          │
                                      ├── L2: SQLite (local, fast, per-workspace)
                                      │     └── Salience tracking + CJK token estimation
                                      │
                                      ├── L3: Markdown (git-synced, human-readable)
                                      │     └── Knowledge files, daily logs
                                      │
                                      └── L4: Cloud DB (cross-workspace, vector search)
                                            └── 18 tables + audit trail + daily reports

The 4-Layer Memory Model / 四層記憶模型

L1  session.md       ← 短暫：當前對話的脈絡（每次載入，compact 控制大小）
      ↓ compact (agent-driven, CJK-aware token estimation)
L2  SQLite memory.db ← 結構化：每個專案獨立，salience 追蹤，毫秒級查詢
      ↓ sync (scheduled, incremental, idempotent)
L3  Markdown files   ← 永久：Git 同步，人類可讀，Obsidian 知識庫
L4  Cloud DB         ← 統一：跨專案向量搜尋，18 張表，完整稽核軌跡

Layer	Storage	Speed	Scope	說明
L1	`session.md`	Instant	Current conversation	當前對話脈絡
L2	SQLite	~1ms	Current workspace	當前專案的結構化記憶
L3	Markdown	~10ms	Shared via git	Git 同步的知識庫
L4	Cloud DB	~100ms	All workspaces	跨所有專案 + 向量搜尋

Key principle: each layer only talks to its neighbor. No skipping layers.

核心原則：每層只與相鄰層互動，不跳層。

L1 ──compact──→ L2 ──sync──→ L4
     ↑ agent        ↑ scheduler
     主動            自動 (30min)

Query: L2 (fast) → L4 (cross-workspace)

MCP Tools (19) / 工具一覽

Write — Capture knowledge as it happens / 寫入——即時捕捉知識

Tool	Description	說明
`memory_store`	Smart store — auto-classifies type (decision/resolved/question/knowledge)	智慧儲存——自動分類類型
`memory_record_decision`	Record a decision (what, why, how)	記錄決策（做什麼、為什麼、怎麼做）
`memory_record_resolved`	Record a resolved issue	記錄已解決的問題
`memory_record_question`	Record an open question (priority 1-3)	記錄未解問題（優先級 1-3）
`memory_record_knowledge`	Record a knowledge item	記錄知識項目

Read — Recall what matters / 讀取——回憶重要的事

Tool	Description	說明
`memory_list`	List recent memories, filter by type	列出最近記憶，可按類型篩選
`memory_get`	Get a specific memory by ID (auto-tracks access)	依 ID 取得（自動追蹤存取）
`memory_get_summary`	Quick counts per workspace	快速查看各表計數

Delete / 刪除

Tool	Description	說明
`memory_delete`	Delete a memory by ID	依 ID 刪除記憶

Search — Find by meaning, not keywords / 搜尋——依語意查找

Tool	Description	說明
`memory_search`	Salience-weighted semantic search across all workspaces	Salience 加權的跨專案語意搜尋

Salience — Important memories surface naturally / 記憶重要性——重要的自然浮上來

Tool	Description	說明
`memory_reinforce`	Manually boost a memory's salience (+20%)	手動強化記憶重要性（+20%）

Trail — Full audit trail / 軌跡——完整稽核

Tool	Description	說明
`memory_audit_search`	Search audit trail (keyword, date, sender, importance)	搜尋稽核軌跡
`memory_audit_stats`	Audit statistics (by importance, direction, category)	稽核統計
`memory_daily_report`	Daily reports (email stats, calendar, narrative)	每日報告
`memory_activity_log`	Agent activity log (scheduled tasks, dispatches)	代理活動日誌

Admin / 管理

Tool	Description	說明
`memory_status`	System overview (local + cloud + trail + embeddings)	系統總覽
`memory_compact`	CJK-aware session health check + compaction trigger	CJK 感知的 session 健康檢查
`memory_oracle_summary`	Cross-workspace summary from cloud	雲端跨專案摘要

Salience Engine / 記憶 Salience 引擎

Not all memories are equal. Some are accessed daily, others are forgotten. The salience engine ensures important memories surface naturally while noise fades away.

不是所有記憶都一樣重要。有些每天被查詢，有些被遺忘。Salience 引擎確保重要的記憶自然浮上來，噪音自然沈下去。

Inspired by memory-lancedb-pro's Weibull decay model, adapted for our multi-layer architecture.

How It Works / 運作方式

Memory born → salience = 1.0
     │
     ├── Accessed (memory_get / memory_search) → salience *= 1.05  (+5% per access)
     ├── Reinforced (memory_reinforce)          → salience *= 1.20  (+20% manual boost)
     │                                            cap at 2.0
     │
     └── Not accessed for 30+ days              → salience *= 0.95  (-5% daily decay)
                                                   floor at 0.01

Search results sorted by: salience DESC, then recency

Every memory_get and memory_search automatically tracks access. No manual intervention needed. Frequently used memories stay relevant. Stale memories gradually fade — but they're never deleted. They just sink lower in search results.

每次 memory_get 和 memory_search 自動追蹤存取。 不需手動操作。常用的記憶保持相關性。陳舊的記憶逐漸淡出——但永不刪除，只是在搜尋結果中排序靠後。

Fields Added to Every Memory / 每筆記憶加入的欄位

Field	Type	Description	說明
`access_count`	Integer	Total times accessed	總存取次數
`last_accessed`	Timestamp	When last read or searched	最後存取時間
`salience`	Float (0.01–2.0)	Current importance score	當前重要性分數

CJK-Aware Context Management / CJK 感知的 Context 管理

Standard token estimation assumes ASCII (~0.25 tokens/char). This underestimates CJK content by 3-6x, causing context window overflow and premature compaction.

標準的 token 估算假設 ASCII（~0.25 tokens/字元）。這低估 CJK 內容 3-6 倍，導致 context window 溢出。

Inspired by lossless-claw-enhanced's character-class approach:

Character Class	Tokens/Char	Examples
ASCII	0.25	`Hello world` → 3 tokens
CJK	1.5	`你好世界` → 6 tokens
Emoji	2.0	Emoji sequences
Hiragana/Katakana	1.5	Japanese text
Korean Hangul	1.5	Korean text

Impact / 影響

Text: "資料庫 schema 部署完成，所有表都已經建好了。"

Standard estimate:  22 chars × 0.25 = 6 tokens   ← WRONG
CJK-aware estimate: 22 chars → 28 tokens          ← CORRECT (4.7x difference)

memory_compact uses CJK-aware estimation to accurately determine when session.md needs compaction:

[workspace] session.md status: COMPACT RECOMMENDED
  Lines: 142 | Chars: 5830 | Estimated tokens: 4102
  (CJK-aware: CJK=1.5tok, ASCII=0.25tok, Emoji=2.0tok)
  Threshold: 100 lines or 3000 tokens
  → Agent should: read session.md, extract items via memory_record_*, trim session.md

Memory Types / 記憶類型

Type	When to Use	Example
Decision	You chose a path	"Use Clerk for auth — lower maintenance"
決策	做出選擇時	「選用 Clerk 做認證——維護成本較低」
Resolved	You fixed something	"JWT race condition — fixed with mutex"
已解決	修好問題時	「JWT race condition——用 mutex 修復」
Question	You need an answer	"Should we add Redis?"
問題	需要答案時	「要不要加 Redis？」
Knowledge	You learned something	"VECTOR columns support cosine similarity"
知識	學到東西時	「VECTOR 欄位支援 cosine similarity」

memory_store auto-classifies: if it contains "decided/chose" → decision, "fixed/solved" → resolved, "?" → question, else → knowledge.

memory_store 自動分類：包含「決定/選擇」→ 決策，「修好/解決」→ 已解決，「？」→ 問題，其他 → 知識。

Audit Trail / 稽核軌跡

"Every interaction leaves a trace." — This is not just a tagline. It's the architecture.
「凡走過必留下痕跡。」 —— 這不只是標語，這是架構。

Table	Scale	Purpose	用途
AUDIT_LOG	Scales to 100K+	Every email, message, and action — timestamped, categorized, suspicious-flagged	每封郵件和動作的完整紀錄
ACTIVITY_LOG	Grows daily	Agent scheduled task logs, dispatches, health checks	代理排程任務、派送、健康檢查
DAILY_REPORTS	1 per day	Auto-generated daily summaries with email stats, calendar, narrative	自動產生的每日摘要
AUDIT_PROGRESS	Per source	Data source processing tracker	資料來源處理進度

Search by date, sender, keyword, importance (H/M/L), suspicious flag. Get aggregate stats by period.

Domain Isolation / 領域隔離

Every workspace belongs to a domain: work, personal, or system.

                    ┌─────── work ───────┐
                    │ project-a          │
                    │ project-b          │  ← Write isolated
                    │ report-automation  │  ← Read: current workspace default
                    └────────────────────┘
                              ↕ cross-domain search (opt-in)
                    ┌────── personal ─────┐
                    │ my-app              │  ← Separate domain
                    │ side-projects       │
                    └─────────────────────┘

Write always goes to the current workspace / 寫入永遠寫到當前專案
Read defaults to current workspace / 讀取預設只看當前專案
Search can span all domains (opt-in) / 搜尋可跨領域（需明確指定）

Database Schema (18 Tables) / 資料庫結構（18 張表）

Table	Purpose	用途
WORKSPACES	Project registry with domain tags	專案註冊表
DECISIONS	Decisions (what/why/how) + salience	決策記錄 + salience
RESOLVED	Resolved issues + salience	已解決問題 + salience
OPEN_QUESTIONS	Prioritized questions + salience	未解問題 + salience
KNOWLEDGE_ITEMS	Knowledge + vector embeddings + salience	知識 + 向量嵌入 + salience
DAILY_LOGS	Daily log metadata	每日日誌
PERSONALITY_TRAITS	Structured user understanding	用戶特質
CROSS_REFERENCES	Links between any records	記錄間關聯
TAXONOMY	Hierarchical categories	階層分類
CONTACTS	People and relationships	聯絡人
SOP	Standard operating procedures	標準作業流程
CHANNELS	Communication channels	通訊管道
ACTIVITY_LOG	Agent action trail	代理動作軌跡
AUDIT_LOG	Full audit trail	完整稽核軌跡
AUDIT_PROGRESS	Processing tracker	處理進度
DAILY_REPORTS	Auto-generated summaries	每日摘要
SYNC_WATERMARK	Incremental sync tracking	增量同步追蹤
SYNC_LOG	Sync history	同步歷史

Supported Cloud Backends / 支援的雲端後端

Backend	Status	Vector Search
SQLite	Built-in (default)	Text search
Oracle AI Database	Supported	Native `VECTOR` + cosine
PostgreSQL + pgvector	Planned	`vector` extension
Supabase	Planned	pgvector + REST API
MySQL / HeatWave	Planned	ML integration

How It Compares / 與其他方案的比較

Feature	MCP Memory Server	OpenMemory	memory-lancedb-pro
Structured memory types	4 types	key-value	6 categories
Cross-workspace search	cloud DB	local only	local only
Vector search	Oracle / pgvector	synthetic embeddings	LanceDB
Salience / decay	access tracking + daily decay	no	Weibull model
CJK token awareness	built-in	no	no
Audit trail	86K+ entries	no	no
Multi-agent support	Claude + Gemini + Codex	Claude + Gemini + Codex	OpenClaw only
Domain isolation	work / personal / system	user ID	scope-based
Cloud sync	scheduled incremental	no	no
Database choice	SQLite + any cloud DB	SQLite	LanceDB
18-table schema	yes	no	no

Configuration / 設定

~/.mcp-memory/config.json:

{
  "workspace_root": "~/Documents/Workspace",
  "workspace_map": "~/path/to/workspace-map.json",
  "cloud_db": {
    "enabled": false,
    "type": "oracle | postgresql | supabase",
    "connection": "..."
  },
  "embedding": {
    "provider": "none | openai | local",
    "model": "text-embedding-3-small"
  }
}

Interactive setup: python setup_config.py

Transport modes:

python -m mcp_memory            # STDIO (Claude Code)
python -m mcp_memory --http     # HTTP (Gemini, Codex, web)
python -m mcp_memory --decay    # Run daily salience decay job

Philosophy / 設計哲學

"The best knowledge system captures everything and surfaces only what's relevant."
「最好的知識系統是捕捉一切，只呈現相關的。」

Structured by default — Decisions and knowledge are different. Treat them differently.
Salience, not deletion — Old memories fade, but never disappear. They can always be found.
Layers, not monoliths — Each layer has a job. Fast local → permanent cloud. No skipping.
CJK-native — Not an afterthought. Built for multilingual from the start.
Agent-agnostic — Your memory belongs to you, not your AI vendor.
Audit everything — Every trace, every action, every day. Full accountability.

結構化為本 —— 決策和知識本質不同，分別儲存。
Salience 而非刪除 —— 舊記憶淡出，但永不消失。隨時可找回。
分層而非單體 —— 每層各司其職。快速本地 → 永久雲端。不跳層。
原生 CJK —— 不是事後補丁。從第一天就為多語言設計。
不綁定 AI —— 你的記憶屬於你，不屬於 AI 廠商。
稽核一切 —— 每個軌跡、每個動作、每一天。完整的可追溯性。

繁體中文版

MCP Memory Server 是什麼？

一個統一的 AI 代理長期記憶系統。安裝後，你的 Claude Code、Gemini CLI、Codex CLI 都能使用 19 個 native MCP tools 來記錄和查詢記憶。

核心功能

19 個 MCP 工具：寫入（5）、讀取（3）、刪除（1）、搜尋（1）、Salience（1）、稽核軌跡（4）、管理（3）
四層記憶架構：session.md → SQLite → Markdown → Cloud DB
Salience 引擎：存取追蹤 + 強化 + 自然衰減，重要的記憶自動浮上來
CJK 感知：繁中/日文/韓文的 token 估算準確度提高 3-6 倍
領域隔離：工作、個人、系統三個領域，寫入隔離，搜尋可跨域
稽核軌跡：AUDIT_LOG + ACTIVITY_LOG + DAILY_REPORTS，完整可追溯
18 張表的雲端 Schema：涵蓋記憶、知識、聯絡人、SOP、分類樹、稽核
離線優先：SQLite 零配置即可運作，雲端資料庫是可選的進階功能

安裝

git clone https://github.com/teddashh/mcp-memory-server.git
cd mcp-memory-server && pip install -e .
python setup_config.py

在 ~/.claude/.mcp.json 加入 server 設定後，重啟 Claude Code 即可使用所有 tools。

Contributing / 貢獻

Issues and PRs welcome.

歡迎提交 Issue 和 Pull Request。

License

MIT

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Maintenance

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–Releases (12mo)

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