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Memory MCP

作者 vk · GitHub ↗ · v1.0.5 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install memory-mcp-cyber-bye
功能描述
Graph-based memory MCP server with 9 consolidated tools, 8-phase auto-linking, persona tracking, emotional memory, adaptive learning, and knowledge graph ent...
使用说明 (SKILL.md)

Memory MCP — Graph-Based Memory & Persona Management

[!IMPORTANT] Dependency Warning: This skill requires the memory MCP server to be running and registered with the Model Context Protocol (MCP) host. It enables persistent memory, knowledge graph entities, context tracking, and cross-session intelligence.

This skill equips the agent to store, search, and manage memories across sessions; track entities and relationships in a knowledge graph; maintain user persona, mood, and learning patterns; and surface proactive suggestions based on memory health.


Guidelines for the Agent

1. Tool Architecture — 9 Consolidated Tools

The memory MCP exposes 9 tools, each with an op (operation) parameter:

Tool Prefix Purpose Operations
memory__memory memory_ Core memory store & search remember, recall, search, context, stats, health, decay, boost, pin, inspect, export, import, trim, suggest, remind, dedup, backup, restore
memory__entity entity_ Knowledge graph entities create, read, update, delete, search
memory__relation relation_ Entity relationships create, delete, search
memory__short_term short_term_ Fast KV storage set, get, list, delete, clear, search
memory__project project_ Projects, tasks, workflows create_project, get_project, list_projects, update_project, delete_project, plan_task, get_task, list_tasks, update_task, complete_task, delete_task, plan_workflow, get_workflow, list_workflows
memory__context context_ Conversation context better, chat_add, chat_get, chat_summary, get_summary
memory__extract extract_ Extract/remember info entities, text, keypoint, thought, note, discovery, mistake, learning, boundary
memory__share share_ Share with others share, shared_with_me, shared_by_me, get_network, person_memories
memory__memory_tool_search Find tools by keyword query

2. Common Parameters

Every tool call requires:

  • userId (required) — Who owns this data. Always pass the user identifier.
  • op (required) — Which operation to perform on the tool.

Each tool also accepts optional context parameters:

  • projectId — Scope to a project
  • sessionId — Conversation thread identifier

3. Core Memory Operations (memory__memory)

Store a Memory

{ "op": "remember", "userId": "u1", "userMessage": "Fixed @AuthService bug!", "agentMessage": "Great work!" }

Auto-features: intent detection, entity extraction (@mentions, CamelCase, URLs), auto-linking.

Search / Recall

{ "op": "recall", "userId": "u1", "query": "auth bug" }
{ "op": "search", "userId": "u1", "query": "database config" }

Context for LLM

{ "op": "context", "userId": "u1", "sessionId": "sess_123", "maxTokens": 2000 }

Memory Health & Maintenance

{ "op": "health", "userId": "u1" }
{ "op": "decay", "userId": "u1", "daysUnused": 7 }
{ "op": "dedup", "userId": "u1", "threshold": 0.8, "autoMerge": false }

Smart Features

{ "op": "suggest", "userId": "u1" }
{ "op": "remind", "userId": "u1", "reminderType": "followup", "title": "Check database", "priority": 8 }
{ "op": "mood", "userId": "u1", "mood": "excited", "intensity": 8, "context": "Launch day!" }
{ "op": "persona", "userId": "u1", "traits": {"creative": true}, "style": "friendly" }
{ "op": "learn", "userId": "u1", "type": "work", "pattern": "prefers morning" }

4. Knowledge Graph (entity + relation)

Create Entity

{ "op": "create", "userId": "u1", "entityType": "Person", "name": "Alice", "properties": {"role": "Engineer"} }

Search Entities

{ "op": "search", "userId": "u1", "entityType": "Person", "search": "alice" }

Create Relation

{ "op": "create", "userId": "u1", "fromId": "e1", "toId": "e2", "type": "WORKS_WITH" }

5. Short-Term KV Storage (memory__short_term)

Fast key-value store for session data. Cleared periodically.

{ "op": "set", "userId": "u1", "key": "active_task", "value": {"id": "t1"} }
{ "op": "get", "userId": "u1", "key": "active_task" }
{ "op": "list", "userId": "u1" }

6. Project & Task Management (memory__project)

{ "op": "create_project", "userId": "u1", "name": "My App", "description": "A new app" }
{ "op": "plan_task", "userId": "u1", "projectId": "p1", "title": "Fix bug", "status": "pending" }
{ "op": "list_tasks", "userId": "u1", "projectId": "p1" }
{ "op": "complete_task", "id": "task_123" }

7. Context Tracking (memory__context)

{ "op": "better", "userId": "u1", "timeRange": "week" }
{ "op": "chat_add", "userId": "u1", "role": "user", "content": "Hello" }
{ "op": "chat_get", "userId": "u1", "limit": 10 }

8. Extract & Remember (memory__extract)

{ "op": "entities", "userId": "u1", "text": "John from Acme called" }
{ "op": "learning", "userId": "u1", "insight": "Tests first" }
{ "op": "discovery", "userId": "u1", "content": "Found new approach" }
{ "op": "mistake", "userId": "u1", "description": "Used wrong API", "resolution": "Update docs" }

9. Share (memory__share)

{ "op": "share", "userId": "u1", "toOwnerId": "u2", "content": "Deadline Sunday" }
{ "op": "shared_with_me", "userId": "u1" }

10. 8-Phase Auto-Linking System

Memories are automatically linked across sessions using:

Phase Method Description
0 Temporal Same conversation flow (strongest)
1 Entity Shared @mentions, CamelCase entities
2 Project Same project context
3 Intent Same detected intent
4 Keyword Content keyword overlap
5 Cross-Project Related across different projects
6 Temporal Chain Within 30-minute window
7 Entity Graph Knowledge graph traversal

Features: bidirectional links, max 15 links per memory, adaptive boost for high-priority items.


11. Intent Detection (Auto)

The system auto-detects intent when storing memories:

Intent Priority Examples
error 80% bug, crash, failed
success 70% fixed, working, completed
learning 80% learned, discovered
question 50% how, why, what
planning 50% will, going to

12. Session Workflow

At session start:

// Get recent context
{ "op": "better", "userId": "u1", "timeRange": "week" }

// Check pending reminders
{ "op": "remind", "userId": "u1" }

// Get health overview
{ "op": "health", "userId": "u1" }

During session:

// Store conversation
{ "op": "remember", "userId": "u1", "userMessage": "...", "agentMessage": "..." }

// Extract entities
{ "op": "entities", "userId": "u1", "text": "..." }

// Track mood
{ "op": "mood", "userId": "u1", "mood": "happy", "context": "..." }

At session end:

// Store summary
{ "op": "chat_summary", "userId": "u1", "summary": "..." }

// Clean up short-term
{ "op": "cleanup", "userId": "u1" }

13. Error Handling

Error Cause Solution
"id required" Missing ID param Add id field
"sessionId required" Missing sessionId Add sessionId field
"mood required" Missing mood Add mood (happy/sad/excited/etc)
"userId required" Missing userId Always pass userId

14. Best Practices

  1. Always pass userId — Every tool call requires it.
  2. Use entity extraction — Extract entities from user messages to build the knowledge graph.
  3. Track mood — Call mood during emotionally significant moments.
  4. Remember at end — Store session summaries for cross-session continuity.
  5. Use context before startingbetter op gives you full context before you begin.
  6. Health check periodically — Run health + decay to keep memory clean.
安全使用建议
Install only if you intentionally want persistent cross-session memory and trust the configured memory MCP server. Before use, confirm where memories are stored, how to delete/export them, whether persona or mood tracking can be disabled, and require explicit approval before sharing any stored memory with another user.
能力评估
Purpose & Capability
The stated purpose is coherent: a graph-based persistent memory skill with persona, mood, learning, project, context, and sharing tools. Those capabilities are disclosed in SKILL.md and README.md, but they are sensitive by nature.
Instruction Scope
The session workflow tells the agent to store conversation content, extract entities, track mood, and save summaries as normal operation, without instructing the agent to ask first, minimize sensitive data, redact secrets, or limit retention.
Install Mechanism
The package contains only README.md and SKILL.md, with no executable scripts, no package dependencies, and clean static/VirusTotal telemetry.
Credentials
Requiring node and a registered memory MCP server is proportionate for this type of skill, but the actual storage, access controls, and retention behavior depend on that external MCP server and are not defined here.
Persistence & Privilege
The skill enables persistent cross-session memory, knowledge graph entities and relationships, persona and mood tracking, exports/backups/imports, and sharing with other owners; deletion and cleanup exist as operations but user-directed controls and approval requirements are not clearly specified.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install memory-mcp-cyber-bye
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /memory-mcp-cyber-bye 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.5
Initial release of Memory MCP with Graph-Based Memory & Persona Management.
元数据
Slug memory-mcp-cyber-bye
版本 1.0.5
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Memory MCP 是什么?

Graph-based memory MCP server with 9 consolidated tools, 8-phase auto-linking, persona tracking, emotional memory, adaptive learning, and knowledge graph ent... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 34 次。

如何安装 Memory MCP?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install memory-mcp-cyber-bye」即可一键安装,无需额外配置。

Memory MCP 是免费的吗?

是的,Memory MCP 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Memory MCP 支持哪些平台?

Memory MCP 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Memory MCP?

由 vk(@cyber-bye)开发并维护,当前版本 v1.0.5。

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