/install memory-mcp-cyber-bye
Memory MCP — Graph-Based Memory & Persona Management
[!IMPORTANT] Dependency Warning: This skill requires the
memoryMCP 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 projectsessionId— 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
- Always pass
userId— Every tool call requires it. - Use entity extraction — Extract entities from user messages to build the knowledge graph.
- Track mood — Call
moodduring emotionally significant moments. - Remember at end — Store session summaries for cross-session continuity.
- Use context before starting —
betterop gives you full context before you begin. - Health check periodically — Run
health+decayto keep memory clean.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install memory-mcp-cyber-bye - 安装完成后,直接呼叫该 Skill 的名称或使用
/memory-mcp-cyber-bye触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
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。