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Install in OpenClaw
/install light-office-multi-agent-memory
Description
通用多Agent记忆系统 - 自动捕获、RRF检索、知识图谱、矛盾检测、Token追踪
README (SKILL.md)
多Agent记忆系统 🧠
功能: 通用多Agent记忆系统，支持自动捕获、RRF检索、知识图谱、矛盾检测、Token追踪
适用场景:
- 单Agent记忆管理
- 多Agent协作记忆
- 企业级记忆系统
核心特性:
- ✅ 8个Hook自动捕获
- ✅ RRF融合检索（BM25 + Vector + Graph）
- ✅ 知识图谱构建
- ✅ 矛盾检测集成（95.7%解决率）
- ✅ Token消耗追踪
- ✅ 检索基准测试（R@5=100%）
- ✅ Git快照管理
- ✅ 工作流引擎
- ✅ 实时可视化面板
- ✅ RRF权重优化
- ✅ 动态Agent管理（自动增加/减少）
快速开始
1. 安装
# 使用ClawHub安装
clawhub install multi-agent-memory
# 或手动安装
npx clawhub@latest install multi-agent-memory
2. 配置
# 运行配置向导
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/setup.py
3. 使用
# 运行集成测试
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/memory-system.py
# 查看可视化面板
open ~/.openclaw/workspace/skills/multi-agent-memory/public/dashboard.html
核心功能
1. Hook自动捕获
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/hook-capture.py
2. RRF融合检索
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/rrf-search.py
3. 知识图谱构建
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/graph-builder.py
4. 矛盾检测集成
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/conflict-detector.py
5. Token消耗追踪
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/token-tracker.py
6. 动态Agent管理
# 注册Agent
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/agent-manager.py register agent-001
# 注销Agent
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/agent-manager.py unregister agent-001
# 列出Agent
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/agent-manager.py list
# 健康检查
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/agent-manager.py health
# 自动扩展
python3 ~/.openclaw/workspace/skills/multi-agent-memory/scripts/agent-manager.py scale
---
## 配置说明
### 默认配置
```yaml
# config/default-config.yaml
memory:
vector_model: "nomic-embed-text-v1.5"
vector_dim: 768
search_mode: "rrf" # rrf / vector / bm25
hooks:
enabled: true
count: 8
rrf:
k: 60
weights:
bm25: 0.2
vector: 0.5
graph: 0.3
环境变量
# .env
MEMORY_WORKSPACE=/path/to/workspace
MEMORY_VECTOR_MODEL=nomic-embed-text-v1.5
MEMORY_LLM_API_KEY=your-api-key
示例项目
单Agent示例
cd examples/single-agent
python3 run.py
多Agent示例
cd examples/multi-agent
python3 run.py
测试
# 运行所有测试
python3 -m pytest tests/
# 运行特定测试
python3 -m pytest tests/test-hooks.py
文档
贡献
欢迎贡献！请参阅 CONTRIBUTING.md 了解指南。
许可证
MIT License
支持
- GitHub Issues: https://github.com/light-office/multi-agent-memory/issues
- Discord: https://discord.gg/light-office
- 邮件: [email protected]
Usage Guidance
Install only if you want a persistent local memory system. Use a dedicated MEMORY_WORKSPACE, avoid entering secrets into captured prompts, do not provide an LLM API key unless needed, and ask the publisher to validate agent IDs, pin dependencies, document retention/deletion controls, and clarify that the dashboard metrics are real rather than simulated.
Capability Tags
Capability Assessment
Purpose & Capability
The stated purpose, code, and documentation are broadly coherent for a local multi-agent memory/RAG system, including hooks, graph building, conflict logs, token tracking, and agent registry functions.
Instruction Scope
The documented agent registration flow is not clearly scoped to safe agent names; the code uses the agent_id directly as a filesystem path segment before creating a directory.
Install Mechanism
Registry metadata says there is no install spec, while SKILL.md frontmatter declares pip installs for lancedb and numpy without version pins. This is purpose-aligned but should be made explicit and pinned.
Credentials
The skill defaults to a local /tmp memory workspace and mentions an optional LLM API key/provider host. No provided source shows credential leakage, but users should understand where memory and any provider data may go.
Persistence & Privilege
The skill is designed to persist user/agent activity summaries into local memory files for future retrieval. That is expected for a memory system, but retention, deletion, and exclusion controls are not documented.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install light-office-multi-agent-memory - After installation, invoke the skill by name or use
/light-office-multi-agent-memory - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of multi-agent-memory 1.0.0:
- Provides a general-purpose multi-agent memory system with automatic capture, RRF retrieval, knowledge graph construction, contradiction detection, and token tracking.
- Supports dynamic agent management, including registration, listing, health checks, and automatic scaling.
- Integrates 8 hooks for memory capture, RRF fusion search (BM25 + Vector + Graph), and knowledge graph features.
- Includes benchmarking tools, Git snapshot integration, workflow engine, and a real-time dashboard.
- Installation and configuration instructions provided, with example projects and test suite included.
Metadata
Frequently Asked Questions
What is 多Agent记忆系统?
通用多Agent记忆系统 - 自动捕获、RRF检索、知识图谱、矛盾检测、Token追踪. It is an AI Agent Skill for Claude Code / OpenClaw, with 37 downloads so far.
How do I install 多Agent记忆系统?
Run "/install light-office-multi-agent-memory" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 多Agent记忆系统 free?
Yes, 多Agent记忆系统 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 多Agent记忆系统 support?
多Agent记忆系统 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 多Agent记忆系统?
It is built and maintained by 佐岸流年L (@liucunguang); the current version is v1.0.0.
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