AgentMemory Skill

Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.

Installation

clawdhub install agent-memory

Usage

from src.memory import AgentMemory

mem = AgentMemory()

# Remember facts
mem.remember("Important information", tags=["category"])

# Learn from experience
mem.learn(
    action="What was done",
    context="situation",
    outcome="positive",  # or "negative"
    insight="What was learned"
)

# Recall memories
facts = mem.recall("search query")
lessons = mem.get_lessons(context="topic")

# Track entities
mem.track_entity("Name", "person", {"role": "engineer"})
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When to Use

• Starting a session: Load relevant context from memory
• After conversations: Store important facts
• After failures: Record lessons learned
• Meeting new people/projects: Track as entities

Integration with Clawdbot

Add to your AGENTS.md or HEARTBEAT.md:

## Memory Protocol

On session start:
1. Load recent lessons: `mem.get_lessons(limit=5)`
2. Check entity context for current task
3. Recall relevant facts

On session end:
1. Extract durable facts from conversation
2. Record any lessons learned
3. Update entity information

Database Location

Default: ~/.agent-memory/memory.db

Custom: AgentMemory(db_path="/path/to/memory.db")

這個 Skill 質量不錯，核心功能完整，文件和測試都比較齊全。它能幫助 AI 記住重要資訊、從錯誤中學習、跟蹤人物專案等，使用簡單方便。主要不足是 Skill 本身的說明文件比較簡單，安裝使用指南不夠詳細；另外命令列工具功能也不夠完整。對於想為 AI Agent 新增記憶功能的開發者來說，這是一個值得一試的基礎元件。