openclaw-master-skills/skills/agent-memory

🧠 AgentMemory

Persistent Memory for AI Agents

Every AI agent session starts fresh. We forget learnings, repeat mistakes, and lose context. AgentMemory solves this.

Built for OpenClaw and Clawdbot agents, but works with any LLM-powered system.

✨ Features

• 📝 Facts - Store and recall information across sessions
• 🎓 Lessons - Learn from successes and failures
• 👤 Entities - Track people, projects, and preferences
• 🔍 Semantic Search - Find relevant memories fast (FTS5)
• 🧹 Auto-cleanup - Forget stale information automatically
• 📦 Zero Dependencies - Just Python + SQLite

🚀 Quick Start

from agent_memory import AgentMemory

# Initialize (creates ~/.agent-memory/memory.db)
mem = AgentMemory()

# Remember facts
mem.remember("Boss prefers brief status updates", tags=["preference", "communication"])
mem.remember("API rate limit is 100 req/min", tags=["technical", "api"])

# Learn from experience
mem.learn(
    action="Used RSI momentum strategy for crypto trading",
    context="trading",
    outcome="negative", 
    insight="RSI alone is insufficient, need confirmation signals"
)

# Track entities
mem.track_entity("Alex", "person", {
    "role": "boss",
    "timezone": "America/New_York",
    "communication_style": "brief and direct"
})

# Recall relevant memories
facts = mem.recall("how does boss like updates?")
# → Returns facts about boss preferences

lessons = mem.get_lessons(context="trading", outcome="negative")
# → Returns failed trading lessons to avoid repeating mistakes

# Stats
print(mem.stats())
# → {'active_facts': 42, 'lessons': 15, 'entities': 8}

📦 Installation

Option 1: ClawdHub (Recommended for Clawdbot/OpenClaw)

clawdhub install agent-memory

Option 2: Git Clone

git clone https://github.com/Dennis-Da-Menace/agent-memory.git
cd agent-memory

Option 3: Copy the file

Just copy src/memory.py to your project. It has zero external dependencies!

📖 API Reference

Facts

# Remember something
fact_id = mem.remember(
    content="Important information",
    tags=["category1", "category2"],
    source="conversation",  # or "observation", "inference"
    confidence=0.9,  # 0-1
    expires_in_days=30  # optional auto-expiry
)

# Search facts
facts = mem.recall(
    query="search terms",
    limit=10,
    tags=["filter_tag"],
    min_confidence=0.5
)

# Update a fact (keeps history)
new_id = mem.supersede(old_fact_id, "Updated information")

# Delete a fact
mem.forget(fact_id)

# Cleanup old facts
deleted = mem.forget_stale(days=30, min_access_count=1)

Lessons

# Record a lesson
lesson_id = mem.learn(
    action="What I did",
    context="Situation/topic",
    outcome="positive",  # or "negative", "neutral"
    insight="What I learned from this"
)

# Get lessons
lessons = mem.get_lessons(
    context="trading",  # optional filter
    outcome="negative",  # optional filter
    limit=10
)

# Mark lesson as applied
mem.apply_lesson(lesson_id)

Entities

# Track an entity
entity_id = mem.track_entity(
    name="Alex",
    entity_type="person",  # or "project", "company", "tool"
    attributes={"role": "boss", "timezone": "EST"}
)

# Get entity
entity = mem.get_entity("Alex", entity_type="person")

# Link facts to entities
mem.link_fact_to_entity("Alex", fact_id)

Utilities

# Statistics
stats = mem.stats()
# {'active_facts': 42, 'superseded_facts': 5, 'lessons': 15, 'entities': 8}

# Export everything
data = mem.export_json()

🔧 Configuration

By default, AgentMemory stores data in ~/.agent-memory/memory.db. You can customize:

# Custom location
mem = AgentMemory(db_path="/path/to/my/memory.db")

# In-memory (for testing)
mem = AgentMemory(db_path=":memory:")

🎯 Use Cases

1. Preference Learning

# When user expresses preference
mem.remember("User prefers dark mode", tags=["preference", "ui"])

# Later, when making UI decisions
prefs = mem.recall("user preference ui", tags=["preference"])

2. Error Prevention

# When something fails
mem.learn(
    action="Deployed to production without tests",
    context="deployment",
    outcome="negative",
    insight="Always run test suite before deploying"
)

# Before deploying
lessons = mem.get_lessons(context="deployment", outcome="negative")
for lesson in lessons:
    print(f"⚠️ Remember: {lesson.insight}")

3. Relationship Context

# Track relationships
mem.track_entity("Alice", "person", {"team": "engineering", "expertise": "backend"})
mem.remember("Alice prefers Slack over email", tags=["communication", "Alice"])

# Before contacting Alice
alice = mem.get_entity("Alice")
alice_facts = mem.recall("Alice communication")

🤝 Contributing

Built by Dennis Da Menace for the OpenClaw community.

Contributions welcome! Please:

1. Fork the repo
2. Create a feature branch
3. Submit a PR

📄 License

MIT License - Use freely in your projects!

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"Memory is the treasury and guardian of all things." - Cicero

Built with 🦀 by an AI agent, for AI agents.