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MyClaw AI
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105 lines
2.8 KiB
Python
105 lines
2.8 KiB
Python
"""
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Basic usage example for AgentMemory
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"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from src.memory import AgentMemory
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# Initialize memory (creates ~/.agent-memory/memory.db by default)
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# Use a temporary path for this example
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mem = AgentMemory(db_path="/tmp/agent-memory-example.db")
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print("🧠 AgentMemory Example\n")
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# ==================== FACTS ====================
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print("📝 Storing facts...")
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mem.remember(
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"Boss prefers brief status updates over long explanations",
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tags=["preference", "communication", "boss"]
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)
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mem.remember(
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"API rate limit for the trading service is 100 requests per minute",
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tags=["technical", "api", "trading"]
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)
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mem.remember(
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"Weekly standup is every Monday at 9am EST",
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tags=["schedule", "meeting"]
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)
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# ==================== LESSONS ====================
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print("🎓 Recording lessons...")
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mem.learn(
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action="Deployed code directly to production without testing",
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context="deployment",
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outcome="negative",
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insight="Always run the full test suite before deploying, no matter how small the change"
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)
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mem.learn(
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action="Used quarter-Kelly position sizing for trades",
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context="trading",
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outcome="positive",
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insight="Conservative position sizing prevents large drawdowns and allows recovery from bad streaks"
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)
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# ==================== ENTITIES ====================
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print("👤 Tracking entities...")
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mem.track_entity("Alex", "person", {
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"role": "boss",
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"timezone": "America/New_York",
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"communication_style": "direct",
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"interests": ["AI", "trading", "automation"]
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})
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mem.track_entity("DataDeck", "project", {
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"type": "SaaS",
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"status": "completed",
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"features": 59,
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"url": "https://datadeck-preview.vercel.app"
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})
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# ==================== RECALL ====================
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print("\n🔍 Recalling memories...\n")
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# Search for communication preferences
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print("Q: How does boss like updates?")
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facts = mem.recall("boss communication updates")
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for f in facts[:3]:
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print(f" → {f.content}")
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print()
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# Get negative lessons about deployment
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print("Q: What went wrong with deployments?")
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lessons = mem.get_lessons(context="deployment", outcome="negative")
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for l in lessons:
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print(f" → Action: {l.action}")
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print(f" Lesson: {l.insight}")
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print()
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# Get entity info
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print("Q: What do I know about Alex?")
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alex = mem.get_entity("Alex", "person")
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if alex:
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print(f" → Name: {alex.name}")
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print(f" → Type: {alex.entity_type}")
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print(f" → Attributes: {alex.attributes}")
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# ==================== STATS ====================
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print("\n📊 Memory stats:")
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stats = mem.stats()
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print(f" Active facts: {stats['active_facts']}")
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print(f" Lessons: {stats['lessons']}")
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print(f" Entities: {stats['entities']}")
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print("\n✅ Example complete!")
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