File size: 2,848 Bytes
06793fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | """
Basic usage example for AgentMemory
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src.memory import AgentMemory
# Initialize memory (creates ~/.agent-memory/memory.db by default)
# Use a temporary path for this example
mem = AgentMemory(db_path="/tmp/agent-memory-example.db")
print("🧠 AgentMemory Example\n")
# ==================== FACTS ====================
print("📝 Storing facts...")
mem.remember(
"Boss prefers brief status updates over long explanations",
tags=["preference", "communication", "boss"]
)
mem.remember(
"API rate limit for the trading service is 100 requests per minute",
tags=["technical", "api", "trading"]
)
mem.remember(
"Weekly standup is every Monday at 9am EST",
tags=["schedule", "meeting"]
)
# ==================== LESSONS ====================
print("🎓 Recording lessons...")
mem.learn(
action="Deployed code directly to production without testing",
context="deployment",
outcome="negative",
insight="Always run the full test suite before deploying, no matter how small the change"
)
mem.learn(
action="Used quarter-Kelly position sizing for trades",
context="trading",
outcome="positive",
insight="Conservative position sizing prevents large drawdowns and allows recovery from bad streaks"
)
# ==================== ENTITIES ====================
print("👤 Tracking entities...")
mem.track_entity("Alex", "person", {
"role": "boss",
"timezone": "America/New_York",
"communication_style": "direct",
"interests": ["AI", "trading", "automation"]
})
mem.track_entity("DataDeck", "project", {
"type": "SaaS",
"status": "completed",
"features": 59,
"url": "https://datadeck-preview.vercel.app"
})
# ==================== RECALL ====================
print("\n🔍 Recalling memories...\n")
# Search for communication preferences
print("Q: How does boss like updates?")
facts = mem.recall("boss communication updates")
for f in facts[:3]:
print(f" → {f.content}")
print()
# Get negative lessons about deployment
print("Q: What went wrong with deployments?")
lessons = mem.get_lessons(context="deployment", outcome="negative")
for l in lessons:
print(f" → Action: {l.action}")
print(f" Lesson: {l.insight}")
print()
# Get entity info
print("Q: What do I know about Alex?")
alex = mem.get_entity("Alex", "person")
if alex:
print(f" → Name: {alex.name}")
print(f" → Type: {alex.entity_type}")
print(f" → Attributes: {alex.attributes}")
# ==================== STATS ====================
print("\n📊 Memory stats:")
stats = mem.stats()
print(f" Active facts: {stats['active_facts']}")
print(f" Lessons: {stats['lessons']}")
print(f" Entities: {stats['entities']}")
print("\n✅ Example complete!")
|