ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/browser-use-rpa/demo_weather.py

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+"""
+Demo: Weather checking with learning capability
+
+This demo shows how the agent learns to check weather and reuses the learned workflow.
+"""
+
+import asyncio
+import logging
+from dotenv import load_dotenv
+
+from browser_use import ChatOpenAI
+from learning_agent import LearningAgent
+from llm_factory import make_llm
+
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+load_dotenv()
+
+
+async def demo_weather_learning():
+    """Demonstrate weather checking with learning."""
+    
+    print("=" * 60)
+    print("WEATHER CHECKING DEMO - LEARNING AGENT")
+    print("=" * 60)
+    
+    # First task - agent will learn
+    print("\n📚 PHASE 1: LEARNING - First weather check (Beijing)")
+    print("-" * 40)
+    
+    task1 = "Check the weather in Beijing"
+    
+    agent1 = LearningAgent(
+        task=task1,
+        llm=make_llm(),
+        knowledge_base_path="./weather_knowledge",
+        headless=False  # Show browser for demo
+    )
+    
+    print(f"Task: {task1}")
+    print("The agent will use browser-use to complete this task from scratch...")
+    
+    result1 = await agent1.run(max_steps=10)
+    
+    print(f"\n✅ Task completed!")
+    print(f"  - Success: {result1['success']}")
+    print(f"  - Execution time: {result1['execution_time']:.2f}s")
+    print(f"  - LLM calls made: {result1['llm_calls']}")
+    print(f"  - Workflow learned: {'Yes' if result1['success'] else 'No'}")
+    
+    # Wait a bit before second task
+    await asyncio.sleep(3)
+    
+    # Second task - agent should reuse learned workflow
+    print("\n🚀 PHASE 2: REPLAY - Second weather check (Shanghai)")
+    print("-" * 40)
+    
+    task2 = "Check the weather in Shanghai"
+    
+    agent2 = LearningAgent(
+        task=task2,
+        llm=make_llm(),
+        knowledge_base_path="./weather_knowledge",
+        headless=False
+    )
+    
+    print(f"Task: {task2}")
+    print("The agent will try to reuse the learned workflow...")
+    
+    result2 = await agent2.run(max_steps=10)
+    
+    print(f"\n✅ Task completed!")
+    print(f"  - Success: {result2['success']}")
+    print(f"  - Execution time: {result2['execution_time']:.2f}s")
+    print(f"  - Replay used: {result2['replay_used']}")
+    
+    if result2['replay_used']:
+        print(f"  - LLM calls saved: {result1['llm_calls']}")
+        speedup = result1['execution_time'] / result2['execution_time']
+        print(f"  - Speed improvement: {speedup:.1f}x faster")
+    else:
+        print(f"  - LLM calls made: {result2['llm_calls']}")
+    
+    # Show knowledge base statistics
+    print("\n📊 KNOWLEDGE BASE STATISTICS")
+    print("-" * 40)
+    
+    kb = agent2.knowledge_base
+    stats = kb.get_statistics()
+    
+    for key, value in stats.items():
+        print(f"  - {key.replace('_', ' ').title()}: {value}")
+    
+    print("\n" + "=" * 60)
+    print("DEMO COMPLETED")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(demo_weather_learning())