""" Test script to demonstrate using the ManagerAgent's research function """ import os from dotenv import load_dotenv from llama_index.llms.anthropic import Anthropic from manager_agent import ManagerAgent from models import TaskPrompt # Load environment variables load_dotenv() # ANSI color codes for prettier output COLOR_RESET = "\033[0m" COLOR_CYAN = "\033[96m" COLOR_GREEN = "\033[92m" COLOR_YELLOW = "\033[93m" COLOR_RED = "\033[91m" def color_text(text, color): return f"{color}{text}{COLOR_RESET}" def main(): # Check if API key is available api_key = os.environ.get("ANTHROPIC_API_KEY") if not api_key: print(color_text("Error: ANTHROPIC_API_KEY not found in environment variables.", COLOR_RED)) print("Please set your Anthropic API key with:") print(" export ANTHROPIC_API_KEY='your-api-key'") print(" or create a .env file with ANTHROPIC_API_KEY=your-api-key") return # Initialize LLM print(color_text("Initializing Anthropic Claude...", COLOR_CYAN)) llm = Anthropic(model="claude-3-5-sonnet-20241022", api_key=api_key) # Initialize ManagerAgent print(color_text("Creating ManagerAgent...", COLOR_CYAN)) manager = ManagerAgent(llm=llm) print(color_text("\nTest 1: Using research function directly", COLOR_GREEN)) query = "What are the latest developments in AI agents and autonomous systems?" print(color_text(f"Research Query: {query}", COLOR_YELLOW)) # Call research function directly report = manager.research(query=query, verbose=True) print(color_text("\n=== Research Report ===", COLOR_GREEN)) print(report) print(color_text("\nTest 2: Using research as a tool through the agent", COLOR_GREEN)) prompt_text = "I need a comprehensive report on recent developments in quantum computing. Please research this topic thoroughly." print(color_text(f"User Prompt: {prompt_text}", COLOR_YELLOW)) # Create task prompt task_prompt = TaskPrompt(text=prompt_text) # Run through agent response = manager.run_task(task_prompt) print(color_text("\n=== Agent Response ===", COLOR_GREEN)) print(response) if __name__ == "__main__": main()