WIP

examples/lite_agent_example.py

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+"""
+Example script demonstrating how to use the LiteAgent.
+
+This example shows how to create and use a LiteAgent for simple interactions
+without the need for a full crew or task-based workflow.
+"""
+
+import asyncio
+from typing import Any, Dict, cast
+
+from pydantic import BaseModel, Field
+
+from crewai.lite_agent import LiteAgent
+from crewai.tools.base_tool import BaseTool
+
+
+# Define custom tools
+class WebSearchTool(BaseTool):
+    """Tool for searching the web for information."""
+
+    name: str = "search_web"
+    description: str = "Search the web for information about a topic."
+
+    def _run(self, query: str) -> str:
+        """Search the web for information about a topic."""
+        # This is a mock implementation
+        if "tokyo" in query.lower():
+            return "Tokyo's population in 2023 was approximately 14 million people in the city proper, and 37 million in the greater metropolitan area."
+        elif "climate change" in query.lower() and "coral" in query.lower():
+            return "Climate change severely impacts coral reefs through: 1) Ocean warming causing coral bleaching, 2) Ocean acidification reducing calcification, 3) Sea level rise affecting light availability, 4) Increased storm frequency damaging reef structures. Sources: NOAA Coral Reef Conservation Program, Global Coral Reef Alliance."
+        else:
+            return f"Found information about {query}: This is a simulated search result for demonstration purposes."
+
+
+class CalculatorTool(BaseTool):
+    """Tool for performing calculations."""
+
+    name: str = "calculate"
+    description: str = "Calculate the result of a mathematical expression."
+
+    def _run(self, expression: str) -> str:
+        """Calculate the result of a mathematical expression."""
+        try:
+            # CAUTION: eval can be dangerous in production code
+            # This is just for demonstration purposes
+            result = eval(expression, {"__builtins__": {}})
+            return f"The result of {expression} is {result}"
+        except Exception as e:
+            return f"Error calculating {expression}: {str(e)}"
+
+
+# Define a custom response format using Pydantic
+class ResearchResult(BaseModel):
+    """Structure for research results."""
+
+    main_findings: str = Field(description="The main findings from the research")
+    key_points: list[str] = Field(description="List of key points")
+    sources: list[str] = Field(description="List of sources used")
+
+
+async def main():
+    # Create tools
+    web_search_tool = WebSearchTool()
+    calculator_tool = CalculatorTool()
+
+    # Create a LiteAgent with a specific role, goal, and backstory
+    agent = LiteAgent(
+        role="Research Analyst",
+        goal="Provide accurate and concise information on requested topics",
+        backstory="You are an expert research analyst with years of experience in gathering and synthesizing information from various sources.",
+        llm="gpt-4",  # You can use any supported LLM
+        tools=[web_search_tool, calculator_tool],
+        verbose=True,
+        response_format=ResearchResult,  # Optional: Use a structured output format
+    )
+
+    # Example 1: Simple query with raw text response
+    print("\n=== Example 1: Simple Query ===")
+    result = await agent.kickoff_async("What is the population of Tokyo in 2023?")
+    print(f"Raw response: {result.raw}")
+
+    # Example 2: Query with structured output
+    print("\n=== Example 2: Structured Output ===")
+    structured_query = """
+    Research the impact of climate change on coral reefs.
+    
+    YOU MUST format your response as a valid JSON object with the following structure:
+    {
+      "main_findings": "A summary of the main findings",
+      "key_points": ["Point 1", "Point 2", "Point 3"],
+      "sources": ["Source 1", "Source 2"]
+    }
+    
+    Include at least 3 key points and 2 sources. Wrap your JSON in ```json and ``` tags.
+    """
+
+    result = await agent.kickoff_async(structured_query)
+
+    if result.pydantic:
+        # Cast to the specific type for better IDE support
+        research_result = cast(ResearchResult, result.pydantic)
+        print(f"Main findings: {research_result.main_findings}")
+        print("\nKey points:")
+        for i, point in enumerate(research_result.key_points, 1):
+            print(f"{i}. {point}")
+        print("\nSources:")
+        for i, source in enumerate(research_result.sources, 1):
+            print(f"{i}. {source}")
+    else:
+        print(f"Raw response: {result.raw}")
+        print(
+            "\nNote: Structured output was not generated. The LLM may need more explicit instructions to format the response as JSON."
+        )
+
+    # Example 3: Multi-turn conversation
+    print("\n=== Example 3: Multi-turn Conversation ===")
+    messages = [
+        {"role": "user", "content": "I'm planning a trip to Japan."},
+        {
+            "role": "assistant",
+            "content": "That sounds exciting! Japan is a beautiful country with rich culture, delicious food, and stunning landscapes. What would you like to know about Japan to help with your trip planning?",
+        },
+        {
+            "role": "user",
+            "content": "What are the best times to visit Tokyo and Kyoto?",
+        },
+    ]
+
+    result = await agent.kickoff_async(messages)
+    print(f"Response: {result.raw}")
+
+    # Print usage metrics if available
+    if result.usage_metrics:
+        print("\nUsage metrics:")
+        for key, value in result.usage_metrics.items():
+            print(f"{key}: {value}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())