Remove deprecated test files and examples for LiteAgent; add comprehensive tests for LiteAgent functionality, including tool usage and structured output handling.

tests/test_lite_agent.py

@@ -0,0 +1,172 @@
+import asyncio
+from typing import cast
+
+import pytest
+from pydantic import BaseModel, Field
+
+from crewai import LLM
+from crewai.lite_agent import LiteAgent
+from crewai.tools import BaseTool
+from crewai.utilities.events import crewai_event_bus
+from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
+
+
+# A simple test tool
+class SecretLookupTool(BaseTool):
+    name: str = "secret_lookup"
+    description: str = "A tool to lookup secrets"
+
+    def _run(self) -> str:
+        return "SUPERSECRETPASSWORD123"
+
+
+# Define Mock Search Tool
+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 21 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."
+
+
+# Define Mock Calculator Tool
+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:
+            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")
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_with_tools():
+    """Test that LiteAgent can use tools."""
+    # Create a LiteAgent with tools
+    llm = LLM(model="gpt-4o-mini")
+    agent = LiteAgent(
+        role="Research Assistant",
+        goal="Find information about the population of Tokyo",
+        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = agent.kickoff(
+        "What is the population of Tokyo and how many people would that be per square kilometer if Tokyo's area is 2,194 square kilometers?"
+    )
+
+    assert (
+        "21 million" in result.raw or "37 million" in result.raw
+    ), "Agent should find Tokyo's population"
+    assert (
+        "per square kilometer" in result.raw
+    ), "Agent should calculate population density"
+
+    received_events = []
+
+    @crewai_event_bus.on(ToolUsageStartedEvent)
+    def event_handler(source, event):
+        received_events.append(event)
+
+    agent.kickoff("What are the effects of climate change on coral reefs?")
+
+    # Verify tool usage events were emitted
+    assert len(received_events) > 0, "Tool usage events should be emitted"
+    event = received_events[0]
+    assert isinstance(event, ToolUsageStartedEvent)
+    assert event.agent_role == "Research Assistant"
+    assert event.tool_name == "search_web"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_structured_output():
+    """Test that LiteAgent can return a simple structured output."""
+
+    class SimpleOutput(BaseModel):
+        """Simple structure for agent outputs."""
+
+        summary: str = Field(description="A brief summary of findings")
+        confidence: int = Field(description="Confidence level from 1-100")
+
+    web_search_tool = WebSearchTool()
+
+    llm = LLM(model="gpt-4o-mini")
+    agent = LiteAgent(
+        role="Info Gatherer",
+        goal="Provide brief information",
+        backstory="You gather and summarize information quickly.",
+        llm=llm,
+        tools=[web_search_tool],
+        verbose=True,
+        response_format=SimpleOutput,
+    )
+
+    result = agent.kickoff(
+        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence"
+    )
+
+    print(f"\n=== Agent Result Type: {type(result)}")
+    print(f"=== Agent Result: {result}")
+    print(f"=== Pydantic: {result.pydantic}")
+
+    assert result.pydantic is not None, "Should return a Pydantic model"
+
+    output = cast(SimpleOutput, result.pydantic)
+
+    assert isinstance(output.summary, str), "Summary should be a string"
+    assert len(output.summary) > 0, "Summary should not be empty"
+    assert isinstance(output.confidence, int), "Confidence should be an integer"
+    assert 1 <= output.confidence <= 100, "Confidence should be between 1 and 100"
+
+    assert "tokyo" in output.summary.lower() or "population" in output.summary.lower()
+
+    assert result.usage_metrics is not None
+
+    return result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_returns_usage_metrics():
+    """Test that LiteAgent returns usage metrics."""
+    llm = LLM(model="gpt-4o-mini")
+    agent = LiteAgent(
+        role="Research Assistant",
+        goal="Find information about the population of Tokyo",
+        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = agent.kickoff(
+        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence"
+    )
+
+    assert result.usage_metrics is not None
+    assert result.usage_metrics["total_tokens"] > 0