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

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Lorenze Jay
2025-03-31 12:13:25 -07:00
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80
examples/flow_lite_agent.py
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@@ -1,80 +0,0 @@
from typing import List, cast
from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
from pydantic import BaseModel, Field
from crewai.flow.flow import Flow, listen, start
from crewai.lite_agent import LiteAgent
# Define a structured output format
class MarketAnalysis(BaseModel):
key_trends: List[str] = Field(description="List of identified market trends")
market_size: str = Field(description="Estimated market size")
competitors: List[str] = Field(description="Major competitors in the space")
# Define flow state
class MarketResearchState(BaseModel):
product: str = ""
analysis: MarketAnalysis | None = None
class MarketResearchFlow(Flow[MarketResearchState]):
@start()
def initialize_research(self):
print(f"Starting market research for {self.state.product}")
@listen(initialize_research)
def analyze_market(self):
# Create a LiteAgent for market research
analyst = LiteAgent(
role="Market Research Analyst",
goal=f"Analyze the market for {self.state.product}",
backstory="You are an experienced market analyst with expertise in "
"identifying market trends and opportunities.",
llm="gpt-4o",
tools=[WebsiteSearchTool()],
verbose=True,
response_format=MarketAnalysis,
)
# Define the research query
query = f"""
Research the market for {self.state.product}. Include:
1. Key market trends
2. Market size
3. Major competitors
Format your response according to the specified structure.
"""
# Execute the analysis
result = analyst.kickoff(query)
self.state.analysis = cast(MarketAnalysis, result.pydantic)
return result.pydantic
@listen(analyze_market)
def present_results(self):
analysis = self.state.analysis
if analysis is None:
print("No analysis results available")
return
print("\nMarket Analysis Results")
print("=====================")
print("\nKey Market Trends:")
for trend in analysis.key_trends:
print(f"- {trend}")
print(f"\nMarket Size: {analysis.market_size}")
print("\nMajor Competitors:")
for competitor in analysis.competitors:
print(f"- {competitor}")
# Usage example
flow = MarketResearchFlow()
result = flow.kickoff(inputs={"product": "AI-powered chatbots"})

140
examples/lite_agent_example.py
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@@ -1,140 +0,0 @@
"""
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 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."
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")
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",
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."
)
print("Usage metrics:")
print(result.usage_metrics)
# # 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())

46
examples/lite_agent_example_2.py
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@@ -1,46 +0,0 @@
from typing import List, cast
from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
from pydantic import BaseModel, Field
from crewai.lite_agent import LiteAgent
# Define a structured output format
class MovieReview(BaseModel):
title: str = Field(description="The title of the movie")
rating: float = Field(description="Rating out of 10")
pros: List[str] = Field(description="List of positive aspects")
cons: List[str] = Field(description="List of negative aspects")
# Create a LiteAgent
critic = LiteAgent(
role="Movie Critic",
goal="Provide insightful movie reviews",
backstory="You are an experienced film critic known for balanced, thoughtful reviews.",
tools=[WebsiteSearchTool()],
verbose=True,
response_format=MovieReview,
)
# Use the agent
query = """
Review the movie 'Inception'. Include:
1. Your rating out of 10
2. Key positive aspects
3. Areas that could be improved
"""
result = critic.kickoff(query)
# Access the structured output
review = cast(MovieReview, result.pydantic)
print(f"\nMovie Review: {review.title}")
print(f"Rating: {review.rating}/10")
print("\nPros:")
for pro in review.pros:
print(f"- {pro}")
print("\nCons:")
for con in review.cons:
print(f"- {con}")

19
src/crewai/lite_agent.py
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@@ -241,12 +241,21 @@ class LiteAgent(BaseModel):
formatted_result: Optional[BaseModel] = None
if self.response_format:
try:
# Cast to BaseModel to ensure type safety
result = self.response_format.model_validate_json(
agent_finish.output
final_answer_match = re.search(
r"Final Answer:\s*(.*?)(?:\n\n|$)",
agent_finish.output,
re.DOTALL,
)
if isinstance(result, BaseModel):
formatted_result = result
if final_answer_match:
json_content = final_answer_match.group(1).strip()
result = self.response_format.model_validate_json(json_content)
if isinstance(result, BaseModel):
formatted_result = result
else:
self._printer.print(
content="Could not find Final Answer section in the output",
color="yellow",
)
except Exception as e:
self._printer.print(
content=f"Failed to parse output into response format: {str(e)}",

2
src/crewai/tools/tool_usage.py
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@@ -26,7 +26,6 @@ from crewai.utilities.events.tool_usage_events import (
ToolSelectionErrorEvent,
ToolUsageErrorEvent,
ToolUsageFinishedEvent,
ToolUsageStartedEvent,
ToolValidateInputErrorEvent,
)
@@ -169,6 +168,7 @@ class ToolUsage:
started_at = time.time()
from_cache = False
result = None
if self.tools_handler and self.tools_handler.cache:
result = self.tools_handler.cache.read(

60
test_lite_agent.py
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@@ -1,60 +0,0 @@
from crewai import LLM
from crewai.lite_agent import LiteAgent
from crewai.tools import BaseTool
# A simple test tool
class SecretLookupTool(BaseTool):
name = "secret_lookup"
description = "A tool to lookup secrets"
def _run(self) -> str:
return "SUPERSECRETPASSWORD123"
# Test with tools
def test_with_tools():
llm = LLM(model="gpt-4o")
agent = LiteAgent(
role="Secret Agent",
goal="Return the secret password",
backstory="I am a secret agent created to return the secret password",
llm=llm,
tools=[SecretLookupTool()],
verbose=True,
)
# Test a simple query
response = agent.kickoff("Hello, can you help me?")
print("\n=== Agent Response ===")
print(response)
# # Test without tools
# def test_without_tools():
# llm = LLM(model="gpt-4o")
# agent = LiteAgent(
# role="Test Agent",
# goal="Test the system prompt formatting",
# backstory="I am a test agent created to verify the system prompt works correctly.",
# llm=llm,
# verbose=True,
# )
# # Get the system prompt
# system_prompt = agent._get_default_system_prompt()
# print("\n=== System Prompt (without tools) ===")
# print(system_prompt)
# # Test a simple query
# response = agent.kickoff("Hello, can you help me?")
# print("\n=== Agent Response ===")
# print(response)
if __name__ == "__main__":
print("Testing LiteAgent with tools...")
test_with_tools()
# print("\n\nTesting LiteAgent without tools...")
# test_without_tools()

245
tests/cassettes/test_lite_agent_returns_usage_metrics.yaml Normal file
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tests/cassettes/test_lite_agent_structured_output.yaml Normal file
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tests/test_lite_agent.py Normal file
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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