diff --git a/examples/flow_lite_agent.py b/examples/flow_lite_agent.py deleted file mode 100644 index 2df35d119..000000000 --- a/examples/flow_lite_agent.py +++ /dev/null @@ -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"}) diff --git a/examples/lite_agent_example.py b/examples/lite_agent_example.py deleted file mode 100644 index 40e064579..000000000 --- a/examples/lite_agent_example.py +++ /dev/null @@ -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()) diff --git a/examples/lite_agent_example_2.py b/examples/lite_agent_example_2.py deleted file mode 100644 index f01589007..000000000 --- a/examples/lite_agent_example_2.py +++ /dev/null @@ -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}") diff --git a/src/crewai/lite_agent.py b/src/crewai/lite_agent.py index 31a607826..d1451c68f 100644 --- a/src/crewai/lite_agent.py +++ b/src/crewai/lite_agent.py @@ -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)}", diff --git a/src/crewai/tools/tool_usage.py b/src/crewai/tools/tool_usage.py index 3221b64bc..6c19925fb 100644 --- a/src/crewai/tools/tool_usage.py +++ b/src/crewai/tools/tool_usage.py @@ -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( diff --git a/test_lite_agent.py b/test_lite_agent.py deleted file mode 100644 index e7296c7cc..000000000 --- a/test_lite_agent.py +++ /dev/null @@ -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() diff --git a/tests/cassettes/test_lite_agent_returns_usage_metrics.yaml b/tests/cassettes/test_lite_agent_returns_usage_metrics.yaml new file mode 100644 index 000000000..4435a7c2b --- /dev/null +++ b/tests/cassettes/test_lite_agent_returns_usage_metrics.yaml @@ -0,0 +1,245 @@ +interactions: +- request: + body: '{"messages": [{"role": "system", "content": "You are Research Assistant. + You are a helpful research assistant who can search for information about the + population of Tokyo.\nYour personal goal is: Find information about the population + of Tokyo\n\nYou ONLY have access to the following tools, and should NEVER make + up tools that are not listed here:\n\nTool Name: search_web\nTool Arguments: + {''query'': {''description'': None, ''type'': ''str''}}\nTool Description: Search + the web for information about a topic.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [search_web], just the name, exactly as + it''s written.\nAction Input: the input to the action, just a simple JSON object, + enclosed in curly braces, using \" to wrap keys and values.\nObservation: the + result of the action\n```\n\nOnce all necessary information is gathered, return + the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n```"}, {"role": "user", "content": + "What is the population of Tokyo? 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You + gather and summarize information quickly.\nYour personal goal is: Provide brief + information\n\nYou ONLY have access to the following tools, and should NEVER + make up tools that are not listed here:\n\nTool Name: search_web\nTool Arguments: + {''query'': {''description'': None, ''type'': ''str''}}\nTool Description: Search + the web for information about a topic.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [search_web], just the name, exactly as + it''s written.\nAction Input: the input to the action, just a simple JSON object, + enclosed in curly braces, using \" to wrap keys and values.\nObservation: the + result of the action\n```\n\nOnce all necessary information is gathered, return + the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n```\nIMPORTANT: Your final + answer MUST contain all the information requested in the following format: {\n \"summary\": + str,\n \"confidence\": int\n}\n\nIMPORTANT: Ensure the final output does not + include any code block markers like ```json or ```python."}, {"role": "user", + "content": "What is the population of Tokyo? 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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