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Feat/individual react agent (#2483)

Browse Source 
* WIP

* WIP

* wip

* wip

* WIP

* More WIP

* Its working but needs a massive clean up

* output type works now

* Usage metrics fixed

* more testing

* WIP

* cleaning up

* Update logger

* 99% done. Need to make docs match new example

* cleanup

* drop hard coded examples

* docs

* Clean up

* Fix errors

* Trying to fix CI issues

* more type checker fixes

* More type checking fixes

* Update LiteAgent documentation for clarity and consistency; replace WebsiteSearchTool with SerperDevTool, and improve formatting in examples.

* fix fingerprinting issues

* fix type-checker

* Fix type-checker issue by adding type ignore comment for cache read in ToolUsage class

* Add optional agent parameter to CrewAgentParser and enhance action handling logic

* Remove unused parameters from ToolUsage instantiation in tests and clean up debug print statement in CrewAgentParser.

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

* Remove unused variable 'result' from ToolUsage class to clean up code.

* Add initialization for 'result' variable in ToolUsage class to resolve type-checker warnings

* Refactor agent_utils.py by removing unused event imports and adding missing commas in function definitions. Update test_events.py to reflect changes in expected event counts and adjust assertions accordingly. Modify test_tools_emits_error_events.yaml to include new headers and update response content for consistency with recent API changes.

* Enhance tests in crew_test.py by verifying cache behavior in test_tools_with_custom_caching and ensuring proper agent initialization with added commas in test_crew_kickoff_for_each_works_with_manager_agent_copy.

* Update agent tests to reflect changes in expected call counts and improve response formatting in YAML cassette. Adjusted mock call count from 2 to 3 and refined interaction formats for clarity and consistency.

* Refactor agent tests to update model versions and improve response formatting in YAML cassettes. Changed model references from 'o1-preview' to 'o3-mini' and adjusted interaction formats for consistency. Enhanced error handling in context length tests and refined mock setups for better clarity.

* Update tool usage logging to ensure tool arguments are consistently formatted as strings. Adjust agent test cases to reflect changes in maximum iterations and expected outputs, enhancing clarity in assertions. Update YAML cassettes to align with new response formats and improve overall consistency across tests.

* Update YAML cassette for LLM tests to reflect changes in response structure and model version. Adjusted request and response headers, including updated content length and user agent. Enhanced token limits and request counts for improved testing accuracy.

* Update tool usage logging to store tool arguments as native types instead of strings, enhancing data integrity and usability.

* Refactor agent tests by removing outdated test cases and updating YAML cassettes to reflect changes in tool usage and response formats. Adjusted request and response headers, including user agent and content length, for improved accuracy in testing. Enhanced interaction formats for consistency across tests.

* Add Excalidraw diagram file for visual representation of input-output flow

Created a new Excalidraw file that includes a diagram illustrating the input box, database, and output box with connecting arrows. This visual aid enhances understanding of the data flow within the application.

* Remove redundant error handling for action and final answer in CrewAgentParser. Update tests to reflect this change by deleting the corresponding test case.

---------

Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
Co-authored-by: Lorenze Jay <lorenzejaytech@gmail.com>
This commit is contained in:
Brandon Hancock (bhancock_ai)
2025-04-02 11:54:46 -04:00
committed by GitHub
parent 9b51e1174c
commit efe27bd570
42 changed files with 21642 additions and 12531 deletions
Download Patch File Download Diff File Expand all files Collapse all files

112
tests/agent_test.py
View File

@@ -9,7 +9,7 @@ import pytest
from crewai import Agent, Crew, Task
from crewai.agents.cache import CacheHandler
from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
from crewai.agents.parser import CrewAgentParser, OutputParserException
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
from crewai.llm import LLM
@@ -18,7 +18,6 @@ from crewai.tools.tool_calling import InstructorToolCalling
from crewai.tools.tool_usage import ToolUsage
from crewai.utilities import RPMController
from crewai.utilities.events import crewai_event_bus
from crewai.utilities.events.llm_events import LLMStreamChunkEvent
from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
@@ -375,7 +374,7 @@ def test_agent_powered_by_new_o_model_family_that_allows_skipping_tool():
role="test role",
goal="test goal",
backstory="test backstory",
llm="o1-preview",
llm=LLM(model="o3-mini"),
max_iter=3,
use_system_prompt=False,
allow_delegation=False,
@@ -401,7 +400,7 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
role="test role",
goal="test goal",
backstory="test backstory",
llm="o1-preview",
llm="o3-mini",
max_iter=3,
use_system_prompt=False,
allow_delegation=False,
@@ -443,7 +442,7 @@ def test_agent_custom_max_iterations():
task=task,
tools=[get_final_answer],
)
assert private_mock.call_count == 2
assert private_mock.call_count == 3
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -531,7 +530,7 @@ def test_agent_moved_on_after_max_iterations():
role="test role",
goal="test goal",
backstory="test backstory",
max_iter=3,
max_iter=5,
allow_delegation=False,
)
@@ -552,6 +551,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
def get_final_answer() -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
agent = Agent(
role="test role",
@@ -573,7 +573,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
task=task,
tools=[get_final_answer],
)
assert output == "The final answer is 42."
assert output == "42"
captured = capsys.readouterr()
assert "Max RPM reached, waiting for next minute to start." in captured.out
moveon.assert_called()
@@ -863,25 +863,6 @@ def test_agent_function_calling_llm():
mock_original_tool_calling.assert_called()
def test_agent_count_formatting_error():
from unittest.mock import patch
agent1 = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
verbose=True,
)
parser = CrewAgentParser(agent=agent1)
with patch.object(Agent, "increment_formatting_errors") as mock_count_errors:
test_text = "This text does not match expected formats."
with pytest.raises(OutputParserException):
parser.parse(test_text)
mock_count_errors.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
from crewai.tools import BaseTool
@@ -1305,46 +1286,55 @@ def test_llm_call_with_error():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_handle_context_length_exceeds_limit():
# Import necessary modules
from crewai.utilities.agent_utils import handle_context_length
from crewai.utilities.i18n import I18N
from crewai.utilities.printer import Printer
# Create mocks for dependencies
printer = Printer()
i18n = I18N()
# Create an agent just for its LLM
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
)
original_action = AgentAction(
tool="test_tool",
tool_input="test_input",
text="test_log",
thought="test_thought",
respect_context_window=True,
)
with patch.object(
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
) as private_mock:
task = Task(
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
expected_output="The final answer",
)
agent.execute_task(
task=task,
)
private_mock.assert_called_once()
with patch.object(
CrewAgentExecutor, "_handle_context_length"
) as mock_handle_context:
mock_handle_context.side_effect = ValueError(
"Context length limit exceeded"
llm = agent.llm
# Create test messages
messages = [
{
"role": "user",
"content": "This is a test message that would exceed context length",
}
]
# Set up test parameters
respect_context_window = True
callbacks = []
# Apply our patch to summarize_messages to force an error
with patch("crewai.utilities.agent_utils.summarize_messages") as mock_summarize:
mock_summarize.side_effect = ValueError("Context length limit exceeded")
# Directly call handle_context_length with our parameters
with pytest.raises(ValueError) as excinfo:
handle_context_length(
respect_context_window=respect_context_window,
printer=printer,
messages=messages,
llm=llm,
callbacks=callbacks,
i18n=i18n,
)
long_input = "This is a very long input. " * 10000
# Attempt to handle context length, expecting the mocked error
with pytest.raises(ValueError) as excinfo:
agent.agent_executor._handle_context_length(
[(original_action, long_input)]
)
assert "Context length limit exceeded" in str(excinfo.value)
mock_handle_context.assert_called_once()
# Verify our patch was called and raised the correct error
assert "Context length limit exceeded" in str(excinfo.value)
mock_summarize.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1353,7 +1343,7 @@ def test_handle_context_length_exceeds_limit_cli_no():
role="test role",
goal="test goal",
backstory="test backstory",
sliding_context_window=False,
respect_context_window=False,
)
task = Task(description="test task", agent=agent, expected_output="test output")
@@ -1369,8 +1359,8 @@ def test_handle_context_length_exceeds_limit_cli_no():
)
private_mock.assert_called_once()
pytest.raises(SystemExit)
with patch.object(
CrewAgentExecutor, "_handle_context_length"
with patch(
"crewai.utilities.agent_utils.handle_context_length"
) as mock_handle_context:
mock_handle_context.assert_not_called()

7
tests/agents/test_crew_agent_parser.py
View File

@@ -227,13 +227,6 @@ def test_missing_action_input_error(parser):
assert "I missed the 'Action Input:' after 'Action:'." in str(exc_info.value)
def test_action_and_final_answer_error(parser):
text = "Thought: I found the information\nAction: search\nAction Input: what is the temperature in SF?\nFinal Answer: The temperature is 100 degrees"
with pytest.raises(OutputParserException) as exc_info:
parser.parse(text)
assert "both perform Action and give a Final Answer" in str(exc_info.value)
def test_safe_repair_json(parser):
invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": Senior Researcher'
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'

351
tests/cassettes/test_agent_custom_max_iterations.yaml
View File

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33
tests/crew_test.py
View File

@@ -350,7 +350,7 @@ def test_hierarchical_process():
assert (
result.raw
== "Here are the 5 interesting ideas along with a compelling paragraph for each that showcases how good an article on the topic could be:\n\n1. **The Evolution and Future of AI Agents in Everyday Life**:\nThe rapid development of AI agents from rudimentary virtual assistants like Siri and Alexa to today's sophisticated systems marks a significant technological leap. This article will explore the evolving landscape of AI agents, detailing their seamless integration into daily activities ranging from managing smart home devices to streamlining workflows. We will examine the multifaceted benefits these agents bring, such as increased efficiency and personalized user experiences, while also addressing ethical concerns like data privacy and algorithmic bias. Looking ahead, we will forecast the advancements slated for the next decade, including AI agents in personalized health coaching and automated legal consultancy. With more advanced machine learning algorithms, the potential for these AI systems to revolutionize our daily lives is immense.\n\n2. **AI in Healthcare: Revolutionizing Diagnostics and Treatment**:\nArtificial Intelligence is poised to revolutionize the healthcare sector by offering unprecedented improvements in diagnostic accuracy and personalized treatments. This article will delve into the transformative power of AI in healthcare, highlighting real-world applications like AI-driven imaging technologies that aid in early disease detection and predictive analytics that enable personalized patient care plans. We will discuss the ethical challenges, such as data privacy and the implications of AI-driven decision-making in medicine. Through compelling case studies, we will showcase successful AI implementations that have made significant impacts, ultimately painting a picture of a future where AI plays a central role in proactive and precise healthcare delivery.\n\n3. **The Role of AI in Enhancing Cybersecurity**:\nAs cyber threats become increasingly sophisticated, AI stands at the forefront of the battle against cybercrime. This article will discuss the crucial role AI plays in detecting and responding to threats in real-time, its capacity to predict and prevent potential attacks, and the inherent challenges of an AI-dependent cybersecurity framework. We will highlight recent advancements in AI-based security tools and provide case studies where AI has been instrumental in mitigating cyber threats effectively. By examining these elements, we'll underline the potential and limitations of AI in creating a more secure digital environment, showcasing how it can adapt to evolving threats faster than traditional methods.\n\n4. **The Intersection of AI and Autonomous Vehicles: Driving Towards a Safer Future**:\nThe prospect of AI-driven autonomous vehicles promises to redefine transportation. This article will explore the technological underpinnings of self-driving cars, their developmental milestones, and the hurdles they face, including regulatory and ethical challenges. We will discuss the profound implications for various industries and employment sectors, coupled with the benefits such as reduced traffic accidents, improved fuel efficiency, and enhanced mobility for people with disabilities. By detailing these aspects, the article will offer a comprehensive overview of how AI-powered autonomous vehicles are steering us towards a safer, more efficient future.\n\n5. **AI and the Future of Work: Embracing Change in the Workplace**:\nAI is transforming the workplace by automating mundane tasks, enabling advanced data analysis, and fostering creativity and strategic decision-making. This article will explore the profound impact of AI on the job market, addressing concerns about job displacement and the evolution of new roles that demand reskilling. We will provide insights into the necessity for upskilling to keep pace with an AI-driven economy. Through interviews with industry experts and narratives from workers who have experienced AI's impact firsthand, we will present a balanced perspective. The aim is to paint a future where humans and AI work in synergy, driving innovation and productivity in a continuously evolving workplace landscape."
== "**1. The Rise of Autonomous AI Agents in Daily Life** \nAs artificial intelligence technology progresses, the integration of autonomous AI agents into everyday life becomes increasingly prominent. These agents, capable of making decisions without human intervention, are reshaping industries from healthcare to finance. Exploring case studies where autonomous AI has successfully decreased operational costs or improved efficiency can reveal not only the benefits but also the ethical implications of delegating decision-making to machines. This topic offers an exciting opportunity to dive into the AI landscape, showcasing current developments such as AI assistants and autonomous vehicles.\n\n**2. Ethical Implications of Generative AI in Creative Industries** \nThe surge of generative AI tools in creative fields, such as art, music, and writing, has sparked a heated debate about authorship and originality. This article could investigate how these tools are being used by artists and creators, examining both the potential for innovation and the risk of devaluing traditional art forms. Highlighting perspectives from creators, legal experts, and ethicists could provide a comprehensive overview of the challenges faced, including copyright concerns and the emotional impact on human artists. This discussion is vital as the creative landscape evolves alongside technological advancements, making it ripe for exploration.\n\n**3. AI in Climate Change Mitigation: Current Solutions and Future Potential** \nAs the world grapples with climate change, AI technology is increasingly being harnessed to develop innovative solutions for sustainability. From predictive analytics that optimize energy consumption to machine learning algorithms that improve carbon capture methods, AI's potential in environmental science is vast. This topic invites an exploration of existing AI applications in climate initiatives, with a focus on groundbreaking research and initiatives aimed at reducing humanity's carbon footprint. Highlighting successful projects and technology partnerships can illustrate the positive impact AI can have on global climate efforts, inspiring further exploration and investment in this area.\n\n**4. The Future of Work: How AI is Reshaping Employment Landscapes** \nThe discussions around AI's impact on the workforce are both urgent and complex, as advances in automation and machine learning continue to transform the job market. This article could delve into the current trends of AI-driven job displacement alongside opportunities for upskilling and the creation of new job roles. By examining case studies of companies that integrate AI effectively and the resulting workforce adaptations, readers can gain valuable insights into preparing for a future where humans and AI collaborate. This exploration highlights the importance of policies that promote workforce resilience in the face of change.\n\n**5. Decentralized AI: Exploring the Role of Blockchain in AI Development** \nAs blockchain technology sweeps through various sectors, its application in AI development presents a fascinating topic worth examining. Decentralized AI could address issues of data privacy, security, and democratization in AI models by allowing users to retain ownership of data while benefiting from AI's capabilities. This article could analyze how decentralized networks are disrupting traditional AI development models, featuring innovative projects that harness the synergy between blockchain and AI. Highlighting potential pitfalls and the future landscape of decentralized AI could stimulate discussion among technologists, entrepreneurs, and policymakers alike.\n\nThese topics not only reflect current trends but also probe deeper into ethical and practical considerations, making them timely and relevant for contemporary audiences."
)
@@ -2157,14 +2157,20 @@ def test_tools_with_custom_caching():
with patch.object(
CacheHandler, "add", wraps=crew._cache_handler.add
) as add_to_cache:
with patch.object(CacheHandler, "read", wraps=crew._cache_handler.read) as _:
result = crew.kickoff()
add_to_cache.assert_called_once_with(
tool="multiplcation_tool",
input={"first_number": 2, "second_number": 6},
output=12,
)
assert result.raw == "3"
result = crew.kickoff()
# Check that add_to_cache was called exactly twice
assert add_to_cache.call_count == 2
# Verify that one of those calls was with the even number that should be cached
add_to_cache.assert_any_call(
tool="multiplcation_tool",
input={"first_number": 2, "second_number": 6},
output=12,
)
assert result.raw == "3"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -4072,14 +4078,14 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy():
role="Researcher",
goal="Conduct thorough research and analysis on AI and AI agents",
backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
allow_delegation=False
allow_delegation=False,
)
writer = Agent(
role="Senior Writer",
goal="Create compelling content about AI and AI agents",
backstory="You're a senior writer, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently writing content for a new client.",
allow_delegation=False
allow_delegation=False,
)
# Define task
@@ -4093,7 +4099,7 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy():
role="Project Manager",
goal="Efficiently manage the crew and ensure high-quality task completion",
backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
allow_delegation=True
allow_delegation=True,
)
# Instantiate crew with a custom manager
@@ -4102,7 +4108,7 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy():
tasks=[task],
manager_agent=manager,
process=Process.hierarchical,
verbose=True
verbose=True,
)
crew_copy = crew.copy()
@@ -4113,4 +4119,3 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy():
assert crew_copy.manager_agent.backstory == crew.manager_agent.backstory
assert isinstance(crew_copy.manager_agent.agent_executor, CrewAgentExecutor)
assert isinstance(crew_copy.manager_agent.cache_handler, CacheHandler)

172
tests/test_lite_agent.py Normal file
View File

@@ -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

65
tests/tools/test_tool_usage.py
View File

@@ -99,9 +99,6 @@ def test_tool_usage_render():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[tool],
original_tools=[tool],
tools_description="Sample tool for testing",
tools_names="random_number_generator",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
@@ -136,9 +133,6 @@ def test_validate_tool_input_booleans_and_none():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
@@ -158,9 +152,6 @@ def test_validate_tool_input_mixed_types():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
@@ -180,9 +171,6 @@ def test_validate_tool_input_single_quotes():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
@@ -202,9 +190,6 @@ def test_validate_tool_input_invalid_json_repairable():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
@@ -224,9 +209,6 @@ def test_validate_tool_input_with_special_characters():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
@@ -245,9 +227,6 @@ def test_validate_tool_input_none_input():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -262,9 +241,6 @@ def test_validate_tool_input_valid_json():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -282,9 +258,6 @@ def test_validate_tool_input_python_dict():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -302,9 +275,6 @@ def test_validate_tool_input_json5_unquoted_keys():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -322,9 +292,6 @@ def test_validate_tool_input_with_trailing_commas():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -355,9 +322,6 @@ def test_validate_tool_input_invalid_input():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=mock_agent,
@@ -388,9 +352,6 @@ def test_validate_tool_input_complex_structure():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -427,9 +388,6 @@ def test_validate_tool_input_code_content():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -450,9 +408,6 @@ def test_validate_tool_input_with_escaped_quotes():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -470,9 +425,6 @@ def test_validate_tool_input_large_json_content():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=None,
agent=MagicMock(),
@@ -512,9 +464,6 @@ def test_tool_selection_error_event_direct():
tool_usage = ToolUsage(
tools_handler=mock_tools_handler,
tools=[test_tool],
original_tools=[test_tool],
tools_description="Test Tool Description",
tools_names="Test Tool",
task=mock_task,
function_calling_llm=None,
agent=mock_agent,
@@ -536,7 +485,8 @@ def test_tool_selection_error_event_direct():
assert event.agent_role == "test_role"
assert event.tool_name == "Non Existent Tool"
assert event.tool_args == {}
assert event.tool_class == "Test Tool Description"
assert "Tool Name: Test Tool" in event.tool_class
assert "A test tool" in event.tool_class
assert "don't exist" in event.error
received_events.clear()
@@ -550,7 +500,7 @@ def test_tool_selection_error_event_direct():
assert event.agent_role == "test_role"
assert event.tool_name == ""
assert event.tool_args == {}
assert event.tool_class == "Test Tool Description"
assert "Test Tool" in event.tool_class
assert "forgot the Action name" in event.error
@@ -591,9 +541,6 @@ def test_tool_validate_input_error_event():
tool_usage = ToolUsage(
tools_handler=mock_tools_handler,
tools=[test_tool],
original_tools=[test_tool],
tools_description="Test Tool Description",
tools_names="Test Tool",
task=mock_task,
function_calling_llm=None,
agent=mock_agent,
@@ -661,9 +608,6 @@ def test_tool_usage_finished_event_with_result():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[test_tool],
original_tools=[test_tool],
tools_description="Test Tool Description",
tools_names="Test Tool",
task=mock_task,
function_calling_llm=None,
agent=mock_agent,
@@ -740,9 +684,6 @@ def test_tool_usage_finished_event_with_cached_result():
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[test_tool],
original_tools=[test_tool],
tools_description="Test Tool Description",
tools_names="Test Tool",
task=mock_task,
function_calling_llm=None,
agent=mock_agent,

61
tests/utilities/cassettes/test_llm_no_stream_chunks_when_streaming_disabled.yaml
View File

@@ -1,7 +1,7 @@
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16620
tests/utilities/cassettes/test_tools_emits_error_events.yaml
View File

File diff suppressed because it is too large Load Diff

7
tests/utilities/test_events.py
View File

@@ -355,7 +355,7 @@ def test_tools_emits_finished_events():
assert received_events[0].agent_key == agent.key
assert received_events[0].agent_role == agent.role
assert received_events[0].tool_name == SayHiTool().name
assert received_events[0].tool_args == {}
assert received_events[0].tool_args == "{}" or received_events[0].tool_args == {}
assert received_events[0].type == "tool_usage_finished"
assert isinstance(received_events[0].timestamp, datetime)
@@ -385,6 +385,7 @@ def test_tools_emits_error_events():
goal="Try to use the error tool",
backstory="You are an assistant that tests error handling",
tools=[ErrorTool()],
llm=LLM(model="gpt-4o-mini"),
)
task = Task(
@@ -396,11 +397,11 @@ def test_tools_emits_error_events():
crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
crew.kickoff()
assert len(received_events) == 75
assert len(received_events) == 48
assert received_events[0].agent_key == agent.key
assert received_events[0].agent_role == agent.role
assert received_events[0].tool_name == "error_tool"
assert received_events[0].tool_args == {}
assert received_events[0].tool_args == "{}" or received_events[0].tool_args == {}
assert str(received_events[0].error) == "Simulated tool error"
assert received_events[0].type == "tool_usage_error"
assert isinstance(received_events[0].timestamp, datetime)