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Merge branch 'main' into devin/1739079368-fix-ollama-embedder-url-config

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This commit is contained in:
João Moura
2025-02-09 16:48:18 -03:00
committed by GitHub
parent 015ce7f550 d6d98ee969
commit 5eefd90512
169 changed files with 21525 additions and 39243 deletions
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264
tests/agent_test.py
View File

@@ -10,6 +10,7 @@ from crewai import Agent, Crew, Task
from crewai.agents.cache import CacheHandler
from crewai.agents.crew_agent_executor import CrewAgentExecutor
from crewai.agents.parser import AgentAction, 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
from crewai.tools import tool
@@ -114,35 +115,6 @@ def test_custom_llm_temperature_preservation():
assert agent.llm.temperature == 0.7
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execute_task():
from langchain_openai import ChatOpenAI
from crewai import Task
agent = Agent(
role="Math Tutor",
goal="Solve math problems accurately",
backstory="You are an experienced math tutor with a knack for explaining complex concepts simply.",
llm=ChatOpenAI(temperature=0.7, model="gpt-4o-mini"),
)
task = Task(
description="Calculate the area of a circle with radius 5 cm.",
expected_output="The calculated area of the circle in square centimeters.",
agent=agent,
)
result = agent.execute_task(task)
assert result is not None
assert (
result
== "The calculated area of the circle is approximately 78.5 square centimeters."
)
assert "square centimeters" in result.lower()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execution():
agent = Agent(
@@ -565,7 +537,7 @@ def test_agent_moved_on_after_max_iterations():
task=task,
tools=[get_final_answer],
)
assert output == "The final answer is 42."
assert output == "42"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -574,7 +546,6 @@ 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",
@@ -641,15 +612,14 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_without_max_rpm_respet_crew_rpm(capsys):
def test_agent_without_max_rpm_respects_crew_rpm(capsys):
from unittest.mock import patch
from crewai.tools import tool
@tool
def get_final_answer() -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
"""Get the final answer but don't give it yet, just re-use this tool non-stop."""
return 42
agent1 = Agent(
@@ -666,23 +636,30 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
role="test role2",
goal="test goal2",
backstory="test backstory2",
max_iter=1,
max_iter=5,
verbose=True,
allow_delegation=False,
)
tasks = [
Task(
description="Just say hi.", agent=agent1, expected_output="Your greeting."
description="Just say hi.",
agent=agent1,
expected_output="Your greeting.",
),
Task(
description="NEVER give a Final Answer, unless you are told otherwise, instead keep using the `get_final_answer` tool non-stop, until you must give you best final answer",
description=(
"NEVER give a Final Answer, unless you are told otherwise, "
"instead keep using the `get_final_answer` tool non-stop, "
"until you must give your best final answer"
),
expected_output="The final answer",
tools=[get_final_answer],
agent=agent2,
),
]
# Set crew's max_rpm to 1 to trigger RPM limit
crew = Crew(agents=[agent1, agent2], tasks=tasks, max_rpm=1, verbose=True)
with patch.object(RPMController, "_wait_for_next_minute") as moveon:
@@ -1206,7 +1183,7 @@ def test_agent_max_retry_limit():
[
mock.call(
{
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
"input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
"tool_names": "",
"tools": "",
"ask_for_human_input": True,
@@ -1214,7 +1191,7 @@ def test_agent_max_retry_limit():
),
mock.call(
{
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
"input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
"tool_names": "",
"tools": "",
"ask_for_human_input": True,
@@ -1445,44 +1422,43 @@ def test_llm_call_with_all_attributes():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_with_ollama_gemma():
def test_agent_with_ollama_llama3():
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
llm=LLM(
model="ollama/gemma2:latest",
base_url="http://localhost:8080",
),
llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"),
)
assert isinstance(agent.llm, LLM)
assert agent.llm.model == "ollama/gemma2:latest"
assert agent.llm.base_url == "http://localhost:8080"
assert agent.llm.model == "ollama/llama3.2:3b"
assert agent.llm.base_url == "http://localhost:11434"
task = "Respond in 20 words. Who are you?"
task = "Respond in 20 words. Which model are you?"
response = agent.llm.call([{"role": "user", "content": task}])
assert response
assert len(response.split()) <= 25 # Allow a little flexibility in word count
assert "Gemma" in response or "AI" in response or "language model" in response
assert "Llama3" in response or "AI" in response or "language model" in response
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_call_with_ollama_gemma():
def test_llm_call_with_ollama_llama3():
llm = LLM(
model="ollama/gemma2:latest",
base_url="http://localhost:8080",
model="ollama/llama3.2:3b",
base_url="http://localhost:11434",
temperature=0.7,
max_tokens=30,
)
messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}]
messages = [
{"role": "user", "content": "Respond in 20 words. Which model are you?"}
]
response = llm.call(messages)
assert response
assert len(response.split()) <= 25 # Allow a little flexibility in word count
assert "Gemma" in response or "AI" in response or "language model" in response
assert "Llama3" in response or "AI" in response or "language model" in response
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1491,7 +1467,7 @@ def test_agent_execute_task_basic():
role="test role",
goal="test goal",
backstory="test backstory",
llm=LLM(model="gpt-3.5-turbo"),
llm="gpt-4o-mini",
)
task = Task(
@@ -1578,7 +1554,7 @@ def test_agent_execute_task_with_ollama():
role="test role",
goal="test goal",
backstory="test backstory",
llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"),
llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"),
)
task = Task(
@@ -1625,3 +1601,181 @@ def test_agent_with_knowledge_sources():
# Assert that the agent provides the correct information
assert "red" in result.raw.lower()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_with_knowledge_sources_works_with_copy():
content = "Brandon's favorite color is red and he likes Mexican food."
string_source = StringKnowledgeSource(content=content)
with patch(
"crewai.knowledge.source.base_knowledge_source.BaseKnowledgeSource",
autospec=True,
) as MockKnowledgeSource:
mock_knowledge_source_instance = MockKnowledgeSource.return_value
mock_knowledge_source_instance.__class__ = BaseKnowledgeSource
mock_knowledge_source_instance.sources = [string_source]
agent = Agent(
role="Information Agent",
goal="Provide information based on knowledge sources",
backstory="You have access to specific knowledge sources.",
llm=LLM(model="gpt-4o-mini"),
knowledge_sources=[string_source],
)
with patch(
"crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
) as MockKnowledgeStorage:
mock_knowledge_storage = MockKnowledgeStorage.return_value
agent.knowledge_storage = mock_knowledge_storage
agent_copy = agent.copy()
assert agent_copy.role == agent.role
assert agent_copy.goal == agent.goal
assert agent_copy.backstory == agent.backstory
assert agent_copy.knowledge_sources is not None
assert len(agent_copy.knowledge_sources) == 1
assert isinstance(agent_copy.knowledge_sources[0], StringKnowledgeSource)
assert agent_copy.knowledge_sources[0].content == content
assert isinstance(agent_copy.llm, LLM)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_litellm_auth_error_handling():
"""Test that LiteLLM authentication errors are handled correctly and not retried."""
from litellm import AuthenticationError as LiteLLMAuthenticationError
# Create an agent with a mocked LLM and max_retry_limit=0
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
llm=LLM(model="gpt-4"),
max_retry_limit=0, # Disable retries for authentication errors
)
# Create a task
task = Task(
description="Test task",
expected_output="Test output",
agent=agent,
)
# Mock the LLM call to raise AuthenticationError
with (
patch.object(LLM, "call") as mock_llm_call,
pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
):
mock_llm_call.side_effect = LiteLLMAuthenticationError(
message="Invalid API key", llm_provider="openai", model="gpt-4"
)
agent.execute_task(task)
# Verify the call was only made once (no retries)
mock_llm_call.assert_called_once()
def test_crew_agent_executor_litellm_auth_error():
"""Test that CrewAgentExecutor handles LiteLLM authentication errors by raising them."""
from litellm.exceptions import AuthenticationError
from crewai.agents.tools_handler import ToolsHandler
from crewai.utilities import Printer
# Create an agent and executor
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
llm=LLM(model="gpt-4", api_key="invalid_api_key"),
)
task = Task(
description="Test task",
expected_output="Test output",
agent=agent,
)
# Create executor with all required parameters
executor = CrewAgentExecutor(
agent=agent,
task=task,
llm=agent.llm,
crew=None,
prompt={"system": "You are a test agent", "user": "Execute the task: {input}"},
max_iter=5,
tools=[],
tools_names="",
stop_words=[],
tools_description="",
tools_handler=ToolsHandler(),
)
# Mock the LLM call to raise AuthenticationError
with (
patch.object(LLM, "call") as mock_llm_call,
patch.object(Printer, "print") as mock_printer,
pytest.raises(AuthenticationError) as exc_info,
):
mock_llm_call.side_effect = AuthenticationError(
message="Invalid API key", llm_provider="openai", model="gpt-4"
)
executor.invoke(
{
"input": "test input",
"tool_names": "",
"tools": "",
}
)
# Verify error handling messages
error_message = f"Error during LLM call: {str(mock_llm_call.side_effect)}"
mock_printer.assert_any_call(
content=error_message,
color="red",
)
# Verify the call was only made once (no retries)
mock_llm_call.assert_called_once()
# Assert that the exception was raised and has the expected attributes
assert exc_info.type is AuthenticationError
assert "Invalid API key".lower() in exc_info.value.message.lower()
assert exc_info.value.llm_provider == "openai"
assert exc_info.value.model == "gpt-4"
def test_litellm_anthropic_error_handling():
"""Test that AnthropicError from LiteLLM is handled correctly and not retried."""
from litellm.llms.anthropic.common_utils import AnthropicError
# Create an agent with a mocked LLM that uses an Anthropic model
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
llm=LLM(model="claude-3.5-sonnet-20240620"),
max_retry_limit=0,
)
# Create a task
task = Task(
description="Test task",
expected_output="Test output",
agent=agent,
)
# Mock the LLM call to raise AnthropicError
with (
patch.object(LLM, "call") as mock_llm_call,
pytest.raises(AnthropicError, match="Test Anthropic error"),
):
mock_llm_call.side_effect = AnthropicError(
status_code=500,
message="Test Anthropic error",
)
agent.execute_task(task)
# Verify the LLM call was only made once (no retries)
mock_llm_call.assert_called_once()

4
tests/agents/agent_builder/base_agent_test.py
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@@ -7,7 +7,7 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.tools.base_tool import BaseTool
class TestAgent(BaseAgent):
class MockAgent(BaseAgent):
def execute_task(
self,
task: Any,
@@ -29,7 +29,7 @@ class TestAgent(BaseAgent):
def test_key():
agent = TestAgent(
agent = MockAgent(
role="test role",
goal="test goal",
backstory="test backstory",

163
tests/cassettes/test_agent_custom_max_iterations.yaml
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497
tests/cassettes/test_agent_execute_task_with_tool.yaml
View File

@@ -2,22 +2,22 @@ interactions:
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tests/cassettes/test_agent_without_max_rpm_respects_crew_rpm.yaml Normal file
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If you use the\\nLlama Materials or any outputs or results of the Llama Materials
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or any derivative works thereof, from a Licensee as part\\nof an integrated
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UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
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Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
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89
tests/cli/cli_test.py
View File

@@ -55,72 +55,83 @@ def test_train_invalid_string_iterations(train_crew, runner):
)
@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
@mock.patch("crewai.cli.reset_memories_command.EntityMemory")
@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
@mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
def test_reset_all_memories(
MockTaskOutputStorageHandler,
MockLongTermMemory,
MockEntityMemory,
MockShortTermMemory,
runner,
):
result = runner.invoke(reset_memories, ["--all"])
MockShortTermMemory().reset.assert_called_once()
MockEntityMemory().reset.assert_called_once()
MockLongTermMemory().reset.assert_called_once()
MockTaskOutputStorageHandler().reset.assert_called_once()
@mock.patch("crewai.cli.reset_memories_command.get_crew")
def test_reset_all_memories(mock_get_crew, runner):
mock_crew = mock.Mock()
mock_get_crew.return_value = mock_crew
result = runner.invoke(reset_memories, ["-a"])
mock_crew.reset_memories.assert_called_once_with(command_type="all")
assert result.output == "All memories have been reset.\n"
@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
def test_reset_short_term_memories(MockShortTermMemory, runner):
@mock.patch("crewai.cli.reset_memories_command.get_crew")
def test_reset_short_term_memories(mock_get_crew, runner):
mock_crew = mock.Mock()
mock_get_crew.return_value = mock_crew
result = runner.invoke(reset_memories, ["-s"])
MockShortTermMemory().reset.assert_called_once()
mock_crew.reset_memories.assert_called_once_with(command_type="short")
assert result.output == "Short term memory has been reset.\n"
@mock.patch("crewai.cli.reset_memories_command.EntityMemory")
def test_reset_entity_memories(MockEntityMemory, runner):
@mock.patch("crewai.cli.reset_memories_command.get_crew")
def test_reset_entity_memories(mock_get_crew, runner):
mock_crew = mock.Mock()
mock_get_crew.return_value = mock_crew
result = runner.invoke(reset_memories, ["-e"])
MockEntityMemory().reset.assert_called_once()
mock_crew.reset_memories.assert_called_once_with(command_type="entity")
assert result.output == "Entity memory has been reset.\n"
@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
def test_reset_long_term_memories(MockLongTermMemory, runner):
@mock.patch("crewai.cli.reset_memories_command.get_crew")
def test_reset_long_term_memories(mock_get_crew, runner):
mock_crew = mock.Mock()
mock_get_crew.return_value = mock_crew
result = runner.invoke(reset_memories, ["-l"])
MockLongTermMemory().reset.assert_called_once()
mock_crew.reset_memories.assert_called_once_with(command_type="long")
assert result.output == "Long term memory has been reset.\n"
@mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
def test_reset_kickoff_outputs(MockTaskOutputStorageHandler, runner):
@mock.patch("crewai.cli.reset_memories_command.get_crew")
def test_reset_kickoff_outputs(mock_get_crew, runner):
mock_crew = mock.Mock()
mock_get_crew.return_value = mock_crew
result = runner.invoke(reset_memories, ["-k"])
MockTaskOutputStorageHandler().reset.assert_called_once()
mock_crew.reset_memories.assert_called_once_with(command_type="kickoff_outputs")
assert result.output == "Latest Kickoff outputs stored has been reset.\n"
@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
def test_reset_multiple_memory_flags(MockShortTermMemory, MockLongTermMemory, runner):
result = runner.invoke(
reset_memories,
[
"-s",
"-l",
],
@mock.patch("crewai.cli.reset_memories_command.get_crew")
def test_reset_multiple_memory_flags(mock_get_crew, runner):
mock_crew = mock.Mock()
mock_get_crew.return_value = mock_crew
result = runner.invoke(reset_memories, ["-s", "-l"])
# Check that reset_memories was called twice with the correct arguments
assert mock_crew.reset_memories.call_count == 2
mock_crew.reset_memories.assert_has_calls(
[mock.call(command_type="long"), mock.call(command_type="short")]
)
MockShortTermMemory().reset.assert_called_once()
MockLongTermMemory().reset.assert_called_once()
assert (
result.output
== "Long term memory has been reset.\nShort term memory has been reset.\n"
)
@mock.patch("crewai.cli.reset_memories_command.get_crew")
def test_reset_knowledge(mock_get_crew, runner):
mock_crew = mock.Mock()
mock_get_crew.return_value = mock_crew
result = runner.invoke(reset_memories, ["--knowledge"])
mock_crew.reset_memories.assert_called_once_with(command_type="knowledge")
assert result.output == "Knowledge has been reset.\n"
def test_reset_no_memory_flags(runner):
result = runner.invoke(
reset_memories,

4
tests/cli/deploy/test_deploy_main.py
View File

@@ -177,12 +177,12 @@ class TestDeployCommand(unittest.TestCase):
def test_get_crew_status(self):
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"name": "TestCrew", "status": "active"}
mock_response.json.return_value = {"name": "InternalCrew", "status": "active"}
self.mock_client.crew_status_by_name.return_value = mock_response
with patch("sys.stdout", new=StringIO()) as fake_out:
self.deploy_command.get_crew_status()
self.assertIn("TestCrew", fake_out.getvalue())
self.assertIn("InternalCrew", fake_out.getvalue())
self.assertIn("active", fake_out.getvalue())
def test_get_crew_logs(self):

9
tests/cli/tools/test_main.py
View File

@@ -28,9 +28,10 @@ def test_create_success(mock_subprocess):
with in_temp_dir():
tool_command = ToolCommand()
with patch.object(tool_command, "login") as mock_login, patch(
"sys.stdout", new=StringIO()
) as fake_out:
with (
patch.object(tool_command, "login") as mock_login,
patch("sys.stdout", new=StringIO()) as fake_out,
):
tool_command.create("test-tool")
output = fake_out.getvalue()
@@ -82,7 +83,7 @@ def test_install_success(mock_get, mock_subprocess_run):
capture_output=False,
text=True,
check=True,
env=unittest.mock.ANY
env=unittest.mock.ANY,
)
assert "Successfully installed sample-tool" in output

2
tests/config/tasks.yaml
View File

@@ -2,7 +2,7 @@ research_task:
description: >
Conduct a thorough research about {topic}
Make sure you find any interesting and relevant information given
the current year is 2024.
the current year is 2025.
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
agent: researcher

33
tests/conftest.py
View File

@@ -1,4 +1,37 @@
# conftest.py
import os
import tempfile
from pathlib import Path
import pytest
from dotenv import load_dotenv
load_result = load_dotenv(override=True)
@pytest.fixture(autouse=True)
def setup_test_environment():
"""Set up test environment with a temporary directory for SQLite storage."""
with tempfile.TemporaryDirectory() as temp_dir:
# Create the directory with proper permissions
storage_dir = Path(temp_dir) / "crewai_test_storage"
storage_dir.mkdir(parents=True, exist_ok=True)
# Validate that the directory was created successfully
if not storage_dir.exists() or not storage_dir.is_dir():
raise RuntimeError(f"Failed to create test storage directory: {storage_dir}")
# Verify directory permissions
try:
# Try to create a test file to verify write permissions
test_file = storage_dir / ".permissions_test"
test_file.touch()
test_file.unlink()
except (OSError, IOError) as e:
raise RuntimeError(f"Test storage directory {storage_dir} is not writable: {e}")
# Set environment variable to point to the test storage directory
os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
yield
# Cleanup is handled automatically when tempfile context exits

996
tests/crew_test.py
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File diff suppressed because it is too large Load Diff

76
tests/flow_test.py
View File

@@ -3,6 +3,7 @@
import asyncio
import pytest
from pydantic import BaseModel
from crewai.flow.flow import Flow, and_, listen, or_, router, start
@@ -265,6 +266,81 @@ def test_flow_with_custom_state():
assert flow.counter == 2
def test_flow_uuid_unstructured():
"""Test that unstructured (dictionary) flow states automatically get a UUID that persists."""
initial_id = None
class UUIDUnstructuredFlow(Flow):
@start()
def first_method(self):
nonlocal initial_id
# Verify ID is automatically generated
assert "id" in self.state
assert isinstance(self.state["id"], str)
# Store initial ID for comparison
initial_id = self.state["id"]
# Add some data to trigger state update
self.state["data"] = "example"
@listen(first_method)
def second_method(self):
# Ensure the ID persists after state updates
assert "id" in self.state
assert self.state["id"] == initial_id
# Update state again to verify ID preservation
self.state["more_data"] = "test"
assert self.state["id"] == initial_id
flow = UUIDUnstructuredFlow()
flow.kickoff()
# Verify ID persists after flow completion
assert flow.state["id"] == initial_id
# Verify UUID format (36 characters, including hyphens)
assert len(flow.state["id"]) == 36
def test_flow_uuid_structured():
"""Test that structured (Pydantic) flow states automatically get a UUID that persists."""
initial_id = None
class MyStructuredState(BaseModel):
counter: int = 0
message: str = "initial"
class UUIDStructuredFlow(Flow[MyStructuredState]):
@start()
def first_method(self):
nonlocal initial_id
# Verify ID is automatically generated and accessible as attribute
assert hasattr(self.state, "id")
assert isinstance(self.state.id, str)
# Store initial ID for comparison
initial_id = self.state.id
# Update some fields to trigger state changes
self.state.counter += 1
self.state.message = "updated"
@listen(first_method)
def second_method(self):
# Ensure the ID persists after state updates
assert hasattr(self.state, "id")
assert self.state.id == initial_id
# Update state again to verify ID preservation
self.state.counter += 1
self.state.message = "final"
assert self.state.id == initial_id
flow = UUIDStructuredFlow()
flow.kickoff()
# Verify ID persists after flow completion
assert flow.state.id == initial_id
# Verify UUID format (36 characters, including hyphens)
assert len(flow.state.id) == 36
# Verify other state fields were properly updated
assert flow.state.counter == 2
assert flow.state.message == "final"
def test_router_with_multiple_conditions():
"""Test a router that triggers when any of multiple steps complete (OR condition),
and another router that triggers only after all specified steps complete (AND condition).

25
tests/knowledge/knowledge_test.py
View File

@@ -578,9 +578,26 @@ def test_multiple_docling_sources():
assert docling_source.content is not None
def test_docling_source_with_local_file():
def test_file_path_validation():
"""Test file path validation for knowledge sources."""
current_dir = Path(__file__).parent
pdf_path = current_dir / "crewai_quickstart.pdf"
docling_source = CrewDoclingSource(file_paths=[pdf_path])
assert docling_source.file_paths == [pdf_path]
assert docling_source.content is not None
# Test valid single file_path
source = PDFKnowledgeSource(file_path=pdf_path)
assert source.safe_file_paths == [pdf_path]
# Test valid file_paths list
source = PDFKnowledgeSource(file_paths=[pdf_path])
assert source.safe_file_paths == [pdf_path]
# Test both file_path and file_paths provided (should use file_paths)
source = PDFKnowledgeSource(file_path=pdf_path, file_paths=[pdf_path])
assert source.safe_file_paths == [pdf_path]
# Test neither file_path nor file_paths provided
with pytest.raises(
ValueError,
match="file_path/file_paths must be a Path, str, or a list of these types",
):
PDFKnowledgeSource()

349
tests/llm_test.py
View File

@@ -1,30 +1,373 @@
import os
from time import sleep
from unittest.mock import MagicMock, patch
import pytest
from pydantic import BaseModel
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
from crewai.llm import LLM
from crewai.tools import tool
from crewai.utilities.token_counter_callback import TokenCalcHandler
# TODO: This test fails without print statement, which makes me think that something is happening asynchronously that we need to eventually fix and dive deeper into at a later date
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_callback_replacement():
llm = LLM(model="gpt-4o-mini")
llm1 = LLM(model="gpt-4o-mini")
llm2 = LLM(model="gpt-4o-mini")
calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess())
calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess())
llm.call(
result1 = llm1.call(
messages=[{"role": "user", "content": "Hello, world!"}],
callbacks=[calc_handler_1],
)
print("result1:", result1)
usage_metrics_1 = calc_handler_1.token_cost_process.get_summary()
print("usage_metrics_1:", usage_metrics_1)
llm.call(
result2 = llm2.call(
messages=[{"role": "user", "content": "Hello, world from another agent!"}],
callbacks=[calc_handler_2],
)
sleep(5)
print("result2:", result2)
usage_metrics_2 = calc_handler_2.token_cost_process.get_summary()
print("usage_metrics_2:", usage_metrics_2)
# The first handler should not have been updated
assert usage_metrics_1.successful_requests == 1
assert usage_metrics_2.successful_requests == 1
assert usage_metrics_1 == calc_handler_1.token_cost_process.get_summary()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_call_with_string_input():
llm = LLM(model="gpt-4o-mini")
# Test the call method with a string input
result = llm.call("Return the name of a random city in the world.")
assert isinstance(result, str)
assert len(result.strip()) > 0 # Ensure the response is not empty
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_call_with_string_input_and_callbacks():
llm = LLM(model="gpt-4o-mini")
calc_handler = TokenCalcHandler(token_cost_process=TokenProcess())
# Test the call method with a string input and callbacks
result = llm.call(
"Tell me a joke.",
callbacks=[calc_handler],
)
usage_metrics = calc_handler.token_cost_process.get_summary()
assert isinstance(result, str)
assert len(result.strip()) > 0
assert usage_metrics.successful_requests == 1
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_call_with_message_list():
llm = LLM(model="gpt-4o-mini")
messages = [{"role": "user", "content": "What is the capital of France?"}]
# Test the call method with a list of messages
result = llm.call(messages)
assert isinstance(result, str)
assert "Paris" in result
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_call_with_tool_and_string_input():
llm = LLM(model="gpt-4o-mini")
def get_current_year() -> str:
"""Returns the current year as a string."""
from datetime import datetime
return str(datetime.now().year)
# Create tool schema
tool_schema = {
"type": "function",
"function": {
"name": "get_current_year",
"description": "Returns the current year as a string.",
"parameters": {
"type": "object",
"properties": {},
"required": [],
},
},
}
# Available functions mapping
available_functions = {"get_current_year": get_current_year}
# Test the call method with a string input and tool
result = llm.call(
"What is the current year?",
tools=[tool_schema],
available_functions=available_functions,
)
assert isinstance(result, str)
assert result == get_current_year()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_call_with_tool_and_message_list():
llm = LLM(model="gpt-4o-mini")
def square_number(number: int) -> int:
"""Returns the square of a number."""
return number * number
# Create tool schema
tool_schema = {
"type": "function",
"function": {
"name": "square_number",
"description": "Returns the square of a number.",
"parameters": {
"type": "object",
"properties": {
"number": {"type": "integer", "description": "The number to square"}
},
"required": ["number"],
},
},
}
# Available functions mapping
available_functions = {"square_number": square_number}
messages = [{"role": "user", "content": "What is the square of 5?"}]
# Test the call method with messages and tool
result = llm.call(
messages,
tools=[tool_schema],
available_functions=available_functions,
)
assert isinstance(result, int)
assert result == 25
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_passes_additional_params():
llm = LLM(
model="gpt-4o-mini",
vertex_credentials="test_credentials",
vertex_project="test_project",
)
messages = [{"role": "user", "content": "Hello, world!"}]
with patch("litellm.completion") as mocked_completion:
# Create mocks for response structure
mock_message = MagicMock()
mock_message.content = "Test response"
mock_choice = MagicMock()
mock_choice.message = mock_message
mock_response = MagicMock()
mock_response.choices = [mock_choice]
mock_response.usage = {
"prompt_tokens": 5,
"completion_tokens": 5,
"total_tokens": 10,
}
# Set up the mocked completion to return the mock response
mocked_completion.return_value = mock_response
result = llm.call(messages)
# Assert that litellm.completion was called once
mocked_completion.assert_called_once()
# Retrieve the actual arguments with which litellm.completion was called
_, kwargs = mocked_completion.call_args
# Check that the additional_params were passed to litellm.completion
assert kwargs["vertex_credentials"] == "test_credentials"
assert kwargs["vertex_project"] == "test_project"
# Also verify that other expected parameters are present
assert kwargs["model"] == "gpt-4o-mini"
assert kwargs["messages"] == messages
# Check the result from llm.call
assert result == "Test response"
def test_get_custom_llm_provider_openrouter():
llm = LLM(model="openrouter/deepseek/deepseek-chat")
assert llm._get_custom_llm_provider() == "openrouter"
def test_get_custom_llm_provider_gemini():
llm = LLM(model="gemini/gemini-1.5-pro")
assert llm._get_custom_llm_provider() == "gemini"
def test_get_custom_llm_provider_openai():
llm = LLM(model="gpt-4")
assert llm._get_custom_llm_provider() == "openai"
def test_validate_call_params_supported():
class DummyResponse(BaseModel):
a: int
# Patch supports_response_schema to simulate a supported model.
with patch("crewai.llm.supports_response_schema", return_value=True):
llm = LLM(
model="openrouter/deepseek/deepseek-chat", response_format=DummyResponse
)
# Should not raise any error.
llm._validate_call_params()
def test_validate_call_params_not_supported():
class DummyResponse(BaseModel):
a: int
# Patch supports_response_schema to simulate an unsupported model.
with patch("crewai.llm.supports_response_schema", return_value=False):
llm = LLM(model="gemini/gemini-1.5-pro", response_format=DummyResponse)
with pytest.raises(ValueError) as excinfo:
llm._validate_call_params()
assert "does not support response_format" in str(excinfo.value)
def test_validate_call_params_no_response_format():
# When no response_format is provided, no validation error should occur.
llm = LLM(model="gemini/gemini-1.5-pro", response_format=None)
llm._validate_call_params()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_o3_mini_reasoning_effort_high():
llm = LLM(
model="o3-mini",
reasoning_effort="high",
)
result = llm.call("What is the capital of France?")
assert isinstance(result, str)
assert "Paris" in result
@pytest.mark.vcr(filter_headers=["authorization"])
def test_o3_mini_reasoning_effort_low():
llm = LLM(
model="o3-mini",
reasoning_effort="low",
)
result = llm.call("What is the capital of France?")
assert isinstance(result, str)
assert "Paris" in result
@pytest.mark.vcr(filter_headers=["authorization"])
def test_o3_mini_reasoning_effort_medium():
llm = LLM(
model="o3-mini",
reasoning_effort="medium",
)
result = llm.call("What is the capital of France?")
assert isinstance(result, str)
assert "Paris" in result
@pytest.mark.vcr(filter_headers=["authorization"])
@pytest.fixture
def anthropic_llm():
"""Fixture providing an Anthropic LLM instance."""
return LLM(model="anthropic/claude-3-sonnet")
@pytest.fixture
def system_message():
"""Fixture providing a system message."""
return {"role": "system", "content": "test"}
@pytest.fixture
def user_message():
"""Fixture providing a user message."""
return {"role": "user", "content": "test"}
def test_anthropic_message_formatting_edge_cases(anthropic_llm):
"""Test edge cases for Anthropic message formatting."""
# Test None messages
with pytest.raises(TypeError, match="Messages cannot be None"):
anthropic_llm._format_messages_for_provider(None)
# Test empty message list
formatted = anthropic_llm._format_messages_for_provider([])
assert len(formatted) == 1
assert formatted[0]["role"] == "user"
assert formatted[0]["content"] == "."
# Test invalid message format
with pytest.raises(TypeError, match="Invalid message format"):
anthropic_llm._format_messages_for_provider([{"invalid": "message"}])
def test_anthropic_model_detection():
"""Test Anthropic model detection with various formats."""
models = [
("anthropic/claude-3", True),
("claude-instant", True),
("claude/v1", True),
("gpt-4", False),
("", False),
("anthropomorphic", False), # Should not match partial words
]
for model, expected in models:
llm = LLM(model=model)
assert llm.is_anthropic == expected, f"Failed for model: {model}"
def test_anthropic_message_formatting(anthropic_llm, system_message, user_message):
"""Test Anthropic message formatting with fixtures."""
# Test when first message is system
formatted = anthropic_llm._format_messages_for_provider([system_message])
assert len(formatted) == 2
assert formatted[0]["role"] == "user"
assert formatted[0]["content"] == "."
assert formatted[1] == system_message
# Test when first message is already user
formatted = anthropic_llm._format_messages_for_provider([user_message])
assert len(formatted) == 1
assert formatted[0] == user_message
# Test with empty message list
formatted = anthropic_llm._format_messages_for_provider([])
assert len(formatted) == 1
assert formatted[0]["role"] == "user"
assert formatted[0]["content"] == "."
# Test with non-Anthropic model (should not modify messages)
non_anthropic_llm = LLM(model="gpt-4")
formatted = non_anthropic_llm._format_messages_for_provider([system_message])
assert len(formatted) == 1
assert formatted[0] == system_message
def test_deepseek_r1_with_open_router():
if not os.getenv("OPEN_ROUTER_API_KEY"):
pytest.skip("OPEN_ROUTER_API_KEY not set; skipping test.")
llm = LLM(
model="openrouter/deepseek/deepseek-r1",
base_url="https://openrouter.ai/api/v1",
api_key=os.getenv("OPEN_ROUTER_API_KEY"),
)
result = llm.call("What is the capital of France?")
assert isinstance(result, str)
assert "Paris" in result

10
tests/project_test.py
View File

@@ -27,7 +27,7 @@ class SimpleCrew:
@CrewBase
class TestCrew:
class InternalCrew:
agents_config = "config/agents.yaml"
tasks_config = "config/tasks.yaml"
@@ -84,7 +84,7 @@ def test_task_memoization():
def test_crew_memoization():
crew = TestCrew()
crew = InternalCrew()
first_call_result = crew.crew()
second_call_result = crew.crew()
@@ -107,7 +107,7 @@ def test_task_name():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_before_kickoff_modification():
crew = TestCrew()
crew = InternalCrew()
inputs = {"topic": "LLMs"}
result = crew.crew().kickoff(inputs=inputs)
assert "bicycles" in result.raw, "Before kickoff function did not modify inputs"
@@ -115,7 +115,7 @@ def test_before_kickoff_modification():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_after_kickoff_modification():
crew = TestCrew()
crew = InternalCrew()
# Assuming the crew execution returns a dict
result = crew.crew().kickoff({"topic": "LLMs"})
@@ -126,7 +126,7 @@ def test_after_kickoff_modification():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_before_kickoff_with_none_input():
crew = TestCrew()
crew = InternalCrew()
crew.crew().kickoff(None)
# Test should pass without raising exceptions

447
tests/task_test.py
View File

@@ -719,61 +719,99 @@ def test_interpolate_inputs():
task = Task(
description="Give me a list of 5 interesting ideas about {topic} to explore for an article, what makes them unique and interesting.",
expected_output="Bullet point list of 5 interesting ideas about {topic}.",
output_file="/tmp/{topic}/output_{date}.txt",
)
task.interpolate_inputs(inputs={"topic": "AI"})
task.interpolate_inputs_and_add_conversation_history(
inputs={"topic": "AI", "date": "2025"}
)
assert (
task.description
== "Give me a list of 5 interesting ideas about AI to explore for an article, what makes them unique and interesting."
)
assert task.expected_output == "Bullet point list of 5 interesting ideas about AI."
assert task.output_file == "/tmp/AI/output_2025.txt"
task.interpolate_inputs(inputs={"topic": "ML"})
task.interpolate_inputs_and_add_conversation_history(
inputs={"topic": "ML", "date": "2025"}
)
assert (
task.description
== "Give me a list of 5 interesting ideas about ML to explore for an article, what makes them unique and interesting."
)
assert task.expected_output == "Bullet point list of 5 interesting ideas about ML."
assert task.output_file == "/tmp/ML/output_2025.txt"
def test_interpolate_only():
"""Test the interpolate_only method for various scenarios including JSON structure preservation."""
task = Task(
description="Unused in this test",
expected_output="Unused in this test"
description="Unused in this test", expected_output="Unused in this test"
)
# Test JSON structure preservation
json_string = '{"info": "Look at {placeholder}", "nested": {"val": "{nestedVal}"}}'
result = task.interpolate_only(
input_string=json_string,
inputs={"placeholder": "the data", "nestedVal": "something else"}
inputs={"placeholder": "the data", "nestedVal": "something else"},
)
assert '"info": "Look at the data"' in result
assert '"val": "something else"' in result
assert "{placeholder}" not in result
assert "{nestedVal}" not in result
# Test normal string interpolation
normal_string = "Hello {name}, welcome to {place}!"
result = task.interpolate_only(
input_string=normal_string,
inputs={"name": "John", "place": "CrewAI"}
input_string=normal_string, inputs={"name": "John", "place": "CrewAI"}
)
assert result == "Hello John, welcome to CrewAI!"
# Test empty string
result = task.interpolate_only(
input_string="",
inputs={"unused": "value"}
)
result = task.interpolate_only(input_string="", inputs={"unused": "value"})
assert result == ""
# Test string with no placeholders
no_placeholders = "Hello, this is a test"
result = task.interpolate_only(
input_string=no_placeholders,
inputs={"unused": "value"}
input_string=no_placeholders, inputs={"unused": "value"}
)
assert result == no_placeholders
def test_interpolate_only_with_dict_inside_expected_output():
"""Test the interpolate_only method for various scenarios including JSON structure preservation."""
task = Task(
description="Unused in this test",
expected_output="Unused in this test: {questions}",
)
json_string = '{"questions": {"main_question": "What is the user\'s name?", "secondary_question": "What is the user\'s age?"}}'
result = task.interpolate_only(
input_string=json_string,
inputs={
"questions": {
"main_question": "What is the user's name?",
"secondary_question": "What is the user's age?",
}
},
)
assert '"main_question": "What is the user\'s name?"' in result
assert '"secondary_question": "What is the user\'s age?"' in result
assert result == json_string
normal_string = "Hello {name}, welcome to {place}!"
result = task.interpolate_only(
input_string=normal_string, inputs={"name": "John", "place": "CrewAI"}
)
assert result == "Hello John, welcome to CrewAI!"
result = task.interpolate_only(input_string="", inputs={"unused": "value"})
assert result == ""
no_placeholders = "Hello, this is a test"
result = task.interpolate_only(
input_string=no_placeholders, inputs={"unused": "value"}
)
assert result == no_placeholders
@@ -868,7 +906,380 @@ def test_key():
assert task.key == hash, "The key should be the hash of the description."
task.interpolate_inputs(inputs={"topic": "AI"})
task.interpolate_inputs_and_add_conversation_history(inputs={"topic": "AI"})
assert (
task.key == hash
), "The key should be the hash of the non-interpolated description."
def test_output_file_validation():
"""Test output file path validation."""
# Valid paths
assert (
Task(
description="Test task",
expected_output="Test output",
output_file="output.txt",
).output_file
== "output.txt"
)
assert (
Task(
description="Test task",
expected_output="Test output",
output_file="/tmp/output.txt",
).output_file
== "tmp/output.txt"
)
assert (
Task(
description="Test task",
expected_output="Test output",
output_file="{dir}/output_{date}.txt",
).output_file
== "{dir}/output_{date}.txt"
)
# Invalid paths
with pytest.raises(ValueError, match="Path traversal"):
Task(
description="Test task",
expected_output="Test output",
output_file="../output.txt",
)
with pytest.raises(ValueError, match="Path traversal"):
Task(
description="Test task",
expected_output="Test output",
output_file="folder/../output.txt",
)
with pytest.raises(ValueError, match="Shell special characters"):
Task(
description="Test task",
expected_output="Test output",
output_file="output.txt | rm -rf /",
)
with pytest.raises(ValueError, match="Shell expansion"):
Task(
description="Test task",
expected_output="Test output",
output_file="~/output.txt",
)
with pytest.raises(ValueError, match="Shell expansion"):
Task(
description="Test task",
expected_output="Test output",
output_file="$HOME/output.txt",
)
with pytest.raises(ValueError, match="Invalid template variable"):
Task(
description="Test task",
expected_output="Test output",
output_file="{invalid-name}/output.txt",
)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_task_execution_times():
researcher = Agent(
role="Researcher",
goal="Make the best research and analysis on content about AI and AI agents",
backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
allow_delegation=False,
)
task = Task(
description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
expected_output="Bullet point list of 5 interesting ideas.",
agent=researcher,
)
assert task.start_time is None
assert task.end_time is None
assert task.execution_duration is None
task.execute_sync(agent=researcher)
assert task.start_time is not None
assert task.end_time is not None
assert task.execution_duration == (task.end_time - task.start_time).total_seconds()
def test_interpolate_with_list_of_strings():
task = Task(
description="Test list interpolation",
expected_output="List: {items}",
)
# Test simple list of strings
input_str = "Available items: {items}"
inputs = {"items": ["apple", "banana", "cherry"]}
result = task.interpolate_only(input_str, inputs)
assert result == f"Available items: {inputs['items']}"
# Test empty list
empty_list_input = {"items": []}
result = task.interpolate_only(input_str, empty_list_input)
assert result == "Available items: []"
def test_interpolate_with_list_of_dicts():
task = Task(
description="Test list of dicts interpolation",
expected_output="People: {people}",
)
input_data = {
"people": [
{"name": "Alice", "age": 30, "skills": ["Python", "AI"]},
{"name": "Bob", "age": 25, "skills": ["Java", "Cloud"]},
]
}
result = task.interpolate_only("{people}", input_data)
parsed_result = eval(result)
assert isinstance(parsed_result, list)
assert len(parsed_result) == 2
assert parsed_result[0]["name"] == "Alice"
assert parsed_result[0]["age"] == 30
assert parsed_result[0]["skills"] == ["Python", "AI"]
assert parsed_result[1]["name"] == "Bob"
assert parsed_result[1]["age"] == 25
assert parsed_result[1]["skills"] == ["Java", "Cloud"]
def test_interpolate_with_nested_structures():
task = Task(
description="Test nested structures",
expected_output="Company: {company}",
)
input_data = {
"company": {
"name": "TechCorp",
"departments": [
{
"name": "Engineering",
"employees": 50,
"tools": ["Git", "Docker", "Kubernetes"],
},
{"name": "Sales", "employees": 20, "regions": {"north": 5, "south": 3}},
],
}
}
result = task.interpolate_only("{company}", input_data)
parsed = eval(result)
assert parsed["name"] == "TechCorp"
assert len(parsed["departments"]) == 2
assert parsed["departments"][0]["tools"] == ["Git", "Docker", "Kubernetes"]
assert parsed["departments"][1]["regions"]["north"] == 5
def test_interpolate_with_special_characters():
task = Task(
description="Test special characters in dicts",
expected_output="Data: {special_data}",
)
input_data = {
"special_data": {
"quotes": """This has "double" and 'single' quotes""",
"unicode": "文字化けテスト",
"symbols": "!@#$%^&*()",
"empty": "",
}
}
result = task.interpolate_only("{special_data}", input_data)
parsed = eval(result)
assert parsed["quotes"] == """This has "double" and 'single' quotes"""
assert parsed["unicode"] == "文字化けテスト"
assert parsed["symbols"] == "!@#$%^&*()"
assert parsed["empty"] == ""
def test_interpolate_mixed_types():
task = Task(
description="Test mixed type interpolation",
expected_output="Mixed: {data}",
)
input_data = {
"data": {
"name": "Test Dataset",
"samples": 1000,
"features": ["age", "income", "location"],
"metadata": {
"source": "public",
"validated": True,
"tags": ["demo", "test", "temp"],
},
}
}
result = task.interpolate_only("{data}", input_data)
parsed = eval(result)
assert parsed["name"] == "Test Dataset"
assert parsed["samples"] == 1000
assert parsed["metadata"]["tags"] == ["demo", "test", "temp"]
def test_interpolate_complex_combination():
task = Task(
description="Test complex combination",
expected_output="Report: {report}",
)
input_data = {
"report": [
{
"month": "January",
"metrics": {"sales": 15000, "expenses": 8000, "profit": 7000},
"top_products": ["Product A", "Product B"],
},
{
"month": "February",
"metrics": {"sales": 18000, "expenses": 8500, "profit": 9500},
"top_products": ["Product C", "Product D"],
},
]
}
result = task.interpolate_only("{report}", input_data)
parsed = eval(result)
assert len(parsed) == 2
assert parsed[0]["month"] == "January"
assert parsed[1]["metrics"]["profit"] == 9500
assert "Product D" in parsed[1]["top_products"]
def test_interpolate_invalid_type_validation():
task = Task(
description="Test invalid type validation",
expected_output="Should never reach here",
)
# Test with invalid top-level type
with pytest.raises(ValueError) as excinfo:
task.interpolate_only("{data}", {"data": set()}) # type: ignore we are purposely testing this failure
assert "Unsupported type set" in str(excinfo.value)
# Test with invalid nested type
invalid_nested = {
"profile": {
"name": "John",
"age": 30,
"tags": {"a", "b", "c"}, # Set is invalid
}
}
with pytest.raises(ValueError) as excinfo:
task.interpolate_only("{data}", {"data": invalid_nested})
assert "Unsupported type set" in str(excinfo.value)
def test_interpolate_custom_object_validation():
task = Task(
description="Test custom object rejection",
expected_output="Should never reach here",
)
class CustomObject:
def __init__(self, value):
self.value = value
def __str__(self):
return str(self.value)
# Test with custom object at top level
with pytest.raises(ValueError) as excinfo:
task.interpolate_only("{obj}", {"obj": CustomObject(5)}) # type: ignore we are purposely testing this failure
assert "Unsupported type CustomObject" in str(excinfo.value)
# Test with nested custom object in dictionary
with pytest.raises(ValueError) as excinfo:
task.interpolate_only(
"{data}", {"data": {"valid": 1, "invalid": CustomObject(5)}}
)
assert "Unsupported type CustomObject" in str(excinfo.value)
# Test with nested custom object in list
with pytest.raises(ValueError) as excinfo:
task.interpolate_only("{data}", {"data": [1, "valid", CustomObject(5)]})
assert "Unsupported type CustomObject" in str(excinfo.value)
# Test with deeply nested custom object
with pytest.raises(ValueError) as excinfo:
task.interpolate_only(
"{data}", {"data": {"level1": {"level2": [{"level3": CustomObject(5)}]}}}
)
assert "Unsupported type CustomObject" in str(excinfo.value)
def test_interpolate_valid_complex_types():
task = Task(
description="Test valid complex types",
expected_output="Validation should pass",
)
# Valid complex structure
valid_data = {
"name": "Valid Dataset",
"stats": {
"count": 1000,
"distribution": [0.2, 0.3, 0.5],
"features": ["age", "income"],
"nested": {"deep": [1, 2, 3], "deeper": {"a": 1, "b": 2.5}},
},
}
# Should not raise any errors
result = task.interpolate_only("{data}", {"data": valid_data})
parsed = eval(result)
assert parsed["name"] == "Valid Dataset"
assert parsed["stats"]["nested"]["deeper"]["b"] == 2.5
def test_interpolate_edge_cases():
task = Task(
description="Test edge cases",
expected_output="Edge case handling",
)
# Test empty dict and list
assert task.interpolate_only("{}", {"data": {}}) == "{}"
assert task.interpolate_only("[]", {"data": []}) == "[]"
# Test numeric types
assert task.interpolate_only("{num}", {"num": 42}) == "42"
assert task.interpolate_only("{num}", {"num": 3.14}) == "3.14"
# Test boolean values (valid JSON types)
assert task.interpolate_only("{flag}", {"flag": True}) == "True"
assert task.interpolate_only("{flag}", {"flag": False}) == "False"
def test_interpolate_valid_types():
task = Task(
description="Test valid types including null and boolean",
expected_output="Should pass validation",
)
# Test with boolean and null values (valid JSON types)
valid_data = {
"name": "Test",
"active": True,
"deleted": False,
"optional": None,
"nested": {"flag": True, "empty": None},
}
result = task.interpolate_only("{data}", {"data": valid_data})
parsed = eval(result)
assert parsed["active"] is True
assert parsed["deleted"] is False
assert parsed["optional"] is None
assert parsed["nested"]["flag"] is True
assert parsed["nested"]["empty"] is None

112
tests/test_flow_default_override.py Normal file
View File

@@ -0,0 +1,112 @@
"""Test that persisted state properly overrides default values."""
from crewai.flow.flow import Flow, FlowState, listen, start
from crewai.flow.persistence import persist
class PoemState(FlowState):
"""Test state model with default values that should be overridden."""
sentence_count: int = 1000 # Default that should be overridden
has_set_count: bool = False # Track whether we've set the count
poem_type: str = ""
def test_default_value_override():
"""Test that persisted state values override class defaults."""
@persist()
class PoemFlow(Flow[PoemState]):
initial_state = PoemState
@start()
def set_sentence_count(self):
if self.state.has_set_count and self.state.sentence_count == 2:
self.state.sentence_count = 3
elif self.state.has_set_count and self.state.sentence_count == 1000:
self.state.sentence_count = 1000
elif self.state.has_set_count and self.state.sentence_count == 5:
self.state.sentence_count = 5
else:
self.state.sentence_count = 2
self.state.has_set_count = True
# First run - should set sentence_count to 2
flow1 = PoemFlow()
flow1.kickoff()
original_uuid = flow1.state.id
assert flow1.state.sentence_count == 2
# Second run - should load sentence_count=2 instead of default 1000
flow2 = PoemFlow()
flow2.kickoff(inputs={"id": original_uuid})
assert flow2.state.sentence_count == 3 # Should load 2, not default 1000
# Fourth run - explicit override should work
flow3 = PoemFlow()
flow3.kickoff(inputs={
"id": original_uuid,
"has_set_count": True,
"sentence_count": 5, # Override persisted value
})
assert flow3.state.sentence_count == 5 # Should use override value
# Third run - should not load sentence_count=2 instead of default 1000
flow4 = PoemFlow()
flow4.kickoff(inputs={"has_set_count": True})
assert flow4.state.sentence_count == 1000 # Should load 1000, not 2
def test_multi_step_default_override():
"""Test default value override with multiple start methods."""
@persist()
class MultiStepPoemFlow(Flow[PoemState]):
initial_state = PoemState
@start()
def set_sentence_count(self):
print("Setting sentence count")
if not self.state.has_set_count:
self.state.sentence_count = 3
self.state.has_set_count = True
@listen(set_sentence_count)
def set_poem_type(self):
print("Setting poem type")
if self.state.sentence_count == 3:
self.state.poem_type = "haiku"
elif self.state.sentence_count == 5:
self.state.poem_type = "limerick"
else:
self.state.poem_type = "free_verse"
@listen(set_poem_type)
def finished(self):
print("finished")
# First run - should set both sentence count and poem type
flow1 = MultiStepPoemFlow()
flow1.kickoff()
original_uuid = flow1.state.id
assert flow1.state.sentence_count == 3
assert flow1.state.poem_type == "haiku"
# Second run - should load persisted state and update poem type
flow2 = MultiStepPoemFlow()
flow2.kickoff(inputs={
"id": original_uuid,
"sentence_count": 5
})
assert flow2.state.sentence_count == 5
assert flow2.state.poem_type == "limerick"
# Third run - new flow without persisted state should use defaults
flow3 = MultiStepPoemFlow()
flow3.kickoff(inputs={
"id": original_uuid
})
assert flow3.state.sentence_count == 5
assert flow3.state.poem_type == "limerick"

176
tests/test_flow_persistence.py Normal file
View File

@@ -0,0 +1,176 @@
"""Test flow state persistence functionality."""
import os
from typing import Dict
import pytest
from pydantic import BaseModel
from crewai.flow.flow import Flow, FlowState, listen, start
from crewai.flow.persistence import persist
from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
class TestState(FlowState):
"""Test state model with required id field."""
counter: int = 0
message: str = ""
def test_persist_decorator_saves_state(tmp_path):
"""Test that @persist decorator saves state in SQLite."""
db_path = os.path.join(tmp_path, "test_flows.db")
persistence = SQLiteFlowPersistence(db_path)
class TestFlow(Flow[Dict[str, str]]):
initial_state = dict() # Use dict instance as initial state
@start()
@persist(persistence)
def init_step(self):
self.state["message"] = "Hello, World!"
self.state["id"] = "test-uuid" # Ensure we have an ID for persistence
# Run flow and verify state is saved
flow = TestFlow(persistence=persistence)
flow.kickoff()
# Load state from DB and verify
saved_state = persistence.load_state(flow.state["id"])
assert saved_state is not None
assert saved_state["message"] == "Hello, World!"
def test_structured_state_persistence(tmp_path):
"""Test persistence with Pydantic model state."""
db_path = os.path.join(tmp_path, "test_flows.db")
persistence = SQLiteFlowPersistence(db_path)
class StructuredFlow(Flow[TestState]):
initial_state = TestState
@start()
@persist(persistence)
def count_up(self):
self.state.counter += 1
self.state.message = f"Count is {self.state.counter}"
# Run flow and verify state changes are saved
flow = StructuredFlow(persistence=persistence)
flow.kickoff()
# Load and verify state
saved_state = persistence.load_state(flow.state.id)
assert saved_state is not None
assert saved_state["counter"] == 1
assert saved_state["message"] == "Count is 1"
def test_flow_state_restoration(tmp_path):
"""Test restoring flow state from persistence with various restoration methods."""
db_path = os.path.join(tmp_path, "test_flows.db")
persistence = SQLiteFlowPersistence(db_path)
# First flow execution to create initial state
class RestorableFlow(Flow[TestState]):
@start()
@persist(persistence)
def set_message(self):
if self.state.message == "":
self.state.message = "Original message"
if self.state.counter == 0:
self.state.counter = 42
# Create and persist initial state
flow1 = RestorableFlow(persistence=persistence)
flow1.kickoff()
original_uuid = flow1.state.id
# Test case 1: Restore using restore_uuid with field override
flow2 = RestorableFlow(persistence=persistence)
flow2.kickoff(inputs={
"id": original_uuid,
"counter": 43
})
# Verify state restoration and selective field override
assert flow2.state.id == original_uuid
assert flow2.state.message == "Original message" # Preserved
assert flow2.state.counter == 43 # Overridden
# Test case 2: Restore using kwargs['id']
flow3 = RestorableFlow(persistence=persistence)
flow3.kickoff(inputs={
"id": original_uuid,
"message": "Updated message"
})
# Verify state restoration and selective field override
assert flow3.state.id == original_uuid
assert flow3.state.counter == 43 # Preserved
assert flow3.state.message == "Updated message" # Overridden
def test_multiple_method_persistence(tmp_path):
"""Test state persistence across multiple method executions."""
db_path = os.path.join(tmp_path, "test_flows.db")
persistence = SQLiteFlowPersistence(db_path)
class MultiStepFlow(Flow[TestState]):
@start()
@persist(persistence)
def step_1(self):
if self.state.counter == 1:
self.state.counter = 99999
self.state.message = "Step 99999"
else:
self.state.counter = 1
self.state.message = "Step 1"
@listen(step_1)
@persist(persistence)
def step_2(self):
if self.state.counter == 1:
self.state.counter = 2
self.state.message = "Step 2"
flow = MultiStepFlow(persistence=persistence)
flow.kickoff()
flow2 = MultiStepFlow(persistence=persistence)
flow2.kickoff(inputs={"id": flow.state.id})
# Load final state
final_state = flow2.state
assert final_state is not None
assert final_state.counter == 2
assert final_state.message == "Step 2"
class NoPersistenceMultiStepFlow(Flow[TestState]):
@start()
@persist(persistence)
def step_1(self):
if self.state.counter == 1:
self.state.counter = 99999
self.state.message = "Step 99999"
else:
self.state.counter = 1
self.state.message = "Step 1"
@listen(step_1)
def step_2(self):
if self.state.counter == 1:
self.state.counter = 2
self.state.message = "Step 2"
flow = NoPersistenceMultiStepFlow(persistence=persistence)
flow.kickoff()
flow2 = NoPersistenceMultiStepFlow(persistence=persistence)
flow2.kickoff(inputs={"id": flow.state.id})
# Load final state
final_state = flow2.state
assert final_state.counter == 99999
assert final_state.message == "Step 99999"

27
tests/test_task_guardrails.py
View File

@@ -15,10 +15,7 @@ def test_task_without_guardrail():
agent.execute_task.return_value = "test result"
agent.crew = None
task = Task(
description="Test task",
expected_output="Output"
)
task = Task(description="Test task", expected_output="Output")
result = task.execute_sync(agent=agent)
assert isinstance(result, TaskOutput)
@@ -27,6 +24,7 @@ def test_task_without_guardrail():
def test_task_with_successful_guardrail():
"""Test that successful guardrail validation passes transformed result."""
def guardrail(result: TaskOutput):
return (True, result.raw.upper())
@@ -35,11 +33,7 @@ def test_task_with_successful_guardrail():
agent.execute_task.return_value = "test result"
agent.crew = None
task = Task(
description="Test task",
expected_output="Output",
guardrail=guardrail
)
task = Task(description="Test task", expected_output="Output", guardrail=guardrail)
result = task.execute_sync(agent=agent)
assert isinstance(result, TaskOutput)
@@ -48,22 +42,20 @@ def test_task_with_successful_guardrail():
def test_task_with_failing_guardrail():
"""Test that failing guardrail triggers retry with error context."""
def guardrail(result: TaskOutput):
return (False, "Invalid format")
agent = Mock()
agent.role = "test_agent"
agent.execute_task.side_effect = [
"bad result",
"good result"
]
agent.execute_task.side_effect = ["bad result", "good result"]
agent.crew = None
task = Task(
description="Test task",
expected_output="Output",
guardrail=guardrail,
max_retries=1
max_retries=1,
)
# First execution fails guardrail, second succeeds
@@ -77,6 +69,7 @@ def test_task_with_failing_guardrail():
def test_task_with_guardrail_retries():
"""Test that guardrail respects max_retries configuration."""
def guardrail(result: TaskOutput):
return (False, "Invalid format")
@@ -89,7 +82,7 @@ def test_task_with_guardrail_retries():
description="Test task",
expected_output="Output",
guardrail=guardrail,
max_retries=2
max_retries=2,
)
with pytest.raises(Exception) as exc_info:
@@ -102,6 +95,7 @@ def test_task_with_guardrail_retries():
def test_guardrail_error_in_context():
"""Test that guardrail error is passed in context for retry."""
def guardrail(result: TaskOutput):
return (False, "Expected JSON, got string")
@@ -113,11 +107,12 @@ def test_guardrail_error_in_context():
description="Test task",
expected_output="Output",
guardrail=guardrail,
max_retries=1
max_retries=1,
)
# Mock execute_task to succeed on second attempt
first_call = True
def execute_task(task, context, tools):
nonlocal first_call
if first_call:

8
tests/tools/test_base_tool.py
View File

@@ -15,7 +15,7 @@ def test_creating_a_tool_using_annotation():
my_tool.description
== "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
)
assert my_tool.args_schema.schema()["properties"] == {
assert my_tool.args_schema.model_json_schema()["properties"] == {
"question": {"title": "Question", "type": "string"}
}
assert (
@@ -29,7 +29,7 @@ def test_creating_a_tool_using_annotation():
converted_tool.description
== "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
)
assert converted_tool.args_schema.schema()["properties"] == {
assert converted_tool.args_schema.model_json_schema()["properties"] == {
"question": {"title": "Question", "type": "string"}
}
assert (
@@ -54,7 +54,7 @@ def test_creating_a_tool_using_baseclass():
my_tool.description
== "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
)
assert my_tool.args_schema.schema()["properties"] == {
assert my_tool.args_schema.model_json_schema()["properties"] == {
"question": {"title": "Question", "type": "string"}
}
assert my_tool.run("What is the meaning of life?") == "What is the meaning of life?"
@@ -66,7 +66,7 @@ def test_creating_a_tool_using_baseclass():
converted_tool.description
== "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
)
assert converted_tool.args_schema.schema()["properties"] == {
assert converted_tool.args_schema.model_json_schema()["properties"] == {
"question": {"title": "Question", "type": "string"}
}
assert (

2
tests/tools/test_structured_tool.py
View File

@@ -25,7 +25,7 @@ def schema_class():
return TestSchema
class TestCrewStructuredTool:
class InternalCrewStructuredTool:
def test_initialization(self, basic_function, schema_class):
"""Test basic initialization of CrewStructuredTool"""
tool = CrewStructuredTool(

362
tests/tools/test_tool_usage.py
View File

@@ -121,3 +121,365 @@ def test_tool_usage_render():
"Tool Name: Random Number Generator\nTool Arguments: {'min_value': {'description': 'The minimum value of the range (inclusive)', 'type': 'int'}, 'max_value': {'description': 'The maximum value of the range (inclusive)', 'type': 'int'}}\nTool Description: Generates a random number within a specified range"
in rendered
)
def test_validate_tool_input_booleans_and_none():
# Create a ToolUsage instance with mocks
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
action=MagicMock(),
)
# Input with booleans and None
tool_input = '{"key1": True, "key2": False, "key3": None}'
expected_arguments = {"key1": True, "key2": False, "key3": None}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
def test_validate_tool_input_mixed_types():
# Create a ToolUsage instance with mocks
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
action=MagicMock(),
)
# Input with mixed types
tool_input = '{"number": 123, "text": "Some text", "flag": True}'
expected_arguments = {"number": 123, "text": "Some text", "flag": True}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
def test_validate_tool_input_single_quotes():
# Create a ToolUsage instance with mocks
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
action=MagicMock(),
)
# Input with single quotes instead of double quotes
tool_input = "{'key': 'value', 'flag': True}"
expected_arguments = {"key": "value", "flag": True}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
def test_validate_tool_input_invalid_json_repairable():
# Create a ToolUsage instance with mocks
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
action=MagicMock(),
)
# Invalid JSON input that can be repaired
tool_input = '{"key": "value", "list": [1, 2, 3,]}'
expected_arguments = {"key": "value", "list": [1, 2, 3]}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
def test_validate_tool_input_with_special_characters():
# Create a ToolUsage instance with mocks
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[],
original_tools=[],
tools_description="",
tools_names="",
task=MagicMock(),
function_calling_llm=MagicMock(),
agent=MagicMock(),
action=MagicMock(),
)
# Input with special characters
tool_input = '{"message": "Hello, world! \u263A", "valid": True}'
expected_arguments = {"message": "Hello, world! ☺", "valid": True}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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(),
action=MagicMock(),
)
arguments = tool_usage._validate_tool_input(None)
assert arguments == {}
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(),
action=MagicMock(),
)
tool_input = '{"key": "value", "number": 42, "flag": true}'
expected_arguments = {"key": "value", "number": 42, "flag": True}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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(),
action=MagicMock(),
)
tool_input = "{'key': 'value', 'number': 42, 'flag': True}"
expected_arguments = {"key": "value", "number": 42, "flag": True}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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(),
action=MagicMock(),
)
tool_input = "{key: 'value', number: 42, flag: true}"
expected_arguments = {"key": "value", "number": 42, "flag": True}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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(),
action=MagicMock(),
)
tool_input = '{"key": "value", "number": 42, "flag": true,}'
expected_arguments = {"key": "value", "number": 42, "flag": True}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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=MagicMock(),
action=MagicMock(),
)
invalid_inputs = [
"Just a string",
"['list', 'of', 'values']",
"12345",
"",
]
for invalid_input in invalid_inputs:
with pytest.raises(Exception) as e_info:
tool_usage._validate_tool_input(invalid_input)
assert (
"Tool input must be a valid dictionary in JSON or Python literal format"
in str(e_info.value)
)
# Test for None input separately
arguments = tool_usage._validate_tool_input(None)
assert arguments == {} # Expecting an empty dictionary
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(),
action=MagicMock(),
)
tool_input = """
{
"user": {
"name": "Alice",
"age": 30
},
"items": [
{"id": 1, "value": "Item1"},
{"id": 2, "value": "Item2",}
],
"active": true,
}
"""
expected_arguments = {
"user": {"name": "Alice", "age": 30},
"items": [
{"id": 1, "value": "Item1"},
{"id": 2, "value": "Item2"},
],
"active": True,
}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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(),
action=MagicMock(),
)
tool_input = '{"filename": "script.py", "content": "def hello():\\n print(\'Hello, world!\')"}'
expected_arguments = {
"filename": "script.py",
"content": "def hello():\n print('Hello, world!')",
}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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(),
action=MagicMock(),
)
tool_input = '{"text": "He said, \\"Hello, world!\\""}'
expected_arguments = {"text": 'He said, "Hello, world!"'}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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(),
action=MagicMock(),
)
# Simulate a large JSON content
tool_input = (
'{"data": ' + json.dumps([{"id": i, "value": i * 2} for i in range(1000)]) + "}"
)
expected_arguments = {"data": [{"id": i, "value": i * 2} for i in range(1000)]}
arguments = tool_usage._validate_tool_input(tool_input)
assert arguments == expected_arguments
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(),
action=MagicMock(),
)
arguments = tool_usage._validate_tool_input(None)
assert arguments == {} # Expecting an empty dictionary

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2
tests/utilities/evaluators/test_crew_evaluator_handler.py
View File

@@ -12,7 +12,7 @@ from crewai.utilities.evaluators.crew_evaluator_handler import (
)
class TestCrewEvaluator:
class InternalCrewEvaluator:
@pytest.fixture
def crew_planner(self):
agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")

6
tests/utilities/evaluators/test_task_evaluator.py
View File

@@ -48,9 +48,9 @@ def test_evaluate_training_data(converter_mock):
mock.call(
llm=original_agent.llm,
text="Assess the quality of the training data based on the llm output, human feedback , and llm "
"output improved result.\n\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
"1\n\nImproved Output:\nImproved output 1\n\nInitial Output:\nInitial output 2\n\nHuman "
"Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- Provide "
"output improved result.\n\nIteration: data1\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
"1\n\nImproved Output:\nImproved output 1\n\n------------------------------------------------\n\nIteration: data2\nInitial Output:\nInitial output 2\n\nHuman "
"Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\n------------------------------------------------\n\nPlease provide:\n- Provide "
"a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's "
"performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific "
"action items for future tasks. Ensure all key and specificpoints from the human feedback are "

316
tests/utilities/test_converter.py
View File

@@ -39,6 +39,22 @@ class NestedModel(BaseModel):
data: SimpleModel
class Address(BaseModel):
street: str
city: str
zip_code: str
class Person(BaseModel):
name: str
age: int
address: Address
class CustomConverter(Converter):
pass
# Fixtures
@pytest.fixture
def mock_agent():
@@ -199,26 +215,23 @@ def test_convert_with_instructions_failure(
# Tests for get_conversion_instructions
def test_get_conversion_instructions_gpt():
mock_llm = Mock()
mock_llm.openai_api_base = None
llm = LLM(model="gpt-4o-mini")
with patch.object(LLM, "supports_function_calling") as supports_function_calling:
supports_function_calling.return_value = True
instructions = get_conversion_instructions(SimpleModel, mock_llm)
instructions = get_conversion_instructions(SimpleModel, llm)
model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
assert (
instructions
== f"I'm gonna convert this raw text into valid JSON.\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
== f"Please convert the following text into valid JSON.\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
)
def test_get_conversion_instructions_non_gpt():
mock_llm = Mock()
with patch.object(LLM, "supports_function_calling") as supports_function_calling:
supports_function_calling.return_value = False
with patch("crewai.utilities.converter.PydanticSchemaParser") as mock_parser:
mock_parser.return_value.get_schema.return_value = "Sample schema"
instructions = get_conversion_instructions(SimpleModel, mock_llm)
assert "Sample schema" in instructions
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
with patch.object(LLM, "supports_function_calling", return_value=False):
instructions = get_conversion_instructions(SimpleModel, llm)
assert '"name": str' in instructions
assert '"age": int' in instructions
# Tests for is_gpt
@@ -232,10 +245,6 @@ def test_supports_function_calling_false():
assert llm.supports_function_calling() is False
class CustomConverter(Converter):
pass
def test_create_converter_with_mock_agent():
mock_agent = MagicMock()
mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
@@ -255,7 +264,7 @@ def test_create_converter_with_mock_agent():
def test_create_converter_with_custom_converter():
converter = create_converter(
converter_cls=CustomConverter,
llm=Mock(),
llm=LLM(model="gpt-4o-mini"),
text="Sample",
model=SimpleModel,
instructions="Convert",
@@ -313,3 +322,278 @@ def test_generate_model_description_dict_field():
description = generate_model_description(ModelWithDictField)
expected_description = '{\n "attributes": Dict[str, int]\n}'
assert description == expected_description
@pytest.mark.vcr(filter_headers=["authorization"])
def test_convert_with_instructions():
llm = LLM(model="gpt-4o-mini")
sample_text = "Name: Alice, Age: 30"
instructions = get_conversion_instructions(SimpleModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=SimpleModel,
instructions=instructions,
)
# Act
output = converter.to_pydantic()
# Assert
assert isinstance(output, SimpleModel)
assert output.name == "Alice"
assert output.age == 30
@pytest.mark.vcr(filter_headers=["authorization"])
def test_converter_with_llama3_2_model():
llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
sample_text = "Name: Alice Llama, Age: 30"
instructions = get_conversion_instructions(SimpleModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=SimpleModel,
instructions=instructions,
)
output = converter.to_pydantic()
assert isinstance(output, SimpleModel)
assert output.name == "Alice Llama"
assert output.age == 30
@pytest.mark.vcr(filter_headers=["authorization"])
def test_converter_with_llama3_1_model():
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
sample_text = "Name: Alice Llama, Age: 30"
instructions = get_conversion_instructions(SimpleModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=SimpleModel,
instructions=instructions,
)
output = converter.to_pydantic()
assert isinstance(output, SimpleModel)
assert output.name == "Alice Llama"
assert output.age == 30
@pytest.mark.vcr(filter_headers=["authorization"])
def test_converter_with_nested_model():
llm = LLM(model="gpt-4o-mini")
sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
instructions = get_conversion_instructions(Person, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=Person,
instructions=instructions,
)
output = converter.to_pydantic()
assert isinstance(output, Person)
assert output.name == "John Doe"
assert output.age == 30
assert isinstance(output.address, Address)
assert output.address.street == "123 Main St"
assert output.address.city == "Anytown"
assert output.address.zip_code == "12345"
# Tests for error handling
def test_converter_error_handling():
llm = Mock(spec=LLM)
llm.supports_function_calling.return_value = False
llm.call.return_value = "Invalid JSON"
sample_text = "Name: Alice, Age: 30"
instructions = get_conversion_instructions(SimpleModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=SimpleModel,
instructions=instructions,
)
with pytest.raises(ConverterError) as exc_info:
output = converter.to_pydantic()
assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
# Tests for retry logic
def test_converter_retry_logic():
llm = Mock(spec=LLM)
llm.supports_function_calling.return_value = False
llm.call.side_effect = [
"Invalid JSON",
"Still invalid",
'{"name": "Retry Alice", "age": 30}',
]
sample_text = "Name: Retry Alice, Age: 30"
instructions = get_conversion_instructions(SimpleModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=SimpleModel,
instructions=instructions,
max_attempts=3,
)
output = converter.to_pydantic()
assert isinstance(output, SimpleModel)
assert output.name == "Retry Alice"
assert output.age == 30
assert llm.call.call_count == 3
# Tests for optional fields
def test_converter_with_optional_fields():
class OptionalModel(BaseModel):
name: str
age: Optional[int]
llm = Mock(spec=LLM)
llm.supports_function_calling.return_value = False
# Simulate the LLM's response with 'age' explicitly set to null
llm.call.return_value = '{"name": "Bob", "age": null}'
sample_text = "Name: Bob, age: None"
instructions = get_conversion_instructions(OptionalModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=OptionalModel,
instructions=instructions,
)
output = converter.to_pydantic()
assert isinstance(output, OptionalModel)
assert output.name == "Bob"
assert output.age is None
# Tests for list fields
def test_converter_with_list_field():
class ListModel(BaseModel):
items: List[int]
llm = Mock(spec=LLM)
llm.supports_function_calling.return_value = False
llm.call.return_value = '{"items": [1, 2, 3]}'
sample_text = "Items: 1, 2, 3"
instructions = get_conversion_instructions(ListModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=ListModel,
instructions=instructions,
)
output = converter.to_pydantic()
assert isinstance(output, ListModel)
assert output.items == [1, 2, 3]
# Tests for enums
from enum import Enum
def test_converter_with_enum():
class Color(Enum):
RED = "red"
GREEN = "green"
BLUE = "blue"
class EnumModel(BaseModel):
name: str
color: Color
llm = Mock(spec=LLM)
llm.supports_function_calling.return_value = False
llm.call.return_value = '{"name": "Alice", "color": "red"}'
sample_text = "Name: Alice, Color: Red"
instructions = get_conversion_instructions(EnumModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=EnumModel,
instructions=instructions,
)
output = converter.to_pydantic()
assert isinstance(output, EnumModel)
assert output.name == "Alice"
assert output.color == Color.RED
# Tests for ambiguous input
def test_converter_with_ambiguous_input():
llm = Mock(spec=LLM)
llm.supports_function_calling.return_value = False
llm.call.return_value = '{"name": "Charlie", "age": "Not an age"}'
sample_text = "Charlie is thirty years old"
instructions = get_conversion_instructions(SimpleModel, llm)
converter = Converter(
llm=llm,
text=sample_text,
model=SimpleModel,
instructions=instructions,
)
with pytest.raises(ConverterError) as exc_info:
output = converter.to_pydantic()
assert "validation error" in str(exc_info.value).lower()
# Tests for function calling support
def test_converter_with_function_calling():
llm = Mock(spec=LLM)
llm.supports_function_calling.return_value = True
instructor = Mock()
instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
converter = Converter(
llm=llm,
text="Name: Eve, Age: 35",
model=SimpleModel,
instructions="Convert this text.",
)
converter._create_instructor = Mock(return_value=instructor)
output = converter.to_pydantic()
assert isinstance(output, SimpleModel)
assert output.name == "Eve"
assert output.age == 35
instructor.to_pydantic.assert_called_once()
def test_generate_model_description_union_field():
class UnionModel(BaseModel):
field: int | str | None
description = generate_model_description(UnionModel)
expected_description = '{\n "field": int | str | None\n}'
assert description == expected_description

84
tests/utilities/test_knowledge_planning.py Normal file
View File

@@ -0,0 +1,84 @@
"""
Tests for verifying the integration of knowledge sources in the planning process.
This module ensures that agent knowledge is properly included during task planning.
"""
from unittest.mock import patch
import pytest
from crewai.agent import Agent
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
from crewai.task import Task
from crewai.utilities.planning_handler import CrewPlanner
@pytest.fixture
def mock_knowledge_source():
"""
Create a mock knowledge source with test content.
Returns:
StringKnowledgeSource:
A knowledge source containing AI-related test content
"""
content = """
Important context about AI:
1. AI systems use machine learning algorithms
2. Neural networks are a key component
3. Training data is essential for good performance
"""
return StringKnowledgeSource(content=content)
@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
def test_knowledge_included_in_planning(mock_chroma):
"""Test that verifies knowledge sources are properly included in planning."""
# Mock ChromaDB collection
mock_collection = mock_chroma.return_value.get_or_create_collection.return_value
mock_collection.add.return_value = None
# Create an agent with knowledge
agent = Agent(
role="AI Researcher",
goal="Research and explain AI concepts",
backstory="Expert in artificial intelligence",
knowledge_sources=[
StringKnowledgeSource(
content="AI systems require careful training and validation."
)
]
)
# Create a task for the agent
task = Task(
description="Explain the basics of AI systems",
expected_output="A clear explanation of AI fundamentals",
agent=agent
)
# Create a crew planner
planner = CrewPlanner([task], None)
# Get the task summary
task_summary = planner._create_tasks_summary()
# Verify that knowledge is included in planning when present
assert "AI systems require careful training" in task_summary, \
"Knowledge content should be present in task summary when knowledge exists"
assert '"agent_knowledge"' in task_summary, \
"agent_knowledge field should be present in task summary when knowledge exists"
# Verify that knowledge is properly formatted
assert isinstance(task.agent.knowledge_sources, list), \
"Knowledge sources should be stored in a list"
assert len(task.agent.knowledge_sources) > 0, \
"At least one knowledge source should be present"
assert task.agent.knowledge_sources[0].content in task_summary, \
"Knowledge source content should be included in task summary"
# Verify that other expected components are still present
assert task.description in task_summary, \
"Task description should be present in task summary"
assert task.expected_output in task_summary, \
"Expected output should be present in task summary"
assert agent.role in task_summary, \
"Agent role should be present in task summary"

96
tests/utilities/test_llm_utils.py Normal file
View File

@@ -0,0 +1,96 @@
import os
from unittest.mock import patch
import pytest
from litellm.exceptions import BadRequestError
from crewai.llm import LLM
from crewai.utilities.llm_utils import create_llm
def test_create_llm_with_llm_instance():
existing_llm = LLM(model="gpt-4o")
llm = create_llm(llm_value=existing_llm)
assert llm is existing_llm
def test_create_llm_with_valid_model_string():
llm = create_llm(llm_value="gpt-4o")
assert isinstance(llm, LLM)
assert llm.model == "gpt-4o"
def test_create_llm_with_invalid_model_string():
with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
llm = create_llm(llm_value="invalid-model")
llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
def test_create_llm_with_unknown_object_missing_attributes():
class UnknownObject:
pass
unknown_obj = UnknownObject()
llm = create_llm(llm_value=unknown_obj)
# Attempt to call the LLM and expect it to raise an error due to missing attributes
with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
def test_create_llm_with_none_uses_default_model():
with patch.dict(os.environ, {}, clear=True):
with patch("crewai.cli.constants.DEFAULT_LLM_MODEL", "gpt-4o"):
llm = create_llm(llm_value=None)
assert isinstance(llm, LLM)
assert llm.model == "gpt-4o-mini"
def test_create_llm_with_unknown_object():
class UnknownObject:
model_name = "gpt-4o"
temperature = 0.7
max_tokens = 1500
unknown_obj = UnknownObject()
llm = create_llm(llm_value=unknown_obj)
assert isinstance(llm, LLM)
assert llm.model == "gpt-4o"
assert llm.temperature == 0.7
assert llm.max_tokens == 1500
def test_create_llm_from_env_with_unaccepted_attributes():
with patch.dict(
os.environ,
{
"OPENAI_MODEL_NAME": "gpt-3.5-turbo",
"AWS_ACCESS_KEY_ID": "fake-access-key",
"AWS_SECRET_ACCESS_KEY": "fake-secret-key",
"AWS_REGION_NAME": "us-west-2",
},
):
llm = create_llm(llm_value=None)
assert isinstance(llm, LLM)
assert llm.model == "gpt-3.5-turbo"
assert not hasattr(llm, "AWS_ACCESS_KEY_ID")
assert not hasattr(llm, "AWS_SECRET_ACCESS_KEY")
assert not hasattr(llm, "AWS_REGION_NAME")
def test_create_llm_with_partial_attributes():
class PartialAttributes:
model_name = "gpt-4o"
# temperature is missing
obj = PartialAttributes()
llm = create_llm(llm_value=obj)
assert isinstance(llm, LLM)
assert llm.model == "gpt-4o"
assert llm.temperature is None # Should handle missing attributes gracefully
def test_create_llm_with_invalid_type():
with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
llm = create_llm(llm_value=42)
llm.call(messages=[{"role": "user", "content": "Hello, world!"}])

75
tests/utilities/test_planning_handler.py
View File

@@ -1,10 +1,14 @@
from unittest.mock import patch
from typing import Optional
from unittest.mock import MagicMock, patch
import pytest
from pydantic import BaseModel
from crewai.agent import Agent
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
from crewai.task import Task
from crewai.tasks.task_output import TaskOutput
from crewai.tools.base_tool import BaseTool
from crewai.utilities.planning_handler import (
CrewPlanner,
PlannerTaskPydanticOutput,
@@ -12,7 +16,7 @@ from crewai.utilities.planning_handler import (
)
class TestCrewPlanner:
class InternalCrewPlanner:
@pytest.fixture
def crew_planner(self):
tasks = [
@@ -92,7 +96,72 @@ class TestCrewPlanner:
tasks_summary = crew_planner._create_tasks_summary()
assert isinstance(tasks_summary, str)
assert tasks_summary.startswith("\n Task Number 1 - Task 1")
assert tasks_summary.endswith('"agent_tools": []\n ')
assert '"agent_tools": "agent has no tools"' in tasks_summary
# Knowledge field should not be present when empty
assert '"agent_knowledge"' not in tasks_summary
@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma):
"""Test task summary generation with both knowledge and tools present."""
# Mock ChromaDB collection
mock_collection = mock_chroma.return_value.get_or_create_collection.return_value
mock_collection.add.return_value = None
# Create mock tools with proper string descriptions and structured tool support
class MockTool(BaseTool):
name: str
description: str
def __init__(self, name: str, description: str):
tool_data = {"name": name, "description": description}
super().__init__(**tool_data)
def __str__(self):
return self.name
def __repr__(self):
return self.name
def to_structured_tool(self):
return self
def _run(self, *args, **kwargs):
pass
def _generate_description(self) -> str:
"""Override _generate_description to avoid args_schema handling."""
return self.description
tool1 = MockTool("tool1", "Tool 1 description")
tool2 = MockTool("tool2", "Tool 2 description")
# Create a task with knowledge and tools
task = Task(
description="Task with knowledge and tools",
expected_output="Expected output",
agent=Agent(
role="Test Agent",
goal="Test Goal",
backstory="Test Backstory",
tools=[tool1, tool2],
knowledge_sources=[
StringKnowledgeSource(content="Test knowledge content")
]
)
)
# Create planner with the new task
planner = CrewPlanner([task], None)
tasks_summary = planner._create_tasks_summary()
# Verify task summary content
assert isinstance(tasks_summary, str)
assert task.description in tasks_summary
assert task.expected_output in tasks_summary
assert '"agent_tools": [tool1, tool2]' in tasks_summary
assert '"agent_knowledge": "[\\"Test knowledge content\\"]"' in tasks_summary
assert task.agent.role in tasks_summary
assert task.agent.goal in tasks_summary
def test_handle_crew_planning_different_llm(self, crew_planner_different_llm):
with patch.object(Task, "execute_sync") as execute:

94
tests/utilities/test_pydantic_schema_parser.py Normal file
View File

@@ -0,0 +1,94 @@
from typing import Any, Dict, List, Optional, Set, Tuple, Union
import pytest
from pydantic import BaseModel, Field
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
def test_simple_model():
class SimpleModel(BaseModel):
field1: int
field2: str
parser = PydanticSchemaParser(model=SimpleModel)
schema = parser.get_schema()
expected_schema = """{
field1: int,
field2: str
}"""
assert schema.strip() == expected_schema.strip()
def test_nested_model():
class NestedModel(BaseModel):
nested_field: int
class ParentModel(BaseModel):
parent_field: str
nested: NestedModel
parser = PydanticSchemaParser(model=ParentModel)
schema = parser.get_schema()
expected_schema = """{
parent_field: str,
nested: NestedModel
{
nested_field: int
}
}"""
assert schema.strip() == expected_schema.strip()
def test_model_with_list():
class ListModel(BaseModel):
list_field: List[int]
parser = PydanticSchemaParser(model=ListModel)
schema = parser.get_schema()
expected_schema = """{
list_field: List[int]
}"""
assert schema.strip() == expected_schema.strip()
def test_model_with_optional_field():
class OptionalModel(BaseModel):
optional_field: Optional[str]
parser = PydanticSchemaParser(model=OptionalModel)
schema = parser.get_schema()
expected_schema = """{
optional_field: Optional[str]
}"""
assert schema.strip() == expected_schema.strip()
def test_model_with_union():
class UnionModel(BaseModel):
union_field: Union[int, str]
parser = PydanticSchemaParser(model=UnionModel)
schema = parser.get_schema()
expected_schema = """{
union_field: Union[int, str]
}"""
assert schema.strip() == expected_schema.strip()
def test_model_with_dict():
class DictModel(BaseModel):
dict_field: Dict[str, int]
parser = PydanticSchemaParser(model=DictModel)
schema = parser.get_schema()
expected_schema = """{
dict_field: Dict[str, int]
}"""
assert schema.strip() == expected_schema.strip()

2
tests/utilities/test_training_handler.py
View File

@@ -4,7 +4,7 @@ import unittest
from crewai.utilities.training_handler import CrewTrainingHandler
class TestCrewTrainingHandler(unittest.TestCase):
class InternalCrewTrainingHandler(unittest.TestCase):
def setUp(self):
self.handler = CrewTrainingHandler("trained_data.pkl")