added tests

This commit is contained in:
lorenzejay
2026-01-29 17:14:25 -08:00
parent 383fcaab9d
commit f63d088115
16 changed files with 6165 additions and 21 deletions

View File

@@ -675,23 +675,6 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(response)
self._track_token_usage_internal(usage)
if "tools" in params and response.content:
tool_uses = [
block
for block in response.content
if isinstance(block, (ToolUseBlock, BetaToolUseBlock))
]
if tool_uses:
if not available_functions:
self._emit_call_completed_event(
response=list(tool_uses),
call_type=LLMCallType.TOOL_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return list(tool_uses)
if _is_pydantic_model_class(response_model) and response.content:
if use_native_structured_output:
for block in response.content:
@@ -708,7 +691,7 @@ class AnthropicCompletion(BaseLLM):
else:
for block in response.content:
if (
isinstance(block, ToolUseBlock)
isinstance(block, (ToolUseBlock, BetaToolUseBlock))
and block.name == "structured_output"
):
structured_data = response_model.model_validate(block.input)
@@ -721,6 +704,23 @@ class AnthropicCompletion(BaseLLM):
)
return structured_data
if "tools" in params and response.content:
tool_uses = [
block
for block in response.content
if isinstance(block, (ToolUseBlock, BetaToolUseBlock))
]
if tool_uses:
if not available_functions:
self._emit_call_completed_event(
response=list(tool_uses),
call_type=LLMCallType.TOOL_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return list(tool_uses)
# Check if Claude wants to use tools
if response.content:
tool_uses = [

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@@ -867,3 +867,86 @@ def test_anthropic_function_calling():
assert len(result) > 0
# Verify the response includes information about Tokyo's weather
assert "tokyo" in result.lower() or "72" in result
# =============================================================================
# Agent Kickoff Structured Output Tests
# =============================================================================
@pytest.mark.vcr()
def test_anthropic_agent_kickoff_structured_output_without_tools():
"""
Test that agent kickoff returns structured output without tools.
This tests native structured output handling for Anthropic models.
"""
from pydantic import BaseModel, Field
class AnalysisResult(BaseModel):
"""Structured output for analysis results."""
topic: str = Field(description="The topic analyzed")
key_points: list[str] = Field(description="Key insights from the analysis")
summary: str = Field(description="Brief summary of findings")
agent = Agent(
role="Analyst",
goal="Provide structured analysis on topics",
backstory="You are an expert analyst who provides clear, structured insights.",
llm=LLM(model="anthropic/claude-3-5-haiku-20241022"),
tools=[],
verbose=True,
)
result = agent.kickoff(
messages="Analyze the benefits of remote work briefly. Keep it concise.",
response_format=AnalysisResult,
)
assert result.pydantic is not None, "Expected pydantic output but got None"
assert isinstance(result.pydantic, AnalysisResult), f"Expected AnalysisResult but got {type(result.pydantic)}"
assert result.pydantic.topic, "Topic should not be empty"
assert len(result.pydantic.key_points) > 0, "Should have at least one key point"
assert result.pydantic.summary, "Summary should not be empty"
@pytest.mark.vcr()
def test_anthropic_agent_kickoff_structured_output_with_tools():
"""
Test that agent kickoff returns structured output after using tools.
This tests post-tool-call structured output handling for Anthropic models.
"""
from pydantic import BaseModel, Field
from crewai.tools import tool
class CalculationResult(BaseModel):
"""Structured output for calculation results."""
operation: str = Field(description="The mathematical operation performed")
result: int = Field(description="The result of the calculation")
explanation: str = Field(description="Brief explanation of the calculation")
@tool
def add_numbers(a: int, b: int) -> int:
"""Add two numbers together and return the sum."""
return a + b
agent = Agent(
role="Calculator",
goal="Perform calculations using available tools",
backstory="You are a calculator assistant that uses tools to compute results.",
llm=LLM(model="anthropic/claude-3-5-haiku-20241022"),
tools=[add_numbers],
verbose=True,
)
result = agent.kickoff(
messages="Calculate 15 + 27 using your add_numbers tool. Report the result.",
response_format=CalculationResult,
)
assert result.pydantic is not None, "Expected pydantic output but got None"
assert isinstance(result.pydantic, CalculationResult), f"Expected CalculationResult but got {type(result.pydantic)}"
assert result.pydantic.result == 42, f"Expected result 42 but got {result.pydantic.result}"
assert result.pydantic.operation, "Operation should not be empty"
assert result.pydantic.explanation, "Explanation should not be empty"

View File

@@ -1215,3 +1215,86 @@ def test_azure_streaming_returns_usage_metrics():
assert result.token_usage.prompt_tokens > 0
assert result.token_usage.completion_tokens > 0
assert result.token_usage.successful_requests >= 1
# =============================================================================
# Agent Kickoff Structured Output Tests
# =============================================================================
@pytest.mark.vcr()
def test_azure_agent_kickoff_structured_output_without_tools():
"""
Test that agent kickoff returns structured output without tools.
This tests native structured output handling for Azure OpenAI models.
"""
from pydantic import BaseModel, Field
class AnalysisResult(BaseModel):
"""Structured output for analysis results."""
topic: str = Field(description="The topic analyzed")
key_points: list[str] = Field(description="Key insights from the analysis")
summary: str = Field(description="Brief summary of findings")
agent = Agent(
role="Analyst",
goal="Provide structured analysis on topics",
backstory="You are an expert analyst who provides clear, structured insights.",
llm=LLM(model="azure/gpt-4o-mini"),
tools=[],
verbose=True,
)
result = agent.kickoff(
messages="Analyze the benefits of remote work briefly. Keep it concise.",
response_format=AnalysisResult,
)
assert result.pydantic is not None, "Expected pydantic output but got None"
assert isinstance(result.pydantic, AnalysisResult), f"Expected AnalysisResult but got {type(result.pydantic)}"
assert result.pydantic.topic, "Topic should not be empty"
assert len(result.pydantic.key_points) > 0, "Should have at least one key point"
assert result.pydantic.summary, "Summary should not be empty"
@pytest.mark.vcr()
def test_azure_agent_kickoff_structured_output_with_tools():
"""
Test that agent kickoff returns structured output after using tools.
This tests post-tool-call structured output handling for Azure OpenAI models.
"""
from pydantic import BaseModel, Field
from crewai.tools import tool
class CalculationResult(BaseModel):
"""Structured output for calculation results."""
operation: str = Field(description="The mathematical operation performed")
result: int = Field(description="The result of the calculation")
explanation: str = Field(description="Brief explanation of the calculation")
@tool
def add_numbers(a: int, b: int) -> int:
"""Add two numbers together and return the sum."""
return a + b
agent = Agent(
role="Calculator",
goal="Perform calculations using available tools",
backstory="You are a calculator assistant that uses tools to compute results.",
llm=LLM(model="azure/gpt-4o-mini"),
tools=[add_numbers],
verbose=True,
)
result = agent.kickoff(
messages="Calculate 15 + 27 using your add_numbers tool. Report the result.",
response_format=CalculationResult,
)
assert result.pydantic is not None, "Expected pydantic output but got None"
assert isinstance(result.pydantic, CalculationResult), f"Expected CalculationResult but got {type(result.pydantic)}"
assert result.pydantic.result == 42, f"Expected result 42 but got {result.pydantic.result}"
assert result.pydantic.operation, "Operation should not be empty"
assert result.pydantic.explanation, "Explanation should not be empty"

View File

@@ -12,7 +12,12 @@ from crewai.task import Task
@pytest.fixture(autouse=True)
def mock_aws_credentials():
"""Automatically mock AWS credentials and boto3 Session for all tests in this module."""
"""Mock AWS credentials and boto3 Session for tests only if real credentials are not set."""
# If real AWS credentials exist, don't mock - allow real API calls
if "AWS_ACCESS_KEY_ID" in os.environ and "AWS_SECRET_ACCESS_KEY" in os.environ:
yield None, None
return
with patch.dict(os.environ, {
"AWS_ACCESS_KEY_ID": "test-access-key",
"AWS_SECRET_ACCESS_KEY": "test-secret-key",

View File

@@ -12,8 +12,11 @@ from crewai.task import Task
@pytest.fixture(autouse=True)
def mock_google_api_key():
"""Automatically mock GOOGLE_API_KEY for all tests in this module."""
with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
"""Mock GOOGLE_API_KEY for tests only if real keys are not set."""
if "GOOGLE_API_KEY" not in os.environ and "GEMINI_API_KEY" not in os.environ:
with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
yield
else:
yield
@@ -927,3 +930,86 @@ def test_gemini_1_5_response_model_uses_response_schema():
# For Gemini 1.5, response_schema should be the Pydantic model itself
# The SDK handles conversion internally
assert schema is TestResponse or isinstance(schema, type)
# =============================================================================
# Agent Kickoff Structured Output Tests
# =============================================================================
@pytest.mark.vcr()
def test_gemini_agent_kickoff_structured_output_without_tools():
"""
Test that agent kickoff returns structured output without tools.
This tests native structured output handling for Gemini models.
"""
from pydantic import BaseModel, Field
class AnalysisResult(BaseModel):
"""Structured output for analysis results."""
topic: str = Field(description="The topic analyzed")
key_points: list[str] = Field(description="Key insights from the analysis")
summary: str = Field(description="Brief summary of findings")
agent = Agent(
role="Analyst",
goal="Provide structured analysis on topics",
backstory="You are an expert analyst who provides clear, structured insights.",
llm=LLM(model="google/gemini-2.0-flash-001"),
tools=[],
verbose=True,
)
result = agent.kickoff(
messages="Analyze the benefits of remote work briefly. Keep it concise.",
response_format=AnalysisResult,
)
assert result.pydantic is not None, "Expected pydantic output but got None"
assert isinstance(result.pydantic, AnalysisResult), f"Expected AnalysisResult but got {type(result.pydantic)}"
assert result.pydantic.topic, "Topic should not be empty"
assert len(result.pydantic.key_points) > 0, "Should have at least one key point"
assert result.pydantic.summary, "Summary should not be empty"
@pytest.mark.vcr()
def test_gemini_agent_kickoff_structured_output_with_tools():
"""
Test that agent kickoff returns structured output after using tools.
This tests post-tool-call structured output handling for Gemini models.
"""
from pydantic import BaseModel, Field
from crewai.tools import tool
class CalculationResult(BaseModel):
"""Structured output for calculation results."""
operation: str = Field(description="The mathematical operation performed")
result: int = Field(description="The result of the calculation")
explanation: str = Field(description="Brief explanation of the calculation")
@tool
def add_numbers(a: int, b: int) -> int:
"""Add two numbers together and return the sum."""
return a + b
agent = Agent(
role="Calculator",
goal="Perform calculations using available tools",
backstory="You are a calculator assistant that uses tools to compute results.",
llm=LLM(model="google/gemini-2.0-flash-001"),
tools=[add_numbers],
verbose=True,
)
result = agent.kickoff(
messages="Calculate 15 + 27 using your add_numbers tool. Report the result.",
response_format=CalculationResult,
)
assert result.pydantic is not None, "Expected pydantic output but got None"
assert isinstance(result.pydantic, CalculationResult), f"Expected CalculationResult but got {type(result.pydantic)}"
assert result.pydantic.result == 42, f"Expected result 42 but got {result.pydantic.result}"
assert result.pydantic.operation, "Operation should not be empty"
assert result.pydantic.explanation, "Explanation should not be empty"

View File

@@ -1397,3 +1397,86 @@ def test_openai_responses_api_both_auto_chains_work_together():
assert params.get("previous_response_id") == "resp_123"
assert "reasoning.encrypted_content" in params["include"]
assert len(params["input"]) == 2 # Reasoning item + message
# =============================================================================
# Agent Kickoff Structured Output Tests
# =============================================================================
@pytest.mark.vcr()
def test_openai_agent_kickoff_structured_output_without_tools():
"""
Test that agent kickoff returns structured output without tools.
This tests native structured output handling for OpenAI models.
"""
from pydantic import BaseModel, Field
class AnalysisResult(BaseModel):
"""Structured output for analysis results."""
topic: str = Field(description="The topic analyzed")
key_points: list[str] = Field(description="Key insights from the analysis")
summary: str = Field(description="Brief summary of findings")
agent = Agent(
role="Analyst",
goal="Provide structured analysis on topics",
backstory="You are an expert analyst who provides clear, structured insights.",
llm=LLM(model="gpt-4o-mini"),
tools=[],
verbose=True,
)
result = agent.kickoff(
messages="Analyze the benefits of remote work briefly. Keep it concise.",
response_format=AnalysisResult,
)
assert result.pydantic is not None, "Expected pydantic output but got None"
assert isinstance(result.pydantic, AnalysisResult), f"Expected AnalysisResult but got {type(result.pydantic)}"
assert result.pydantic.topic, "Topic should not be empty"
assert len(result.pydantic.key_points) > 0, "Should have at least one key point"
assert result.pydantic.summary, "Summary should not be empty"
@pytest.mark.vcr()
def test_openai_agent_kickoff_structured_output_with_tools():
"""
Test that agent kickoff returns structured output after using tools.
This tests post-tool-call structured output handling for OpenAI models.
"""
from pydantic import BaseModel, Field
from crewai.tools import tool
class CalculationResult(BaseModel):
"""Structured output for calculation results."""
operation: str = Field(description="The mathematical operation performed")
result: int = Field(description="The result of the calculation")
explanation: str = Field(description="Brief explanation of the calculation")
@tool
def add_numbers(a: int, b: int) -> int:
"""Add two numbers together and return the sum."""
return a + b
agent = Agent(
role="Calculator",
goal="Perform calculations using available tools",
backstory="You are a calculator assistant that uses tools to compute results.",
llm=LLM(model="gpt-4o-mini"),
tools=[add_numbers],
verbose=True,
)
result = agent.kickoff(
messages="Calculate 15 + 27 using your add_numbers tool. Report the result.",
response_format=CalculationResult,
)
assert result.pydantic is not None, "Expected pydantic output but got None"
assert isinstance(result.pydantic, CalculationResult), f"Expected CalculationResult but got {type(result.pydantic)}"
assert result.pydantic.result == 42, f"Expected result 42 but got {result.pydantic.result}"
assert result.pydantic.operation, "Operation should not be empty"
assert result.pydantic.explanation, "Explanation should not be empty"