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Code Issues Actions 7 Packages Projects Releases Wiki Activity

Merge branch 'main' into lorenze/feat/grep-tool

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This commit is contained in:
Lorenze Jay
2026-02-11 14:23:35 -08:00
committed by GitHub
parent e65940816b 0341e5aee7
commit 25835ca795
59 changed files with 8834 additions and 1692 deletions
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7
lib/crewai-tools/tests/tools/brave_search_tool_test.py
View File

@@ -33,8 +33,11 @@ def test_brave_tool_search(mock_get, brave_tool):
mock_get.return_value.json.return_value = mock_response
result = brave_tool.run(query="test")
assert "Test Title" in result
assert "http://test.com" in result
data = json.loads(result)
assert isinstance(data, list)
assert len(data) >= 1
assert data[0]["title"] == "Test Title"
assert data[0]["url"] == "http://test.com"
@patch("requests.get")

4
lib/crewai/pyproject.toml
View File

@@ -14,7 +14,7 @@ dependencies = [
"instructor>=1.3.3",
# Text Processing
"pdfplumber~=0.11.4",
"regex~=2024.9.11",
"regex~=2026.1.15",
# Telemetry and Monitoring
"opentelemetry-api~=1.34.0",
"opentelemetry-sdk~=1.34.0",
@@ -36,7 +36,7 @@ dependencies = [
"json5~=0.10.0",
"portalocker~=2.7.0",
"pydantic-settings~=2.10.1",
"mcp~=1.23.1",
"mcp~=1.26.0",
"uv~=0.9.13",
"aiosqlite~=0.21.0",
]

6
lib/crewai/src/crewai/cli/create_crew.py
View File

@@ -143,6 +143,12 @@ def create_folder_structure(
(folder_path / "src" / folder_name).mkdir(parents=True)
(folder_path / "src" / folder_name / "tools").mkdir(parents=True)
(folder_path / "src" / folder_name / "config").mkdir(parents=True)
# Copy AGENTS.md to project root (top-level projects only)
package_dir = Path(__file__).parent
agents_md_src = package_dir / "templates" / "AGENTS.md"
if agents_md_src.exists():
shutil.copy2(agents_md_src, folder_path / "AGENTS.md")
return folder_path, folder_name, class_name

6
lib/crewai/src/crewai/cli/create_flow.py
View File

@@ -1,3 +1,4 @@
import shutil
from pathlib import Path
import click
@@ -34,6 +35,11 @@ def create_flow(name):
package_dir = Path(__file__).parent
templates_dir = package_dir / "templates" / "flow"
# Copy AGENTS.md to project root
agents_md_src = package_dir / "templates" / "AGENTS.md"
if agents_md_src.exists():
shutil.copy2(agents_md_src, project_root / "AGENTS.md")
# List of template files to copy
root_template_files = [".gitignore", "pyproject.toml", "README.md"]
src_template_files = ["__init__.py", "main.py"]

1017
lib/crewai/src/crewai/cli/templates/AGENTS.md Normal file
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File diff suppressed because it is too large Load Diff

6
lib/crewai/src/crewai/cli/tools/main.py
View File

@@ -2,6 +2,7 @@ import base64
from json import JSONDecodeError
import os
from pathlib import Path
import shutil
import subprocess
import tempfile
from typing import Any
@@ -55,6 +56,11 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
tree_find_and_replace(project_root, "{{folder_name}}", folder_name)
tree_find_and_replace(project_root, "{{class_name}}", class_name)
# Copy AGENTS.md to project root
agents_md_src = Path(__file__).parent.parent / "templates" / "AGENTS.md"
if agents_md_src.exists():
shutil.copy2(agents_md_src, project_root / "AGENTS.md")
old_directory = os.getcwd()
os.chdir(project_root)
try:

14
lib/crewai/src/crewai/crew.py
View File

@@ -187,6 +187,7 @@ class Crew(FlowTrackable, BaseModel):
_task_output_handler: TaskOutputStorageHandler = PrivateAttr(
default_factory=TaskOutputStorageHandler
)
_kickoff_event_id: str | None = PrivateAttr(default=None)
name: str | None = Field(default="crew")
cache: bool = Field(default=True)
@@ -759,7 +760,11 @@ class Crew(FlowTrackable, BaseModel):
except Exception as e:
crewai_event_bus.emit(
self,
CrewKickoffFailedEvent(error=str(e), crew_name=self.name),
CrewKickoffFailedEvent(
error=str(e),
crew_name=self.name,
started_event_id=self._kickoff_event_id,
),
)
raise
finally:
@@ -949,7 +954,11 @@ class Crew(FlowTrackable, BaseModel):
except Exception as e:
crewai_event_bus.emit(
self,
CrewKickoffFailedEvent(error=str(e), crew_name=self.name),
CrewKickoffFailedEvent(
error=str(e),
crew_name=self.name,
started_event_id=self._kickoff_event_id,
),
)
raise
finally:
@@ -1524,6 +1533,7 @@ class Crew(FlowTrackable, BaseModel):
crew_name=self.name,
output=final_task_output,
total_tokens=self.token_usage.total_tokens,
started_event_id=self._kickoff_event_id,
),
)

7
lib/crewai/src/crewai/crews/utils.py
View File

@@ -265,10 +265,9 @@ def prepare_kickoff(
normalized = {}
normalized = before_callback(normalized)
future = crewai_event_bus.emit(
crew,
CrewKickoffStartedEvent(crew_name=crew.name, inputs=normalized),
)
started_event = CrewKickoffStartedEvent(crew_name=crew.name, inputs=normalized)
crew._kickoff_event_id = started_event.event_id
future = crewai_event_bus.emit(crew, started_event)
if future is not None:
try:
future.result()

2
lib/crewai/src/crewai/llms/providers/anthropic/completion.py
View File

@@ -1580,10 +1580,12 @@ class AnthropicCompletion(BaseLLM):
usage = response.usage
input_tokens = getattr(usage, "input_tokens", 0)
output_tokens = getattr(usage, "output_tokens", 0)
cache_read_tokens = getattr(usage, "cache_read_input_tokens", 0) or 0
return {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": input_tokens + output_tokens,
"cached_prompt_tokens": cache_read_tokens,
}
return {"total_tokens": 0}

15
lib/crewai/src/crewai/llms/providers/azure/completion.py
View File

@@ -425,8 +425,9 @@ class AzureCompletion(BaseLLM):
"stream": self.stream,
}
model_extras: dict[str, Any] = {}
if self.stream:
params["model_extras"] = {"stream_options": {"include_usage": True}}
model_extras["stream_options"] = {"include_usage": True}
if response_model and self.is_openai_model:
model_description = generate_model_description(response_model)
@@ -464,6 +465,13 @@ class AzureCompletion(BaseLLM):
params["tools"] = self._convert_tools_for_interference(tools)
params["tool_choice"] = "auto"
prompt_cache_key = self.additional_params.get("prompt_cache_key")
if prompt_cache_key:
model_extras["prompt_cache_key"] = prompt_cache_key
if model_extras:
params["model_extras"] = model_extras
additional_params = self.additional_params
additional_drop_params = additional_params.get("additional_drop_params")
drop_params = additional_params.get("drop_params")
@@ -1063,10 +1071,15 @@ class AzureCompletion(BaseLLM):
"""Extract token usage from Azure response."""
if hasattr(response, "usage") and response.usage:
usage = response.usage
cached_tokens = 0
prompt_details = getattr(usage, "prompt_tokens_details", None)
if prompt_details:
cached_tokens = getattr(prompt_details, "cached_tokens", 0) or 0
return {
"prompt_tokens": getattr(usage, "prompt_tokens", 0),
"completion_tokens": getattr(usage, "completion_tokens", 0),
"total_tokens": getattr(usage, "total_tokens", 0),
"cached_prompt_tokens": cached_tokens,
}
return {"total_tokens": 0}

2
lib/crewai/src/crewai/llms/providers/gemini/completion.py
View File

@@ -1295,11 +1295,13 @@ class GeminiCompletion(BaseLLM):
"""Extract token usage from Gemini response."""
if response.usage_metadata:
usage = response.usage_metadata
cached_tokens = getattr(usage, "cached_content_token_count", 0) or 0
return {
"prompt_token_count": getattr(usage, "prompt_token_count", 0),
"candidates_token_count": getattr(usage, "candidates_token_count", 0),
"total_token_count": getattr(usage, "total_token_count", 0),
"total_tokens": getattr(usage, "total_token_count", 0),
"cached_prompt_tokens": cached_tokens,
}
return {"total_tokens": 0}

230
lib/crewai/src/crewai/llms/providers/openai/completion.py
View File

@@ -1094,11 +1094,7 @@ class OpenAICompletion(BaseLLM):
if reasoning_items:
self._last_reasoning_items = reasoning_items
if event.response and event.response.usage:
usage = {
"prompt_tokens": event.response.usage.input_tokens,
"completion_tokens": event.response.usage.output_tokens,
"total_tokens": event.response.usage.total_tokens,
}
usage = self._extract_responses_token_usage(event.response)
self._track_token_usage_internal(usage)
# If parse_tool_outputs is enabled, return structured result
@@ -1222,11 +1218,7 @@ class OpenAICompletion(BaseLLM):
if reasoning_items:
self._last_reasoning_items = reasoning_items
if event.response and event.response.usage:
usage = {
"prompt_tokens": event.response.usage.input_tokens,
"completion_tokens": event.response.usage.output_tokens,
"total_tokens": event.response.usage.total_tokens,
}
usage = self._extract_responses_token_usage(event.response)
self._track_token_usage_internal(usage)
# If parse_tool_outputs is enabled, return structured result
@@ -1310,11 +1302,18 @@ class OpenAICompletion(BaseLLM):
def _extract_responses_token_usage(self, response: Response) -> dict[str, Any]:
"""Extract token usage from Responses API response."""
if response.usage:
return {
result = {
"prompt_tokens": response.usage.input_tokens,
"completion_tokens": response.usage.output_tokens,
"total_tokens": response.usage.total_tokens,
}
# Extract cached prompt tokens from input_tokens_details
input_details = getattr(response.usage, "input_tokens_details", None)
if input_details:
result["cached_prompt_tokens"] = (
getattr(input_details, "cached_tokens", 0) or 0
)
return result
return {"total_tokens": 0}
def _extract_builtin_tool_outputs(self, response: Response) -> ResponsesAPIResult:
@@ -1696,6 +1695,99 @@ class OpenAICompletion(BaseLLM):
return content
def _finalize_streaming_response(
self,
full_response: str,
tool_calls: dict[int, dict[str, Any]],
usage_data: dict[str, int],
params: dict[str, Any],
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str | list[dict[str, Any]]:
"""Finalize a streaming response with usage tracking, tool call handling, and events.
Args:
full_response: The accumulated text response from the stream.
tool_calls: Accumulated tool calls from the stream, keyed by index.
usage_data: Token usage data from the stream.
params: The completion parameters containing messages.
available_functions: Available functions for tool calling.
from_task: Task that initiated the call.
from_agent: Agent that initiated the call.
Returns:
Tool calls list when tools were invoked without available_functions,
tool execution result when available_functions is provided,
or the text response string.
"""
self._track_token_usage_internal(usage_data)
if tool_calls and not available_functions:
tool_calls_list = [
{
"id": call_data["id"],
"type": "function",
"function": {
"name": call_data["name"],
"arguments": call_data["arguments"],
},
"index": call_data["index"],
}
for call_data in tool_calls.values()
]
self._emit_call_completed_event(
response=tool_calls_list,
call_type=LLMCallType.TOOL_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return tool_calls_list
if tool_calls and available_functions:
for call_data in tool_calls.values():
function_name = call_data["name"]
arguments = call_data["arguments"]
if not function_name or not arguments:
continue
if function_name not in available_functions:
logging.warning(
f"Function '{function_name}' not found in available functions"
)
continue
try:
function_args = json.loads(arguments)
except json.JSONDecodeError as e:
logging.error(f"Failed to parse streamed tool arguments: {e}")
continue
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
return result
full_response = self._apply_stop_words(full_response)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return full_response
def _handle_streaming_completion(
self,
params: dict[str, Any],
@@ -1703,7 +1795,7 @@ class OpenAICompletion(BaseLLM):
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | BaseModel:
) -> str | list[dict[str, Any]] | BaseModel:
"""Handle streaming chat completion."""
full_response = ""
tool_calls: dict[int, dict[str, Any]] = {}
@@ -1820,54 +1912,20 @@ class OpenAICompletion(BaseLLM):
response_id=response_id_stream,
)
self._track_token_usage_internal(usage_data)
if tool_calls and available_functions:
for call_data in tool_calls.values():
function_name = call_data["name"]
arguments = call_data["arguments"]
# Skip if function name is empty or arguments are empty
if not function_name or not arguments:
continue
# Check if function exists in available functions
if function_name not in available_functions:
logging.warning(
f"Function '{function_name}' not found in available functions"
)
continue
try:
function_args = json.loads(arguments)
except json.JSONDecodeError as e:
logging.error(f"Failed to parse streamed tool arguments: {e}")
continue
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
return result
full_response = self._apply_stop_words(full_response)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
result = self._finalize_streaming_response(
full_response=full_response,
tool_calls=tool_calls,
usage_data=usage_data,
params=params,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return self._invoke_after_llm_call_hooks(
params["messages"], full_response, from_agent
)
if isinstance(result, str):
return self._invoke_after_llm_call_hooks(
params["messages"], result, from_agent
)
return result
async def _ahandle_completion(
self,
@@ -2016,7 +2074,7 @@ class OpenAICompletion(BaseLLM):
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | BaseModel:
) -> str | list[dict[str, Any]] | BaseModel:
"""Handle async streaming chat completion."""
full_response = ""
tool_calls: dict[int, dict[str, Any]] = {}
@@ -2142,51 +2200,16 @@ class OpenAICompletion(BaseLLM):
response_id=response_id_stream,
)
self._track_token_usage_internal(usage_data)
if tool_calls and available_functions:
for call_data in tool_calls.values():
function_name = call_data["name"]
arguments = call_data["arguments"]
if not function_name or not arguments:
continue
if function_name not in available_functions:
logging.warning(
f"Function '{function_name}' not found in available functions"
)
continue
try:
function_args = json.loads(arguments)
except json.JSONDecodeError as e:
logging.error(f"Failed to parse streamed tool arguments: {e}")
continue
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
return result
full_response = self._apply_stop_words(full_response)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
return self._finalize_streaming_response(
full_response=full_response,
tool_calls=tool_calls,
usage_data=usage_data,
params=params,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return full_response
def supports_function_calling(self) -> bool:
"""Check if the model supports function calling."""
return not self.is_o1_model
@@ -2240,11 +2263,18 @@ class OpenAICompletion(BaseLLM):
"""Extract token usage from OpenAI ChatCompletion or ChatCompletionChunk response."""
if hasattr(response, "usage") and response.usage:
usage = response.usage
return {
result = {
"prompt_tokens": getattr(usage, "prompt_tokens", 0),
"completion_tokens": getattr(usage, "completion_tokens", 0),
"total_tokens": getattr(usage, "total_tokens", 0),
}
# Extract cached prompt tokens from prompt_tokens_details
prompt_details = getattr(usage, "prompt_tokens_details", None)
if prompt_details:
result["cached_prompt_tokens"] = (
getattr(prompt_details, "cached_tokens", 0) or 0
)
return result
return {"total_tokens": 0}
def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:

332
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_cached_prompt_tokens.yaml Normal file
View File

@@ -0,0 +1,332 @@
interactions:
- request:
body: '{"max_tokens":4096,"messages":[{"role":"user","content":[{"type":"text","text":"Say
hello in one word.","cache_control":{"type":"ephemeral"}}]}],"model":"claude-sonnet-4-5-20250929","stream":false,"system":"You
are a helpful assistant. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. This is padding text to ensure the prompt
is large enough for caching. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. This is padding text to ensure the prompt
is large enough for caching. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. This is padding text to ensure the prompt
is large enough for caching. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. This is padding text to ensure the prompt
is large enough for caching. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. This is padding text to ensure the prompt
is large enough for caching. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. This is padding text to ensure the prompt
is large enough for caching. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. This is padding text to ensure the prompt
is large enough for caching. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. This is padding text to ensure the prompt
is large enough for caching. This is padding text to ensure the prompt is large
enough for caching. This is padding text to ensure the prompt is large enough
for caching. This is padding text to ensure the prompt is large enough for caching.
This is padding text to ensure the prompt is large enough for caching. This
is padding text to ensure the prompt is large enough for caching. This is padding
text to ensure the prompt is large enough for caching. This is padding text
to ensure the prompt is large enough for caching. This is padding text to ensure
the prompt is large enough for caching. "}'
headers:
User-Agent:
- X-USER-AGENT-XXX
accept:
- application/json
accept-encoding:
- ACCEPT-ENCODING-XXX
anthropic-version:
- '2023-06-01'
connection:
- keep-alive
content-length:
- '5918'
content-type:
- application/json
host:
- api.anthropic.com
x-api-key:
- X-API-KEY-XXX
x-stainless-arch:
- X-STAINLESS-ARCH-XXX
x-stainless-async:
- 'false'
x-stainless-lang:
- python
x-stainless-os:
- X-STAINLESS-OS-XXX
x-stainless-package-version:
- 0.73.0
x-stainless-retry-count:
- '0'
x-stainless-runtime:
- CPython
x-stainless-runtime-version:
- 3.13.3
x-stainless-timeout:
- NOT_GIVEN
method: POST
uri: https://api.anthropic.com/v1/messages
response:
body:
string: '{"model":"claude-sonnet-4-5-20250929","id":"msg_013xTaKq41TFn6drdxt1mFdx","type":"message","role":"assistant","content":[{"type":"text","text":"Hello!"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":0,"cache_read_input_tokens":1217,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"}}'
headers:
CF-RAY:
- CF-RAY-XXX
Connection:
- keep-alive
Content-Security-Policy:
- CSP-FILTERED
Content-Type:
- application/json
Date:
- Tue, 10 Feb 2026 18:27:40 GMT
Server:
- cloudflare
Transfer-Encoding:
- chunked
X-Robots-Tag:
- none
anthropic-organization-id:
- ANTHROPIC-ORGANIZATION-ID-XXX
anthropic-ratelimit-input-tokens-limit:
- ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
anthropic-ratelimit-input-tokens-remaining:
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anthropic-ratelimit-input-tokens-reset:
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anthropic-ratelimit-output-tokens-limit:
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131
lib/crewai/tests/llms/anthropic/test_anthropic.py
View File

@@ -990,3 +990,134 @@ def test_anthropic_agent_kickoff_structured_output_with_tools():
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"
@pytest.mark.vcr()
def test_anthropic_cached_prompt_tokens():
"""
Test that Anthropic correctly extracts and tracks cached_prompt_tokens
from cache_read_input_tokens. Uses cache_control to enable prompt caching
and sends the same large prompt twice so the second call hits the cache.
"""
# Anthropic requires cache_control blocks and >=1024 tokens for caching
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant. {padding}"
llm = LLM(model="anthropic/claude-sonnet-4-5-20250929")
def _ephemeral_user(text: str):
return [{"type": "text", "text": text, "cache_control": {"type": "ephemeral"}}]
# First call: creates the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": _ephemeral_user("Say hello in one word.")},
])
# Second call: same system prompt should hit the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": _ephemeral_user("Say goodbye in one word.")},
])
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.prompt_tokens > 0
assert usage.completion_tokens > 0
assert usage.successful_requests == 2
# The second call should have cached prompt tokens
assert usage.cached_prompt_tokens > 0
@pytest.mark.vcr()
def test_anthropic_streaming_cached_prompt_tokens():
"""
Test that Anthropic streaming correctly extracts and tracks cached_prompt_tokens.
"""
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant. {padding}"
llm = LLM(model="anthropic/claude-sonnet-4-5-20250929", stream=True)
def _ephemeral_user(text: str):
return [{"type": "text", "text": text, "cache_control": {"type": "ephemeral"}}]
# First call: creates the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": _ephemeral_user("Say hello in one word.")},
])
# Second call: same system prompt should hit the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": _ephemeral_user("Say goodbye in one word.")},
])
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.successful_requests == 2
# The second call should have cached prompt tokens
assert usage.cached_prompt_tokens > 0
@pytest.mark.vcr()
def test_anthropic_cached_prompt_tokens_with_tools():
"""
Test that Anthropic correctly tracks cached_prompt_tokens when tools are used.
The large system prompt should be cached across tool-calling requests.
"""
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant that uses tools. {padding}"
def get_weather(location: str) -> str:
return f"The weather in {location} is sunny and 72°F"
tools = [
{
"name": "get_weather",
"description": "Get the current weather for a location",
"input_schema": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name"
}
},
"required": ["location"],
},
}
]
llm = LLM(model="anthropic/claude-sonnet-4-5-20250929")
def _ephemeral_user(text: str):
return [{"type": "text", "text": text, "cache_control": {"type": "ephemeral"}}]
# First call with tool: creates the cache
llm.call(
[
{"role": "system", "content": system_msg},
{"role": "user", "content": _ephemeral_user("What is the weather in Tokyo?")},
],
tools=tools,
available_functions={"get_weather": get_weather},
)
# Second call with same system prompt + tools: should hit the cache
llm.call(
[
{"role": "system", "content": system_msg},
{"role": "user", "content": _ephemeral_user("What is the weather in Paris?")},
],
tools=tools,
available_functions={"get_weather": get_weather},
)
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.prompt_tokens > 0
assert usage.successful_requests == 2
# The second call should have cached prompt tokens
assert usage.cached_prompt_tokens > 0

1
lib/crewai/tests/llms/azure/test_azure.py
View File

@@ -102,7 +102,6 @@ def test_azure_tool_use_conversation_flow():
# Verify that the API was called
assert mock_complete.called
@pytest.mark.usefixtures("mock_azure_credentials")
def test_azure_completion_module_is_imported():
"""

153
lib/crewai/tests/llms/google/test_google.py
View File

@@ -42,65 +42,6 @@ def test_gemini_completion_is_used_when_gemini_provider():
assert llm.provider == "gemini"
assert llm.model == "gemini-2.0-flash-001"
def test_gemini_tool_use_conversation_flow():
"""
Test that the Gemini completion properly handles tool use conversation flow
"""
from unittest.mock import Mock, patch
from crewai.llms.providers.gemini.completion import GeminiCompletion
# Create GeminiCompletion instance
completion = GeminiCompletion(model="gemini-2.0-flash-001")
# Mock tool function
def mock_weather_tool(location: str) -> str:
return f"The weather in {location} is sunny and 75°F"
available_functions = {"get_weather": mock_weather_tool}
# Mock the Google Gemini client responses
with patch.object(completion.client.models, 'generate_content') as mock_generate:
# Mock function call in response
mock_function_call = Mock()
mock_function_call.name = "get_weather"
mock_function_call.args = {"location": "San Francisco"}
mock_part = Mock()
mock_part.function_call = mock_function_call
mock_content = Mock()
mock_content.parts = [mock_part]
mock_candidate = Mock()
mock_candidate.content = mock_content
mock_response = Mock()
mock_response.candidates = [mock_candidate]
mock_response.text = "Based on the weather data, it's a beautiful day in San Francisco with sunny skies and 75°F temperature."
mock_response.usage_metadata = Mock()
mock_response.usage_metadata.prompt_token_count = 100
mock_response.usage_metadata.candidates_token_count = 50
mock_response.usage_metadata.total_token_count = 150
mock_generate.return_value = mock_response
# Test the call
messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
result = completion.call(
messages=messages,
available_functions=available_functions
)
# Verify the tool was executed and returned the result
assert result == "The weather in San Francisco is sunny and 75°F"
# Verify that the API was called
assert mock_generate.called
def test_gemini_completion_module_is_imported():
"""
Test that the completion module is properly imported when using Google provider
@@ -1114,3 +1055,97 @@ def test_gemini_structured_output_preserves_json_with_stop_word_patterns():
assert "Action:" in result.action_taken
assert "Observation:" in result.observation_result
assert "Final Answer:" in result.final_answer
@pytest.mark.vcr()
def test_gemini_cached_prompt_tokens():
"""
Test that Gemini correctly extracts and tracks cached_prompt_tokens
from cached_content_token_count in the usage metadata.
Sends two calls with the same large prompt to trigger caching.
"""
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant. {padding}"
llm = LLM(model="google/gemini-2.5-flash")
# First call
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": "Say hello in one word."},
])
# Second call: same system prompt
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": "Say goodbye in one word."},
])
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.prompt_tokens > 0
assert usage.completion_tokens > 0
assert usage.successful_requests == 2
# cached_prompt_tokens should be populated (may be 0 if Gemini
# doesn't cache for this particular request, but the field should exist)
assert usage.cached_prompt_tokens >= 0
@pytest.mark.vcr()
def test_gemini_cached_prompt_tokens_with_tools():
"""
Test that Gemini correctly tracks cached_prompt_tokens when tools are used.
The large system prompt should be cached across tool-calling requests.
"""
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant that uses tools. {padding}"
def get_weather(location: str) -> str:
return f"The weather in {location} is sunny and 72°F"
tools = [
{
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name"
}
},
"required": ["location"],
},
}
]
llm = LLM(model="google/gemini-2.5-flash")
# First call with tool
llm.call(
[
{"role": "system", "content": system_msg},
{"role": "user", "content": "What is the weather in Tokyo?"},
],
tools=tools,
available_functions={"get_weather": get_weather},
)
# Second call with same system prompt + tools
llm.call(
[
{"role": "system", "content": system_msg},
{"role": "user", "content": "What is the weather in Paris?"},
],
tools=tools,
available_functions={"get_weather": get_weather},
)
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.prompt_tokens > 0
assert usage.successful_requests == 2
# cached_prompt_tokens should be populated (may be 0 if Gemini
# doesn't cache for this particular request, but the field should exist)
assert usage.cached_prompt_tokens >= 0

377
lib/crewai/tests/llms/openai/test_openai.py
View File

@@ -1,6 +1,7 @@
import os
import sys
import types
from typing import Any
from unittest.mock import patch, MagicMock
import openai
import pytest
@@ -1578,3 +1579,379 @@ def test_openai_structured_output_preserves_json_with_stop_word_patterns():
assert "Action:" in result.action_taken
assert "Observation:" in result.observation_result
assert "Final Answer:" in result.final_answer
@pytest.mark.vcr()
def test_openai_completions_cached_prompt_tokens():
"""
Test that the Chat Completions API correctly extracts and tracks
cached_prompt_tokens from prompt_tokens_details.cached_tokens.
Sends the same large prompt twice so the second call hits the cache.
"""
# Build a large system prompt to trigger prompt caching (>1024 tokens)
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant. {padding}"
llm = OpenAICompletion(model="gpt-4.1")
# First call: creates the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": "Say hello in one word."},
])
# Second call: same system prompt should hit the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": "Say goodbye in one word."},
])
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.prompt_tokens > 0
assert usage.completion_tokens > 0
assert usage.successful_requests == 2
# The second call should have cached prompt tokens
assert usage.cached_prompt_tokens > 0
@pytest.mark.vcr()
def test_openai_responses_api_cached_prompt_tokens():
"""
Test that the Responses API correctly extracts and tracks
cached_prompt_tokens from input_tokens_details.cached_tokens.
"""
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant. {padding}"
llm = OpenAICompletion(model="gpt-4.1", api="responses")
# First call: creates the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": "Say hello in one word."},
])
# Second call: same system prompt should hit the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": "Say goodbye in one word."},
])
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.prompt_tokens > 0
assert usage.completion_tokens > 0
assert usage.successful_requests == 2
# The second call should have cached prompt tokens
assert usage.cached_prompt_tokens > 0
@pytest.mark.vcr()
def test_openai_streaming_cached_prompt_tokens():
"""
Test that streaming Chat Completions API correctly extracts and tracks
cached_prompt_tokens.
"""
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant. {padding}"
llm = OpenAICompletion(model="gpt-4.1", stream=True)
# First call: creates the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": "Say hello in one word."},
])
# Second call: same system prompt should hit the cache
llm.call([
{"role": "system", "content": system_msg},
{"role": "user", "content": "Say goodbye in one word."},
])
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.successful_requests == 2
# The second call should have cached prompt tokens
assert usage.cached_prompt_tokens > 0
@pytest.mark.vcr()
def test_openai_completions_cached_prompt_tokens_with_tools():
"""
Test that the Chat Completions API correctly tracks cached_prompt_tokens
when tools are used. The large system prompt should be cached across calls.
"""
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant that uses tools. {padding}"
def get_weather(location: str) -> str:
return f"The weather in {location} is sunny and 72°F"
tools = [
{
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name"
}
},
"required": ["location"],
"additionalProperties": False,
},
}
]
llm = OpenAICompletion(model="gpt-4.1")
# First call with tool: creates the cache
llm.call(
[
{"role": "system", "content": system_msg},
{"role": "user", "content": "What is the weather in Tokyo?"},
],
tools=tools,
available_functions={"get_weather": get_weather},
)
# Second call with same system prompt + tools: should hit the cache
llm.call(
[
{"role": "system", "content": system_msg},
{"role": "user", "content": "What is the weather in Paris?"},
],
tools=tools,
available_functions={"get_weather": get_weather},
)
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.prompt_tokens > 0
assert usage.successful_requests == 2
# The second call should have cached prompt tokens
assert usage.cached_prompt_tokens > 0
@pytest.mark.vcr()
def test_openai_responses_api_cached_prompt_tokens_with_tools():
"""
Test that the Responses API correctly tracks cached_prompt_tokens
when function tools are used.
"""
padding = "This is padding text to ensure the prompt is large enough for caching. " * 80
system_msg = f"You are a helpful assistant that uses tools. {padding}"
def get_weather(location: str) -> str:
return f"The weather in {location} is sunny and 72°F"
tools = [
{
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name"
}
},
"required": ["location"],
},
}
]
llm = OpenAICompletion(model="gpt-4.1", api='response')
# First call with tool
llm.call(
[
{"role": "system", "content": system_msg},
{"role": "user", "content": "What is the weather in Tokyo?"},
],
tools=tools,
available_functions={"get_weather": get_weather},
)
# Second call: same system prompt + tools should hit cache
llm.call(
[
{"role": "system", "content": system_msg},
{"role": "user", "content": "What is the weather in Paris?"},
],
tools=tools,
available_functions={"get_weather": get_weather},
)
usage = llm.get_token_usage_summary()
assert usage.total_tokens > 0
assert usage.successful_requests == 2
assert usage.cached_prompt_tokens > 0
def test_openai_streaming_returns_tool_calls_without_available_functions():
"""Test that streaming returns tool calls list when available_functions is None.
This mirrors the non-streaming path where tool_calls are returned for
the executor to handle. Reproduces the bug where streaming with tool
calls would return empty text instead of tool_calls when
available_functions was not provided (as the crew executor does).
"""
llm = LLM(model="openai/gpt-4o-mini", stream=True)
mock_chunk_1 = MagicMock()
mock_chunk_1.choices = [MagicMock()]
mock_chunk_1.choices[0].delta = MagicMock()
mock_chunk_1.choices[0].delta.content = None
mock_chunk_1.choices[0].delta.tool_calls = [MagicMock()]
mock_chunk_1.choices[0].delta.tool_calls[0].index = 0
mock_chunk_1.choices[0].delta.tool_calls[0].id = "call_abc123"
mock_chunk_1.choices[0].delta.tool_calls[0].function = MagicMock()
mock_chunk_1.choices[0].delta.tool_calls[0].function.name = "calculator"
mock_chunk_1.choices[0].delta.tool_calls[0].function.arguments = '{"expr'
mock_chunk_1.choices[0].finish_reason = None
mock_chunk_1.usage = None
mock_chunk_1.id = "chatcmpl-1"
mock_chunk_2 = MagicMock()
mock_chunk_2.choices = [MagicMock()]
mock_chunk_2.choices[0].delta = MagicMock()
mock_chunk_2.choices[0].delta.content = None
mock_chunk_2.choices[0].delta.tool_calls = [MagicMock()]
mock_chunk_2.choices[0].delta.tool_calls[0].index = 0
mock_chunk_2.choices[0].delta.tool_calls[0].id = None
mock_chunk_2.choices[0].delta.tool_calls[0].function = MagicMock()
mock_chunk_2.choices[0].delta.tool_calls[0].function.name = None
mock_chunk_2.choices[0].delta.tool_calls[0].function.arguments = 'ession": "1+1"}'
mock_chunk_2.choices[0].finish_reason = None
mock_chunk_2.usage = None
mock_chunk_2.id = "chatcmpl-1"
mock_chunk_3 = MagicMock()
mock_chunk_3.choices = [MagicMock()]
mock_chunk_3.choices[0].delta = MagicMock()
mock_chunk_3.choices[0].delta.content = None
mock_chunk_3.choices[0].delta.tool_calls = None
mock_chunk_3.choices[0].finish_reason = "tool_calls"
mock_chunk_3.usage = MagicMock()
mock_chunk_3.usage.prompt_tokens = 10
mock_chunk_3.usage.completion_tokens = 5
mock_chunk_3.id = "chatcmpl-1"
with patch.object(
llm.client.chat.completions, "create", return_value=iter([mock_chunk_1, mock_chunk_2, mock_chunk_3])
):
result = llm.call(
messages=[{"role": "user", "content": "Calculate 1+1"}],
tools=[{
"type": "function",
"function": {
"name": "calculator",
"description": "Calculate expression",
"parameters": {"type": "object", "properties": {"expression": {"type": "string"}}},
},
}],
available_functions=None,
)
assert isinstance(result, list), f"Expected list of tool calls, got {type(result)}: {result}"
assert len(result) == 1
assert result[0]["function"]["name"] == "calculator"
assert result[0]["function"]["arguments"] == '{"expression": "1+1"}'
assert result[0]["id"] == "call_abc123"
assert result[0]["type"] == "function"
@pytest.mark.asyncio
async def test_openai_async_streaming_returns_tool_calls_without_available_functions():
"""Test that async streaming returns tool calls list when available_functions is None.
Same as the sync test but for the async path (_ahandle_streaming_completion).
"""
llm = LLM(model="openai/gpt-4o-mini", stream=True)
mock_chunk_1 = MagicMock()
mock_chunk_1.choices = [MagicMock()]
mock_chunk_1.choices[0].delta = MagicMock()
mock_chunk_1.choices[0].delta.content = None
mock_chunk_1.choices[0].delta.tool_calls = [MagicMock()]
mock_chunk_1.choices[0].delta.tool_calls[0].index = 0
mock_chunk_1.choices[0].delta.tool_calls[0].id = "call_abc123"
mock_chunk_1.choices[0].delta.tool_calls[0].function = MagicMock()
mock_chunk_1.choices[0].delta.tool_calls[0].function.name = "calculator"
mock_chunk_1.choices[0].delta.tool_calls[0].function.arguments = '{"expr'
mock_chunk_1.choices[0].finish_reason = None
mock_chunk_1.usage = None
mock_chunk_1.id = "chatcmpl-1"
mock_chunk_2 = MagicMock()
mock_chunk_2.choices = [MagicMock()]
mock_chunk_2.choices[0].delta = MagicMock()
mock_chunk_2.choices[0].delta.content = None
mock_chunk_2.choices[0].delta.tool_calls = [MagicMock()]
mock_chunk_2.choices[0].delta.tool_calls[0].index = 0
mock_chunk_2.choices[0].delta.tool_calls[0].id = None
mock_chunk_2.choices[0].delta.tool_calls[0].function = MagicMock()
mock_chunk_2.choices[0].delta.tool_calls[0].function.name = None
mock_chunk_2.choices[0].delta.tool_calls[0].function.arguments = 'ession": "1+1"}'
mock_chunk_2.choices[0].finish_reason = None
mock_chunk_2.usage = None
mock_chunk_2.id = "chatcmpl-1"
mock_chunk_3 = MagicMock()
mock_chunk_3.choices = [MagicMock()]
mock_chunk_3.choices[0].delta = MagicMock()
mock_chunk_3.choices[0].delta.content = None
mock_chunk_3.choices[0].delta.tool_calls = None
mock_chunk_3.choices[0].finish_reason = "tool_calls"
mock_chunk_3.usage = MagicMock()
mock_chunk_3.usage.prompt_tokens = 10
mock_chunk_3.usage.completion_tokens = 5
mock_chunk_3.id = "chatcmpl-1"
class MockAsyncStream:
"""Async iterator that mimics OpenAI's async streaming response."""
def __init__(self, chunks: list[Any]) -> None:
self._chunks = chunks
self._index = 0
def __aiter__(self) -> "MockAsyncStream":
return self
async def __anext__(self) -> Any:
if self._index >= len(self._chunks):
raise StopAsyncIteration
chunk = self._chunks[self._index]
self._index += 1
return chunk
async def mock_create(**kwargs: Any) -> MockAsyncStream:
return MockAsyncStream([mock_chunk_1, mock_chunk_2, mock_chunk_3])
with patch.object(
llm.async_client.chat.completions, "create", side_effect=mock_create
):
result = await llm.acall(
messages=[{"role": "user", "content": "Calculate 1+1"}],
tools=[{
"type": "function",
"function": {
"name": "calculator",
"description": "Calculate expression",
"parameters": {"type": "object", "properties": {"expression": {"type": "string"}}},
},
}],
available_functions=None,
)
assert isinstance(result, list), f"Expected list of tool calls, got {type(result)}: {result}"
assert len(result) == 1
assert result[0]["function"]["name"] == "calculator"
assert result[0]["function"]["arguments"] == '{"expression": "1+1"}'
assert result[0]["id"] == "call_abc123"
assert result[0]["type"] == "function"