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feat: emit token usage data in LLMCallCompletedEvent

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Add usage field to LLMCallCompletedEvent and wire it through BaseLLM._emit_call_completed_event and all provider implementations (OpenAI, Anthropic, Gemini, Bedrock, Azure, LiteLLM)
This commit is contained in:
Lucas Gomide
2026-03-31 08:19:19 -03:00
parent 3283a00e31
commit 8521aa1d8c
12 changed files with 444 additions and 14 deletions
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1
lib/crewai/src/crewai/events/types/llm_events.py
View File

@@ -57,6 +57,7 @@ class LLMCallCompletedEvent(LLMEventBase):
messages: str | list[dict[str, Any]] | None = None
response: Any
call_type: LLMCallType
usage: dict[str, Any] | None = None
class LLMCallFailedEvent(LLMEventBase):

37
lib/crewai/src/crewai/llm.py
View File

@@ -970,21 +970,25 @@ class LLM(BaseLLM):
)
result = instructor_instance.to_pydantic()
structured_response = result.model_dump_json()
usage_dict = self._usage_to_dict(usage_info)
self._handle_emit_call_events(
response=structured_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_dict,
)
return structured_response
usage_dict = self._usage_to_dict(usage_info)
self._handle_emit_call_events(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_dict,
)
return full_response
@@ -994,12 +998,14 @@ class LLM(BaseLLM):
return tool_result
# --- 10) Emit completion event and return response
usage_dict = self._usage_to_dict(usage_info)
self._handle_emit_call_events(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_dict,
)
return full_response
@@ -1021,6 +1027,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=self._usage_to_dict(usage_info),
)
return full_response
@@ -1172,6 +1179,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=None,
)
return structured_response
@@ -1202,6 +1210,8 @@ class LLM(BaseLLM):
raise LLMContextLengthExceededError(error_msg) from e
raise
response_usage = self._usage_to_dict(getattr(response, "usage", None))
# --- 2) Handle structured output response (when response_model is provided)
if response_model is not None:
# When using instructor/response_model, litellm returns a Pydantic model instance
@@ -1213,6 +1223,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=response_usage,
)
return structured_response
@@ -1244,6 +1255,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=response_usage,
)
return text_response
@@ -1267,6 +1279,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=response_usage,
)
return text_response
@@ -1316,6 +1329,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=None,
)
return structured_response
@@ -1342,6 +1356,8 @@ class LLM(BaseLLM):
raise LLMContextLengthExceededError(error_msg) from e
raise
response_usage = self._usage_to_dict(getattr(response, "usage", None))
if response_model is not None:
if isinstance(response, BaseModel):
structured_response = response.model_dump_json()
@@ -1351,6 +1367,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=response_usage,
)
return structured_response
@@ -1380,6 +1397,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=response_usage,
)
return text_response
@@ -1402,6 +1420,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=response_usage,
)
return text_response
@@ -1548,12 +1567,14 @@ class LLM(BaseLLM):
if result is not None:
return result
usage_dict = self._usage_to_dict(usage_info)
self._handle_emit_call_events(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params.get("messages"),
usage=usage_dict,
)
return full_response
@@ -1575,6 +1596,7 @@ class LLM(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("messages"),
usage=self._usage_to_dict(usage_info),
)
return full_response
raise
@@ -1961,6 +1983,18 @@ class LLM(BaseLLM):
)
raise
@staticmethod
def _usage_to_dict(usage: Any) -> dict[str, Any] | None:
if usage is None:
return None
if isinstance(usage, dict):
return usage
if hasattr(usage, "model_dump"):
return usage.model_dump()
if hasattr(usage, "__dict__"):
return {k: v for k, v in vars(usage).items() if not k.startswith("_")}
return None
def _handle_emit_call_events(
self,
response: Any,
@@ -1968,6 +2002,7 @@ class LLM(BaseLLM):
from_task: Task | None = None,
from_agent: Agent | None = None,
messages: str | list[LLMMessage] | None = None,
usage: dict[str, Any] | None = None,
) -> None:
"""Handle the events for the LLM call.
@@ -1977,6 +2012,7 @@ class LLM(BaseLLM):
from_task: Optional task object
from_agent: Optional agent object
messages: Optional messages object
usage: Optional token usage data
"""
crewai_event_bus.emit(
self,
@@ -1988,6 +2024,7 @@ class LLM(BaseLLM):
from_agent=from_agent,
model=self.model,
call_id=get_current_call_id(),
usage=usage,
),
)

2
lib/crewai/src/crewai/llms/base_llm.py
View File

@@ -460,6 +460,7 @@ class BaseLLM(BaseModel, ABC):
from_task: Task | None = None,
from_agent: Agent | None = None,
messages: str | list[LLMMessage] | None = None,
usage: dict[str, Any] | None = None,
) -> None:
"""Emit LLM call completed event."""
from crewai.utilities.serialization import to_serializable
@@ -474,6 +475,7 @@ class BaseLLM(BaseModel, ABC):
from_agent=from_agent,
model=self.model,
call_id=get_current_call_id(),
usage=usage,
),
)

16
lib/crewai/src/crewai/llms/providers/anthropic/completion.py
View File

@@ -811,6 +811,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
else:
@@ -826,6 +827,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
@@ -848,6 +850,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return list(tool_uses)
@@ -879,6 +882,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
if usage.get("total_tokens", 0) > 0:
@@ -1028,6 +1032,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
for block in final_message.content:
@@ -1042,6 +1047,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
@@ -1071,6 +1077,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return self._invoke_after_llm_call_hooks(
@@ -1241,6 +1248,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=follow_up_params["messages"],
usage=follow_up_usage,
)
# Log combined token usage
@@ -1332,6 +1340,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
else:
@@ -1347,6 +1356,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
@@ -1367,6 +1377,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return list(tool_uses)
@@ -1390,6 +1401,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
if usage.get("total_tokens", 0) > 0:
@@ -1527,6 +1539,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
for block in final_message.content:
@@ -1541,6 +1554,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
@@ -1569,6 +1583,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return full_response
@@ -1627,6 +1642,7 @@ class AnthropicCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=follow_up_params["messages"],
usage=follow_up_usage,
)
total_usage = {

8
lib/crewai/src/crewai/llms/providers/azure/completion.py
View File

@@ -569,6 +569,7 @@ class AzureCompletion(BaseLLM):
params: AzureCompletionParams,
from_task: Any | None = None,
from_agent: Any | None = None,
usage: dict[str, Any] | None = None,
) -> BaseModel:
"""Validate content against response model and emit completion event.
@@ -594,6 +595,7 @@ class AzureCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_data
@@ -643,6 +645,7 @@ class AzureCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return list(message.tool_calls)
@@ -680,6 +683,7 @@ class AzureCompletion(BaseLLM):
params=params,
from_task=from_task,
from_agent=from_agent,
usage=usage,
)
content = self._apply_stop_words(content)
@@ -691,6 +695,7 @@ class AzureCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return self._invoke_after_llm_call_hooks(
@@ -826,6 +831,7 @@ class AzureCompletion(BaseLLM):
params=params,
from_task=from_task,
from_agent=from_agent,
usage=usage_data,
)
# If there are tool_calls but no available_functions, return them
@@ -848,6 +854,7 @@ class AzureCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_data,
)
return formatted_tool_calls
@@ -884,6 +891,7 @@ class AzureCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_data,
)
return self._invoke_after_llm_call_hooks(

24
lib/crewai/src/crewai/llms/providers/bedrock/completion.py
View File

@@ -664,8 +664,9 @@ class BedrockCompletion(BaseLLM):
)
# Track token usage according to AWS response format
if "usage" in response:
self._track_token_usage_internal(response["usage"])
usage = response.get("usage")
if usage:
self._track_token_usage_internal(usage)
stop_reason = response.get("stopReason")
if stop_reason:
@@ -705,6 +706,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage,
)
return result
except Exception as e:
@@ -727,6 +729,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage,
)
return non_structured_output_tool_uses
@@ -806,6 +809,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage,
)
return self._invoke_after_llm_call_hooks(
@@ -936,6 +940,7 @@ class BedrockCompletion(BaseLLM):
tool_use_id: str | None = None
tool_use_index = 0
accumulated_tool_input = ""
usage_data: dict[str, Any] | None = None
try:
response = self._client.converse_stream(
@@ -1045,6 +1050,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage_data,
)
return result # type: ignore[return-value]
except Exception as e:
@@ -1112,6 +1118,7 @@ class BedrockCompletion(BaseLLM):
metadata = event["metadata"]
if "usage" in metadata:
usage_metrics = metadata["usage"]
usage_data = usage_metrics
self._track_token_usage_internal(usage_metrics)
logging.debug(f"Token usage: {usage_metrics}")
if "trace" in metadata:
@@ -1141,6 +1148,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage_data,
)
return full_response
@@ -1252,8 +1260,9 @@ class BedrockCompletion(BaseLLM):
**body,
)
if "usage" in response:
self._track_token_usage_internal(response["usage"])
usage = response.get("usage")
if usage:
self._track_token_usage_internal(usage)
stop_reason = response.get("stopReason")
if stop_reason:
@@ -1292,6 +1301,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage,
)
return result
except Exception as e:
@@ -1314,6 +1324,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage,
)
return non_structured_output_tool_uses
@@ -1388,6 +1399,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage,
)
return text_content
@@ -1508,6 +1520,7 @@ class BedrockCompletion(BaseLLM):
tool_use_id: str | None = None
tool_use_index = 0
accumulated_tool_input = ""
usage_data: dict[str, Any] | None = None
try:
async_client = await self._ensure_async_client()
@@ -1619,6 +1632,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage_data,
)
return result # type: ignore[return-value]
except Exception as e:
@@ -1691,6 +1705,7 @@ class BedrockCompletion(BaseLLM):
metadata = event["metadata"]
if "usage" in metadata:
usage_metrics = metadata["usage"]
usage_data = usage_metrics
self._track_token_usage_internal(usage_metrics)
logging.debug(f"Token usage: {usage_metrics}")
if "trace" in metadata:
@@ -1720,6 +1735,7 @@ class BedrockCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages,
usage=usage_data,
)
return self._invoke_after_llm_call_hooks(

16
lib/crewai/src/crewai/llms/providers/gemini/completion.py
View File

@@ -665,6 +665,7 @@ class GeminiCompletion(BaseLLM):
messages_for_event: list[LLMMessage],
from_task: Any | None = None,
from_agent: Any | None = None,
usage: dict[str, Any] | None = None,
) -> BaseModel:
"""Validate content against response model and emit completion event.
@@ -690,6 +691,7 @@ class GeminiCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
usage=usage,
)
return structured_data
@@ -705,6 +707,7 @@ class GeminiCompletion(BaseLLM):
response_model: type[BaseModel] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
usage: dict[str, Any] | None = None,
) -> str | BaseModel:
"""Finalize completion response with validation and event emission.
@@ -728,6 +731,7 @@ class GeminiCompletion(BaseLLM):
messages_for_event=messages_for_event,
from_task=from_task,
from_agent=from_agent,
usage=usage,
)
self._emit_call_completed_event(
@@ -736,6 +740,7 @@ class GeminiCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
usage=usage,
)
return self._invoke_after_llm_call_hooks(
@@ -749,6 +754,7 @@ class GeminiCompletion(BaseLLM):
contents: list[types.Content],
from_task: Any | None = None,
from_agent: Any | None = None,
usage: dict[str, Any] | None = None,
) -> BaseModel:
"""Validate and emit event for structured_output tool call.
@@ -773,6 +779,7 @@ class GeminiCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=self._convert_contents_to_dict(contents),
usage=usage,
)
return validated_data
except Exception as e:
@@ -791,6 +798,7 @@ class GeminiCompletion(BaseLLM):
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
usage: dict[str, Any] | None = None,
) -> str | Any:
"""Process response, execute function calls, and finalize completion.
@@ -831,6 +839,7 @@ class GeminiCompletion(BaseLLM):
contents=contents,
from_task=from_task,
from_agent=from_agent,
usage=usage,
)
# Filter out structured_output from function calls returned to executor
@@ -852,6 +861,7 @@ class GeminiCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=self._convert_contents_to_dict(contents),
usage=usage,
)
return non_structured_output_parts
@@ -893,6 +903,7 @@ class GeminiCompletion(BaseLLM):
response_model=effective_response_model,
from_task=from_task,
from_agent=from_agent,
usage=usage,
)
def _process_stream_chunk(
@@ -1013,6 +1024,7 @@ class GeminiCompletion(BaseLLM):
contents=contents,
from_task=from_task,
from_agent=from_agent,
usage=usage_data,
)
non_structured_output_calls = {
@@ -1041,6 +1053,7 @@ class GeminiCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=self._convert_contents_to_dict(contents),
usage=usage_data,
)
return formatted_function_calls
@@ -1081,6 +1094,7 @@ class GeminiCompletion(BaseLLM):
response_model=effective_response_model,
from_task=from_task,
from_agent=from_agent,
usage=usage_data,
)
def _handle_completion(
@@ -1118,6 +1132,7 @@ class GeminiCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
usage=usage,
)
def _handle_streaming_completion(
@@ -1196,6 +1211,7 @@ class GeminiCompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
usage=usage,
)
async def _ahandle_streaming_completion(

29
lib/crewai/src/crewai/llms/providers/openai/completion.py
View File

@@ -809,6 +809,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return parsed_result
@@ -821,6 +822,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return function_calls
@@ -858,6 +860,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return structured_result
except ValueError as e:
@@ -871,6 +874,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
content = self._invoke_after_llm_call_hooks(
@@ -941,6 +945,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return parsed_result
@@ -953,6 +958,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return function_calls
@@ -990,6 +996,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return structured_result
except ValueError as e:
@@ -1003,6 +1010,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
except NotFoundError as e:
@@ -1045,6 +1053,7 @@ class OpenAICompletion(BaseLLM):
full_response = ""
function_calls: list[dict[str, Any]] = []
final_response: Response | None = None
usage: dict[str, Any] = {"total_tokens": 0}
stream = self._client.responses.create(**params)
response_id_stream = None
@@ -1102,6 +1111,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return parsed_result
@@ -1138,6 +1148,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return structured_result
except ValueError as e:
@@ -1151,6 +1162,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return self._invoke_after_llm_call_hooks(
@@ -1169,6 +1181,7 @@ class OpenAICompletion(BaseLLM):
full_response = ""
function_calls: list[dict[str, Any]] = []
final_response: Response | None = None
usage: dict[str, Any] = {"total_tokens": 0}
stream = await self._async_client.responses.create(**params)
response_id_stream = None
@@ -1226,6 +1239,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return parsed_result
@@ -1262,6 +1276,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return structured_result
except ValueError as e:
@@ -1275,6 +1290,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params.get("input", []),
usage=usage,
)
return full_response
@@ -1580,6 +1596,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return parsed_object
@@ -1601,6 +1618,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return list(message.tool_calls)
@@ -1639,6 +1657,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_result
except ValueError as e:
@@ -1652,6 +1671,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
if usage.get("total_tokens", 0) > 0:
@@ -1736,6 +1756,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_data,
)
return tool_calls_list
@@ -1778,6 +1799,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_data,
)
return full_response
@@ -1831,6 +1853,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return parsed_result
@@ -1955,6 +1978,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return parsed_object
@@ -1978,6 +2002,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return list(message.tool_calls)
@@ -2016,6 +2041,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
return structured_result
except ValueError as e:
@@ -2029,6 +2055,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage,
)
if usage.get("total_tokens", 0) > 0:
@@ -2113,6 +2140,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_data,
)
return parsed_object
@@ -2124,6 +2152,7 @@ class OpenAICompletion(BaseLLM):
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
usage=usage_data,
)
return accumulated_content

108
lib/crewai/tests/cassettes/utilities/test_llm_completed_event_includes_usage.yaml Normal file
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x-stainless-retry-count:
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x-stainless-runtime:
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x-stainless-runtime-version:
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uri: https://api.openai.com/v1/chat/completions
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176
lib/crewai/tests/events/test_llm_usage_event.py Normal file
View File

@@ -0,0 +1,176 @@
from typing import Any
from unittest.mock import patch
import pytest
from pydantic import BaseModel
from crewai.events.event_bus import CrewAIEventsBus
from crewai.events.types.llm_events import LLMCallCompletedEvent, LLMCallType
from crewai.llm import LLM
from crewai.llms.base_llm import BaseLLM
class TestLLMCallCompletedEventUsageField:
def test_accepts_usage_dict(self):
event = LLMCallCompletedEvent(
response="hello",
call_type=LLMCallType.LLM_CALL,
call_id="test-id",
usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
)
assert event.usage == {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30,
}
def test_usage_defaults_to_none(self):
event = LLMCallCompletedEvent(
response="hello",
call_type=LLMCallType.LLM_CALL,
call_id="test-id",
)
assert event.usage is None
def test_accepts_none_usage(self):
event = LLMCallCompletedEvent(
response="hello",
call_type=LLMCallType.LLM_CALL,
call_id="test-id",
usage=None,
)
assert event.usage is None
def test_accepts_nested_usage_dict(self):
usage = {
"prompt_tokens": 100,
"completion_tokens": 200,
"total_tokens": 300,
"prompt_tokens_details": {"cached_tokens": 50},
}
event = LLMCallCompletedEvent(
response="hello",
call_type=LLMCallType.LLM_CALL,
call_id="test-id",
usage=usage,
)
assert event.usage["prompt_tokens_details"]["cached_tokens"] == 50
class TestUsageToDict:
def test_none_returns_none(self):
assert LLM._usage_to_dict(None) is None
def test_dict_passes_through(self):
usage = {"prompt_tokens": 10, "total_tokens": 30}
assert LLM._usage_to_dict(usage) is usage
def test_pydantic_model_uses_model_dump(self):
class Usage(BaseModel):
prompt_tokens: int = 10
completion_tokens: int = 20
total_tokens: int = 30
result = LLM._usage_to_dict(Usage())
assert result == {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30,
}
def test_object_with_dict_attr(self):
class UsageObj:
def __init__(self):
self.prompt_tokens = 5
self.completion_tokens = 15
self.total_tokens = 20
result = LLM._usage_to_dict(UsageObj())
assert result == {
"prompt_tokens": 5,
"completion_tokens": 15,
"total_tokens": 20,
}
def test_object_with_dict_excludes_private_attrs(self):
class UsageObj:
def __init__(self):
self.total_tokens = 42
self._internal = "hidden"
result = LLM._usage_to_dict(UsageObj())
assert result == {"total_tokens": 42}
assert "_internal" not in result
def test_unsupported_type_returns_none(self):
assert LLM._usage_to_dict(42) is None
assert LLM._usage_to_dict("string") is None
class _StubLLM(BaseLLM):
"""Minimal concrete BaseLLM for testing event emission."""
model: str = "test-model"
def call(self, *args: Any, **kwargs: Any) -> str:
return ""
async def acall(self, *args: Any, **kwargs: Any) -> str:
return ""
def supports_function_calling(self) -> bool:
return False
def supports_stop_words(self) -> bool:
return True
class TestEmitCallCompletedEventPassesUsage:
@pytest.fixture
def mock_emit(self):
with patch.object(CrewAIEventsBus, "emit") as mock:
yield mock
@pytest.fixture
def llm(self):
return _StubLLM(model="test-model")
def test_usage_is_passed_to_event(self, mock_emit, llm):
usage_data = {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
llm._emit_call_completed_event(
response="hello",
call_type=LLMCallType.LLM_CALL,
messages="test prompt",
usage=usage_data,
)
mock_emit.assert_called_once()
event = mock_emit.call_args[1]["event"]
assert isinstance(event, LLMCallCompletedEvent)
assert event.usage == usage_data
def test_none_usage_is_passed_to_event(self, mock_emit, llm):
llm._emit_call_completed_event(
response="hello",
call_type=LLMCallType.LLM_CALL,
messages="test prompt",
usage=None,
)
mock_emit.assert_called_once()
event = mock_emit.call_args[1]["event"]
assert isinstance(event, LLMCallCompletedEvent)
assert event.usage is None
def test_usage_omitted_defaults_to_none(self, mock_emit, llm):
llm._emit_call_completed_event(
response="hello",
call_type=LLMCallType.LLM_CALL,
messages="test prompt",
)
mock_emit.assert_called_once()
event = mock_emit.call_args[1]["event"]
assert isinstance(event, LLMCallCompletedEvent)
assert event.usage is None

12
lib/crewai/tests/test_llm.py
View File

@@ -752,11 +752,7 @@ def test_litellm_retry_catches_litellm_unsupported_params_error(caplog):
raise litellm_error
return MagicMock(
choices=[MagicMock(message=MagicMock(content="Paris", tool_calls=None))],
usage=MagicMock(
prompt_tokens=10,
completion_tokens=5,
total_tokens=15,
),
usage={"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
)
with patch("litellm.completion", side_effect=mock_completion):
@@ -787,11 +783,7 @@ def test_litellm_retry_catches_openai_api_stop_error(caplog):
raise api_error
return MagicMock(
choices=[MagicMock(message=MagicMock(content="Paris", tool_calls=None))],
usage=MagicMock(
prompt_tokens=10,
completion_tokens=5,
total_tokens=15,
),
usage={"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
)
with patch("litellm.completion", side_effect=mock_completion):

29
lib/crewai/tests/utilities/test_events.py
View File

@@ -879,6 +879,35 @@ def test_llm_emits_call_started_event():
assert started_events[0].task_id is None
@pytest.mark.vcr()
def test_llm_completed_event_includes_usage():
completed_events: list[LLMCallCompletedEvent] = []
condition = threading.Condition()
@crewai_event_bus.on(LLMCallCompletedEvent)
def handle_llm_call_completed(source, event):
with condition:
completed_events.append(event)
condition.notify()
llm = LLM(model="gpt-4o-mini")
llm.call("Say hello")
with condition:
success = condition.wait_for(
lambda: len(completed_events) >= 1,
timeout=10,
)
assert success, "Timeout waiting for LLMCallCompletedEvent"
event = completed_events[0]
assert event.usage is not None
assert isinstance(event.usage, dict)
assert event.usage.get("prompt_tokens", 0) > 0
assert event.usage.get("completion_tokens", 0) > 0
assert event.usage.get("total_tokens", 0) > 0
@pytest.mark.vcr()
def test_llm_emits_call_failed_event():
received_events = []