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Adding usage info in llm.py (#4172)

Browse Source 
* Adding usage info everywhere

* Changing the check

* Changing the logic

* Adding tests

* Adding casellets

* Minor change

* Fixing testcase

* remove the duplicated test case, thanks to cursor

* Adding async test cases

* Updating test case

---------

Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
This commit is contained in:
Vidit Ostwal
2026-01-08 00:12:27 +05:30
committed by GitHub
parent 09014215a9
commit 1c4f44af80
6 changed files with 715 additions and 10 deletions
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28
lib/crewai/src/crewai/llm.py
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@@ -925,11 +925,12 @@ class LLM(BaseLLM):
except Exception as e:
logging.debug(f"Error checking for tool calls: {e}")
# Track token usage and log callbacks if available in streaming mode
if usage_info:
self._track_token_usage_internal(usage_info)
self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
if not tool_calls or not available_functions:
# Track token usage and log callbacks if available in streaming mode
if usage_info:
self._track_token_usage_internal(usage_info)
self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
if response_model and self.is_litellm:
instructor_instance = InternalInstructor(
@@ -962,12 +963,7 @@ class LLM(BaseLLM):
if tool_result is not None:
return tool_result
# --- 10) Track token usage and log callbacks if available in streaming mode
if usage_info:
self._track_token_usage_internal(usage_info)
self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
# --- 11) Emit completion event and return response
# --- 10) Emit completion event and return response
self._handle_emit_call_events(
response=full_response,
call_type=LLMCallType.LLM_CALL,
@@ -1148,6 +1144,10 @@ class LLM(BaseLLM):
if response_model:
params["response_model"] = response_model
response = litellm.completion(**params)
if hasattr(response,"usage") and not isinstance(response.usage, type) and response.usage:
usage_info = response.usage
self._track_token_usage_internal(usage_info)
except ContextWindowExceededError as e:
# Convert litellm's context window error to our own exception type
@@ -1273,6 +1273,10 @@ class LLM(BaseLLM):
params["response_model"] = response_model
response = await litellm.acompletion(**params)
if hasattr(response,"usage") and not isinstance(response.usage, type) and response.usage:
usage_info = response.usage
self._track_token_usage_internal(usage_info)
except ContextWindowExceededError as e:
raise LLMContextLengthExceededError(str(e)) from e
@@ -1359,6 +1363,7 @@ class LLM(BaseLLM):
"""
full_response = ""
chunk_count = 0
usage_info = None
accumulated_tool_args: defaultdict[int, AccumulatedToolArgs] = defaultdict(
@@ -1444,6 +1449,9 @@ class LLM(BaseLLM):
end_time=0,
)
if usage_info:
self._track_token_usage_internal(usage_info)
if accumulated_tool_args and available_functions:
# Convert accumulated tool args to ChatCompletionDeltaToolCall objects
tool_calls_list: list[ChatCompletionDeltaToolCall] = [

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lib/crewai/tests/cassettes/test_usage_info_non_streaming_with_acall.yaml Normal file
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113
lib/crewai/tests/test_llm.py
View File

@@ -877,3 +877,116 @@ def test_validate_model_in_constants():
LLM._validate_model_in_constants("anthropic.claude-future-v1:0", "bedrock")
is True
)
@pytest.mark.vcr(record_mode="once",decode_compressed_response=True)
def test_usage_info_non_streaming_with_call():
llm = LLM(model="gpt-4o-mini", is_litellm=True)
assert llm._token_usage == {
"total_tokens": 0,
"prompt_tokens": 0,
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
}
assert llm.stream is False
with patch.object(
llm, "_handle_non_streaming_response", wraps=llm._handle_non_streaming_response
) as mock_handle:
llm.call("Tell me a joke.")
mock_handle.assert_called_once()
assert llm._token_usage["total_tokens"] > 0
assert llm._token_usage["prompt_tokens"] > 0
assert llm._token_usage["completion_tokens"] > 0
assert llm._token_usage["successful_requests"] == 1
@pytest.mark.vcr(record_mode="once",decode_compressed_response=True)
def test_usage_info_streaming_with_call():
llm = LLM(model="gpt-4o-mini", is_litellm=True, stream=True)
assert llm._token_usage == {
"total_tokens": 0,
"prompt_tokens": 0,
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
}
assert llm.stream is True
with patch.object(
llm, "_handle_streaming_response", wraps=llm._handle_streaming_response
) as mock_handle:
llm.call("Tell me a joke.")
mock_handle.assert_called_once()
assert llm._token_usage["total_tokens"] > 0
assert llm._token_usage["prompt_tokens"] > 0
assert llm._token_usage["completion_tokens"] > 0
assert llm._token_usage["successful_requests"] == 1
@pytest.mark.asyncio
@pytest.mark.vcr(record_mode="once",decode_compressed_response=True,match_on=["method", "scheme", "host", "path", "body"])
async def test_usage_info_non_streaming_with_acall():
llm = LLM(
model="openai/gpt-4o-mini",
is_litellm=True,
stream=False,
)
# sanity check
assert llm._token_usage == {
"total_tokens": 0,
"prompt_tokens": 0,
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
}
with patch.object(
llm, "_ahandle_non_streaming_response", wraps=llm._ahandle_non_streaming_response
) as mock_handle:
result = await llm.acall("Tell me a joke.")
mock_handle.assert_called_once()
# token usage assertions (robust)
assert llm._token_usage["successful_requests"] == 1
assert llm._token_usage["prompt_tokens"] > 0
assert llm._token_usage["completion_tokens"] > 0
assert llm._token_usage["total_tokens"] > 0
assert len(result) > 0
@pytest.mark.asyncio
@pytest.mark.vcr(record_mode="none",decode_compressed_response=True,match_on=["method", "scheme", "host", "path", "body"])
async def test_usage_info_streaming_with_acall():
llm = LLM(
model="gpt-4o-mini",
is_litellm=True,
stream=True,
)
assert llm.stream is True
assert llm._token_usage == {
"total_tokens": 0,
"prompt_tokens": 0,
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
}
with patch.object(
llm, "_ahandle_streaming_response", wraps=llm._ahandle_streaming_response
) as mock_handle:
result = await llm.acall("Tell me a joke.")
mock_handle.assert_called_once()
assert llm._token_usage["successful_requests"] == 1
assert llm._token_usage["prompt_tokens"] > 0
assert llm._token_usage["completion_tokens"] > 0
assert llm._token_usage["total_tokens"] > 0
assert len(result) > 0