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supporting prompt cache results show (#4447)

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* supporting prompt cache

* droped azure tests

* fix tests

---------

Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
This commit is contained in:
Lorenze Jay
2026-02-11 14:07:15 -08:00
committed by GitHub
parent 397d14c772
commit 0341e5aee7
18 changed files with 4085 additions and 73 deletions
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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}

30
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:
@@ -2264,11 +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
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@@ -0,0 +1,332 @@
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This is padding text to ensure the prompt is large enough for caching. This
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This is padding text to ensure the prompt is large enough for caching. This
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This is padding text to ensure the prompt is large enough for caching. This
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the prompt is large enough for caching. This is padding text to ensure the prompt
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enough for caching. This is padding text to ensure the prompt is large enough
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This is padding text to ensure the prompt is large enough for caching. This
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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
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This is padding text to ensure the prompt is large enough for caching. This
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336
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_cached_prompt_tokens_with_tools.yaml Normal file
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This is padding text to ensure the prompt is large enough for caching. This
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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

212
lib/crewai/tests/llms/openai/test_openai.py
View File

@@ -1581,6 +1581,218 @@ def test_openai_structured_output_preserves_json_with_stop_word_patterns():
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.