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fix: ensure token usage recording, validate response model on stream
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This commit is contained in:
Greyson LaLonde
2025-12-10 20:32:10 -05:00
committed by GitHub
parent 8e99d490b0
commit bdafe0fac7
312 changed files with 7846 additions and 33124 deletions
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4
conftest.py
View File

@@ -136,6 +136,10 @@ def _filter_request_headers(request: Request) -> Request: # type: ignore[no-any
def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any]:
"""Filter sensitive headers from response before recording."""
# Remove Content-Encoding to prevent decompression issues on replay
for encoding_header in ["Content-Encoding", "content-encoding"]:
response["headers"].pop(encoding_header, None)
for header_name, replacement in HEADERS_TO_FILTER.items():
for variant in [header_name, header_name.upper(), header_name.title()]:
if variant in response["headers"]:

2
lib/crewai/pyproject.toml
View File

@@ -84,7 +84,7 @@ bedrock = [
"boto3~=1.40.45",
]
google-genai = [
"google-genai~=1.2.0",
"google-genai~=1.49.0",
]
azure-ai-inference = [
"azure-ai-inference~=1.0.0b9",

104
lib/crewai/src/crewai/llms/providers/anthropic/completion.py
View File

@@ -679,6 +679,49 @@ class AnthropicCompletion(BaseLLM):
params["messages"], full_response, from_agent
)
def _execute_tools_and_collect_results(
self,
tool_uses: list[ToolUseBlock],
available_functions: dict[str, Any],
from_task: Any | None = None,
from_agent: Any | None = None,
) -> list[dict[str, Any]]:
"""Execute tools and collect results in Anthropic format.
Args:
tool_uses: List of tool use blocks from Claude's response
available_functions: Available functions for tool calling
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Returns:
List of tool result dictionaries in Anthropic format
"""
tool_results = []
for tool_use in tool_uses:
function_name = tool_use.name
function_args = tool_use.input
result = self._handle_tool_execution(
function_name=function_name,
function_args=cast(dict[str, Any], function_args),
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
tool_result = {
"type": "tool_result",
"tool_use_id": tool_use.id,
"content": str(result)
if result is not None
else "Tool execution completed",
}
tool_results.append(tool_result)
return tool_results
def _handle_tool_use_conversation(
self,
initial_response: Message,
@@ -696,33 +739,10 @@ class AnthropicCompletion(BaseLLM):
3. We send tool results back to Claude
4. Claude processes results and generates final response
"""
# Execute all requested tools and collect results
tool_results = []
tool_results = self._execute_tools_and_collect_results(
tool_uses, available_functions, from_task, from_agent
)
for tool_use in tool_uses:
function_name = tool_use.name
function_args = tool_use.input
# Execute the tool
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,
)
# Create tool result in Anthropic format
tool_result = {
"type": "tool_result",
"tool_use_id": tool_use.id,
"content": str(result)
if result is not None
else "Tool execution completed",
}
tool_results.append(tool_result)
# Prepare follow-up conversation with tool results
follow_up_params = params.copy()
# Add Claude's tool use response to conversation
@@ -810,7 +830,7 @@ class AnthropicCompletion(BaseLLM):
logging.error(f"Tool follow-up conversation failed: {e}")
# Fallback: return the first tool result if follow-up fails
if tool_results:
return tool_results[0]["content"]
return cast(str, tool_results[0]["content"])
raise e
async def _ahandle_completion(
@@ -1003,28 +1023,9 @@ class AnthropicCompletion(BaseLLM):
3. We send tool results back to Claude
4. Claude processes results and generates final response
"""
tool_results = []
for tool_use in tool_uses:
function_name = tool_use.name
function_args = tool_use.input
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,
)
tool_result = {
"type": "tool_result",
"tool_use_id": tool_use.id,
"content": str(result)
if result is not None
else "Tool execution completed",
}
tool_results.append(tool_result)
tool_results = self._execute_tools_and_collect_results(
tool_uses, available_functions, from_task, from_agent
)
follow_up_params = params.copy()
@@ -1079,7 +1080,7 @@ class AnthropicCompletion(BaseLLM):
logging.error(f"Tool follow-up conversation failed: {e}")
if tool_results:
return tool_results[0]["content"]
return cast(str, tool_results[0]["content"])
raise e
def supports_function_calling(self) -> bool:
@@ -1115,7 +1116,8 @@ class AnthropicCompletion(BaseLLM):
# Default context window size for Claude models
return int(200000 * CONTEXT_WINDOW_USAGE_RATIO)
def _extract_anthropic_token_usage(self, response: Message) -> dict[str, Any]:
@staticmethod
def _extract_anthropic_token_usage(response: Message) -> dict[str, Any]:
"""Extract token usage from Anthropic response."""
if hasattr(response, "usage") and response.usage:
usage = response.usage

731
lib/crewai/src/crewai/llms/providers/azure/completion.py
View File

@@ -3,7 +3,7 @@ from __future__ import annotations
import json
import logging
import os
from typing import TYPE_CHECKING, Any
from typing import TYPE_CHECKING, Any, TypedDict
from pydantic import BaseModel
from typing_extensions import Self
@@ -18,7 +18,6 @@ from crewai.utilities.types import LLMMessage
if TYPE_CHECKING:
from crewai.llms.hooks.base import BaseInterceptor
from crewai.tools.base_tool import BaseTool
try:
@@ -31,6 +30,8 @@ try:
from azure.ai.inference.models import (
ChatCompletions,
ChatCompletionsToolCall,
ChatCompletionsToolDefinition,
FunctionDefinition,
JsonSchemaFormat,
StreamingChatCompletionsUpdate,
)
@@ -50,6 +51,24 @@ except ImportError:
) from None
class AzureCompletionParams(TypedDict, total=False):
"""Type definition for Azure chat completion parameters."""
messages: list[LLMMessage]
stream: bool
model_extras: dict[str, Any]
response_format: JsonSchemaFormat
model: str
temperature: float
top_p: float
frequency_penalty: float
presence_penalty: float
max_tokens: int
stop: list[str]
tools: list[ChatCompletionsToolDefinition]
tool_choice: str
class AzureCompletion(BaseLLM):
"""Azure AI Inference native completion implementation.
@@ -156,7 +175,8 @@ class AzureCompletion(BaseLLM):
and "/openai/deployments/" in self.endpoint
)
def _validate_and_fix_endpoint(self, endpoint: str, model: str) -> str:
@staticmethod
def _validate_and_fix_endpoint(endpoint: str, model: str) -> str:
"""Validate and fix Azure endpoint URL format.
Azure OpenAI endpoints should be in the format:
@@ -179,10 +199,75 @@ class AzureCompletion(BaseLLM):
return endpoint
def _handle_api_error(
self,
error: Exception,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> None:
"""Handle API errors with appropriate logging and events.
Args:
error: The exception that occurred
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Raises:
The original exception after logging and emitting events
"""
if isinstance(error, HttpResponseError):
if error.status_code == 401:
error_msg = "Azure authentication failed. Check your API key."
elif error.status_code == 404:
error_msg = (
f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
)
elif error.status_code == 429:
error_msg = "Azure API rate limit exceeded. Please retry later."
else:
error_msg = (
f"Azure API HTTP error: {error.status_code} - {error.message}"
)
else:
error_msg = f"Azure API call failed: {error!s}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise error
def _handle_completion_error(
self,
error: Exception,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> None:
"""Handle completion-specific errors including context length checks.
Args:
error: The exception that occurred
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Raises:
LLMContextLengthExceededError if context window exceeded, otherwise the original exception
"""
if is_context_length_exceeded(error):
logging.error(f"Context window exceeded: {error}")
raise LLMContextLengthExceededError(str(error)) from error
error_msg = f"Azure API call failed: {error!s}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise error
def call(
self,
messages: str | list[LLMMessage],
tools: list[dict[str, BaseTool]] | None = None,
tools: list[dict[str, Any]] | None = None,
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
@@ -198,6 +283,7 @@ class AzureCompletion(BaseLLM):
available_functions: Available functions for tool calling
from_task: Task that initiated the call
from_agent: Agent that initiated the call
response_model: Response model
Returns:
Chat completion response or tool call result
@@ -242,35 +328,13 @@ class AzureCompletion(BaseLLM):
response_model,
)
except HttpResponseError as e:
if e.status_code == 401:
error_msg = "Azure authentication failed. Check your API key."
elif e.status_code == 404:
error_msg = (
f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
)
elif e.status_code == 429:
error_msg = "Azure API rate limit exceeded. Please retry later."
else:
error_msg = f"Azure API HTTP error: {e.status_code} - {e.message}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise
except Exception as e:
error_msg = f"Azure API call failed: {e!s}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise
return self._handle_api_error(e, from_task, from_agent) # type: ignore[func-returns-value]
async def acall(
async def acall( # type: ignore[return]
self,
messages: str | list[LLMMessage],
tools: list[dict[str, BaseTool]] | None = None,
tools: list[dict[str, Any]] | None = None,
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
@@ -324,37 +388,15 @@ class AzureCompletion(BaseLLM):
response_model,
)
except HttpResponseError as e:
if e.status_code == 401:
error_msg = "Azure authentication failed. Check your API key."
elif e.status_code == 404:
error_msg = (
f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
)
elif e.status_code == 429:
error_msg = "Azure API rate limit exceeded. Please retry later."
else:
error_msg = f"Azure API HTTP error: {e.status_code} - {e.message}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise
except Exception as e:
error_msg = f"Azure API call failed: {e!s}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise
self._handle_api_error(e, from_task, from_agent)
def _prepare_completion_params(
self,
messages: list[LLMMessage],
tools: list[dict[str, Any]] | None = None,
response_model: type[BaseModel] | None = None,
) -> dict[str, Any]:
) -> AzureCompletionParams:
"""Prepare parameters for Azure AI Inference chat completion.
Args:
@@ -365,11 +407,14 @@ class AzureCompletion(BaseLLM):
Returns:
Parameters dictionary for Azure API
"""
params = {
params: AzureCompletionParams = {
"messages": messages,
"stream": self.stream,
}
if self.stream:
params["model_extras"] = {"stream_options": {"include_usage": True}}
if response_model and self.is_openai_model:
model_description = generate_model_description(response_model)
json_schema_info = model_description["json_schema"]
@@ -412,37 +457,42 @@ class AzureCompletion(BaseLLM):
if drop_params and isinstance(additional_drop_params, list):
for drop_param in additional_drop_params:
params.pop(drop_param, None)
if isinstance(drop_param, str):
params.pop(drop_param, None) # type: ignore[misc]
return params
def _convert_tools_for_interference(
def _convert_tools_for_interference( # type: ignore[override]
self, tools: list[dict[str, Any]]
) -> list[dict[str, Any]]:
"""Convert CrewAI tool format to Azure OpenAI function calling format."""
) -> list[ChatCompletionsToolDefinition]:
"""Convert CrewAI tool format to Azure OpenAI function calling format.
Args:
tools: List of CrewAI tool definitions
Returns:
List of Azure ChatCompletionsToolDefinition objects
"""
from crewai.llms.providers.utils.common import safe_tool_conversion
azure_tools = []
azure_tools: list[ChatCompletionsToolDefinition] = []
for tool in tools:
name, description, parameters = safe_tool_conversion(tool, "Azure")
azure_tool = {
"type": "function",
"function": {
"name": name,
"description": description,
},
}
function_def = FunctionDefinition(
name=name,
description=description,
parameters=parameters
if isinstance(parameters, dict)
else dict(parameters)
if parameters
else None,
)
if parameters:
if isinstance(parameters, dict):
azure_tool["function"]["parameters"] = parameters # type: ignore
else:
azure_tool["function"]["parameters"] = dict(parameters)
tool_def = ChatCompletionsToolDefinition(function=function_def)
azure_tools.append(azure_tool)
azure_tools.append(tool_def)
return azure_tools
@@ -471,148 +521,239 @@ class AzureCompletion(BaseLLM):
return azure_messages
def _handle_completion(
def _validate_and_emit_structured_output(
self,
params: dict[str, Any],
available_functions: dict[str, Any] | None = None,
content: str,
response_model: type[BaseModel],
params: AzureCompletionParams,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Handle non-streaming chat completion."""
# Make API call
) -> str:
"""Validate content against response model and emit completion event.
Args:
content: Response content to validate
response_model: Pydantic model for validation
params: Completion parameters containing messages
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Returns:
Validated and serialized JSON string
Raises:
ValueError: If validation fails
"""
try:
response: ChatCompletions = self.client.complete(**params)
structured_data = response_model.model_validate_json(content)
structured_json = structured_data.model_dump_json()
if not response.choices:
raise ValueError("No choices returned from Azure API")
choice = response.choices[0]
message = choice.message
# Extract and track token usage
usage = self._extract_azure_token_usage(response)
self._track_token_usage_internal(usage)
if response_model and self.is_openai_model:
content = message.content or ""
try:
structured_data = response_model.model_validate_json(content)
structured_json = structured_data.model_dump_json()
self._emit_call_completed_event(
response=structured_json,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return structured_json
except Exception as e:
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
logging.error(error_msg)
raise ValueError(error_msg) from e
# Handle tool calls
if message.tool_calls and available_functions:
tool_call = message.tool_calls[0] # Handle first tool call
if isinstance(tool_call, ChatCompletionsToolCall):
function_name = tool_call.function.name
try:
function_args = json.loads(tool_call.function.arguments)
except json.JSONDecodeError as e:
logging.error(f"Failed to parse tool arguments: {e}")
function_args = {}
# Execute tool
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
# Extract content
content = message.content or ""
# Apply stop words
content = self._apply_stop_words(content)
# Emit completion event and return content
self._emit_call_completed_event(
response=content,
response=structured_json,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
content = self._invoke_after_llm_call_hooks(
params["messages"], content, from_agent
)
return structured_json
except Exception as e:
if is_context_length_exceeded(e):
logging.error(f"Context window exceeded: {e}")
raise LLMContextLengthExceededError(str(e)) from e
error_msg = f"Azure API call failed: {e!s}"
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise e
raise ValueError(error_msg) from e
return content
def _handle_streaming_completion(
def _process_completion_response(
self,
params: dict[str, Any],
response: ChatCompletions,
params: AzureCompletionParams,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Process completion response with usage tracking, tool execution, and events.
Args:
response: Chat completion response from Azure API
params: 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
response_model: Pydantic model for structured output
Returns:
Response content or structured output
"""
if not response.choices:
raise ValueError("No choices returned from Azure API")
choice = response.choices[0]
message = choice.message
# Extract and track token usage
usage = self._extract_azure_token_usage(response)
self._track_token_usage_internal(usage)
if response_model and self.is_openai_model:
content = message.content or ""
return self._validate_and_emit_structured_output(
content=content,
response_model=response_model,
params=params,
from_task=from_task,
from_agent=from_agent,
)
# Handle tool calls
if message.tool_calls and available_functions:
tool_call = message.tool_calls[0] # Handle first tool call
if isinstance(tool_call, ChatCompletionsToolCall):
function_name = tool_call.function.name
try:
function_args = json.loads(tool_call.function.arguments)
except json.JSONDecodeError as e:
logging.error(f"Failed to parse tool arguments: {e}")
function_args = {}
# Execute tool
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
# Extract content
content = message.content or ""
# Apply stop words
content = self._apply_stop_words(content)
# Emit completion event and return content
self._emit_call_completed_event(
response=content,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return self._invoke_after_llm_call_hooks(
params["messages"], content, from_agent
)
def _handle_completion(
self,
params: AzureCompletionParams,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Handle non-streaming chat completion."""
try:
# Cast params to Any to avoid type checking issues with TypedDict unpacking
response: ChatCompletions = self.client.complete(**params) # type: ignore[assignment,arg-type]
return self._process_completion_response(
response=response,
params=params,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
)
except Exception as e:
return self._handle_completion_error(e, from_task, from_agent) # type: ignore[func-returns-value]
def _process_streaming_update(
self,
update: StreamingChatCompletionsUpdate,
full_response: str,
tool_calls: dict[str, dict[str, str]],
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str:
"""Handle streaming chat completion."""
full_response = ""
tool_calls = {}
"""Process a single streaming update chunk.
# Make streaming API call
for update in self.client.complete(**params):
if isinstance(update, StreamingChatCompletionsUpdate):
if update.choices:
choice = update.choices[0]
if choice.delta and choice.delta.content:
content_delta = choice.delta.content
full_response += content_delta
self._emit_stream_chunk_event(
chunk=content_delta,
from_task=from_task,
from_agent=from_agent,
)
Args:
update: Streaming update from Azure API
full_response: Accumulated response content
tool_calls: Dictionary of accumulated tool calls
from_task: Task that initiated the call
from_agent: Agent that initiated the call
# Handle tool call streaming
if choice.delta and choice.delta.tool_calls:
for tool_call in choice.delta.tool_calls:
call_id = tool_call.id or "default"
if call_id not in tool_calls:
tool_calls[call_id] = {
"name": "",
"arguments": "",
}
Returns:
Updated full_response string
"""
if update.choices:
choice = update.choices[0]
if choice.delta and choice.delta.content:
content_delta = choice.delta.content
full_response += content_delta
self._emit_stream_chunk_event(
chunk=content_delta,
from_task=from_task,
from_agent=from_agent,
)
if tool_call.function and tool_call.function.name:
tool_calls[call_id]["name"] = tool_call.function.name
if tool_call.function and tool_call.function.arguments:
tool_calls[call_id]["arguments"] += (
tool_call.function.arguments
)
if choice.delta and choice.delta.tool_calls:
for tool_call in choice.delta.tool_calls:
call_id = tool_call.id or "default"
if call_id not in tool_calls:
tool_calls[call_id] = {
"name": "",
"arguments": "",
}
if tool_call.function and tool_call.function.name:
tool_calls[call_id]["name"] = tool_call.function.name
if tool_call.function and tool_call.function.arguments:
tool_calls[call_id]["arguments"] += tool_call.function.arguments
return full_response
def _finalize_streaming_response(
self,
full_response: str,
tool_calls: dict[str, dict[str, str]],
usage_data: dict[str, int],
params: AzureCompletionParams,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Finalize streaming response with usage tracking, tool execution, and events.
Args:
full_response: The complete streamed response content
tool_calls: Dictionary of tool calls accumulated during streaming
usage_data: Token usage data from the stream
params: 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
response_model: Pydantic model for structured output validation
Returns:
Final response content after processing, or structured output
"""
self._track_token_usage_internal(usage_data)
# Handle structured output validation
if response_model and self.is_openai_model:
return self._validate_and_emit_structured_output(
content=full_response,
response_model=response_model,
params=params,
from_task=from_task,
from_agent=from_agent,
)
# Handle completed tool calls
if tool_calls and available_functions:
@@ -653,9 +794,52 @@ class AzureCompletion(BaseLLM):
params["messages"], full_response, from_agent
)
def _handle_streaming_completion(
self,
params: AzureCompletionParams,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Handle streaming chat completion."""
full_response = ""
tool_calls: dict[str, dict[str, Any]] = {}
usage_data = {"total_tokens": 0}
for update in self.client.complete(**params): # type: ignore[arg-type]
if isinstance(update, StreamingChatCompletionsUpdate):
if update.usage:
usage = update.usage
usage_data = {
"prompt_tokens": usage.prompt_tokens,
"completion_tokens": usage.completion_tokens,
"total_tokens": usage.total_tokens,
}
continue
full_response = self._process_streaming_update(
update=update,
full_response=full_response,
tool_calls=tool_calls,
from_task=from_task,
from_agent=from_agent,
)
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,
response_model=response_model,
)
async def _ahandle_completion(
self,
params: dict[str, Any],
params: AzureCompletionParams,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
@@ -663,160 +847,64 @@ class AzureCompletion(BaseLLM):
) -> str | Any:
"""Handle non-streaming chat completion asynchronously."""
try:
response: ChatCompletions = await self.async_client.complete(**params)
if not response.choices:
raise ValueError("No choices returned from Azure API")
choice = response.choices[0]
message = choice.message
usage = self._extract_azure_token_usage(response)
self._track_token_usage_internal(usage)
if response_model and self.is_openai_model:
content = message.content or ""
try:
structured_data = response_model.model_validate_json(content)
structured_json = structured_data.model_dump_json()
self._emit_call_completed_event(
response=structured_json,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return structured_json
except Exception as e:
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
logging.error(error_msg)
raise ValueError(error_msg) from e
if message.tool_calls and available_functions:
tool_call = message.tool_calls[0] # Handle first tool call
if isinstance(tool_call, ChatCompletionsToolCall):
function_name = tool_call.function.name
try:
function_args = json.loads(tool_call.function.arguments)
except json.JSONDecodeError as e:
logging.error(f"Failed to parse tool arguments: {e}")
function_args = {}
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
content = message.content or ""
content = self._apply_stop_words(content)
self._emit_call_completed_event(
response=content,
call_type=LLMCallType.LLM_CALL,
# Cast params to Any to avoid type checking issues with TypedDict unpacking
response: ChatCompletions = await self.async_client.complete(**params) # type: ignore[assignment,arg-type]
return self._process_completion_response(
response=response,
params=params,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
response_model=response_model,
)
except Exception as e:
if is_context_length_exceeded(e):
logging.error(f"Context window exceeded: {e}")
raise LLMContextLengthExceededError(str(e)) from e
error_msg = f"Azure API call failed: {e!s}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise e
return content
return self._handle_completion_error(e, from_task, from_agent) # type: ignore[func-returns-value]
async def _ahandle_streaming_completion(
self,
params: dict[str, Any],
params: AzureCompletionParams,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str:
) -> str | Any:
"""Handle streaming chat completion asynchronously."""
full_response = ""
tool_calls = {}
tool_calls: dict[str, dict[str, Any]] = {}
stream = await self.async_client.complete(**params)
async for update in stream:
usage_data = {"total_tokens": 0}
stream = await self.async_client.complete(**params) # type: ignore[arg-type]
async for update in stream: # type: ignore[union-attr]
if isinstance(update, StreamingChatCompletionsUpdate):
if update.choices:
choice = update.choices[0]
if choice.delta and choice.delta.content:
content_delta = choice.delta.content
full_response += content_delta
self._emit_stream_chunk_event(
chunk=content_delta,
from_task=from_task,
from_agent=from_agent,
)
if choice.delta and choice.delta.tool_calls:
for tool_call in choice.delta.tool_calls:
call_id = tool_call.id or "default"
if call_id not in tool_calls:
tool_calls[call_id] = {
"name": "",
"arguments": "",
}
if tool_call.function and tool_call.function.name:
tool_calls[call_id]["name"] = tool_call.function.name
if tool_call.function and tool_call.function.arguments:
tool_calls[call_id]["arguments"] += (
tool_call.function.arguments
)
if tool_calls and available_functions:
for call_data in tool_calls.values():
function_name = call_data["name"]
try:
function_args = json.loads(call_data["arguments"])
except json.JSONDecodeError as e:
logging.error(f"Failed to parse streamed tool arguments: {e}")
if hasattr(update, "usage") and update.usage:
usage = update.usage
usage_data = {
"prompt_tokens": getattr(usage, "prompt_tokens", 0),
"completion_tokens": getattr(usage, "completion_tokens", 0),
"total_tokens": getattr(usage, "total_tokens", 0),
}
continue
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions,
full_response = self._process_streaming_update(
update=update,
full_response=full_response,
tool_calls=tool_calls,
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"],
response_model=response_model,
)
return full_response
def supports_function_calling(self) -> bool:
"""Check if the model supports function calling."""
# Azure OpenAI models support function calling
@@ -860,7 +948,8 @@ class AzureCompletion(BaseLLM):
# Default context window size
return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
def _extract_azure_token_usage(self, response: ChatCompletions) -> dict[str, Any]:
@staticmethod
def _extract_azure_token_usage(response: ChatCompletions) -> dict[str, Any]:
"""Extract token usage from Azure response."""
if hasattr(response, "usage") and response.usage:
usage = response.usage

600
lib/crewai/src/crewai/llms/providers/gemini/completion.py
View File

@@ -3,7 +3,7 @@ from __future__ import annotations
import logging
import os
import re
from typing import TYPE_CHECKING, Any
from typing import TYPE_CHECKING, Any, Literal, cast
from pydantic import BaseModel
@@ -105,6 +105,7 @@ class GeminiCompletion(BaseLLM):
self.stream = stream
self.safety_settings = safety_settings or {}
self.stop_sequences = stop_sequences or []
self.tools: list[dict[str, Any]] | None = None
# Model-specific settings
version_match = re.search(r"gemini-(\d+(?:\.\d+)?)", model.lower())
@@ -223,10 +224,11 @@ class GeminiCompletion(BaseLLM):
Args:
messages: Input messages for the chat completion
tools: List of tool/function definitions
callbacks: Callback functions (not used as token counts are handled by the reponse)
callbacks: Callback functions (not used as token counts are handled by the response)
available_functions: Available functions for tool calling
from_task: Task that initiated the call
from_agent: Agent that initiated the call
response_model: Response model to use.
Returns:
Chat completion response or tool call result
@@ -267,7 +269,6 @@ class GeminiCompletion(BaseLLM):
return self._handle_completion(
formatted_content,
system_instruction,
config,
available_functions,
from_task,
@@ -309,6 +310,7 @@ class GeminiCompletion(BaseLLM):
available_functions: Available functions for tool calling
from_task: Task that initiated the call
from_agent: Agent that initiated the call
response_model: Response model to use.
Returns:
Chat completion response or tool call result
@@ -344,7 +346,6 @@ class GeminiCompletion(BaseLLM):
return await self._ahandle_completion(
formatted_content,
system_instruction,
config,
available_functions,
from_task,
@@ -497,35 +498,113 @@ class GeminiCompletion(BaseLLM):
return contents, system_instruction
def _handle_completion(
def _validate_and_emit_structured_output(
self,
content: str,
response_model: type[BaseModel],
messages_for_event: list[LLMMessage],
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str:
"""Validate content against response model and emit completion event.
Args:
content: Response content to validate
response_model: Pydantic model for validation
messages_for_event: Messages to include in event
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Returns:
Validated and serialized JSON string
Raises:
ValueError: If validation fails
"""
try:
structured_data = response_model.model_validate_json(content)
structured_json = structured_data.model_dump_json()
self._emit_call_completed_event(
response=structured_json,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
)
return structured_json
except Exception as e:
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
logging.error(error_msg)
raise ValueError(error_msg) from e
def _finalize_completion_response(
self,
content: str,
contents: list[types.Content],
response_model: type[BaseModel] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str:
"""Finalize completion response with validation and event emission.
Args:
content: The response content
contents: Original contents for event conversion
response_model: Pydantic model for structured output validation
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Returns:
Final response content after processing
"""
messages_for_event = self._convert_contents_to_dict(contents)
# Handle structured output validation
if response_model:
return self._validate_and_emit_structured_output(
content=content,
response_model=response_model,
messages_for_event=messages_for_event,
from_task=from_task,
from_agent=from_agent,
)
self._emit_call_completed_event(
response=content,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
)
return self._invoke_after_llm_call_hooks(
messages_for_event, content, from_agent
)
def _process_response_with_tools(
self,
response: GenerateContentResponse,
contents: list[types.Content],
system_instruction: str | None,
config: types.GenerateContentConfig,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Handle non-streaming content generation."""
try:
# The API accepts list[Content] but mypy is overly strict about variance
contents_for_api: Any = contents
response = self.client.models.generate_content(
model=self.model,
contents=contents_for_api,
config=config,
)
"""Process response, execute function calls, and finalize completion.
usage = self._extract_token_usage(response)
except Exception as e:
if is_context_length_exceeded(e):
logging.error(f"Context window exceeded: {e}")
raise LLMContextLengthExceededError(str(e)) from e
raise e from e
self._track_token_usage_internal(usage)
Args:
response: The completion response
contents: Original contents for event conversion
available_functions: Available functions for function calling
from_task: Task that initiated the call
from_agent: Agent that initiated the call
response_model: Pydantic model for structured output validation
Returns:
Final response content or function call result
"""
if response.candidates and (self.tools or available_functions):
candidate = response.candidates[0]
if candidate.content and candidate.content.parts:
@@ -554,61 +633,90 @@ class GeminiCompletion(BaseLLM):
content = response.text or ""
content = self._apply_stop_words(content)
messages_for_event = self._convert_contents_to_dict(contents)
self._emit_call_completed_event(
response=content,
call_type=LLMCallType.LLM_CALL,
return self._finalize_completion_response(
content=content,
contents=contents,
response_model=response_model,
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
)
return self._invoke_after_llm_call_hooks(
messages_for_event, content, from_agent
)
def _handle_streaming_completion(
def _process_stream_chunk(
self,
chunk: GenerateContentResponse,
full_response: str,
function_calls: dict[str, dict[str, Any]],
usage_data: dict[str, int],
from_task: Any | None = None,
from_agent: Any | None = None,
) -> tuple[str, dict[str, dict[str, Any]], dict[str, int]]:
"""Process a single streaming chunk.
Args:
chunk: The streaming chunk response
full_response: Accumulated response text
function_calls: Accumulated function calls
usage_data: Accumulated usage data
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Returns:
Tuple of (updated full_response, updated function_calls, updated usage_data)
"""
if chunk.usage_metadata:
usage_data = self._extract_token_usage(chunk)
if chunk.text:
full_response += chunk.text
self._emit_stream_chunk_event(
chunk=chunk.text,
from_task=from_task,
from_agent=from_agent,
)
if chunk.candidates:
candidate = chunk.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
if hasattr(part, "function_call") and part.function_call:
call_id = part.function_call.name or "default"
if call_id not in function_calls:
function_calls[call_id] = {
"name": part.function_call.name,
"args": dict(part.function_call.args)
if part.function_call.args
else {},
}
return full_response, function_calls, usage_data
def _finalize_streaming_response(
self,
full_response: str,
function_calls: dict[str, dict[str, Any]],
usage_data: dict[str, int],
contents: list[types.Content],
config: types.GenerateContentConfig,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str:
"""Handle streaming content generation."""
full_response = ""
function_calls: dict[str, dict[str, Any]] = {}
"""Finalize streaming response with usage tracking, function execution, and events.
# The API accepts list[Content] but mypy is overly strict about variance
contents_for_api: Any = contents
for chunk in self.client.models.generate_content_stream(
model=self.model,
contents=contents_for_api,
config=config,
):
if chunk.text:
full_response += chunk.text
self._emit_stream_chunk_event(
chunk=chunk.text,
from_task=from_task,
from_agent=from_agent,
)
Args:
full_response: The complete streamed response content
function_calls: Dictionary of function calls accumulated during streaming
usage_data: Token usage data from the stream
contents: Original contents for event conversion
available_functions: Available functions for function calling
from_task: Task that initiated the call
from_agent: Agent that initiated the call
response_model: Pydantic model for structured output validation
if chunk.candidates:
candidate = chunk.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
if hasattr(part, "function_call") and part.function_call:
call_id = part.function_call.name or "default"
if call_id not in function_calls:
function_calls[call_id] = {
"name": part.function_call.name,
"args": dict(part.function_call.args)
if part.function_call.args
else {},
}
Returns:
Final response content after processing
"""
self._track_token_usage_internal(usage_data)
# Handle completed function calls
if function_calls and available_functions:
@@ -636,24 +744,95 @@ class GeminiCompletion(BaseLLM):
if result is not None:
return result
messages_for_event = self._convert_contents_to_dict(contents)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
return self._finalize_completion_response(
content=full_response,
contents=contents,
response_model=response_model,
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
)
return self._invoke_after_llm_call_hooks(
messages_for_event, full_response, from_agent
def _handle_completion(
self,
contents: list[types.Content],
config: types.GenerateContentConfig,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Handle non-streaming content generation."""
try:
# The API accepts list[Content] but mypy is overly strict about variance
contents_for_api: Any = contents
response = self.client.models.generate_content(
model=self.model,
contents=contents_for_api,
config=config,
)
usage = self._extract_token_usage(response)
except Exception as e:
if is_context_length_exceeded(e):
logging.error(f"Context window exceeded: {e}")
raise LLMContextLengthExceededError(str(e)) from e
raise e from e
self._track_token_usage_internal(usage)
return self._process_response_with_tools(
response=response,
contents=contents,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
)
def _handle_streaming_completion(
self,
contents: list[types.Content],
config: types.GenerateContentConfig,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str:
"""Handle streaming content generation."""
full_response = ""
function_calls: dict[str, dict[str, Any]] = {}
usage_data = {"total_tokens": 0}
# The API accepts list[Content] but mypy is overly strict about variance
contents_for_api: Any = contents
for chunk in self.client.models.generate_content_stream(
model=self.model,
contents=contents_for_api,
config=config,
):
full_response, function_calls, usage_data = self._process_stream_chunk(
chunk=chunk,
full_response=full_response,
function_calls=function_calls,
usage_data=usage_data,
from_task=from_task,
from_agent=from_agent,
)
return self._finalize_streaming_response(
full_response=full_response,
function_calls=function_calls,
usage_data=usage_data,
contents=contents,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
)
async def _ahandle_completion(
self,
contents: list[types.Content],
system_instruction: str | None,
config: types.GenerateContentConfig,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
@@ -679,46 +858,15 @@ class GeminiCompletion(BaseLLM):
self._track_token_usage_internal(usage)
if response.candidates and (self.tools or available_functions):
candidate = response.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
if hasattr(part, "function_call") and part.function_call:
function_name = part.function_call.name
if function_name is None:
continue
function_args = (
dict(part.function_call.args)
if part.function_call.args
else {}
)
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions or {},
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
return result
content = response.text or ""
content = self._apply_stop_words(content)
messages_for_event = self._convert_contents_to_dict(contents)
self._emit_call_completed_event(
response=content,
call_type=LLMCallType.LLM_CALL,
return self._process_response_with_tools(
response=response,
contents=contents,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
response_model=response_model,
)
return content
async def _ahandle_streaming_completion(
self,
contents: list[types.Content],
@@ -731,6 +879,7 @@ class GeminiCompletion(BaseLLM):
"""Handle async streaming content generation."""
full_response = ""
function_calls: dict[str, dict[str, Any]] = {}
usage_data = {"total_tokens": 0}
# The API accepts list[Content] but mypy is overly strict about variance
contents_for_api: Any = contents
@@ -740,214 +889,24 @@ class GeminiCompletion(BaseLLM):
config=config,
)
async for chunk in stream:
if chunk.text:
full_response += chunk.text
self._emit_stream_chunk_event(
chunk=chunk.text,
from_task=from_task,
from_agent=from_agent,
)
if chunk.candidates:
candidate = chunk.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
if hasattr(part, "function_call") and part.function_call:
call_id = part.function_call.name or "default"
if call_id not in function_calls:
function_calls[call_id] = {
"name": part.function_call.name,
"args": dict(part.function_call.args)
if part.function_call.args
else {},
}
if function_calls and available_functions:
for call_data in function_calls.values():
function_name = call_data["name"]
function_args = call_data["args"]
# Skip if function_name is None
if not isinstance(function_name, str):
continue
# Ensure function_args is a dict
if not isinstance(function_args, dict):
function_args = {}
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
messages_for_event = self._convert_contents_to_dict(contents)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
)
return self._invoke_after_llm_call_hooks(
messages_for_event, full_response, from_agent
)
async def _ahandle_completion(
self,
contents: list[types.Content],
system_instruction: str | None,
config: types.GenerateContentConfig,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Handle async non-streaming content generation."""
try:
# The API accepts list[Content] but mypy is overly strict about variance
contents_for_api: Any = contents
response = await self.client.aio.models.generate_content(
model=self.model,
contents=contents_for_api,
config=config,
full_response, function_calls, usage_data = self._process_stream_chunk(
chunk=chunk,
full_response=full_response,
function_calls=function_calls,
usage_data=usage_data,
from_task=from_task,
from_agent=from_agent,
)
usage = self._extract_token_usage(response)
except Exception as e:
if is_context_length_exceeded(e):
logging.error(f"Context window exceeded: {e}")
raise LLMContextLengthExceededError(str(e)) from e
raise e from e
self._track_token_usage_internal(usage)
if response.candidates and (self.tools or available_functions):
candidate = response.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
if hasattr(part, "function_call") and part.function_call:
function_name = part.function_call.name
if function_name is None:
continue
function_args = (
dict(part.function_call.args)
if part.function_call.args
else {}
)
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions or {},
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
return result
content = response.text or ""
content = self._apply_stop_words(content)
messages_for_event = self._convert_contents_to_dict(contents)
self._emit_call_completed_event(
response=content,
call_type=LLMCallType.LLM_CALL,
return self._finalize_streaming_response(
full_response=full_response,
function_calls=function_calls,
usage_data=usage_data,
contents=contents,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
)
return content
async def _ahandle_streaming_completion(
self,
contents: list[types.Content],
config: types.GenerateContentConfig,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
response_model: type[BaseModel] | None = None,
) -> str:
"""Handle async streaming content generation."""
full_response = ""
function_calls: dict[str, dict[str, Any]] = {}
# The API accepts list[Content] but mypy is overly strict about variance
contents_for_api: Any = contents
stream = await self.client.aio.models.generate_content_stream(
model=self.model,
contents=contents_for_api,
config=config,
)
async for chunk in stream:
if chunk.text:
full_response += chunk.text
self._emit_stream_chunk_event(
chunk=chunk.text,
from_task=from_task,
from_agent=from_agent,
)
if chunk.candidates:
candidate = chunk.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
if hasattr(part, "function_call") and part.function_call:
call_id = part.function_call.name or "default"
if call_id not in function_calls:
function_calls[call_id] = {
"name": part.function_call.name,
"args": dict(part.function_call.args)
if part.function_call.args
else {},
}
if function_calls and available_functions:
for call_data in function_calls.values():
function_name = call_data["name"]
function_args = call_data["args"]
# Skip if function_name is None
if not isinstance(function_name, str):
continue
# Ensure function_args is a dict
if not isinstance(function_args, dict):
function_args = {}
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
messages_for_event = self._convert_contents_to_dict(contents)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=messages_for_event,
)
return self._invoke_after_llm_call_hooks(
messages_for_event, full_response, from_agent
response_model=response_model,
)
def supports_function_calling(self) -> bool:
@@ -1009,12 +968,12 @@ class GeminiCompletion(BaseLLM):
}
return {"total_tokens": 0}
@staticmethod
def _convert_contents_to_dict(
self,
contents: list[types.Content],
) -> list[LLMMessage]:
"""Convert contents to dict format."""
result: list[dict[str, str]] = []
result: list[LLMMessage] = []
for content_obj in contents:
role = content_obj.role
if role == "model":
@@ -1027,5 +986,10 @@ class GeminiCompletion(BaseLLM):
part.text for part in parts if hasattr(part, "text") and part.text
)
result.append({"role": role, "content": content})
result.append(
LLMMessage(
role=cast(Literal["user", "assistant", "system"], role),
content=content,
)
)
return result

57
lib/crewai/src/crewai/llms/providers/openai/completion.py
View File

@@ -297,6 +297,7 @@ class OpenAICompletion(BaseLLM):
}
if self.stream:
params["stream"] = self.stream
params["stream_options"] = {"include_usage": True}
params.update(self.additional_params)
@@ -544,18 +545,21 @@ class OpenAICompletion(BaseLLM):
)
final_completion = stream.get_final_completion()
if final_completion and final_completion.choices:
parsed_result = final_completion.choices[0].message.parsed
if parsed_result:
structured_json = parsed_result.model_dump_json()
self._emit_call_completed_event(
response=structured_json,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return structured_json
if final_completion:
usage = self._extract_openai_token_usage(final_completion)
self._track_token_usage_internal(usage)
if final_completion.choices:
parsed_result = final_completion.choices[0].message.parsed
if parsed_result:
structured_json = parsed_result.model_dump_json()
self._emit_call_completed_event(
response=structured_json,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return structured_json
logging.error("Failed to get parsed result from stream")
return ""
@@ -564,7 +568,13 @@ class OpenAICompletion(BaseLLM):
self.client.chat.completions.create(**params)
)
usage_data = {"total_tokens": 0}
for completion_chunk in completion_stream:
if hasattr(completion_chunk, "usage") and completion_chunk.usage:
usage_data = self._extract_openai_token_usage(completion_chunk)
continue
if not completion_chunk.choices:
continue
@@ -593,6 +603,8 @@ class OpenAICompletion(BaseLLM):
if tool_call.function and tool_call.function.arguments:
tool_calls[call_id]["arguments"] += tool_call.function.arguments
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"]
@@ -785,7 +797,12 @@ class OpenAICompletion(BaseLLM):
] = await self.async_client.chat.completions.create(**params)
accumulated_content = ""
usage_data = {"total_tokens": 0}
async for chunk in completion_stream:
if hasattr(chunk, "usage") and chunk.usage:
usage_data = self._extract_openai_token_usage(chunk)
continue
if not chunk.choices:
continue
@@ -800,6 +817,8 @@ class OpenAICompletion(BaseLLM):
from_agent=from_agent,
)
self._track_token_usage_internal(usage_data)
try:
parsed_object = response_model.model_validate_json(accumulated_content)
structured_json = parsed_object.model_dump_json()
@@ -828,7 +847,13 @@ class OpenAICompletion(BaseLLM):
ChatCompletionChunk
] = await self.async_client.chat.completions.create(**params)
usage_data = {"total_tokens": 0}
async for chunk in stream:
if hasattr(chunk, "usage") and chunk.usage:
usage_data = self._extract_openai_token_usage(chunk)
continue
if not chunk.choices:
continue
@@ -857,6 +882,8 @@ class OpenAICompletion(BaseLLM):
if tool_call.function and tool_call.function.arguments:
tool_calls[call_id]["arguments"] += tool_call.function.arguments
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"]
@@ -944,8 +971,10 @@ class OpenAICompletion(BaseLLM):
# Default context window size
return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
def _extract_openai_token_usage(self, response: ChatCompletion) -> dict[str, Any]:
"""Extract token usage from OpenAI ChatCompletion response."""
def _extract_openai_token_usage(
self, response: ChatCompletion | ChatCompletionChunk
) -> dict[str, Any]:
"""Extract token usage from OpenAI ChatCompletion or ChatCompletionChunk response."""
if hasattr(response, "usage") and response.usage:
usage = response.usage
return {

12
lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_direct_llm_call_hooks_integration.yaml
View File

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21
lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_lite_agent_hooks_integration_with_real_llm.yaml
View File

@@ -1,12 +1,6 @@
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74
lib/crewai/tests/cassettes/agents/test_agent_custom_max_iterations.yaml
View File

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message: OK
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just the name, exactly as it''s written.\nAction Input: the input to the action,
just a simple JSON object, enclosed in curly braces, using \" to wrap keys and
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2
lib/crewai/tests/cassettes/knowledge/test_docling_source.yaml
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4
lib/crewai/tests/cassettes/knowledge/test_multiple_docling_sources.yaml
View File

@@ -1857,8 +1857,6 @@ interactions:
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Connection:
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Content-Encoding:
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20
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_basic_call.yaml
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uri: https://api.anthropic.com/v1/messages
response:
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headers:
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Date:

25
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_conversation.yaml
View File

@@ -1,7 +1,6 @@
interactions:
- request:
body: '{"max_tokens":4096,"messages":[{"role":"user","content":"My name is Alice."},{"role":"assistant","content":"Hello
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body: '{"max_tokens":4096,"messages":[{"role":"user","content":"My name is Alice."},{"role":"assistant","content":"Hello Alice! Nice to meet you."},{"role":"user","content":"What is my name?"}],"model":"claude-sonnet-4-0","stream":false}'
headers:
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uri: https://api.anthropic.com/v1/messages
response:
body:
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code: 200
message: OK
- request:
body: '{"max_tokens":4096,"messages":[{"role":"user","content":"My name is Alice."},{"role":"assistant","content":"Hello
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headers:
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uri: https://api.anthropic.com/v1/messages
response:
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Content-Type:
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Date:

36
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_multiple_calls.yaml
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@@ -42,19 +42,12 @@ interactions:
uri: https://api.anthropic.com/v1/messages
response:
body:
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response:
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12
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_stop_sequences.yaml
View File

@@ -1,7 +1,6 @@
interactions:
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body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Count from 1 to
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body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Count from 1 to 10"}],"model":"claude-sonnet-4-0","stop_sequences":["END","STOP"],"stream":false}'
headers:
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24
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_max_tokens.yaml
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interactions:
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14
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_json.yaml
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14
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_none.yaml
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14
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_model.yaml
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24
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_system_message.yaml
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24
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_temperature.yaml
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16
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_tools.yaml
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34
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_function_calling.yaml
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30
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml
View File

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33
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking.yaml
View File

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35
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking_blocks_preserved_across_turns.yaml
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lib/crewai/tests/cassettes/llms/azure/test_azure_async_non_streaming.yaml
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