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Merge branch 'main' into gl/fix/ensure-complete-tool-signature

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This commit is contained in:
Greyson LaLonde
2025-12-02 09:07:56 -05:00
committed by GitHub
parent 273491f61a 20704742e2
commit c5f0abfc9f
73 changed files with 10348 additions and 1799 deletions
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20
lib/crewai-tools/pyproject.toml
View File

@@ -8,17 +8,17 @@ authors = [
]
requires-python = ">=3.10, <3.14"
dependencies = [
"lancedb>=0.5.4",
"pytube>=15.0.0",
"requests>=2.32.5",
"docker>=7.1.0",
"lancedb~=0.5.4",
"pytube~=15.0.0",
"requests~=2.32.5",
"docker~=7.1.0",
"crewai==1.6.1",
"lancedb>=0.5.4",
"tiktoken>=0.8.0",
"beautifulsoup4>=4.13.4",
"python-docx>=1.2.0",
"youtube-transcript-api>=1.2.2",
"pymupdf>=1.26.6",
"lancedb~=0.5.4",
"tiktoken~=0.8.0",
"beautifulsoup4~=4.13.4",
"python-docx~=1.2.0",
"youtube-transcript-api~=1.2.2",
"pymupdf~=1.26.6",
]

80
lib/crewai/pyproject.toml
View File

@@ -9,35 +9,35 @@ authors = [
requires-python = ">=3.10, <3.14"
dependencies = [
# Core Dependencies
"pydantic>=2.11.9",
"openai>=1.13.3",
"pydantic~=2.11.9",
"openai~=1.83.0",
"instructor>=1.3.3",
# Text Processing
"pdfplumber>=0.11.4",
"regex>=2024.9.11",
"pdfplumber~=0.11.4",
"regex~=2024.9.11",
# Telemetry and Monitoring
"opentelemetry-api>=1.30.0",
"opentelemetry-sdk>=1.30.0",
"opentelemetry-exporter-otlp-proto-http>=1.30.0",
"opentelemetry-api~=1.34.0",
"opentelemetry-sdk~=1.34.0",
"opentelemetry-exporter-otlp-proto-http~=1.34.0",
# Data Handling
"chromadb~=1.1.0",
"tokenizers>=0.20.3",
"openpyxl>=3.1.5",
"tokenizers~=0.20.3",
"openpyxl~=3.1.5",
# Authentication and Security
"python-dotenv>=1.1.1",
"pyjwt>=2.9.0",
"python-dotenv~=1.1.1",
"pyjwt~=2.9.0",
# Configuration and Utils
"click>=8.1.7",
"appdirs>=1.4.4",
"jsonref>=1.1.0",
"json-repair==0.25.2",
"uv>=0.4.25",
"tomli-w>=1.1.0",
"tomli>=2.0.2",
"json5>=0.10.0",
"portalocker==2.7.0",
"pydantic-settings>=2.10.1",
"mcp>=1.16.0",
"click~=8.1.7",
"appdirs~=1.4.4",
"jsonref~=1.1.0",
"json-repair~=0.25.2",
"tomli-w~=1.1.0",
"tomli~=2.0.2",
"json5~=0.10.0",
"portalocker~=2.7.0",
"pydantic-settings~=2.10.1",
"mcp~=1.16.0",
"uv~=0.9.13",
]
[project.urls]
@@ -53,50 +53,48 @@ tools = [
embeddings = [
"tiktoken~=0.8.0"
]
pdfplumber = [
"pdfplumber>=0.11.4",
]
pandas = [
"pandas>=2.2.3",
"pandas~=2.2.3",
]
openpyxl = [
"openpyxl>=3.1.5",
"openpyxl~=3.1.5",
]
mem0 = ["mem0ai>=0.1.94"]
mem0 = ["mem0ai~=0.1.94"]
docling = [
"docling>=2.12.0",
"docling~=2.63.0",
]
qdrant = [
"qdrant-client[fastembed]>=1.14.3",
"qdrant-client[fastembed]~=1.14.3",
]
aws = [
"boto3>=1.40.38",
"boto3~=1.40.38",
"aiobotocore~=2.25.2",
]
watson = [
"ibm-watsonx-ai>=1.3.39",
"ibm-watsonx-ai~=1.3.39",
]
voyageai = [
"voyageai>=0.3.5",
"voyageai~=0.3.5",
]
litellm = [
"litellm>=1.74.9",
"litellm~=1.74.9",
]
bedrock = [
"boto3>=1.40.45",
"boto3~=1.40.45",
]
google-genai = [
"google-genai>=1.2.0",
"google-genai~=1.2.0",
]
azure-ai-inference = [
"azure-ai-inference>=1.0.0b9",
"azure-ai-inference~=1.0.0b9",
]
anthropic = [
"anthropic>=0.69.0",
"anthropic~=0.71.0",
]
a2a = [
a2a = [
"a2a-sdk~=0.3.10",
"httpx-auth>=0.23.1",
"httpx-sse>=0.4.0",
"httpx-auth~=0.23.1",
"httpx-sse~=0.4.0",
]

416
lib/crewai/src/crewai/llm.py
View File

@@ -57,7 +57,12 @@ if TYPE_CHECKING:
from litellm.litellm_core_utils.get_supported_openai_params import (
get_supported_openai_params,
)
from litellm.types.utils import ChatCompletionDeltaToolCall, Choices, ModelResponse
from litellm.types.utils import (
ChatCompletionDeltaToolCall,
Choices,
Function,
ModelResponse,
)
from litellm.utils import supports_response_schema
from crewai.agent.core import Agent
@@ -73,7 +78,12 @@ try:
from litellm.litellm_core_utils.get_supported_openai_params import (
get_supported_openai_params,
)
from litellm.types.utils import ChatCompletionDeltaToolCall, Choices, ModelResponse
from litellm.types.utils import (
ChatCompletionDeltaToolCall,
Choices,
Function,
ModelResponse,
)
from litellm.utils import supports_response_schema
LITELLM_AVAILABLE = True
@@ -84,6 +94,7 @@ except ImportError:
ContextWindowExceededError = Exception # type: ignore
get_supported_openai_params = None # type: ignore
ChatCompletionDeltaToolCall = None # type: ignore
Function = None # type: ignore
ModelResponse = None # type: ignore
supports_response_schema = None # type: ignore
CustomLogger = None # type: ignore
@@ -610,7 +621,9 @@ class LLM(BaseLLM):
self.callbacks = callbacks
self.context_window_size = 0
self.reasoning_effort = reasoning_effort
self.additional_params = kwargs
self.additional_params = {
k: v for k, v in kwargs.items() if k not in ("is_litellm", "provider")
}
self.is_anthropic = self._is_anthropic_model(model)
self.stream = stream
self.interceptor = interceptor
@@ -1204,6 +1217,281 @@ class LLM(BaseLLM):
)
return text_response
async def _ahandle_non_streaming_response(
self,
params: dict[str, Any],
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Task | None = None,
from_agent: Agent | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Handle an async non-streaming response from the LLM.
Args:
params: Parameters for the completion call
callbacks: Optional list of callback functions
available_functions: Dict of available functions
from_task: Optional Task that invoked the LLM
from_agent: Optional Agent that invoked the LLM
response_model: Optional Response model
Returns:
str: The response text
"""
if response_model and self.is_litellm:
from crewai.utilities.internal_instructor import InternalInstructor
messages = params.get("messages", [])
if not messages:
raise ValueError("Messages are required when using response_model")
combined_content = "\n\n".join(
f"{msg['role'].upper()}: {msg['content']}" for msg in messages
)
instructor_instance = InternalInstructor(
content=combined_content,
model=response_model,
llm=self,
)
result = instructor_instance.to_pydantic()
structured_response = result.model_dump_json()
self._handle_emit_call_events(
response=structured_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return structured_response
try:
if response_model:
params["response_model"] = response_model
response = await litellm.acompletion(**params)
except ContextWindowExceededError as e:
raise LLMContextLengthExceededError(str(e)) from e
if response_model is not None:
if isinstance(response, BaseModel):
structured_response = response.model_dump_json()
self._handle_emit_call_events(
response=structured_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return structured_response
response_message = cast(Choices, cast(ModelResponse, response).choices)[
0
].message
text_response = response_message.content or ""
if callbacks and len(callbacks) > 0:
for callback in callbacks:
if hasattr(callback, "log_success_event"):
usage_info = getattr(response, "usage", None)
if usage_info:
callback.log_success_event(
kwargs=params,
response_obj={"usage": usage_info},
start_time=0,
end_time=0,
)
tool_calls = getattr(response_message, "tool_calls", [])
if (not tool_calls or not available_functions) and text_response:
self._handle_emit_call_events(
response=text_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return text_response
if tool_calls and not available_functions and not text_response:
return tool_calls
tool_result = self._handle_tool_call(
tool_calls, available_functions, from_task, from_agent
)
if tool_result is not None:
return tool_result
self._handle_emit_call_events(
response=text_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return text_response
async def _ahandle_streaming_response(
self,
params: dict[str, Any],
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Task | None = None,
from_agent: Agent | None = None,
response_model: type[BaseModel] | None = None,
) -> Any:
"""Handle an async streaming response from the LLM.
Args:
params: Parameters for the completion call
callbacks: Optional list of callback functions
available_functions: Dict of available functions
from_task: Optional task object
from_agent: Optional agent object
response_model: Optional response model
Returns:
str: The complete response text
"""
full_response = ""
chunk_count = 0
usage_info = None
accumulated_tool_args: defaultdict[int, AccumulatedToolArgs] = defaultdict(
AccumulatedToolArgs
)
params["stream"] = True
params["stream_options"] = {"include_usage": True}
try:
async for chunk in await litellm.acompletion(**params):
chunk_count += 1
chunk_content = None
try:
choices = None
if isinstance(chunk, dict) and "choices" in chunk:
choices = chunk["choices"]
elif hasattr(chunk, "choices"):
if not isinstance(chunk.choices, type):
choices = chunk.choices
if hasattr(chunk, "usage") and chunk.usage is not None:
usage_info = chunk.usage
if choices and len(choices) > 0:
first_choice = choices[0]
delta = None
if isinstance(first_choice, dict):
delta = first_choice.get("delta", {})
elif hasattr(first_choice, "delta"):
delta = first_choice.delta
if delta:
if isinstance(delta, dict):
chunk_content = delta.get("content")
elif hasattr(delta, "content"):
chunk_content = delta.content
tool_calls: list[ChatCompletionDeltaToolCall] | None = None
if isinstance(delta, dict):
tool_calls = delta.get("tool_calls")
elif hasattr(delta, "tool_calls"):
tool_calls = delta.tool_calls
if tool_calls:
for tool_call in tool_calls:
idx = tool_call.index
if tool_call.function:
if tool_call.function.name:
accumulated_tool_args[
idx
].function.name = tool_call.function.name
if tool_call.function.arguments:
accumulated_tool_args[
idx
].function.arguments += (
tool_call.function.arguments
)
except (AttributeError, KeyError, IndexError, TypeError):
pass
if chunk_content:
full_response += chunk_content
crewai_event_bus.emit(
self,
event=LLMStreamChunkEvent(
chunk=chunk_content,
from_task=from_task,
from_agent=from_agent,
),
)
if callbacks and len(callbacks) > 0 and usage_info:
for callback in callbacks:
if hasattr(callback, "log_success_event"):
callback.log_success_event(
kwargs=params,
response_obj={"usage": usage_info},
start_time=0,
end_time=0,
)
if accumulated_tool_args and available_functions:
# Convert accumulated tool args to ChatCompletionDeltaToolCall objects
tool_calls_list: list[ChatCompletionDeltaToolCall] = [
ChatCompletionDeltaToolCall(
index=idx,
function=Function(
name=tool_arg.function.name,
arguments=tool_arg.function.arguments,
),
)
for idx, tool_arg in accumulated_tool_args.items()
if tool_arg.function.name
]
if tool_calls_list:
result = self._handle_streaming_tool_calls(
tool_calls=tool_calls_list,
accumulated_tool_args=accumulated_tool_args,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
return result
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"),
)
return full_response
except ContextWindowExceededError as e:
raise LLMContextLengthExceededError(str(e)) from e
except Exception:
if chunk_count == 0:
raise
if full_response:
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"),
)
return full_response
raise
def _handle_tool_call(
self,
tool_calls: list[Any],
@@ -1421,6 +1709,128 @@ class LLM(BaseLLM):
)
raise
async def acall(
self,
messages: str | list[LLMMessage],
tools: list[dict[str, BaseTool]] | None = None,
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Task | None = None,
from_agent: Agent | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Async high-level LLM call method.
Args:
messages: Input messages for the LLM.
Can be a string or list of message dictionaries.
If string, it will be converted to a single user message.
If list, each dict must have 'role' and 'content' keys.
tools: Optional list of tool schemas for function calling.
Each tool should define its name, description, and parameters.
callbacks: Optional list of callback functions to be executed
during and after the LLM call.
available_functions: Optional dict mapping function names to callables
that can be invoked by the LLM.
from_task: Optional Task that invoked the LLM
from_agent: Optional Agent that invoked the LLM
response_model: Optional Model that contains a pydantic response model.
Returns:
Union[str, Any]: Either a text response from the LLM (str) or
the result of a tool function call (Any).
Raises:
TypeError: If messages format is invalid
ValueError: If response format is not supported
LLMContextLengthExceededError: If input exceeds model's context limit
"""
crewai_event_bus.emit(
self,
event=LLMCallStartedEvent(
messages=messages,
tools=tools,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
model=self.model,
),
)
self._validate_call_params()
if isinstance(messages, str):
messages = [{"role": "user", "content": messages}]
if "o1" in self.model.lower():
for message in messages:
if message.get("role") == "system":
msg_role: Literal["assistant"] = "assistant"
message["role"] = msg_role
with suppress_warnings():
if callbacks and len(callbacks) > 0:
self.set_callbacks(callbacks)
try:
params = self._prepare_completion_params(messages, tools)
if self.stream:
return await self._ahandle_streaming_response(
params=params,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
)
return await self._ahandle_non_streaming_response(
params=params,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
)
except LLMContextLengthExceededError:
raise
except Exception as e:
unsupported_stop = "Unsupported parameter" in str(
e
) and "'stop'" in str(e)
if unsupported_stop:
if (
"additional_drop_params" in self.additional_params
and isinstance(
self.additional_params["additional_drop_params"], list
)
):
self.additional_params["additional_drop_params"].append("stop")
else:
self.additional_params = {"additional_drop_params": ["stop"]}
logging.info("Retrying LLM call without the unsupported 'stop'")
return await self.acall(
messages,
tools=tools,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
)
crewai_event_bus.emit(
self,
event=LLMCallFailedEvent(
error=str(e), from_task=from_task, from_agent=from_agent
),
)
raise
def _handle_emit_call_events(
self,
response: Any,

38
lib/crewai/src/crewai/llms/base_llm.py
View File

@@ -158,6 +158,44 @@ class BaseLLM(ABC):
RuntimeError: If the LLM request fails for other reasons.
"""
async def acall(
self,
messages: str | list[LLMMessage],
tools: list[dict[str, BaseTool]] | None = None,
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Task | None = None,
from_agent: Agent | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Call the LLM with the given messages.
Args:
messages: Input messages for the LLM.
Can be a string or list of message dictionaries.
If string, it will be converted to a single user message.
If list, each dict must have 'role' and 'content' keys.
tools: Optional list of tool schemas for function calling.
Each tool should define its name, description, and parameters.
callbacks: Optional list of callback functions to be executed
during and after the LLM call.
available_functions: Optional dict mapping function names to callables
that can be invoked by the LLM.
from_task: Optional task caller to be used for the LLM call.
from_agent: Optional agent caller to be used for the LLM call.
response_model: Optional response model to be used for the LLM call.
Returns:
Either a text response from the LLM (str) or
the result of a tool function call (Any).
Raises:
ValueError: If the messages format is invalid.
TimeoutError: If the LLM request times out.
RuntimeError: If the LLM request fails for other reasons.
"""
raise NotImplementedError
def _convert_tools_for_interference(
self, tools: list[dict[str, BaseTool]]
) -> list[dict[str, BaseTool]]:

354
lib/crewai/src/crewai/llms/providers/anthropic/completion.py
View File

@@ -9,7 +9,7 @@ from pydantic import BaseModel
from crewai.events.types.llm_events import LLMCallType
from crewai.llms.base_llm import BaseLLM
from crewai.llms.hooks.transport import HTTPTransport
from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
from crewai.utilities.agent_utils import is_context_length_exceeded
from crewai.utilities.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededError,
@@ -21,7 +21,7 @@ if TYPE_CHECKING:
from crewai.llms.hooks.base import BaseInterceptor
try:
from anthropic import Anthropic
from anthropic import Anthropic, AsyncAnthropic
from anthropic.types import Message
from anthropic.types.tool_use_block import ToolUseBlock
import httpx
@@ -84,15 +84,20 @@ class AnthropicCompletion(BaseLLM):
self.client = Anthropic(**self._get_client_params())
async_client_params = self._get_client_params()
if self.interceptor:
async_transport = AsyncHTTPTransport(interceptor=self.interceptor)
async_http_client = httpx.AsyncClient(transport=async_transport)
async_client_params["http_client"] = async_http_client
self.async_client = AsyncAnthropic(**async_client_params)
# Store completion parameters
self.max_tokens = max_tokens
self.top_p = top_p
self.stream = stream
self.stop_sequences = stop_sequences or []
# Model-specific settings
self.is_claude_3 = "claude-3" in model.lower()
self.supports_tools = self.is_claude_3 # Claude 3+ supports tool use
self.supports_tools = True
@property
def stop(self) -> list[str]:
@@ -213,6 +218,72 @@ class AnthropicCompletion(BaseLLM):
)
raise
async def acall(
self,
messages: str | list[LLMMessage],
tools: list[dict[str, Any]] | None = None,
callbacks: list[Any] | None = None,
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:
"""Async call to Anthropic messages API.
Args:
messages: Input messages for the chat completion
tools: List of tool/function definitions
callbacks: Callback functions (not used in native implementation)
available_functions: Available functions for tool calling
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Returns:
Chat completion response or tool call result
"""
try:
self._emit_call_started_event(
messages=messages,
tools=tools,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
formatted_messages, system_message = self._format_messages_for_anthropic(
messages
)
completion_params = self._prepare_completion_params(
formatted_messages, system_message, tools
)
if self.stream:
return await self._ahandle_streaming_completion(
completion_params,
available_functions,
from_task,
from_agent,
response_model,
)
return await self._ahandle_completion(
completion_params,
available_functions,
from_task,
from_agent,
response_model,
)
except Exception as e:
error_msg = f"Anthropic 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
def _prepare_completion_params(
self,
messages: list[LLMMessage],
@@ -546,7 +617,7 @@ class AnthropicCompletion(BaseLLM):
# Execute the tool
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args, # type: ignore
function_args=function_args,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
@@ -626,6 +697,275 @@ class AnthropicCompletion(BaseLLM):
return tool_results[0]["content"]
raise e
async def _ahandle_completion(
self,
params: dict[str, Any],
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 async message completion."""
if response_model:
structured_tool = {
"name": "structured_output",
"description": "Returns structured data according to the schema",
"input_schema": response_model.model_json_schema(),
}
params["tools"] = [structured_tool]
params["tool_choice"] = {"type": "tool", "name": "structured_output"}
try:
response: Message = await self.async_client.messages.create(**params)
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
usage = self._extract_anthropic_token_usage(response)
self._track_token_usage_internal(usage)
if response_model and response.content:
tool_uses = [
block for block in response.content if isinstance(block, ToolUseBlock)
]
if tool_uses and tool_uses[0].name == "structured_output":
structured_data = tool_uses[0].input
structured_json = json.dumps(structured_data)
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 response.content and available_functions:
tool_uses = [
block for block in response.content if isinstance(block, ToolUseBlock)
]
if tool_uses:
return await self._ahandle_tool_use_conversation(
response,
tool_uses,
params,
available_functions,
from_task,
from_agent,
)
content = ""
if response.content:
for content_block in response.content:
if hasattr(content_block, "text"):
content += content_block.text
content = self._apply_stop_words(content)
self._emit_call_completed_event(
response=content,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
if usage.get("total_tokens", 0) > 0:
logging.info(f"Anthropic API usage: {usage}")
return content
async def _ahandle_streaming_completion(
self,
params: dict[str, Any],
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 message completion."""
if response_model:
structured_tool = {
"name": "structured_output",
"description": "Returns structured data according to the schema",
"input_schema": response_model.model_json_schema(),
}
params["tools"] = [structured_tool]
params["tool_choice"] = {"type": "tool", "name": "structured_output"}
full_response = ""
stream_params = {k: v for k, v in params.items() if k != "stream"}
async with self.async_client.messages.stream(**stream_params) as stream:
async for event in stream:
if hasattr(event, "delta") and hasattr(event.delta, "text"):
text_delta = event.delta.text
full_response += text_delta
self._emit_stream_chunk_event(
chunk=text_delta,
from_task=from_task,
from_agent=from_agent,
)
final_message: Message = await stream.get_final_message()
usage = self._extract_anthropic_token_usage(final_message)
self._track_token_usage_internal(usage)
if response_model and final_message.content:
tool_uses = [
block
for block in final_message.content
if isinstance(block, ToolUseBlock)
]
if tool_uses and tool_uses[0].name == "structured_output":
structured_data = tool_uses[0].input
structured_json = json.dumps(structured_data)
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_message.content and available_functions:
tool_uses = [
block
for block in final_message.content
if isinstance(block, ToolUseBlock)
]
if tool_uses:
return await self._ahandle_tool_use_conversation(
final_message,
tool_uses,
params,
available_functions,
from_task,
from_agent,
)
full_response = self._apply_stop_words(full_response)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return full_response
async def _ahandle_tool_use_conversation(
self,
initial_response: Message,
tool_uses: list[ToolUseBlock],
params: dict[str, Any],
available_functions: dict[str, Any],
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str:
"""Handle the complete async tool use conversation flow.
This implements the proper Anthropic tool use pattern:
1. Claude requests tool use
2. We execute the tools
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)
follow_up_params = params.copy()
assistant_message = {"role": "assistant", "content": initial_response.content}
user_message = {"role": "user", "content": tool_results}
follow_up_params["messages"] = params["messages"] + [
assistant_message,
user_message,
]
try:
final_response: Message = await self.async_client.messages.create(
**follow_up_params
)
follow_up_usage = self._extract_anthropic_token_usage(final_response)
self._track_token_usage_internal(follow_up_usage)
final_content = ""
if final_response.content:
for content_block in final_response.content:
if hasattr(content_block, "text"):
final_content += content_block.text
final_content = self._apply_stop_words(final_content)
self._emit_call_completed_event(
response=final_content,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=follow_up_params["messages"],
)
total_usage = {
"input_tokens": follow_up_usage.get("input_tokens", 0),
"output_tokens": follow_up_usage.get("output_tokens", 0),
"total_tokens": follow_up_usage.get("total_tokens", 0),
}
if total_usage.get("total_tokens", 0) > 0:
logging.info(f"Anthropic API tool conversation usage: {total_usage}")
return final_content
except Exception as e:
if is_context_length_exceeded(e):
logging.error(f"Context window exceeded in tool follow-up: {e}")
raise LLMContextLengthExceededError(str(e)) from e
logging.error(f"Tool follow-up conversation failed: {e}")
if tool_results:
return tool_results[0]["content"]
raise e
def supports_function_calling(self) -> bool:
"""Check if the model supports function calling."""
return self.supports_tools

269
lib/crewai/src/crewai/llms/providers/azure/completion.py
View File

@@ -6,6 +6,7 @@ import os
from typing import TYPE_CHECKING, Any
from pydantic import BaseModel
from typing_extensions import Self
from crewai.utilities.agent_utils import is_context_length_exceeded
from crewai.utilities.converter import generate_model_description
@@ -24,6 +25,9 @@ try:
from azure.ai.inference import (
ChatCompletionsClient,
)
from azure.ai.inference.aio import (
ChatCompletionsClient as AsyncChatCompletionsClient,
)
from azure.ai.inference.models import (
ChatCompletions,
ChatCompletionsToolCall,
@@ -135,6 +139,8 @@ class AzureCompletion(BaseLLM):
self.client = ChatCompletionsClient(**client_kwargs) # type: ignore[arg-type]
self.async_client = AsyncChatCompletionsClient(**client_kwargs) # type: ignore[arg-type]
self.top_p = top_p
self.frequency_penalty = frequency_penalty
self.presence_penalty = presence_penalty
@@ -258,6 +264,88 @@ class AzureCompletion(BaseLLM):
)
raise
async def acall(
self,
messages: str | list[LLMMessage],
tools: list[dict[str, BaseTool]] | None = None,
callbacks: list[Any] | None = None,
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:
"""Call Azure AI Inference chat completions API asynchronously.
Args:
messages: Input messages for the chat completion
tools: List of tool/function definitions
callbacks: Callback functions (not used in native implementation)
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:
Chat completion response or tool call result
"""
try:
self._emit_call_started_event(
messages=messages,
tools=tools,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
formatted_messages = self._format_messages_for_azure(messages)
completion_params = self._prepare_completion_params(
formatted_messages, tools, response_model
)
if self.stream:
return await self._ahandle_streaming_completion(
completion_params,
available_functions,
from_task,
from_agent,
response_model,
)
return await self._ahandle_completion(
completion_params,
available_functions,
from_task,
from_agent,
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
def _prepare_completion_params(
self,
messages: list[LLMMessage],
@@ -556,6 +644,170 @@ class AzureCompletion(BaseLLM):
return full_response
async def _ahandle_completion(
self,
params: dict[str, Any],
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 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,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
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
async def _ahandle_streaming_completion(
self,
params: dict[str, Any],
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 chat completion asynchronously."""
full_response = ""
tool_calls = {}
stream = await self.async_client.complete(**params)
async for update in stream:
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}")
continue
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
return result
full_response = self._apply_stop_words(full_response)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return full_response
def supports_function_calling(self) -> bool:
"""Check if the model supports function calling."""
# Azure OpenAI models support function calling
@@ -609,3 +861,20 @@ class AzureCompletion(BaseLLM):
"total_tokens": getattr(usage, "total_tokens", 0),
}
return {"total_tokens": 0}
async def aclose(self) -> None:
"""Close the async client and clean up resources.
This ensures proper cleanup of the underlying aiohttp session
to avoid unclosed connector warnings.
"""
if hasattr(self.async_client, "close"):
await self.async_client.close()
async def __aenter__(self) -> Self:
"""Async context manager entry."""
return self
async def __aexit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
"""Async context manager exit."""
await self.aclose()

476
lib/crewai/src/crewai/llms/providers/bedrock/completion.py
View File

@@ -1,6 +1,8 @@
from __future__ import annotations
from collections.abc import Mapping, Sequence
from contextlib import AsyncExitStack
import json
import logging
import os
from typing import TYPE_CHECKING, Any, TypedDict, cast
@@ -42,6 +44,16 @@ except ImportError:
'AWS Bedrock native provider not available, to install: uv add "crewai[bedrock]"'
) from None
try:
from aiobotocore.session import ( # type: ignore[import-untyped]
get_session as get_aiobotocore_session,
)
AIOBOTOCORE_AVAILABLE = True
except ImportError:
AIOBOTOCORE_AVAILABLE = False
get_aiobotocore_session = None
if TYPE_CHECKING:
@@ -221,6 +233,15 @@ class BedrockCompletion(BaseLLM):
self.client = session.client("bedrock-runtime", config=config)
self.region_name = region_name
self.aws_access_key_id = aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID")
self.aws_secret_access_key = aws_secret_access_key or os.getenv(
"AWS_SECRET_ACCESS_KEY"
)
self.aws_session_token = aws_session_token or os.getenv("AWS_SESSION_TOKEN")
self._async_exit_stack = AsyncExitStack() if AIOBOTOCORE_AVAILABLE else None
self._async_client_initialized = False
# Store completion parameters
self.max_tokens = max_tokens
self.top_p = top_p
@@ -354,6 +375,110 @@ class BedrockCompletion(BaseLLM):
)
raise
async def acall(
self,
messages: str | list[LLMMessage],
tools: list[dict[Any, Any]] | None = None,
callbacks: list[Any] | None = None,
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:
"""Async call to AWS Bedrock Converse API.
Args:
messages: Input messages as string or list of message dicts.
tools: Optional list of tool definitions.
callbacks: Optional list of callback handlers.
available_functions: Optional dict mapping function names to callables.
from_task: Optional task context for events.
from_agent: Optional agent context for events.
response_model: Optional Pydantic model for structured output.
Returns:
Generated text response or structured output.
Raises:
NotImplementedError: If aiobotocore is not installed.
LLMContextLengthExceededError: If context window is exceeded.
"""
if not AIOBOTOCORE_AVAILABLE:
raise NotImplementedError(
"Async support for AWS Bedrock requires aiobotocore. "
'Install with: uv add "crewai[bedrock-async]"'
)
try:
self._emit_call_started_event(
messages=messages,
tools=tools,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
formatted_messages, system_message = self._format_messages_for_converse(
messages # type: ignore[arg-type]
)
body: BedrockConverseRequestBody = {
"inferenceConfig": self._get_inference_config(),
}
if system_message:
body["system"] = cast(
"list[SystemContentBlockTypeDef]",
cast(object, [{"text": system_message}]),
)
if tools:
tool_config: ToolConfigurationTypeDef = {
"tools": cast(
"Sequence[ToolTypeDef]",
cast(object, self._format_tools_for_converse(tools)),
)
}
body["toolConfig"] = tool_config
if self.guardrail_config:
guardrail_config: GuardrailConfigurationTypeDef = cast(
"GuardrailConfigurationTypeDef", cast(object, self.guardrail_config)
)
body["guardrailConfig"] = guardrail_config
if self.additional_model_request_fields:
body["additionalModelRequestFields"] = (
self.additional_model_request_fields
)
if self.additional_model_response_field_paths:
body["additionalModelResponseFieldPaths"] = (
self.additional_model_response_field_paths
)
if self.stream:
return await self._ahandle_streaming_converse(
formatted_messages, body, available_functions, from_task, from_agent
)
return await self._ahandle_converse(
formatted_messages, body, available_functions, from_task, from_agent
)
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"AWS Bedrock 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
def _handle_converse(
self,
messages: list[dict[str, Any]],
@@ -565,6 +690,341 @@ class BedrockCompletion(BaseLLM):
role = event["messageStart"].get("role")
logging.debug(f"Streaming message started with role: {role}")
elif "contentBlockStart" in event:
start = event["contentBlockStart"].get("start", {})
if "toolUse" in start:
current_tool_use = start["toolUse"]
tool_use_id = current_tool_use.get("toolUseId")
logging.debug(
f"Tool use started in stream: {json.dumps(current_tool_use)} (ID: {tool_use_id})"
)
elif "contentBlockDelta" in event:
delta = event["contentBlockDelta"]["delta"]
if "text" in delta:
text_chunk = delta["text"]
logging.debug(f"Streaming text chunk: {text_chunk[:50]}...")
full_response += text_chunk
self._emit_stream_chunk_event(
chunk=text_chunk,
from_task=from_task,
from_agent=from_agent,
)
elif "toolUse" in delta and current_tool_use:
tool_input = delta["toolUse"].get("input", "")
if tool_input:
logging.debug(f"Tool input delta: {tool_input}")
elif "contentBlockStop" in event:
logging.debug("Content block stopped in stream")
if current_tool_use and available_functions:
function_name = current_tool_use["name"]
function_args = cast(
dict[str, Any], current_tool_use.get("input", {})
)
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 tool_result is not None and tool_use_id:
messages.append(
{
"role": "assistant",
"content": [{"toolUse": current_tool_use}],
}
)
messages.append(
{
"role": "user",
"content": [
{
"toolResult": {
"toolUseId": tool_use_id,
"content": [
{"text": str(tool_result)}
],
}
}
],
}
)
return self._handle_converse(
messages,
body,
available_functions,
from_task,
from_agent,
)
current_tool_use = None
tool_use_id = None
elif "messageStop" in event:
stop_reason = event["messageStop"].get("stopReason")
logging.debug(f"Streaming message stopped: {stop_reason}")
if stop_reason == "max_tokens":
logging.warning(
"Streaming response truncated due to max_tokens"
)
elif stop_reason == "content_filtered":
logging.warning(
"Streaming response filtered due to content policy"
)
break
elif "metadata" in event:
metadata = event["metadata"]
if "usage" in metadata:
usage_metrics = metadata["usage"]
self._track_token_usage_internal(usage_metrics)
logging.debug(f"Token usage: {usage_metrics}")
if "trace" in metadata:
logging.debug(
f"Trace information available: {metadata['trace']}"
)
except ClientError as e:
error_msg = self._handle_client_error(e)
raise RuntimeError(error_msg) from e
except BotoCoreError as e:
error_msg = f"Bedrock streaming connection error: {e}"
logging.error(error_msg)
raise ConnectionError(error_msg) from e
full_response = self._apply_stop_words(full_response)
if not full_response or full_response.strip() == "":
logging.warning("Bedrock streaming returned empty content, using fallback")
full_response = (
"I apologize, but I couldn't generate a response. Please try again."
)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=messages,
)
return full_response
async def _ensure_async_client(self) -> Any:
"""Ensure async client is initialized and return it."""
if not self._async_client_initialized and get_aiobotocore_session:
if self._async_exit_stack is None:
raise RuntimeError(
"Async exit stack not initialized - aiobotocore not available"
)
session = get_aiobotocore_session()
client = await self._async_exit_stack.enter_async_context(
session.create_client(
"bedrock-runtime",
region_name=self.region_name,
aws_access_key_id=self.aws_access_key_id,
aws_secret_access_key=self.aws_secret_access_key,
aws_session_token=self.aws_session_token,
)
)
self._async_client = client
self._async_client_initialized = True
return self._async_client
async def _ahandle_converse(
self,
messages: list[dict[str, Any]],
body: BedrockConverseRequestBody,
available_functions: Mapping[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str:
"""Handle async non-streaming converse API call."""
try:
if not messages:
raise ValueError("Messages cannot be empty")
for i, msg in enumerate(messages):
if (
not isinstance(msg, dict)
or "role" not in msg
or "content" not in msg
):
raise ValueError(f"Invalid message format at index {i}")
async_client = await self._ensure_async_client()
response = await async_client.converse(
modelId=self.model_id,
messages=cast(
"Sequence[MessageTypeDef | MessageOutputTypeDef]",
cast(object, messages),
),
**body,
)
if "usage" in response:
self._track_token_usage_internal(response["usage"])
stop_reason = response.get("stopReason")
if stop_reason:
logging.debug(f"Response stop reason: {stop_reason}")
if stop_reason == "max_tokens":
logging.warning("Response truncated due to max_tokens limit")
elif stop_reason == "content_filtered":
logging.warning("Response was filtered due to content policy")
output = response.get("output", {})
message = output.get("message", {})
content = message.get("content", [])
if not content:
logging.warning("No content in Bedrock response")
return (
"I apologize, but I received an empty response. Please try again."
)
text_content = ""
for content_block in content:
if "text" in content_block:
text_content += content_block["text"]
elif "toolUse" in content_block and available_functions:
tool_use_block = content_block["toolUse"]
tool_use_id = tool_use_block.get("toolUseId")
function_name = tool_use_block["name"]
function_args = tool_use_block.get("input", {})
logging.debug(
f"Tool use requested: {function_name} with ID {tool_use_id}"
)
tool_result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=dict(available_functions),
from_task=from_task,
from_agent=from_agent,
)
if tool_result is not None:
messages.append(
{
"role": "assistant",
"content": [{"toolUse": tool_use_block}],
}
)
messages.append(
{
"role": "user",
"content": [
{
"toolResult": {
"toolUseId": tool_use_id,
"content": [{"text": str(tool_result)}],
}
}
],
}
)
return await self._ahandle_converse(
messages, body, available_functions, from_task, from_agent
)
text_content = self._apply_stop_words(text_content)
if not text_content or text_content.strip() == "":
logging.warning("Extracted empty text content from Bedrock response")
text_content = "I apologize, but I couldn't generate a proper response. Please try again."
self._emit_call_completed_event(
response=text_content,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=messages,
)
return text_content
except ClientError as e:
error_code = e.response.get("Error", {}).get("Code", "Unknown")
error_msg = e.response.get("Error", {}).get("Message", str(e))
logging.error(f"AWS Bedrock ClientError ({error_code}): {error_msg}")
if error_code == "ValidationException":
if "last turn" in error_msg and "user message" in error_msg:
raise ValueError(
f"Conversation format error: {error_msg}. Check message alternation."
) from e
raise ValueError(f"Request validation failed: {error_msg}") from e
if error_code == "AccessDeniedException":
raise PermissionError(
f"Access denied to model {self.model_id}: {error_msg}"
) from e
if error_code == "ResourceNotFoundException":
raise ValueError(f"Model {self.model_id} not found: {error_msg}") from e
if error_code == "ThrottlingException":
raise RuntimeError(
f"API throttled, please retry later: {error_msg}"
) from e
if error_code == "ModelTimeoutException":
raise TimeoutError(f"Model request timed out: {error_msg}") from e
if error_code == "ServiceQuotaExceededException":
raise RuntimeError(f"Service quota exceeded: {error_msg}") from e
if error_code == "ModelNotReadyException":
raise RuntimeError(
f"Model {self.model_id} not ready: {error_msg}"
) from e
if error_code == "ModelErrorException":
raise RuntimeError(f"Model error: {error_msg}") from e
if error_code == "InternalServerException":
raise RuntimeError(f"Internal server error: {error_msg}") from e
if error_code == "ServiceUnavailableException":
raise RuntimeError(f"Service unavailable: {error_msg}") from e
raise RuntimeError(f"Bedrock API error ({error_code}): {error_msg}") from e
except BotoCoreError as e:
error_msg = f"Bedrock connection error: {e}"
logging.error(error_msg)
raise ConnectionError(error_msg) from e
except Exception as e:
error_msg = f"Unexpected error in Bedrock converse call: {e}"
logging.error(error_msg)
raise RuntimeError(error_msg) from e
async def _ahandle_streaming_converse(
self,
messages: list[dict[str, Any]],
body: BedrockConverseRequestBody,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str:
"""Handle async streaming converse API call."""
full_response = ""
current_tool_use = None
tool_use_id = None
try:
async_client = await self._ensure_async_client()
response = await async_client.converse_stream(
modelId=self.model_id,
messages=cast(
"Sequence[MessageTypeDef | MessageOutputTypeDef]",
cast(object, messages),
),
**body,
)
stream = response.get("stream")
if stream:
async for event in stream:
if "messageStart" in event:
role = event["messageStart"].get("role")
logging.debug(f"Streaming message started with role: {role}")
elif "contentBlockStart" in event:
start = event["contentBlockStart"].get("start", {})
if "toolUse" in start:
@@ -590,17 +1050,14 @@ class BedrockCompletion(BaseLLM):
if tool_input:
logging.debug(f"Tool input delta: {tool_input}")
# Content block stop - end of a content block
elif "contentBlockStop" in event:
logging.debug("Content block stopped in stream")
# If we were accumulating a tool use, it's now complete
if current_tool_use and available_functions:
function_name = current_tool_use["name"]
function_args = cast(
dict[str, Any], current_tool_use.get("input", {})
)
# Execute tool
tool_result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
@@ -610,7 +1067,6 @@ class BedrockCompletion(BaseLLM):
)
if tool_result is not None and tool_use_id:
# Continue conversation with tool result
messages.append(
{
"role": "assistant",
@@ -634,8 +1090,7 @@ class BedrockCompletion(BaseLLM):
}
)
# Recursive call - note this switches to non-streaming
return self._handle_converse(
return await self._ahandle_converse(
messages,
body,
available_functions,
@@ -643,10 +1098,9 @@ class BedrockCompletion(BaseLLM):
from_agent,
)
current_tool_use = None
tool_use_id = None
current_tool_use = None
tool_use_id = None
# Message stop - end of entire message
elif "messageStop" in event:
stop_reason = event["messageStop"].get("stopReason")
logging.debug(f"Streaming message stopped: {stop_reason}")
@@ -660,7 +1114,6 @@ class BedrockCompletion(BaseLLM):
)
break
# Metadata - contains usage information and trace details
elif "metadata" in event:
metadata = event["metadata"]
if "usage" in metadata:
@@ -680,17 +1133,14 @@ class BedrockCompletion(BaseLLM):
logging.error(error_msg)
raise ConnectionError(error_msg) from e
# Apply stop words to full response
full_response = self._apply_stop_words(full_response)
# Ensure we don't return empty content
if not full_response or full_response.strip() == "":
logging.warning("Bedrock streaming returned empty content, using fallback")
full_response = (
"I apologize, but I couldn't generate a response. Please try again."
)
# Emit completion event
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,

365
lib/crewai/src/crewai/llms/providers/gemini/completion.py
View File

@@ -1,13 +1,14 @@
from __future__ import annotations
import logging
import os
import re
from typing import Any, cast
from typing import TYPE_CHECKING, Any
from pydantic import BaseModel
from crewai.events.types.llm_events import LLMCallType
from crewai.llms.base_llm import BaseLLM
from crewai.llms.hooks.base import BaseInterceptor
from crewai.utilities.agent_utils import is_context_length_exceeded
from crewai.utilities.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededError,
@@ -15,10 +16,15 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
from crewai.utilities.types import LLMMessage
if TYPE_CHECKING:
from crewai.llms.hooks.base import BaseInterceptor
try:
from google import genai # type: ignore[import-untyped]
from google.genai import types # type: ignore[import-untyped]
from google.genai.errors import APIError # type: ignore[import-untyped]
from google import genai
from google.genai import types
from google.genai.errors import APIError
from google.genai.types import GenerateContentResponse, Schema
except ImportError:
raise ImportError(
'Google Gen AI native provider not available, to install: uv add "crewai[google-genai]"'
@@ -102,7 +108,9 @@ class GeminiCompletion(BaseLLM):
# Model-specific settings
version_match = re.search(r"gemini-(\d+(?:\.\d+)?)", model.lower())
self.supports_tools = bool(version_match and float(version_match.group(1)) >= 1.5)
self.supports_tools = bool(
version_match and float(version_match.group(1)) >= 1.5
)
@property
def stop(self) -> list[str]:
@@ -128,7 +136,7 @@ class GeminiCompletion(BaseLLM):
else:
self.stop_sequences = []
def _initialize_client(self, use_vertexai: bool = False) -> genai.Client: # type: ignore[no-any-unimported]
def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:
"""Initialize the Google Gen AI client with proper parameter handling.
Args:
@@ -277,7 +285,84 @@ class GeminiCompletion(BaseLLM):
)
raise
def _prepare_generation_config( # type: ignore[no-any-unimported]
async def acall(
self,
messages: str | list[LLMMessage],
tools: list[dict[str, Any]] | None = None,
callbacks: list[Any] | None = None,
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:
"""Async call to Google Gemini generate content API.
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 response)
available_functions: Available functions for tool calling
from_task: Task that initiated the call
from_agent: Agent that initiated the call
Returns:
Chat completion response or tool call result
"""
try:
self._emit_call_started_event(
messages=messages,
tools=tools,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
self.tools = tools
formatted_content, system_instruction = self._format_messages_for_gemini(
messages
)
config = self._prepare_generation_config(
system_instruction, tools, response_model
)
if self.stream:
return await self._ahandle_streaming_completion(
formatted_content,
config,
available_functions,
from_task,
from_agent,
response_model,
)
return await self._ahandle_completion(
formatted_content,
system_instruction,
config,
available_functions,
from_task,
from_agent,
response_model,
)
except APIError as e:
error_msg = f"Google Gemini API error: {e.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"Google Gemini 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
def _prepare_generation_config(
self,
system_instruction: str | None = None,
tools: list[dict[str, Any]] | None = None,
@@ -294,7 +379,7 @@ class GeminiCompletion(BaseLLM):
GenerateContentConfig object for Gemini API
"""
self.tools = tools
config_params = {}
config_params: dict[str, Any] = {}
# Add system instruction if present
if system_instruction:
@@ -329,7 +414,7 @@ class GeminiCompletion(BaseLLM):
return types.GenerateContentConfig(**config_params)
def _convert_tools_for_interference( # type: ignore[no-any-unimported]
def _convert_tools_for_interference( # type: ignore[override]
self, tools: list[dict[str, Any]]
) -> list[types.Tool]:
"""Convert CrewAI tool format to Gemini function declaration format."""
@@ -346,7 +431,7 @@ class GeminiCompletion(BaseLLM):
)
# Add parameters if present - ensure parameters is a dict
if parameters and isinstance(parameters, dict):
if parameters and isinstance(parameters, Schema):
function_declaration.parameters = parameters
gemini_tool = types.Tool(function_declarations=[function_declaration])
@@ -354,7 +439,7 @@ class GeminiCompletion(BaseLLM):
return gemini_tools
def _format_messages_for_gemini( # type: ignore[no-any-unimported]
def _format_messages_for_gemini(
self, messages: str | list[LLMMessage]
) -> tuple[list[types.Content], str | None]:
"""Format messages for Gemini API.
@@ -373,32 +458,41 @@ class GeminiCompletion(BaseLLM):
# Use base class formatting first
base_formatted = super()._format_messages(messages)
contents = []
contents: list[types.Content] = []
system_instruction: str | None = None
for message in base_formatted:
role = message.get("role")
content = message.get("content", "")
role = message["role"]
content = message["content"]
# Convert content to string if it's a list
if isinstance(content, list):
text_content = " ".join(
str(item.get("text", "")) if isinstance(item, dict) else str(item)
for item in content
)
else:
text_content = str(content) if content else ""
if role == "system":
# Extract system instruction - Gemini handles it separately
if system_instruction:
system_instruction += f"\n\n{content}"
system_instruction += f"\n\n{text_content}"
else:
system_instruction = cast(str, content)
system_instruction = text_content
else:
# Convert role for Gemini (assistant -> model)
gemini_role = "model" if role == "assistant" else "user"
# Create Content object
gemini_content = types.Content(
role=gemini_role, parts=[types.Part.from_text(text=content)]
role=gemini_role, parts=[types.Part.from_text(text=text_content)]
)
contents.append(gemini_content)
return contents, system_instruction
def _handle_completion( # type: ignore[no-any-unimported]
def _handle_completion(
self,
contents: list[types.Content],
system_instruction: str | None,
@@ -409,14 +503,14 @@ class GeminiCompletion(BaseLLM):
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Handle non-streaming content generation."""
api_params = {
"model": self.model,
"contents": contents,
"config": config,
}
try:
response = self.client.models.generate_content(**api_params)
# 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:
@@ -433,6 +527,8 @@ class GeminiCompletion(BaseLLM):
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
@@ -442,7 +538,7 @@ class GeminiCompletion(BaseLLM):
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions, # type: ignore
available_functions=available_functions or {},
from_task=from_task,
from_agent=from_agent,
)
@@ -450,7 +546,7 @@ class GeminiCompletion(BaseLLM):
if result is not None:
return result
content = response.text if hasattr(response, "text") else ""
content = response.text or ""
content = self._apply_stop_words(content)
messages_for_event = self._convert_contents_to_dict(contents)
@@ -465,7 +561,7 @@ class GeminiCompletion(BaseLLM):
return content
def _handle_streaming_completion( # type: ignore[no-any-unimported]
def _handle_streaming_completion(
self,
contents: list[types.Content],
config: types.GenerateContentConfig,
@@ -476,16 +572,16 @@ class GeminiCompletion(BaseLLM):
) -> str:
"""Handle streaming content generation."""
full_response = ""
function_calls = {}
function_calls: dict[str, dict[str, Any]] = {}
api_params = {
"model": self.model,
"contents": contents,
"config": config,
}
for chunk in self.client.models.generate_content_stream(**api_params):
if hasattr(chunk, "text") and chunk.text:
# 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,
@@ -493,7 +589,7 @@ class GeminiCompletion(BaseLLM):
from_agent=from_agent,
)
if hasattr(chunk, "candidates") and chunk.candidates:
if chunk.candidates:
candidate = chunk.candidates[0]
if candidate.content and candidate.content.parts:
for part in candidate.content.parts:
@@ -513,6 +609,14 @@ class GeminiCompletion(BaseLLM):
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 = {}
# Execute tool
result = self._handle_tool_execution(
function_name=function_name,
@@ -537,6 +641,154 @@ class GeminiCompletion(BaseLLM):
return full_response
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,
)
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,
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 full_response
def supports_function_calling(self) -> bool:
"""Check if the model supports function calling."""
return self.supports_tools
@@ -583,9 +835,10 @@ class GeminiCompletion(BaseLLM):
# Default context window size for Gemini models
return int(1048576 * CONTEXT_WINDOW_USAGE_RATIO) # 1M tokens
def _extract_token_usage(self, response: dict[str, Any]) -> dict[str, Any]:
@staticmethod
def _extract_token_usage(response: GenerateContentResponse) -> dict[str, Any]:
"""Extract token usage from Gemini response."""
if hasattr(response, "usage_metadata"):
if response.usage_metadata:
usage = response.usage_metadata
return {
"prompt_token_count": getattr(usage, "prompt_token_count", 0),
@@ -595,21 +848,23 @@ class GeminiCompletion(BaseLLM):
}
return {"total_tokens": 0}
def _convert_contents_to_dict( # type: ignore[no-any-unimported]
def _convert_contents_to_dict(
self,
contents: list[types.Content],
) -> list[dict[str, str]]:
"""Convert contents to dict format."""
return [
{
"role": "assistant"
if content_obj.role == "model"
else content_obj.role,
"content": " ".join(
part.text
for part in content_obj.parts
if hasattr(part, "text") and part.text
),
}
for content_obj in contents
]
result: list[dict[str, str]] = []
for content_obj in contents:
role = content_obj.role
if role == "model":
role = "assistant"
elif role is None:
role = "user"
parts = content_obj.parts or []
content = " ".join(
part.text for part in parts if hasattr(part, "text") and part.text
)
result.append({"role": role, "content": content})
return result

343
lib/crewai/src/crewai/llms/providers/openai/completion.py
View File

@@ -1,13 +1,13 @@
from __future__ import annotations
from collections.abc import Iterator
from collections.abc import AsyncIterator, Iterator
import json
import logging
import os
from typing import TYPE_CHECKING, Any
import httpx
from openai import APIConnectionError, NotFoundError, OpenAI
from openai import APIConnectionError, AsyncOpenAI, NotFoundError, OpenAI
from openai.types.chat import ChatCompletion, ChatCompletionChunk
from openai.types.chat.chat_completion import Choice
from openai.types.chat.chat_completion_chunk import ChoiceDelta
@@ -15,7 +15,7 @@ from pydantic import BaseModel
from crewai.events.types.llm_events import LLMCallType
from crewai.llms.base_llm import BaseLLM
from crewai.llms.hooks.transport import HTTPTransport
from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
from crewai.utilities.agent_utils import is_context_length_exceeded
from crewai.utilities.converter import generate_model_description
from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -101,6 +101,14 @@ class OpenAICompletion(BaseLLM):
self.client = OpenAI(**client_config)
async_client_config = self._get_client_params()
if self.interceptor:
async_transport = AsyncHTTPTransport(interceptor=self.interceptor)
async_http_client = httpx.AsyncClient(transport=async_transport)
async_client_config["http_client"] = async_http_client
self.async_client = AsyncOpenAI(**async_client_config)
# Completion parameters
self.top_p = top_p
self.frequency_penalty = frequency_penalty
@@ -210,6 +218,71 @@ class OpenAICompletion(BaseLLM):
)
raise
async def acall(
self,
messages: str | list[LLMMessage],
tools: list[dict[str, BaseTool]] | None = None,
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Task | None = None,
from_agent: Agent | None = None,
response_model: type[BaseModel] | None = None,
) -> str | Any:
"""Async call to OpenAI chat completion API.
Args:
messages: Input messages for the chat completion
tools: list of tool/function definitions
callbacks: Callback functions (not used in native implementation)
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 for structured output.
Returns:
Chat completion response or tool call result
"""
try:
self._emit_call_started_event(
messages=messages,
tools=tools,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
formatted_messages = self._format_messages(messages)
completion_params = self._prepare_completion_params(
messages=formatted_messages, tools=tools
)
if self.stream:
return await self._ahandle_streaming_completion(
params=completion_params,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
)
return await self._ahandle_completion(
params=completion_params,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
response_model=response_model,
)
except Exception as e:
error_msg = f"OpenAI 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
def _prepare_completion_params(
self, messages: list[LLMMessage], tools: list[dict[str, BaseTool]] | None = None
) -> dict[str, Any]:
@@ -352,10 +425,10 @@ class OpenAICompletion(BaseLLM):
if message.tool_calls and available_functions:
tool_call = message.tool_calls[0]
function_name = tool_call.function.name # type: ignore[union-attr]
function_name = tool_call.function.name
try:
function_args = json.loads(tool_call.function.arguments) # type: ignore[union-attr]
function_args = json.loads(tool_call.function.arguments)
except json.JSONDecodeError as e:
logging.error(f"Failed to parse tool arguments: {e}")
function_args = {}
@@ -564,6 +637,266 @@ class OpenAICompletion(BaseLLM):
return full_response
async def _ahandle_completion(
self,
params: dict[str, Any],
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 async chat completion."""
try:
if response_model:
parse_params = {
k: v for k, v in params.items() if k != "response_format"
}
parsed_response = await self.async_client.beta.chat.completions.parse(
**parse_params,
response_format=response_model,
)
math_reasoning = parsed_response.choices[0].message
if math_reasoning.refusal:
pass
usage = self._extract_openai_token_usage(parsed_response)
self._track_token_usage_internal(usage)
parsed_object = parsed_response.choices[0].message.parsed
if parsed_object:
structured_json = parsed_object.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
response: ChatCompletion = await self.async_client.chat.completions.create(
**params
)
usage = self._extract_openai_token_usage(response)
self._track_token_usage_internal(usage)
choice: Choice = response.choices[0]
message = choice.message
if message.tool_calls and available_functions:
tool_call = message.tool_calls[0]
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)
if self.response_format and isinstance(self.response_format, type):
try:
structured_result = self._validate_structured_output(
content, self.response_format
)
self._emit_call_completed_event(
response=structured_result,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return structured_result
except ValueError as e:
logging.warning(f"Structured output validation failed: {e}")
self._emit_call_completed_event(
response=content,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
if usage.get("total_tokens", 0) > 0:
logging.info(f"OpenAI API usage: {usage}")
except NotFoundError as e:
error_msg = f"Model {self.model} not found: {e}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise ValueError(error_msg) from e
except APIConnectionError as e:
error_msg = f"Failed to connect to OpenAI API: {e}"
logging.error(error_msg)
self._emit_call_failed_event(
error=error_msg, from_task=from_task, from_agent=from_agent
)
raise ConnectionError(error_msg) from e
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"OpenAI 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 from e
return content
async def _ahandle_streaming_completion(
self,
params: dict[str, Any],
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 chat completion."""
full_response = ""
tool_calls = {}
if response_model:
completion_stream: AsyncIterator[
ChatCompletionChunk
] = await self.async_client.chat.completions.create(**params)
accumulated_content = ""
async for chunk in completion_stream:
if not chunk.choices:
continue
choice = chunk.choices[0]
delta: ChoiceDelta = choice.delta
if delta.content:
accumulated_content += delta.content
self._emit_stream_chunk_event(
chunk=delta.content,
from_task=from_task,
from_agent=from_agent,
)
try:
parsed_object = response_model.model_validate_json(accumulated_content)
structured_json = parsed_object.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:
logging.error(f"Failed to parse structured output from stream: {e}")
self._emit_call_completed_event(
response=accumulated_content,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return accumulated_content
stream: AsyncIterator[
ChatCompletionChunk
] = await self.async_client.chat.completions.create(**params)
async for chunk in stream:
if not chunk.choices:
continue
choice = chunk.choices[0]
chunk_delta: ChoiceDelta = choice.delta
if chunk_delta.content:
full_response += chunk_delta.content
self._emit_stream_chunk_event(
chunk=chunk_delta.content,
from_task=from_task,
from_agent=from_agent,
)
if chunk_delta.tool_calls:
for tool_call in chunk_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"]
arguments = call_data["arguments"]
if not function_name or not arguments:
continue
if function_name not in available_functions:
logging.warning(
f"Function '{function_name}' not found in available functions"
)
continue
try:
function_args = json.loads(arguments)
except json.JSONDecodeError as e:
logging.error(f"Failed to parse streamed tool arguments: {e}")
continue
result = self._handle_tool_execution(
function_name=function_name,
function_args=function_args,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
return result
full_response = self._apply_stop_words(full_response)
self._emit_call_completed_event(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return full_response
def supports_function_calling(self) -> bool:
"""Check if the model supports function calling."""
return not self.is_o1_model

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lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_basic_call.yaml Normal file
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lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_conversation.yaml Normal file
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398
lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_multiple_calls.yaml Normal file
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"""Tests for Anthropic async completion functionality."""
import json
import logging
import pytest
import tiktoken
from pydantic import BaseModel
from crewai.llm import LLM
from crewai.llms.providers.anthropic.completion import AnthropicCompletion
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_basic_call():
"""Test basic async call with Anthropic."""
llm = LLM(model="anthropic/claude-sonnet-4-0")
result = await llm.acall("Say hello")
assert result is not None
assert isinstance(result, str)
assert len(result) > 0
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_with_temperature():
"""Test async call with temperature parameter."""
llm = LLM(model="anthropic/claude-sonnet-4-0", temperature=0.1)
result = await llm.acall("Say the word 'test' once")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_with_max_tokens():
"""Test async call with max_tokens parameter."""
llm = LLM(model="anthropic/claude-sonnet-4-0", max_tokens=10)
result = await llm.acall("Write a very long story about a dragon.")
assert result is not None
assert isinstance(result, str)
encoder = tiktoken.get_encoding("cl100k_base")
token_count = len(encoder.encode(result))
assert token_count <= 10
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_with_system_message():
"""Test async call with system message."""
llm = LLM(model="anthropic/claude-sonnet-4-0")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is 2+2?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_conversation():
"""Test async call with conversation history."""
llm = LLM(model="anthropic/claude-sonnet-4-0")
messages = [
{"role": "user", "content": "My name is Alice."},
{"role": "assistant", "content": "Hello Alice! Nice to meet you."},
{"role": "user", "content": "What is my name?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_stop_sequences():
"""Test async call with stop sequences."""
llm = LLM(
model="anthropic/claude-sonnet-4-0",
stop_sequences=["END", "STOP"]
)
result = await llm.acall("Count from 1 to 10")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_multiple_calls():
"""Test making multiple async calls in sequence."""
llm = LLM(model="anthropic/claude-sonnet-4-0")
result1 = await llm.acall("What is 1+1?")
result2 = await llm.acall("What is 2+2?")
assert result1 is not None
assert result2 is not None
assert isinstance(result1, str)
assert isinstance(result2, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_with_response_format_none():
"""Test async call with response_format set to None."""
llm = LLM(model="anthropic/claude-sonnet-4-0", response_format=None)
result = await llm.acall("Tell me a short fact")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_with_response_format_json():
"""Test async call with JSON response format."""
llm = LLM(model="anthropic/claude-sonnet-4-0", response_format={"type": "json_object"})
result = await llm.acall("Return a JSON object devoid of ```json{x}```, where x is the json object, with a 'greeting' field")
assert isinstance(result, str)
deserialized_result = json.loads(result)
assert isinstance(deserialized_result, dict)
assert isinstance(deserialized_result["greeting"], str)
class GreetingResponse(BaseModel):
"""Response model for greeting test."""
greeting: str
language: str
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_with_response_model():
"""Test async call with Pydantic response_model for structured output."""
llm = LLM(model="anthropic/claude-sonnet-4-0")
result = await llm.acall(
"Say hello in French",
response_model=GreetingResponse
)
model = GreetingResponse.model_validate_json(result)
assert isinstance(model, GreetingResponse)
assert isinstance(model.greeting, str)
assert isinstance(model.language, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_anthropic_async_with_tools():
"""Test async call with tools."""
llm = AnthropicCompletion(model="claude-sonnet-4-0")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
result = await llm.acall(
"What's the weather in San Francisco?",
tools=tools
)
logging.debug("result: %s", result)
assert result is not None
assert isinstance(result, str)

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"""Tests for Azure async completion functionality."""
import pytest
import tiktoken
from crewai.llm import LLM
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_azure_async_non_streaming():
"""Test basic async non-streaming call."""
llm = LLM(model="azure/gpt-4o-mini", stream=False)
result = await llm.acall("Say hello")
assert result is not None
assert isinstance(result, str)
assert len(result) > 0
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_azure_async_multiple_calls():
"""Test making multiple async calls in sequence."""
llm = LLM(model="azure/gpt-4o-mini", stream=False)
result1 = await llm.acall("What is 1+1?")
result2 = await llm.acall("What is 2+2?")
assert result1 is not None
assert result2 is not None
assert isinstance(result1, str)
assert isinstance(result2, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_azure_async_with_temperature():
"""Test async call with temperature parameter."""
llm = LLM(model="azure/gpt-4o-mini", temperature=0.1, stream=False)
result = await llm.acall("Say the word 'test' once")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_azure_async_with_max_tokens():
"""Test async call with max_tokens parameter."""
llm = LLM(model="azure/gpt-4o-mini", max_tokens=10, stream=False)
result = await llm.acall("Write a very long story about a dragon.")
assert result is not None
assert isinstance(result, str)
encoder = tiktoken.get_encoding("cl100k_base")
token_count = len(encoder.encode(result))
assert token_count <= 10
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_azure_async_with_system_message():
"""Test async call with system message."""
llm = LLM(model="azure/gpt-4o-mini", stream=False)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is 2+2?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_azure_async_with_parameters():
"""Test async call with multiple parameters."""
llm = LLM(
model="azure/gpt-4o-mini",
temperature=0.7,
max_tokens=100,
top_p=0.9,
frequency_penalty=0.5,
presence_penalty=0.3,
stream=False
)
result = await llm.acall("Tell me a short fact")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_azure_async_conversation():
"""Test async call with conversation history."""
llm = LLM(model="azure/gpt-4o-mini", stream=False)
messages = [
{"role": "user", "content": "My name is Alice."},
{"role": "assistant", "content": "Hello Alice! Nice to meet you."},
{"role": "user", "content": "What is my name?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)

127
lib/crewai/tests/llms/bedrock/test_bedrock_async.py Normal file
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@@ -0,0 +1,127 @@
"""Tests for Bedrock async completion functionality.
Note: These tests are skipped in CI because VCR.py does not support
aiobotocore's HTTP session. The cassettes were recorded locally but
cannot be played back properly in CI.
"""
import pytest
import tiktoken
from crewai.llm import LLM
SKIP_REASON = "VCR does not support aiobotocore async HTTP client"
@pytest.mark.vcr()
@pytest.mark.asyncio
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_basic_call():
"""Test basic async call with Bedrock."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0")
result = await llm.acall("Say hello")
assert result is not None
assert isinstance(result, str)
assert len(result) > 0
@pytest.mark.vcr()
@pytest.mark.asyncio
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_with_temperature():
"""Test async call with temperature parameter."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0", temperature=0.1)
result = await llm.acall("Say the word 'test' once")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_with_max_tokens():
"""Test async call with max_tokens parameter."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0", max_tokens=10)
result = await llm.acall("Write a very long story about a dragon.")
assert result is not None
assert isinstance(result, str)
encoder = tiktoken.get_encoding("cl100k_base")
token_count = len(encoder.encode(result))
assert token_count <= 10
@pytest.mark.vcr()
@pytest.mark.asyncio
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_with_system_message():
"""Test async call with system message."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is 2+2?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_conversation():
"""Test async call with conversation history."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0")
messages = [
{"role": "user", "content": "My name is Alice."},
{"role": "assistant", "content": "Hello Alice! Nice to meet you."},
{"role": "user", "content": "What is my name?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_multiple_calls():
"""Test making multiple async calls in sequence."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0")
result1 = await llm.acall("What is 1+1?")
result2 = await llm.acall("What is 2+2?")
assert result1 is not None
assert result2 is not None
assert isinstance(result1, str)
assert isinstance(result2, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_with_parameters():
"""Test async call with multiple parameters."""
llm = LLM(
model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0",
temperature=0.7,
max_tokens=100,
top_p=0.9
)
result = await llm.acall("Tell me a short fact")
assert result is not None
assert isinstance(result, str)

114
lib/crewai/tests/llms/google/test_google_async.py Normal file
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@@ -0,0 +1,114 @@
"""Tests for Google (Gemini) async completion functionality."""
import pytest
import tiktoken
from crewai.llm import LLM
from crewai.llms.providers.gemini.completion import GeminiCompletion
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_gemini_async_basic_call():
"""Test basic async call with Gemini."""
llm = LLM(model="gemini/gemini-3-pro-preview")
result = await llm.acall("Say hello")
assert result is not None
assert isinstance(result, str)
assert len(result) > 0
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_gemini_async_with_temperature():
"""Test async call with temperature parameter."""
llm = LLM(model="gemini/gemini-3-pro-preview", temperature=0.1)
result = await llm.acall("Say the word 'test' once")
assert result is not None
assert isinstance(result, str)
@pytest.mark.asyncio
@pytest.mark.vcr
async def test_gemini_async_with_max_tokens():
"""Test async call with max_tokens parameter."""
llm = GeminiCompletion(model="gemini-3-pro-preview", max_output_tokens=1000)
result = await llm.acall("Write a very short story about a dragon.")
assert result is not None
assert isinstance(result, str)
encoder = tiktoken.get_encoding("cl100k_base")
token_count = len(encoder.encode(result))
assert token_count <= 1000
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_gemini_async_with_system_message():
"""Test async call with system message."""
llm = LLM(model="gemini/gemini-3-pro-preview")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is 2+2?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_gemini_async_conversation():
"""Test async call with conversation history."""
llm = LLM(model="gemini/gemini-3-pro-preview")
messages = [
{"role": "user", "content": "My name is Alice."},
{"role": "assistant", "content": "Hello Alice! Nice to meet you."},
{"role": "user", "content": "What is my name?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_gemini_async_multiple_calls():
"""Test making multiple async calls in sequence."""
llm = LLM(model="gemini/gemini-3-pro-preview")
result1 = await llm.acall("What is 1+1?")
result2 = await llm.acall("What is 2+2?")
assert result1 is not None
assert result2 is not None
assert isinstance(result1, str)
assert isinstance(result2, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_gemini_async_with_parameters():
"""Test async call with multiple parameters."""
llm = LLM(
model="gemini/gemini-3-pro-preview",
temperature=0.7,
max_output_tokens=1000,
top_p=0.9
)
result = await llm.acall("Tell me a short fact")
assert result is not None
assert isinstance(result, str)

1
lib/crewai/tests/llms/litellm/__init__.py Normal file
View File

@@ -0,0 +1 @@
"""LiteLLM fallback tests."""

156
lib/crewai/tests/llms/litellm/test_litellm_async.py Normal file
View File

@@ -0,0 +1,156 @@
"""Tests for LiteLLM fallback async completion functionality."""
import pytest
import tiktoken
from crewai.llm import LLM
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_basic_call():
"""Test basic async call with LiteLLM fallback."""
llm = LLM(model="gpt-4o-mini", is_litellm=True)
result = await llm.acall("Say hello")
assert result is not None
assert isinstance(result, str)
assert len(result) > 0
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_with_temperature():
"""Test async call with temperature parameter."""
llm = LLM(model="gpt-4o-mini", is_litellm=True, temperature=0.1)
result = await llm.acall("Say the word 'test' once")
assert result is not None
assert isinstance(result, str)
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_with_max_tokens():
"""Test async call with max_tokens parameter."""
llm = LLM(model="gpt-4o-mini", is_litellm=True, max_tokens=10)
result = await llm.acall("Write a very long story about a dragon.")
assert result is not None
assert isinstance(result, str)
encoder = tiktoken.get_encoding("cl100k_base")
token_count = len(encoder.encode(result))
assert token_count <= 10
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_with_system_message():
"""Test async call with system message."""
llm = LLM(model="gpt-4o-mini", is_litellm=True)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is 2+2?"},
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_conversation():
"""Test async call with conversation history."""
llm = LLM(model="gpt-4o-mini", is_litellm=True)
messages = [
{"role": "user", "content": "My name is Alice."},
{"role": "assistant", "content": "Hello Alice! Nice to meet you."},
{"role": "user", "content": "What is my name?"},
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_multiple_calls():
"""Test making multiple async calls in sequence."""
llm = LLM(model="gpt-4o-mini", is_litellm=True)
result1 = await llm.acall("What is 1+1?")
result2 = await llm.acall("What is 2+2?")
assert result1 is not None
assert result2 is not None
assert isinstance(result1, str)
assert isinstance(result2, str)
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_with_parameters():
"""Test async call with multiple parameters."""
llm = LLM(
model="gpt-4o-mini",
is_litellm=True,
temperature=0.7,
max_tokens=100,
top_p=0.9,
frequency_penalty=0.5,
presence_penalty=0.3,
)
result = await llm.acall("Tell me a short fact")
assert result is not None
assert isinstance(result, str)
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_streaming():
"""Test async streaming call with LiteLLM fallback."""
llm = LLM(model="gpt-4o-mini", is_litellm=True, stream=True)
result = await llm.acall("Say hello world")
assert result is not None
assert isinstance(result, str)
assert len(result) > 0
@pytest.mark.asyncio
@pytest.mark.vcr
@pytest.mark.skip(reason="cassettes do not read properly but were generated correctly.")
async def test_litellm_async_streaming_with_parameters():
"""Test async streaming call with multiple parameters."""
llm = LLM(
model="gpt-4o-mini",
is_litellm=True,
stream=True,
temperature=0.5,
max_tokens=50,
)
result = await llm.acall("Count from 1 to 5")
assert result is not None
assert isinstance(result, str)

6
lib/crewai/tests/llms/openai/test_openai.py
View File

@@ -475,10 +475,14 @@ def test_openai_get_client_params_priority_order():
params3 = llm3._get_client_params()
assert params3["base_url"] == "https://env.openai.com/v1"
def test_openai_get_client_params_no_base_url():
def test_openai_get_client_params_no_base_url(monkeypatch):
"""
Test that _get_client_params works correctly when no base_url is specified
"""
# Clear env vars that could set base_url
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.delenv("OPENAI_API_BASE", raising=False)
llm = OpenAICompletion(model="gpt-4o")
client_params = llm._get_client_params()
# When no base_url is provided, it should not be in the params (filtered out as None)

139
lib/crewai/tests/llms/openai/test_openai_async.py Normal file
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@@ -0,0 +1,139 @@
"""Tests for OpenAI async completion functionality."""
import pytest
import tiktoken
from crewai.llm import LLM
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_basic_call():
"""Test basic async call with OpenAI."""
llm = LLM(model="gpt-4o-mini")
result = await llm.acall("Say hello")
assert result is not None
assert isinstance(result, str)
assert len(result) > 0
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_with_temperature():
"""Test async call with temperature parameter."""
llm = LLM(model="gpt-4o-mini", temperature=0.1)
result = await llm.acall("Say the word 'test' once")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_with_max_tokens():
"""Test async call with max_tokens parameter."""
llm = LLM(model="gpt-4o-mini", max_tokens=10)
result = await llm.acall("Write a very long story about a dragon.")
assert result is not None
assert isinstance(result, str)
encoder = tiktoken.get_encoding("cl100k_base")
token_count = len(encoder.encode(result))
assert token_count <= 10
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_with_system_message():
"""Test async call with system message."""
llm = LLM(model="gpt-4o-mini")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is 2+2?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_conversation():
"""Test async call with conversation history."""
llm = LLM(model="gpt-4o-mini")
messages = [
{"role": "user", "content": "My name is Alice."},
{"role": "assistant", "content": "Hello Alice! Nice to meet you."},
{"role": "user", "content": "What is my name?"}
]
result = await llm.acall(messages)
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_multiple_calls():
"""Test making multiple async calls in sequence."""
llm = LLM(model="gpt-4o-mini")
result1 = await llm.acall("What is 1+1?")
result2 = await llm.acall("What is 2+2?")
assert result1 is not None
assert result2 is not None
assert isinstance(result1, str)
assert isinstance(result2, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_with_response_format_none():
"""Test async call with response_format set to None."""
llm = LLM(model="gpt-4o-mini", response_format=None)
result = await llm.acall("Tell me a short fact")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_with_response_format_json():
"""Test async call with JSON response format."""
llm = LLM(model="gpt-4o-mini", response_format={"type": "json_object"})
result = await llm.acall("Return a JSON object with a 'greeting' field")
assert result is not None
assert isinstance(result, str)
@pytest.mark.vcr()
@pytest.mark.asyncio
async def test_openai_async_with_parameters():
"""Test async call with multiple parameters."""
llm = LLM(
model="gpt-4o-mini",
temperature=0.7,
max_tokens=100,
top_p=0.9,
frequency_penalty=0.5,
presence_penalty=0.3
)
result = await llm.acall("Tell me a short fact")
assert result is not None
assert isinstance(result, str)

9
lib/crewai/tests/test_custom_llm.py
View File

@@ -77,6 +77,9 @@ class CustomLLM(BaseLLM):
"""
return 4096
async def acall(self, messages, tools=None, callbacks=None, available_functions=None, from_task=None, from_agent=None, response_model=None):
raise NotImplementedError
@pytest.mark.vcr()
def test_custom_llm_implementation():
@@ -192,6 +195,9 @@ class JWTAuthLLM(BaseLLM):
"""Return a default context window size."""
return 8192
async def acall(self, messages, tools=None, callbacks=None, available_functions=None, from_task=None, from_agent=None, response_model=None):
raise NotImplementedError
def test_custom_llm_with_jwt_auth():
"""Test a custom LLM implementation with JWT authentication."""
@@ -339,6 +345,9 @@ class TimeoutHandlingLLM(BaseLLM):
"""
return 8192
async def acall(self, messages, tools=None, callbacks=None, available_functions=None, from_task=None, from_agent=None, response_model=None):
raise NotImplementedError
def test_timeout_handling_llm():
"""Test a custom LLM implementation with timeout handling and retry logic."""

12
lib/devtools/pyproject.toml
View File

@@ -10,12 +10,12 @@ requires-python = ">=3.10, <3.14"
classifiers = ["Private :: Do Not Upload"]
private = true
dependencies = [
"click>=8.3.0",
"toml>=0.10.2",
"openai>=1.0.0",
"python-dotenv>=1.1.1",
"pygithub>=1.59.1",
"rich>=13.9.4",
"click~=8.1.7",
"toml~=0.10.2",
"openai~=1.83.0",
"python-dotenv~=1.1.1",
"pygithub~=1.59.1",
"rich~=13.9.4",
]
[project.scripts]