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crewAI/src/crewai/llm.py
Greyson LaLonde 3e97393f58 chore: improve typing and consolidate utilities 
- add type annotations across utility modules  
- refactor printer system, agent utils, and imports for consistency  
- remove unused modules, constants, and redundant patterns  
- improve runtime type checks, exception handling, and guardrail validation  
- standardize warning suppression and logging utilities  
- fix llm typing, threading/typing edge cases, and test behavior
2025-09-23 11:33:46 -04:00

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import io
import json
import logging
import os
import sys
import threading
from collections import defaultdict
from collections.abc import Callable
from datetime import datetime
from typing import (
Any,
Final,
Literal,
TextIO,
TypedDict,
cast,
)
from dotenv import load_dotenv
from litellm.types.utils import ChatCompletionDeltaToolCall
from pydantic import BaseModel, Field
from crewai.events.event_bus import crewai_event_bus
from crewai.events.types.llm_events import (
LLMCallCompletedEvent,
LLMCallFailedEvent,
LLMCallStartedEvent,
LLMCallType,
LLMStreamChunkEvent,
)
from crewai.events.types.tool_usage_events import (
ToolUsageErrorEvent,
ToolUsageFinishedEvent,
ToolUsageStartedEvent,
)
from crewai.llms.base_llm import BaseLLM
from crewai.utilities.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededError,
)
from crewai.utilities.logger_utils import suppress_warnings
with suppress_warnings():
import litellm
from litellm import Choices, CustomLogger
from litellm.exceptions import ContextWindowExceededError
from litellm.litellm_core_utils.get_supported_openai_params import (
get_supported_openai_params,
)
from litellm.types.utils import ModelResponse
from litellm.utils import supports_response_schema
load_dotenv()
litellm.suppress_debug_info = True
class FilteredStream(io.TextIOBase):
_lock = None
def __init__(self, original_stream: TextIO):
self._original_stream = original_stream
self._lock = threading.Lock()
def write(self, s: str) -> int:
if not self._lock:
self._lock = threading.Lock()
with self._lock:
lower_s = s.lower()
# Skip common noisy LiteLLM banners and any other lines that contain "litellm"
if (
"litellm.info:" in lower_s
or "Consider using a smaller input or implementing a text splitting strategy"
in lower_s
):
return 0
return self._original_stream.write(s)
def flush(self):
with self._lock:
return self._original_stream.flush()
def __getattr__(self, name):
"""Delegate attribute access to the wrapped original stream.
This ensures compatibility with libraries (e.g., Rich) that rely on
attributes such as `encoding`, `isatty`, `buffer`, etc., which may not
be explicitly defined on this proxy class.
"""
return getattr(self._original_stream, name)
# Delegate common properties/methods explicitly so they aren't shadowed by
# the TextIOBase defaults (e.g., .encoding returns None by default, which
# confuses Rich). These explicit pass-throughs ensure the wrapped Console
# still sees a fully-featured stream.
@property
def encoding(self):
return getattr(self._original_stream, "encoding", "utf-8")
def isatty(self):
return self._original_stream.isatty()
def fileno(self):
return self._original_stream.fileno()
def writable(self):
return True
# Apply the filtered stream globally so that any subsequent writes containing the filtered
# keywords (e.g., "litellm") are hidden from terminal output. We guard against double
# wrapping to ensure idempotency in environments where this module might be reloaded.
if not isinstance(sys.stdout, FilteredStream):
sys.stdout = FilteredStream(sys.stdout)
if not isinstance(sys.stderr, FilteredStream):
sys.stderr = FilteredStream(sys.stderr)
MIN_CONTEXT: Final[int] = 1024
MAX_CONTEXT: Final[int] = 2097152 # Current max from gemini-1.5-pro
ANTHROPIC_PREFIXES: Final[tuple[str, str, str]] = ("anthropic/", "claude-", "claude/")
LLM_CONTEXT_WINDOW_SIZES: Final[dict[str, int]] = {
# openai
"gpt-4": 8192,
"gpt-4o": 128000,
"gpt-4o-mini": 200000,
"gpt-4-turbo": 128000,
"gpt-4.1": 1047576, # Based on official docs
"gpt-4.1-mini-2025-04-14": 1047576,
"gpt-4.1-nano-2025-04-14": 1047576,
"o1-preview": 128000,
"o1-mini": 128000,
"o3-mini": 200000,
"o4-mini": 200000,
# gemini
"gemini-2.0-flash": 1048576,
"gemini-2.0-flash-thinking-exp-01-21": 32768,
"gemini-2.0-flash-lite-001": 1048576,
"gemini-2.0-flash-001": 1048576,
"gemini-2.5-flash-preview-04-17": 1048576,
"gemini-2.5-pro-exp-03-25": 1048576,
"gemini-1.5-pro": 2097152,
"gemini-1.5-flash": 1048576,
"gemini-1.5-flash-8b": 1048576,
"gemini/gemma-3-1b-it": 32000,
"gemini/gemma-3-4b-it": 128000,
"gemini/gemma-3-12b-it": 128000,
"gemini/gemma-3-27b-it": 128000,
# deepseek
"deepseek-chat": 128000,
# groq
"gemma2-9b-it": 8192,
"gemma-7b-it": 8192,
"llama3-groq-70b-8192-tool-use-preview": 8192,
"llama3-groq-8b-8192-tool-use-preview": 8192,
"llama-3.1-70b-versatile": 131072,
"llama-3.1-8b-instant": 131072,
"llama-3.2-1b-preview": 8192,
"llama-3.2-3b-preview": 8192,
"llama-3.2-11b-text-preview": 8192,
"llama-3.2-90b-text-preview": 8192,
"llama3-70b-8192": 8192,
"llama3-8b-8192": 8192,
"mixtral-8x7b-32768": 32768,
"llama-3.3-70b-versatile": 128000,
"llama-3.3-70b-instruct": 128000,
# sambanova
"Meta-Llama-3.3-70B-Instruct": 131072,
"QwQ-32B-Preview": 8192,
"Qwen2.5-72B-Instruct": 8192,
"Qwen2.5-Coder-32B-Instruct": 8192,
"Meta-Llama-3.1-405B-Instruct": 8192,
"Meta-Llama-3.1-70B-Instruct": 131072,
"Meta-Llama-3.1-8B-Instruct": 131072,
"Llama-3.2-90B-Vision-Instruct": 16384,
"Llama-3.2-11B-Vision-Instruct": 16384,
"Meta-Llama-3.2-3B-Instruct": 4096,
"Meta-Llama-3.2-1B-Instruct": 16384,
# bedrock
"us.amazon.nova-pro-v1:0": 300000,
"us.amazon.nova-micro-v1:0": 128000,
"us.amazon.nova-lite-v1:0": 300000,
"us.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
"us.anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
"us.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
"us.anthropic.claude-3-7-sonnet-20250219-v1:0": 200000,
"us.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
"us.anthropic.claude-3-opus-20240229-v1:0": 200000,
"us.anthropic.claude-3-haiku-20240307-v1:0": 200000,
"us.meta.llama3-2-11b-instruct-v1:0": 128000,
"us.meta.llama3-2-3b-instruct-v1:0": 131000,
"us.meta.llama3-2-90b-instruct-v1:0": 128000,
"us.meta.llama3-2-1b-instruct-v1:0": 131000,
"us.meta.llama3-1-8b-instruct-v1:0": 128000,
"us.meta.llama3-1-70b-instruct-v1:0": 128000,
"us.meta.llama3-3-70b-instruct-v1:0": 128000,
"us.meta.llama3-1-405b-instruct-v1:0": 128000,
"eu.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
"eu.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
"eu.anthropic.claude-3-haiku-20240307-v1:0": 200000,
"eu.meta.llama3-2-3b-instruct-v1:0": 131000,
"eu.meta.llama3-2-1b-instruct-v1:0": 131000,
"apac.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
"apac.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
"apac.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
"apac.anthropic.claude-3-haiku-20240307-v1:0": 200000,
"amazon.nova-pro-v1:0": 300000,
"amazon.nova-micro-v1:0": 128000,
"amazon.nova-lite-v1:0": 300000,
"anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
"anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
"anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
"anthropic.claude-3-7-sonnet-20250219-v1:0": 200000,
"anthropic.claude-3-sonnet-20240229-v1:0": 200000,
"anthropic.claude-3-opus-20240229-v1:0": 200000,
"anthropic.claude-3-haiku-20240307-v1:0": 200000,
"anthropic.claude-v2:1": 200000,
"anthropic.claude-v2": 100000,
"anthropic.claude-instant-v1": 100000,
"meta.llama3-1-405b-instruct-v1:0": 128000,
"meta.llama3-1-70b-instruct-v1:0": 128000,
"meta.llama3-1-8b-instruct-v1:0": 128000,
"meta.llama3-70b-instruct-v1:0": 8000,
"meta.llama3-8b-instruct-v1:0": 8000,
"amazon.titan-text-lite-v1": 4000,
"amazon.titan-text-express-v1": 8000,
"cohere.command-text-v14": 4000,
"ai21.j2-mid-v1": 8191,
"ai21.j2-ultra-v1": 8191,
"ai21.jamba-instruct-v1:0": 256000,
"mistral.mistral-7b-instruct-v0:2": 32000,
"mistral.mixtral-8x7b-instruct-v0:1": 32000,
# mistral
"mistral-tiny": 32768,
"mistral-small-latest": 32768,
"mistral-medium-latest": 32768,
"mistral-large-latest": 32768,
"mistral-large-2407": 32768,
"mistral-large-2402": 32768,
"mistral/mistral-tiny": 32768,
"mistral/mistral-small-latest": 32768,
"mistral/mistral-medium-latest": 32768,
"mistral/mistral-large-latest": 32768,
"mistral/mistral-large-2407": 32768,
"mistral/mistral-large-2402": 32768,
}
DEFAULT_CONTEXT_WINDOW_SIZE: Final[int] = 8192
CONTEXT_WINDOW_USAGE_RATIO: Final[float] = 0.85
class Delta(TypedDict):
content: str | None
role: str | None
class StreamingChoices(TypedDict):
delta: Delta
index: int
finish_reason: str | None
class FunctionArgs(BaseModel):
name: str = ""
arguments: str = ""
class AccumulatedToolArgs(BaseModel):
function: FunctionArgs = Field(default_factory=FunctionArgs)
class LLM(BaseLLM):
completion_cost: float | None = None
def __init__(
self,
model: str,
timeout: float | int | None = None,
temperature: float | None = None,
top_p: float | None = None,
n: int | None = None,
stop: str | list[str] | None = None,
max_completion_tokens: int | None = None,
max_tokens: int | None = None,
presence_penalty: float | None = None,
frequency_penalty: float | None = None,
logit_bias: dict[int, float] | None = None,
response_format: type[BaseModel] | None = None,
seed: int | None = None,
logprobs: int | None = None,
top_logprobs: int | None = None,
base_url: str | None = None,
api_base: str | None = None,
api_version: str | None = None,
api_key: str | None = None,
callbacks: list[Any] | None = None,
reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
stream: bool = False,
**kwargs,
):
self.model = model
self.timeout = timeout
self.temperature = temperature
self.top_p = top_p
self.n = n
self.max_completion_tokens = max_completion_tokens
self.max_tokens = max_tokens
self.presence_penalty = presence_penalty
self.frequency_penalty = frequency_penalty
self.logit_bias = logit_bias
self.response_format = response_format
self.seed = seed
self.logprobs = logprobs
self.top_logprobs = top_logprobs
self.base_url = base_url
self.api_base = api_base
self.api_version = api_version
self.api_key = api_key
self.callbacks = callbacks
self.context_window_size = 0
self.reasoning_effort = reasoning_effort
self.additional_params = kwargs
self.is_anthropic = self._is_anthropic_model(model)
self.stream = stream
litellm.drop_params = True
# Normalize self.stop to always be a List[str]
if stop is None:
self.stop: list[str] = []
elif isinstance(stop, str):
self.stop = [stop]
else:
self.stop = stop
self.set_callbacks(callbacks or [])
self.set_env_callbacks()
@staticmethod
def _is_anthropic_model(model: str) -> bool:
"""Determine if the model is from Anthropic provider.
Args:
model: The model identifier string.
Returns:
bool: True if the model is from Anthropic, False otherwise.
"""
return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
def _prepare_completion_params(
self,
messages: str | list[dict[str, str]],
tools: list[dict] | None = None,
) -> dict[str, Any]:
"""Prepare parameters for the completion call.
Args:
messages: Input messages for the LLM
tools: Optional list of tool schemas
Returns:
Dict[str, Any]: Parameters for the completion call
"""
# --- 1) Format messages according to provider requirements
if isinstance(messages, str):
messages = [{"role": "user", "content": messages}]
formatted_messages = self._format_messages_for_provider(messages)
# --- 2) Prepare the parameters for the completion call
params = {
"model": self.model,
"messages": formatted_messages,
"timeout": self.timeout,
"temperature": self.temperature,
"top_p": self.top_p,
"n": self.n,
"stop": self.stop,
"max_tokens": self.max_tokens or self.max_completion_tokens,
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"logit_bias": self.logit_bias,
"response_format": self.response_format,
"seed": self.seed,
"logprobs": self.logprobs,
"top_logprobs": self.top_logprobs,
"api_base": self.api_base,
"base_url": self.base_url,
"api_version": self.api_version,
"api_key": self.api_key,
"stream": self.stream,
"tools": tools,
"reasoning_effort": self.reasoning_effort,
**self.additional_params,
}
# Remove None values from params
return {k: v for k, v in params.items() if v is not None}
def _handle_streaming_response(
self,
params: dict[str, Any],
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str:
"""Handle a 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
Returns:
str: The complete response text
Raises:
Exception: If no content is received from the streaming response
"""
# --- 1) Initialize response tracking
full_response = ""
last_chunk = None
chunk_count = 0
usage_info = None
accumulated_tool_args: defaultdict[int, AccumulatedToolArgs] = defaultdict(
AccumulatedToolArgs
)
# --- 2) Make sure stream is set to True and include usage metrics
params["stream"] = True
params["stream_options"] = {"include_usage": True}
try:
# --- 3) Process each chunk in the stream
for chunk in litellm.completion(**params):
chunk_count += 1
last_chunk = chunk
# Extract content from the chunk
chunk_content = None
# Safely extract content from various chunk formats
try:
# Try to access choices safely
choices = None
if isinstance(chunk, dict) and "choices" in chunk:
choices = chunk["choices"]
elif hasattr(chunk, "choices"):
# Check if choices is not a type but an actual attribute with value
if not isinstance(chunk.choices, type):
choices = chunk.choices
# Try to extract usage information if available
if isinstance(chunk, dict) and "usage" in chunk:
usage_info = chunk["usage"]
elif hasattr(chunk, "usage"):
# Check if usage is not a type but an actual attribute with value
if not isinstance(chunk.usage, type):
usage_info = chunk.usage
if choices and len(choices) > 0:
choice = choices[0]
# Handle different delta formats
delta = None
if isinstance(choice, dict) and "delta" in choice:
delta = choice["delta"]
elif hasattr(choice, "delta"):
delta = choice.delta
# Extract content from delta
if delta:
# Handle dict format
if isinstance(delta, dict):
if "content" in delta and delta["content"] is not None:
chunk_content = delta["content"]
# Handle object format
elif hasattr(delta, "content"):
chunk_content = delta.content
# Handle case where content might be None or empty
if chunk_content is None and isinstance(delta, dict):
# Some models might send empty content chunks
chunk_content = ""
# Enable tool calls using streaming
if "tool_calls" in delta:
tool_calls = delta["tool_calls"]
if tool_calls:
result = self._handle_streaming_tool_calls(
tool_calls=tool_calls,
accumulated_tool_args=accumulated_tool_args,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
if result is not None:
chunk_content = result
except Exception as e:
logging.debug(f"Error extracting content from chunk: {e}")
logging.debug(f"Chunk format: {type(chunk)}, content: {chunk}")
# Only add non-None content to the response
if chunk_content is not None:
# Add the chunk content to the full response
full_response += chunk_content
# Emit the chunk event
if not hasattr(crewai_event_bus, "emit"):
raise Exception("crewai_event_bus must have an `emit` method")
crewai_event_bus.emit(
self,
event=LLMStreamChunkEvent(
chunk=chunk_content,
from_task=from_task,
from_agent=from_agent,
),
)
# --- 4) Fallback to non-streaming if no content received
if not full_response.strip() and chunk_count == 0:
logging.warning(
"No chunks received in streaming response, falling back to non-streaming"
)
non_streaming_params = params.copy()
non_streaming_params["stream"] = False
non_streaming_params.pop(
"stream_options", None
) # Remove stream_options for non-streaming call
return self._handle_non_streaming_response(
non_streaming_params,
callbacks,
available_functions,
from_task,
from_agent,
)
# --- 5) Handle empty response with chunks
if not full_response.strip() and chunk_count > 0:
logging.warning(
f"Received {chunk_count} chunks but no content was extracted"
)
if last_chunk is not None:
try:
# Try to extract content from the last chunk's message
choices = None
if isinstance(last_chunk, dict) and "choices" in last_chunk:
choices = last_chunk["choices"]
elif hasattr(last_chunk, "choices"):
if not isinstance(last_chunk.choices, type):
choices = last_chunk.choices
if choices and len(choices) > 0:
choice = choices[0]
# Try to get content from message
message = None
if isinstance(choice, dict) and "message" in choice:
message = choice["message"]
elif hasattr(choice, "message"):
message = choice.message
if message:
content = None
if isinstance(message, dict) and "content" in message:
content = message["content"]
elif hasattr(message, "content"):
content = message.content
if content:
full_response = content
logging.info(
f"Extracted content from last chunk message: {full_response}"
)
except Exception as e:
logging.debug(f"Error extracting content from last chunk: {e}")
logging.debug(
f"Last chunk format: {type(last_chunk)}, content: {last_chunk}"
)
# --- 6) If still empty, raise an error instead of using a default response
if not full_response.strip() and len(accumulated_tool_args) == 0:
raise Exception(
"No content received from streaming response. Received empty chunks or failed to extract content."
)
# --- 7) Check for tool calls in the final response
tool_calls = None
try:
if last_chunk:
choices = None
if isinstance(last_chunk, dict) and "choices" in last_chunk:
choices = last_chunk["choices"]
elif hasattr(last_chunk, "choices"):
if not isinstance(last_chunk.choices, type):
choices = last_chunk.choices
if choices and len(choices) > 0:
choice = choices[0]
message = None
if isinstance(choice, dict) and "message" in choice:
message = choice["message"]
elif hasattr(choice, "message"):
message = choice.message
if message:
if isinstance(message, dict) and "tool_calls" in message:
tool_calls = message["tool_calls"]
elif hasattr(message, "tool_calls"):
tool_calls = message.tool_calls
except Exception as e:
logging.debug(f"Error checking for tool calls: {e}")
# --- 8) If no tool calls or no available functions, return the text response directly
if not tool_calls or not available_functions:
# Log token usage if available in streaming mode
self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
# Emit completion event and return response
self._handle_emit_call_events(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return full_response
# --- 9) Handle tool calls if present
tool_result = self._handle_tool_call(tool_calls, available_functions)
if tool_result is not None:
return tool_result
# --- 10) Log token usage if available in streaming mode
self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
# --- 11) Emit completion event and return response
self._handle_emit_call_events(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return full_response
except ContextWindowExceededError as e:
# Catch context window errors from litellm and convert them to our own exception type.
# This exception is handled by CrewAgentExecutor._invoke_loop() which can then
# decide whether to summarize the content or abort based on the respect_context_window flag.
raise LLMContextLengthExceededError(str(e)) from e
except Exception as e:
logging.error(f"Error in streaming response: {e!s}")
if full_response.strip():
logging.warning(f"Returning partial response despite error: {e!s}")
self._handle_emit_call_events(
response=full_response,
call_type=LLMCallType.LLM_CALL,
from_task=from_task,
from_agent=from_agent,
messages=params["messages"],
)
return full_response
# Emit failed event and re-raise the exception
if not hasattr(crewai_event_bus, "emit"):
raise AttributeError(
"crewai_event_bus must have an 'emit' method"
) from e
crewai_event_bus.emit(
self,
event=LLMCallFailedEvent(
error=str(e), from_task=from_task, from_agent=from_agent
),
)
raise Exception(f"Failed to get streaming response: {e!s}") from e
def _handle_streaming_tool_calls(
self,
tool_calls: list[ChatCompletionDeltaToolCall],
accumulated_tool_args: defaultdict[int, AccumulatedToolArgs],
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> None | str:
for tool_call in tool_calls:
current_tool_accumulator = accumulated_tool_args[tool_call.index]
if tool_call.function.name:
current_tool_accumulator.function.name = tool_call.function.name
if tool_call.function.arguments:
current_tool_accumulator.function.arguments += (
tool_call.function.arguments
)
if not hasattr(crewai_event_bus, "emit"):
raise AttributeError("crewai_event_bus must have an 'emit' method")
crewai_event_bus.emit(
self,
event=LLMStreamChunkEvent(
tool_call=tool_call.to_dict(),
chunk=tool_call.function.arguments,
from_task=from_task,
from_agent=from_agent,
),
)
if (
current_tool_accumulator.function.name
and current_tool_accumulator.function.arguments
and available_functions
):
try:
json.loads(current_tool_accumulator.function.arguments)
return self._handle_tool_call(
[current_tool_accumulator],
available_functions,
)
except json.JSONDecodeError:
continue
return None
@staticmethod
def _handle_streaming_callbacks(
callbacks: list[Any] | None,
usage_info: dict[str, Any] | None,
last_chunk: Any | None,
) -> None:
"""Handle callbacks with usage info for streaming responses.
Args:
callbacks: Optional list of callback functions
usage_info: Usage information collected during streaming
last_chunk: The last chunk received from the streaming response
"""
if callbacks and len(callbacks) > 0:
for callback in callbacks:
if hasattr(callback, "log_success_event"):
# Use the usage_info we've been tracking
if not usage_info:
# Try to get usage from the last chunk if we haven't already
try:
if last_chunk:
if (
isinstance(last_chunk, dict)
and "usage" in last_chunk
):
usage_info = last_chunk["usage"]
elif hasattr(last_chunk, "usage"):
if not isinstance(last_chunk.usage, type):
usage_info = last_chunk.usage
except Exception as e:
logging.debug(f"Error extracting usage info: {e}")
if usage_info:
callback.log_success_event(
kwargs={}, # We don't have the original params here
response_obj={"usage": usage_info},
start_time=0,
end_time=0,
)
def _handle_non_streaming_response(
self,
params: dict[str, Any],
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str | Any:
"""Handle a 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
Returns:
str: The response text
"""
# --- 1) Make the completion call
try:
# Attempt to make the completion call, but catch context window errors
# and convert them to our own exception type for consistent handling
# across the codebase. This allows CrewAgentExecutor to handle context
# length issues appropriately.
response = litellm.completion(**params)
except ContextWindowExceededError as e:
# Convert litellm's context window error to our own exception type
# for consistent handling in the rest of the codebase
raise LLMContextLengthExceededError(str(e)) from e
# --- 2) Extract response message and content
response_message = cast(Choices, cast(ModelResponse, response).choices)[
0
].message
text_response = response_message.content or ""
# --- 3) Handle callbacks with usage info
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,
)
# --- 4) Check for tool calls
tool_calls = getattr(response_message, "tool_calls", [])
# --- 5) If no tool calls or no available functions, return the text response directly as long as there is a text response
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
# --- 6) If there is no text response, no available functions, but there are tool calls, return the tool calls
if tool_calls and not available_functions and not text_response:
return tool_calls
# --- 7) Handle tool calls if present
tool_result = self._handle_tool_call(
tool_calls, available_functions, from_task, from_agent
)
if tool_result is not None:
return tool_result
# --- 8) If tool call handling didn't return a result, emit completion event and return 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
def _handle_tool_call(
self,
tool_calls: list[Any],
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str | None:
"""Handle a tool call from the LLM.
Args:
tool_calls: List of tool calls from the LLM
available_functions: Dict of available functions
from_task: Optional Task that invoked the LLM
from_agent: Optional Agent that invoked the LLM
Returns:
The result of the tool call, or None if no tool call was made
"""
# --- 1) Validate tool calls and available functions
if not tool_calls or not available_functions:
return None
# --- 2) Extract function name from first tool call
tool_call = tool_calls[0]
function_name = tool_call.function.name
function_args = {} # Initialize to empty dict to avoid unbound variable
# --- 3) Check if function is available
if function_name in available_functions:
try:
# --- 3.1) Parse function arguments
function_args = json.loads(tool_call.function.arguments)
fn = available_functions[function_name]
# --- 3.2) Execute function
if not hasattr(crewai_event_bus, "emit"):
raise AttributeError("crewai_event_bus must have an 'emit' method")
started_at = datetime.now()
crewai_event_bus.emit(
self,
event=ToolUsageStartedEvent(
tool_name=function_name,
tool_args=function_args,
from_agent=from_agent,
from_task=from_task,
),
)
result = fn(**function_args)
crewai_event_bus.emit(
self,
event=ToolUsageFinishedEvent(
output=result,
tool_name=function_name,
tool_args=function_args,
started_at=started_at,
finished_at=datetime.now(),
from_task=from_task,
from_agent=from_agent,
),
)
# --- 3.3) Emit success event
self._handle_emit_call_events(
response=result,
call_type=LLMCallType.TOOL_CALL,
from_task=from_task,
from_agent=from_agent,
)
return result
except Exception as e:
# --- 3.4) Handle execution errors
fn = available_functions.get(
function_name, lambda: None
) # Ensure fn is always a callable
logging.error(f"Error executing function '{function_name}': {e}")
if not hasattr(crewai_event_bus, "emit"):
raise AttributeError(
"crewai_event_bus must have an 'emit' method"
) from e
crewai_event_bus.emit(
self,
event=LLMCallFailedEvent(error=f"Tool execution error: {e!s}"),
)
crewai_event_bus.emit(
self,
event=ToolUsageErrorEvent(
tool_name=function_name,
tool_args=function_args,
error=f"Tool execution error: {e!s}",
from_task=from_task,
from_agent=from_agent,
),
)
return None
def call(
self,
messages: str | list[dict[str, str]],
tools: list[dict] | None = None,
callbacks: list[Any] | None = None,
available_functions: dict[str, Any] | None = None,
from_task: Any | None = None,
from_agent: Any | None = None,
) -> str | Any:
"""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
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
"""
# --- 1) Emit call started event
if not hasattr(crewai_event_bus, "emit"):
raise AttributeError("crewai_event_bus must have an 'emit' method")
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,
),
)
# --- 2) Validate parameters before proceeding with the call
self._validate_call_params()
# --- 3) Convert string messages to proper format if needed
if isinstance(messages, str):
messages = [{"role": "user", "content": messages}]
# --- 4) Handle O1 model special case (system messages not supported)
if "o1" in self.model.lower():
for message in messages:
if message.get("role") == "system":
message["role"] = "assistant"
# --- 5) Set up callbacks if provided
with suppress_warnings():
if callbacks and len(callbacks) > 0:
self.set_callbacks(callbacks)
try:
# --- 6) Prepare parameters for the completion call
params = self._prepare_completion_params(messages, tools)
# --- 7) Make the completion call and handle response
if self.stream:
return self._handle_streaming_response(
params, callbacks, available_functions, from_task, from_agent
)
return self._handle_non_streaming_response(
params, callbacks, available_functions, from_task, from_agent
)
except LLMContextLengthExceededError:
# Re-raise LLMContextLengthExceededError as it should be handled
# by the CrewAgentExecutor._invoke_loop method, which can then decide
# whether to summarize the content or abort based on the respect_context_window flag
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 self.call(
messages,
tools=tools,
callbacks=callbacks,
available_functions=available_functions,
from_task=from_task,
from_agent=from_agent,
)
if not hasattr(crewai_event_bus, "emit"):
raise AttributeError(
"crewai_event_bus must have an 'emit' method"
) from e
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,
call_type: LLMCallType,
from_task: Any | None = None,
from_agent: Any | None = None,
messages: str | list[dict[str, Any]] | None = None,
):
"""Handle the events for the LLM call.
Args:
response (str): The response from the LLM call.
call_type (str): The type of call, either "tool_call" or "llm_call".
from_task: Optional task object
from_agent: Optional agent object
messages: Optional messages object
"""
if not hasattr(crewai_event_bus, "emit"):
raise AttributeError("crewai_event_bus must have an 'emit' method")
crewai_event_bus.emit(
self,
event=LLMCallCompletedEvent(
messages=messages,
response=response,
call_type=call_type,
from_task=from_task,
from_agent=from_agent,
model=self.model,
),
)
def _format_messages_for_provider(
self, messages: list[dict[str, str]]
) -> list[dict[str, str]]:
"""Format messages according to provider requirements.
Args:
messages: List of message dictionaries with 'role' and 'content' keys.
Can be empty or None.
Returns:
List of formatted messages according to provider requirements.
For Anthropic models, ensures first message has 'user' role.
Raises:
TypeError: If messages is None or contains invalid message format.
"""
if messages is None:
raise TypeError("Messages cannot be None")
# Validate message format first
for msg in messages:
if not isinstance(msg, dict) or "role" not in msg or "content" not in msg:
raise TypeError(
"Invalid message format. Each message must be a dict with 'role' and 'content' keys"
)
# Handle O1 models specially
if "o1" in self.model.lower():
formatted_messages = []
for msg in messages:
# Convert system messages to assistant messages
if msg["role"] == "system":
formatted_messages.append(
{"role": "assistant", "content": msg["content"]}
)
else:
formatted_messages.append(msg)
return formatted_messages
# Handle Mistral models - they require the last message to have a role of 'user' or 'tool'
if "mistral" in self.model.lower():
# Check if the last message has a role of 'assistant'
if messages and messages[-1]["role"] == "assistant":
return [*messages, {"role": "user", "content": "Please continue."}]
return messages
# TODO: Remove this code after merging PR https://github.com/BerriAI/litellm/pull/10917
# Ollama doesn't supports last message to be 'assistant'
if (
"ollama" in self.model.lower()
and messages
and messages[-1]["role"] == "assistant"
):
return [*messages, {"role": "user", "content": ""}]
# Handle Anthropic models
if not self.is_anthropic:
return messages
# Anthropic requires messages to start with 'user' role
if not messages or messages[0]["role"] == "system":
# If first message is system or empty, add a placeholder user message
return [{"role": "user", "content": "."}, *messages]
return messages
def _get_custom_llm_provider(self) -> str | None:
"""
Derives the custom_llm_provider from the model string.
- For example, if the model is "openrouter/deepseek/deepseek-chat", returns "openrouter".
- If the model is "gemini/gemini-1.5-pro", returns "gemini".
- If there is no '/', defaults to "openai".
"""
if "/" in self.model:
return self.model.partition("/")[0]
return None
def _validate_call_params(self) -> None:
"""
Validate parameters before making a call. Currently this only checks if
a response_format is provided and whether the model supports it.
The custom_llm_provider is dynamically determined from the model:
- E.g., "openrouter/deepseek/deepseek-chat" yields "openrouter"
- "gemini/gemini-1.5-pro" yields "gemini"
- If no slash is present, "openai" is assumed.
"""
provider = self._get_custom_llm_provider()
if self.response_format is not None and not supports_response_schema(
model=self.model,
custom_llm_provider=provider,
):
raise ValueError(
f"The model {self.model} does not support response_format for provider '{provider}'. "
"Please remove response_format or use a supported model."
)
def supports_function_calling(self) -> bool:
try:
provider = self._get_custom_llm_provider()
return litellm.utils.supports_function_calling(
self.model, custom_llm_provider=provider
)
except Exception as e:
logging.error(f"Failed to check function calling support: {e!s}")
return False
def supports_stop_words(self) -> bool:
try:
params = get_supported_openai_params(model=self.model)
return params is not None and "stop" in params
except Exception as e:
logging.error(f"Failed to get supported params: {e!s}")
return False
def get_context_window_size(self) -> int:
"""
Returns the context window size, using 75% of the maximum to avoid
cutting off messages mid-thread.
Raises:
ValueError: If a model's context window size is outside valid bounds (1024-2097152)
"""
if self.context_window_size != 0:
return self.context_window_size
# Validate all context window sizes
for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
if value < MIN_CONTEXT or value > MAX_CONTEXT:
raise ValueError(
f"Context window for {key} must be between {MIN_CONTEXT} and {MAX_CONTEXT}"
)
self.context_window_size = int(
DEFAULT_CONTEXT_WINDOW_SIZE * CONTEXT_WINDOW_USAGE_RATIO
)
for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
if self.model.startswith(key):
self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
return self.context_window_size
@staticmethod
def set_callbacks(callbacks: list[Any]):
"""
Attempt to keep a single set of callbacks in litellm by removing old
duplicates and adding new ones.
"""
with suppress_warnings():
callback_types = [type(callback) for callback in callbacks]
for callback in litellm.success_callback[:]:
if type(callback) in callback_types:
litellm.success_callback.remove(callback)
for callback in litellm._async_success_callback[:]:
if type(callback) in callback_types:
litellm._async_success_callback.remove(callback)
litellm.callbacks = callbacks
@staticmethod
def set_env_callbacks() -> None:
"""Sets the success and failure callbacks for the LiteLLM library from environment variables.
This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS`
environment variables, which should contain comma-separated lists of callback names.
It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`,
respectively.
If the environment variables are not set or are empty, the corresponding callback lists
will be set to empty lists.
Examples:
LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
LITELLM_FAILURE_CALLBACKS="langfuse"
This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
`litellm.failure_callback` to ["langfuse"].
"""
with suppress_warnings():
success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
success_callbacks: list[str | Callable[..., Any] | CustomLogger] = []
if success_callbacks_str:
success_callbacks = [
cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
]
failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
if failure_callbacks_str:
failure_callbacks: list[str | Callable[..., Any] | CustomLogger] = [
cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
]
litellm.success_callback = success_callbacks
litellm.failure_callback = failure_callbacks
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