crewAI/src/crewai/llm.py

import json
import logging
import os
import sys
import threading
import warnings
from contextlib import contextmanager
from typing import Any, Dict, List, Optional, Union, cast

from dotenv import load_dotenv

with warnings.catch_warnings():
    warnings.simplefilter("ignore", UserWarning)
    import litellm
    from litellm import Choices, get_supported_openai_params
    from litellm.types.utils import ModelResponse


from crewai.utilities.exceptions.context_window_exceeding_exception import (
    LLMContextLengthExceededException,
)

load_dotenv()


class FilteredStream:
    def __init__(self, original_stream):
        self._original_stream = original_stream
        self._lock = threading.Lock()

    def write(self, s) -> int:
        with self._lock:
            # Filter out extraneous messages from LiteLLM
            if (
                "Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new"
                in s
                or "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True`"
                in s
            ):
                return 0
            return self._original_stream.write(s)

    def flush(self):
        with self._lock:
            return self._original_stream.flush()


LLM_CONTEXT_WINDOW_SIZES = {
    # openai
    "gpt-4": 8192,
    "gpt-4o": 128000,
    "gpt-4o-mini": 128000,
    "gpt-4-turbo": 128000,
    "o1-preview": 128000,
    "o1-mini": 128000,
    # gemini
    "gemini-2.0-flash": 1048576,
    "gemini-1.5-pro": 2097152,
    "gemini-1.5-flash": 1048576,
    "gemini-1.5-flash-8b": 1048576,
    # 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,
}

DEFAULT_CONTEXT_WINDOW_SIZE = 8192
CONTEXT_WINDOW_USAGE_RATIO = 0.75


@contextmanager
def suppress_warnings():
    with warnings.catch_warnings():
        warnings.filterwarnings("ignore")
        warnings.filterwarnings(
            "ignore", message="open_text is deprecated*", category=DeprecationWarning
        )

        # Redirect stdout and stderr
        old_stdout = sys.stdout
        old_stderr = sys.stderr
        sys.stdout = FilteredStream(old_stdout)
        sys.stderr = FilteredStream(old_stderr)
        try:
            yield
        finally:
            sys.stdout = old_stdout
            sys.stderr = old_stderr


class LLM:
    def __init__(
        self,
        model: str,
        timeout: Optional[Union[float, int]] = None,
        temperature: Optional[float] = None,
        top_p: Optional[float] = None,
        n: Optional[int] = None,
        stop: Optional[Union[str, List[str]]] = None,
        max_completion_tokens: Optional[int] = None,
        max_tokens: Optional[int] = None,
        presence_penalty: Optional[float] = None,
        frequency_penalty: Optional[float] = None,
        logit_bias: Optional[Dict[int, float]] = None,
        response_format: Optional[Dict[str, Any]] = None,
        seed: Optional[int] = None,
        logprobs: Optional[int] = None,
        top_logprobs: Optional[int] = None,
        base_url: Optional[str] = None,
        api_version: Optional[str] = None,
        api_key: Optional[str] = None,
        callbacks: List[Any] = [],
    ):
        self.model = model
        self.timeout = timeout
        self.temperature = temperature
        self.top_p = top_p
        self.n = n
        self.stop = stop
        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_version = api_version
        self.api_key = api_key
        self.callbacks = callbacks
        self.context_window_size = 0

        litellm.drop_params = True

        self.set_callbacks(callbacks)
        self.set_env_callbacks()

    def call(
        self,
        messages: List[Dict[str, str]],
        tools: Optional[List[dict]] = None,
        callbacks: Optional[List[Any]] = None,
        available_functions: Optional[Dict[str, Any]] = None,
    ) -> str:
        """
        High-level call method that:
          1) Calls litellm.completion
          2) Checks for function/tool calls
          3) If a tool call is found:
               a) executes the function
               b) returns the result
          4) If no tool call, returns the text response

        :param messages: The conversation messages
        :param tools: Optional list of function schemas for function calling
        :param callbacks: Optional list of callbacks
        :param available_functions: A dictionary mapping function_name -> actual Python function
        :return: Final text response from the LLM or the tool result
        """
        with suppress_warnings():
            if callbacks and len(callbacks) > 0:
                self.set_callbacks(callbacks)

            try:
                # --- 1) Make the completion call
                params = {
                    "model": self.model,
                    "messages": 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.base_url,
                    "api_version": self.api_version,
                    "api_key": self.api_key,
                    "stream": False,
                    "tools": tools,  # pass the tool schema
                }

                params = {k: v for k, v in params.items() if v is not None}

                response = litellm.completion(**params)
                response_message = cast(Choices, cast(ModelResponse, response).choices)[
                    0
                ].message
                text_response = response_message.content or ""
                tool_calls = getattr(response_message, "tool_calls", [])
                
                # Ensure callbacks get the full response object 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,
                                )

                # --- 2) If no tool calls, return the text response
                if not tool_calls or not available_functions:
                    return text_response

                # --- 3) Handle the tool call
                tool_call = tool_calls[0]
                function_name = tool_call.function.name

                if function_name in available_functions:
                    try:
                        function_args = json.loads(tool_call.function.arguments)
                    except json.JSONDecodeError as e:
                        logging.warning(f"Failed to parse function arguments: {e}")
                        return text_response

                    fn = available_functions[function_name]
                    try:
                        # Call the actual tool function
                        result = fn(**function_args)

                        return result

                    except Exception as e:
                        logging.error(
                            f"Error executing function '{function_name}': {e}"
                        )
                        return text_response

                else:
                    logging.warning(
                        f"Tool call requested unknown function '{function_name}'"
                    )
                    return text_response

            except Exception as e:
                if not LLMContextLengthExceededException(
                    str(e)
                )._is_context_limit_error(str(e)):
                    logging.error(f"LiteLLM call failed: {str(e)}")
                raise

    def supports_function_calling(self) -> bool:
        try:
            params = get_supported_openai_params(model=self.model)
            return "response_format" in params
        except Exception as e:
            logging.error(f"Failed to get supported params: {str(e)}")
            return False

    def supports_stop_words(self) -> bool:
        try:
            params = get_supported_openai_params(model=self.model)
            return "stop" in params
        except Exception as e:
            logging.error(f"Failed to get supported params: {str(e)}")
            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.
        """
        if self.context_window_size != 0:
            return self.context_window_size

        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

    def set_callbacks(self, 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

    def set_env_callbacks(self):
        """
        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.

        Example:
            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 = []
            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", "")
            failure_callbacks = []
            if failure_callbacks_str:
                failure_callbacks = [
                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
                ]

                litellm.success_callback = success_callbacks
                litellm.failure_callback = failure_callbacks