fix llm_utils.py and other type errors

src/crewai/task.py

@@ -502,8 +502,11 @@ class Task(BaseModel):
             )
             print("crew_chat_messages:", inputs["crew_chat_messages"])
 
+            # Ensure that inputs["crew_chat_messages"] is a string
+            crew_chat_messages_json = str(inputs["crew_chat_messages"])
+
             try:
-                crew_chat_messages = json.loads(inputs["crew_chat_messages"])
+                crew_chat_messages = json.loads(crew_chat_messages_json)
             except json.JSONDecodeError as e:
                 print("An error occurred while parsing crew chat messages:", e)
                 raise

src/crewai/utilities/llm_utils.py

@@ -1,5 +1,5 @@
 import os
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 from packaging import version
 
@@ -21,8 +21,6 @@ def create_llm(
             - LLM: Already instantiated LLM, returned as-is.
             - Any: Attempt to extract known attributes like model_name, temperature, etc.
             - None: Use environment-based or fallback default model.
-        default_model (str): The fallback model name to use if llm_value is None
-                             and no environment variable is set.
 
     Returns:
         An LLM instance if successful, or None if something fails.
@@ -46,30 +44,33 @@ def create_llm(
     if llm_value is None:
         return _llm_via_environment_or_fallback()
 
-    # 4) Otherwise, attempt to extract relevant attributes from an unknown object (like a config)
-    #    e.g. follow the approach used in agent.py
+    # 4) Otherwise, attempt to extract relevant attributes from an unknown object
     try:
-        llm_params = {
-            "model": (
-                getattr(llm_value, "model_name", None)
-                or getattr(llm_value, "deployment_name", None)
-                or str(llm_value)
-            ),
-            "temperature": getattr(llm_value, "temperature", None),
-            "max_tokens": getattr(llm_value, "max_tokens", None),
-            "logprobs": getattr(llm_value, "logprobs", None),
-            "timeout": getattr(llm_value, "timeout", None),
-            "max_retries": getattr(llm_value, "max_retries", None),
-            "api_key": getattr(llm_value, "api_key", None),
-            "base_url": getattr(llm_value, "base_url", None),
-            "organization": getattr(llm_value, "organization", None),
-        }
-        # Remove None values
-        llm_params = {k: v for k, v in llm_params.items() if v is not None}
-        created_llm = LLM(**llm_params)
+        # Extract attributes with explicit types
+        model = (
+            getattr(llm_value, "model_name", None)
+            or getattr(llm_value, "deployment_name", None)
+            or str(llm_value)
+        )
+        temperature: Optional[float] = getattr(llm_value, "temperature", None)
+        max_tokens: Optional[int] = getattr(llm_value, "max_tokens", None)
+        logprobs: Optional[int] = getattr(llm_value, "logprobs", None)
+        timeout: Optional[float] = getattr(llm_value, "timeout", None)
+        api_key: Optional[str] = getattr(llm_value, "api_key", None)
+        base_url: Optional[str] = getattr(llm_value, "base_url", None)
+
+        created_llm = LLM(
+            model=model,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            logprobs=logprobs,
+            timeout=timeout,
+            api_key=api_key,
+            base_url=base_url,
+        )
         print(
             "LLM created with extracted parameters; "
-            f"model='{llm_params.get('model', 'UNKNOWN')}'"
+            f"model='{model}'"
         )
         return created_llm
     except Exception as e:
@@ -77,7 +78,7 @@ def create_llm(
         return None
 
 
-def create_chat_llm(default_model: str = "gpt-4") -> Optional[LLM]:
+def create_chat_llm() -> Optional[LLM]:
     """
     Creates a Chat LLM with additional checks, such as verifying crewAI version
     or reading from pyproject.toml. Then calls `create_llm(None, default_model)`.
@@ -115,12 +116,55 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
         or os.environ.get("MODEL")
         or DEFAULT_LLM_MODEL
     )
-    llm_params = {"model": model_name}
+
+    # Initialize parameters with correct types
+    model: str = model_name
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    max_completion_tokens: Optional[int] = None
+    logprobs: Optional[int] = None
+    timeout: Optional[float] = None
+    api_key: Optional[str] = None
+    base_url: Optional[str] = None
+    api_version: Optional[str] = None
+    presence_penalty: Optional[float] = None
+    frequency_penalty: Optional[float] = None
+    top_p: Optional[float] = None
+    n: Optional[int] = None
+    stop: Optional[Union[str, List[str]]] = None
+    logit_bias: Optional[Dict[int, float]] = None
+    response_format: Optional[Dict[str, Any]] = None
+    seed: Optional[int] = None
+    top_logprobs: Optional[int] = None
+    callbacks: List[Any] = []
 
     # Optional base URL from env
     api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get("OPENAI_BASE_URL")
     if api_base:
-        llm_params["base_url"] = api_base
+        base_url = api_base
+
+    # Initialize llm_params dictionary
+    llm_params: Dict[str, Any] = {
+        "model": model,
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+        "max_completion_tokens": max_completion_tokens,
+        "logprobs": logprobs,
+        "timeout": timeout,
+        "api_key": api_key,
+        "base_url": base_url,
+        "api_version": api_version,
+        "presence_penalty": presence_penalty,
+        "frequency_penalty": frequency_penalty,
+        "top_p": top_p,
+        "n": n,
+        "stop": stop,
+        "logit_bias": logit_bias,
+        "response_format": response_format,
+        "seed": seed,
+        "top_logprobs": top_logprobs,
+        "callbacks": callbacks,
+    }
 
     UNACCEPTED_ATTRIBUTES = [
         "AWS_ACCESS_KEY_ID",
@@ -135,14 +179,17 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
             if key_name and key_name not in UNACCEPTED_ATTRIBUTES:
                 env_value = os.environ.get(key_name)
                 if env_value:
-                    # Map environment variable names to recognized LITELLM_PARAMS if any
+                    # Map environment variable names to recognized parameters
                     param_key = _normalize_key_name(key_name.lower())
                     llm_params[param_key] = env_value
             elif env_var.get("default", False):
                 for key, value in env_var.items():
                     if key not in ["prompt", "key_name", "default"]:
                         if key in os.environ:
-                            llm_params[key] = value
+                            llm_params[key] = os.environ[key]
+
+    # Remove None values
+    llm_params = {k: v for k, v in llm_params.items() if v is not None}
 
     # Try creating the LLM
     try:
@@ -150,7 +197,7 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
         print(f"LLM created with model='{model_name}'")
         return new_llm
     except Exception as e:
-        print(f"Error instantiating LLM from environment/fallback: {e}")
+        print(f"Error instantiating LLM from environment/fallback: {type(e).__name__}: {e}")
         return None
 
 

src/crewai/utilities/token_counter_callback.py

@@ -1,4 +1,5 @@
 import warnings
+from typing import Any, Dict, Optional
 
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.types.utils import Usage
@@ -7,10 +8,16 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 
 
 class TokenCalcHandler(CustomLogger):
-    def __init__(self, token_cost_process: TokenProcess):
+    def __init__(self, token_cost_process: Optional[TokenProcess]):
         self.token_cost_process = token_cost_process
 
-    def log_success_event(self, kwargs, response_obj, start_time, end_time):
+    def log_success_event(
+        self,
+        kwargs: Dict[str, Any],
+        response_obj: Dict[str, Any],
+        start_time: float,
+        end_time: float,
+    ) -> None:
         if self.token_cost_process is None:
             return