Brandon/eng 266 conversation crew v1 (#1843)

* worked on foundation for new conversational crews. Now going to work on chatting.

* core loop should be working and ready for testing.

* high level chat working

* its alive!!

* Added in Joaos feedback to steer crew chats back towards the purpose of the crew

* properly return tool call result

* accessing crew directly instead of through uv commands

* everything is working for conversation now

* Fix linting

* fix llm_utils.py and other type errors

* fix more type errors

* fixing type error

* More fixing of types

* fix failing tests

* Fix more failing tests

* adding tests. cleaing up pr.

* improve

* drop old functions

* improve type hintings

src/crewai/utilities/llm_utils.py

@@ -0,0 +1,186 @@
+import os
+from typing import Any, Dict, List, Optional, Union
+
+from packaging import version
+
+from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
+from crewai.cli.utils import read_toml
+from crewai.cli.version import get_crewai_version
+from crewai.llm import LLM
+
+
+def create_llm(
+    llm_value: Union[str, LLM, Any, None] = None,
+) -> Optional[LLM]:
+    """
+    Creates or returns an LLM instance based on the given llm_value.
+
+    Args:
+        llm_value (str | LLM | Any | None):
+            - str: The model name (e.g., "gpt-4").
+            - 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.
+
+    Returns:
+        An LLM instance if successful, or None if something fails.
+    """
+
+    # 1) If llm_value is already an LLM object, return it directly
+    if isinstance(llm_value, LLM):
+        return llm_value
+
+    # 2) If llm_value is a string (model name)
+    if isinstance(llm_value, str):
+        try:
+            created_llm = LLM(model=llm_value)
+            return created_llm
+        except Exception as e:
+            print(f"Failed to instantiate LLM with model='{llm_value}': {e}")
+            return None
+
+    # 3) If llm_value is None, parse environment variables or use default
+    if llm_value is None:
+        return _llm_via_environment_or_fallback()
+
+    # 4) Otherwise, attempt to extract relevant attributes from an unknown object
+    try:
+        # 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='{model}'")
+        return created_llm
+    except Exception as e:
+        print(f"Error instantiating LLM from unknown object type: {e}")
+        return None
+
+
+def _llm_via_environment_or_fallback() -> Optional[LLM]:
+    """
+    Helper function: if llm_value is None, we load environment variables or fallback default model.
+    """
+    model_name = (
+        os.environ.get("OPENAI_MODEL_NAME")
+        or os.environ.get("MODEL")
+        or DEFAULT_LLM_MODEL
+    )
+
+    # 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:
+        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",
+        "AWS_SECRET_ACCESS_KEY",
+        "AWS_REGION_NAME",
+    ]
+    set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
+
+    if set_provider in ENV_VARS:
+        env_vars_for_provider = ENV_VARS[set_provider]
+        if isinstance(env_vars_for_provider, (list, tuple)):
+            for env_var in env_vars_for_provider:
+                key_name = env_var.get("key_name")
+                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 parameters
+                        param_key = _normalize_key_name(key_name.lower())
+                        llm_params[param_key] = env_value
+                elif isinstance(env_var, dict):
+                    if env_var.get("default", False):
+                        for key, value in env_var.items():
+                            if key not in ["prompt", "key_name", "default"]:
+                                llm_params[key.lower()] = value
+                else:
+                    print(
+                        f"Expected env_var to be a dictionary, but got {type(env_var)}"
+                    )
+
+    # Remove None values
+    llm_params = {k: v for k, v in llm_params.items() if v is not None}
+
+    # Try creating the LLM
+    try:
+        new_llm = LLM(**llm_params)
+        return new_llm
+    except Exception as e:
+        print(
+            f"Error instantiating LLM from environment/fallback: {type(e).__name__}: {e}"
+        )
+        return None
+
+
+def _normalize_key_name(key_name: str) -> str:
+    """
+    Maps environment variable names to recognized litellm parameter keys,
+    using patterns from LITELLM_PARAMS.
+    """
+    for pattern in LITELLM_PARAMS:
+        if pattern in key_name:
+            return pattern
+    return key_name

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