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/agent.py

@@ -21,6 +21,7 @@ from crewai.tools.base_tool import Tool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
+from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -139,89 +140,9 @@ class Agent(BaseAgent):
     def post_init_setup(self):
         self._set_knowledge()
         self.agent_ops_agent_name = self.role
-        unaccepted_attributes = [
-            "AWS_ACCESS_KEY_ID",
-            "AWS_SECRET_ACCESS_KEY",
-            "AWS_REGION_NAME",
-        ]
 
-        # Handle different cases for self.llm
-        if isinstance(self.llm, str):
-            # If it's a string, create an LLM instance
-            self.llm = LLM(model=self.llm)
-        elif isinstance(self.llm, LLM):
-            # If it's already an LLM instance, keep it as is
-            pass
-        elif self.llm is None:
-            # Determine the model name from environment variables or use default
-            model_name = (
-                os.environ.get("OPENAI_MODEL_NAME")
-                or os.environ.get("MODEL")
-                or "gpt-4o-mini"
-            )
-            llm_params = {"model": model_name}
-
-            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
-                "OPENAI_BASE_URL"
-            )
-            if api_base:
-                llm_params["base_url"] = api_base
-
-            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
-
-            # Iterate over all environment variables to find matching API keys or use defaults
-            for provider, env_vars in ENV_VARS.items():
-                if provider == set_provider:
-                    for env_var in env_vars:
-                        # Check if the environment variable is set
-                        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:
-                                key_name = key_name.lower()
-                                for pattern in LITELLM_PARAMS:
-                                    if pattern in key_name:
-                                        key_name = pattern
-                                        break
-                                llm_params[key_name] = env_value
-                        # Check for default values if the environment variable is not set
-                        elif env_var.get("default", False):
-                            for key, value in env_var.items():
-                                if key not in ["prompt", "key_name", "default"]:
-                                    # Only add default if the key is already set in os.environ
-                                    if key in os.environ:
-                                        llm_params[key] = value
-
-            self.llm = LLM(**llm_params)
-        else:
-            # For any other type, attempt to extract relevant attributes
-            llm_params = {
-                "model": getattr(self.llm, "model_name", None)
-                or getattr(self.llm, "deployment_name", None)
-                or str(self.llm),
-                "temperature": getattr(self.llm, "temperature", None),
-                "max_tokens": getattr(self.llm, "max_tokens", None),
-                "logprobs": getattr(self.llm, "logprobs", None),
-                "timeout": getattr(self.llm, "timeout", None),
-                "max_retries": getattr(self.llm, "max_retries", None),
-                "api_key": getattr(self.llm, "api_key", None),
-                "base_url": getattr(self.llm, "base_url", None),
-                "organization": getattr(self.llm, "organization", None),
-            }
-            # Remove None values to avoid passing unnecessary parameters
-            llm_params = {k: v for k, v in llm_params.items() if v is not None}
-            self.llm = LLM(**llm_params)
-
-        # Similar handling for function_calling_llm
-        if self.function_calling_llm:
-            if isinstance(self.function_calling_llm, str):
-                self.function_calling_llm = LLM(model=self.function_calling_llm)
-            elif not isinstance(self.function_calling_llm, LLM):
-                self.function_calling_llm = LLM(
-                    model=getattr(self.function_calling_llm, "model_name", None)
-                    or getattr(self.function_calling_llm, "deployment_name", None)
-                    or str(self.function_calling_llm)
-                )
+        self.llm = create_llm(self.llm)
+        self.function_calling_llm = create_llm(self.function_calling_llm)
 
         if not self.agent_executor:
             self._setup_agent_executor()
@@ -413,6 +334,7 @@ class Agent(BaseAgent):
 
     def get_multimodal_tools(self) -> List[Tool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
+
         return [AddImageTool()]
 
     def get_code_execution_tools(self):

src/crewai/cli/cli.py

@@ -1,11 +1,13 @@
+import os
 from importlib.metadata import version as get_version
-from typing import Optional
+from typing import Optional, Tuple
 
 import click
 
 from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
+from crewai.cli.crew_chat import run_chat
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
@@ -342,5 +344,15 @@ def flow_add_crew(crew_name):
     add_crew_to_flow(crew_name)
 
 
+@crewai.command()
+def chat():
+    """
+    Start a conversation with the Crew, collecting user-supplied inputs,
+    and using the Chat LLM to generate responses.
+    """
+    click.echo("Starting a conversation with the Crew")
+    run_chat()
+
+
 if __name__ == "__main__":
     crewai()

src/crewai/cli/constants.py

@@ -158,6 +158,8 @@ MODELS = {
     ],
 }
 
+DEFAULT_LLM_MODEL = "gpt-4o-mini"
+
 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
 
 

src/crewai/cli/crew_chat.py

@@ -0,0 +1,413 @@
+import json
+import re
+import sys
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+import click
+import tomli
+
+from crewai.crew import Crew
+from crewai.llm import LLM
+from crewai.types.crew_chat import ChatInputField, ChatInputs
+from crewai.utilities.llm_utils import create_llm
+
+
+def run_chat():
+    """
+    Runs an interactive chat loop using the Crew's chat LLM with function calling.
+    Incorporates crew_name, crew_description, and input fields to build a tool schema.
+    Exits if crew_name or crew_description are missing.
+    """
+    crew, crew_name = load_crew_and_name()
+    chat_llm = initialize_chat_llm(crew)
+    if not chat_llm:
+        return
+
+    crew_chat_inputs = generate_crew_chat_inputs(crew, crew_name, chat_llm)
+    crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
+    system_message = build_system_message(crew_chat_inputs)
+
+    # Call the LLM to generate the introductory message
+    introductory_message = chat_llm.call(
+        messages=[{"role": "system", "content": system_message}]
+    )
+    click.secho(f"\nAssistant: {introductory_message}\n", fg="green")
+
+    messages = [
+        {"role": "system", "content": system_message},
+        {"role": "assistant", "content": introductory_message},
+    ]
+
+    available_functions = {
+        crew_chat_inputs.crew_name: create_tool_function(crew, messages),
+    }
+
+    click.secho(
+        "\nEntering an interactive chat loop with function-calling.\n"
+        "Type 'exit' or Ctrl+C to quit.\n",
+        fg="cyan",
+    )
+
+    chat_loop(chat_llm, messages, crew_tool_schema, available_functions)
+
+
+def initialize_chat_llm(crew: Crew) -> Optional[LLM]:
+    """Initializes the chat LLM and handles exceptions."""
+    try:
+        return create_llm(crew.chat_llm)
+    except Exception as e:
+        click.secho(
+            f"Unable to find a Chat LLM. Please make sure you set chat_llm on the crew: {e}",
+            fg="red",
+        )
+        return None
+
+
+def build_system_message(crew_chat_inputs: ChatInputs) -> str:
+    """Builds the initial system message for the chat."""
+    required_fields_str = (
+        ", ".join(
+            f"{field.name} (desc: {field.description or 'n/a'})"
+            for field in crew_chat_inputs.inputs
+        )
+        or "(No required fields detected)"
+    )
+
+    return (
+        "You are a helpful AI assistant for the CrewAI platform. "
+        "Your primary purpose is to assist users with the crew's specific tasks. "
+        "You can answer general questions, but should guide users back to the crew's purpose afterward. "
+        "For example, after answering a general question, remind the user of your main purpose, such as generating a research report, and prompt them to specify a topic or task related to the crew's purpose. "
+        "You have a function (tool) you can call by name if you have all required inputs. "
+        f"Those required inputs are: {required_fields_str}. "
+        "Once you have them, call the function. "
+        "Please keep your responses concise and friendly. "
+        "If a user asks a question outside the crew's scope, provide a brief answer and remind them of the crew's purpose. "
+        "After calling the tool, be prepared to take user feedback and make adjustments as needed. "
+        "If you are ever unsure about a user's request or need clarification, ask the user for more information."
+        "Before doing anything else, introduce yourself with a friendly message like: 'Hey! I'm here to help you with [crew's purpose]. Could you please provide me with [inputs] so we can get started?' "
+        "For example: 'Hey! I'm here to help you with uncovering and reporting cutting-edge developments through thorough research and detailed analysis. Could you please provide me with a topic you're interested in? This will help us generate a comprehensive research report and detailed analysis.'"
+        f"\nCrew Name: {crew_chat_inputs.crew_name}"
+        f"\nCrew Description: {crew_chat_inputs.crew_description}"
+    )
+
+
+def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
+    """Creates a wrapper function for running the crew tool with messages."""
+
+    def run_crew_tool_with_messages(**kwargs):
+        return run_crew_tool(crew, messages, **kwargs)
+
+    return run_crew_tool_with_messages
+
+
+def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
+    """Main chat loop for interacting with the user."""
+    while True:
+        try:
+            user_input = click.prompt("You", type=str)
+            if user_input.strip().lower() in ["exit", "quit"]:
+                click.echo("Exiting chat. Goodbye!")
+                break
+
+            messages.append({"role": "user", "content": user_input})
+            final_response = chat_llm.call(
+                messages=messages,
+                tools=[crew_tool_schema],
+                available_functions=available_functions,
+            )
+
+            messages.append({"role": "assistant", "content": final_response})
+            click.secho(f"\nAssistant: {final_response}\n", fg="green")
+
+        except KeyboardInterrupt:
+            click.echo("\nExiting chat. Goodbye!")
+            break
+        except Exception as e:
+            click.secho(f"An error occurred: {e}", fg="red")
+            break
+
+
+def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict:
+    """
+    Dynamically build a Littellm 'function' schema for the given crew.
+
+    crew_name: The name of the crew (used for the function 'name').
+    crew_inputs: A ChatInputs object containing crew_description
+                 and a list of input fields (each with a name & description).
+    """
+    properties = {}
+    for field in crew_inputs.inputs:
+        properties[field.name] = {
+            "type": "string",
+            "description": field.description or "No description provided",
+        }
+
+    required_fields = [field.name for field in crew_inputs.inputs]
+
+    return {
+        "type": "function",
+        "function": {
+            "name": crew_inputs.crew_name,
+            "description": crew_inputs.crew_description or "No crew description",
+            "parameters": {
+                "type": "object",
+                "properties": properties,
+                "required": required_fields,
+            },
+        },
+    }
+
+
+def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
+    """
+    Runs the crew using crew.kickoff(inputs=kwargs) and returns the output.
+
+    Args:
+        crew (Crew): The crew instance to run.
+        messages (List[Dict[str, str]]): The chat messages up to this point.
+        **kwargs: The inputs collected from the user.
+
+    Returns:
+        str: The output from the crew's execution.
+
+    Raises:
+        SystemExit: Exits the chat if an error occurs during crew execution.
+    """
+    try:
+        # Serialize 'messages' to JSON string before adding to kwargs
+        kwargs["crew_chat_messages"] = json.dumps(messages)
+
+        # Run the crew with the provided inputs
+        crew_output = crew.kickoff(inputs=kwargs)
+
+        # Convert CrewOutput to a string to send back to the user
+        result = str(crew_output)
+
+        return result
+    except Exception as e:
+        # Exit the chat and show the error message
+        click.secho("An error occurred while running the crew:", fg="red")
+        click.secho(str(e), fg="red")
+        sys.exit(1)
+
+
+def load_crew_and_name() -> Tuple[Crew, str]:
+    """
+    Loads the crew by importing the crew class from the user's project.
+
+    Returns:
+        Tuple[Crew, str]: A tuple containing the Crew instance and the name of the crew.
+    """
+    # Get the current working directory
+    cwd = Path.cwd()
+
+    # Path to the pyproject.toml file
+    pyproject_path = cwd / "pyproject.toml"
+    if not pyproject_path.exists():
+        raise FileNotFoundError("pyproject.toml not found in the current directory.")
+
+    # Load the pyproject.toml file using 'tomli'
+    with pyproject_path.open("rb") as f:
+        pyproject_data = tomli.load(f)
+
+    # Get the project name from the 'project' section
+    project_name = pyproject_data["project"]["name"]
+    folder_name = project_name
+
+    # Derive the crew class name from the project name
+    # E.g., if project_name is 'my_project', crew_class_name is 'MyProject'
+    crew_class_name = project_name.replace("_", " ").title().replace(" ", "")
+
+    # Add the 'src' directory to sys.path
+    src_path = cwd / "src"
+    if str(src_path) not in sys.path:
+        sys.path.insert(0, str(src_path))
+
+    # Import the crew module
+    crew_module_name = f"{folder_name}.crew"
+    try:
+        crew_module = __import__(crew_module_name, fromlist=[crew_class_name])
+    except ImportError as e:
+        raise ImportError(f"Failed to import crew module {crew_module_name}: {e}")
+
+    # Get the crew class from the module
+    try:
+        crew_class = getattr(crew_module, crew_class_name)
+    except AttributeError:
+        raise AttributeError(
+            f"Crew class {crew_class_name} not found in module {crew_module_name}"
+        )
+
+    # Instantiate the crew
+    crew_instance = crew_class().crew()
+    return crew_instance, crew_class_name
+
+
+def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInputs:
+    """
+    Generates the ChatInputs required for the crew by analyzing the tasks and agents.
+
+    Args:
+        crew (Crew): The crew object containing tasks and agents.
+        crew_name (str): The name of the crew.
+        chat_llm: The chat language model to use for AI calls.
+
+    Returns:
+        ChatInputs: An object containing the crew's name, description, and input fields.
+    """
+    # Extract placeholders from tasks and agents
+    required_inputs = fetch_required_inputs(crew)
+
+    # Generate descriptions for each input using AI
+    input_fields = []
+    for input_name in required_inputs:
+        description = generate_input_description_with_ai(input_name, crew, chat_llm)
+        input_fields.append(ChatInputField(name=input_name, description=description))
+
+    # Generate crew description using AI
+    crew_description = generate_crew_description_with_ai(crew, chat_llm)
+
+    return ChatInputs(
+        crew_name=crew_name, crew_description=crew_description, inputs=input_fields
+    )
+
+
+def fetch_required_inputs(crew: Crew) -> Set[str]:
+    """
+    Extracts placeholders from the crew's tasks and agents.
+
+    Args:
+        crew (Crew): The crew object.
+
+    Returns:
+        Set[str]: A set of placeholder names.
+    """
+    placeholder_pattern = re.compile(r"\{(.+?)\}")
+    required_inputs: Set[str] = set()
+
+    # Scan tasks
+    for task in crew.tasks:
+        text = f"{task.description or ''} {task.expected_output or ''}"
+        required_inputs.update(placeholder_pattern.findall(text))
+
+    # Scan agents
+    for agent in crew.agents:
+        text = f"{agent.role or ''} {agent.goal or ''} {agent.backstory or ''}"
+        required_inputs.update(placeholder_pattern.findall(text))
+
+    return required_inputs
+
+
+def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) -> str:
+    """
+    Generates an input description using AI based on the context of the crew.
+
+    Args:
+        input_name (str): The name of the input placeholder.
+        crew (Crew): The crew object.
+        chat_llm: The chat language model to use for AI calls.
+
+    Returns:
+        str: A concise description of the input.
+    """
+    # Gather context from tasks and agents where the input is used
+    context_texts = []
+    placeholder_pattern = re.compile(r"\{(.+?)\}")
+
+    for task in crew.tasks:
+        if (
+            f"{{{input_name}}}" in task.description
+            or f"{{{input_name}}}" in task.expected_output
+        ):
+            # Replace placeholders with input names
+            task_description = placeholder_pattern.sub(
+                lambda m: m.group(1), task.description
+            )
+            expected_output = placeholder_pattern.sub(
+                lambda m: m.group(1), task.expected_output
+            )
+            context_texts.append(f"Task Description: {task_description}")
+            context_texts.append(f"Expected Output: {expected_output}")
+    for agent in crew.agents:
+        if (
+            f"{{{input_name}}}" in agent.role
+            or f"{{{input_name}}}" in agent.goal
+            or f"{{{input_name}}}" in agent.backstory
+        ):
+            # Replace placeholders with input names
+            agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role)
+            agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal)
+            agent_backstory = placeholder_pattern.sub(
+                lambda m: m.group(1), agent.backstory
+            )
+            context_texts.append(f"Agent Role: {agent_role}")
+            context_texts.append(f"Agent Goal: {agent_goal}")
+            context_texts.append(f"Agent Backstory: {agent_backstory}")
+
+    context = "\n".join(context_texts)
+    if not context:
+        # If no context is found for the input, raise an exception as per instruction
+        raise ValueError(f"No context found for input '{input_name}'.")
+
+    prompt = (
+        f"Based on the following context, write a concise description (15 words or less) of the input '{input_name}'.\n"
+        "Provide only the description, without any extra text or labels. Do not include placeholders like '{topic}' in the description.\n"
+        "Context:\n"
+        f"{context}"
+    )
+    response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
+    description = response.strip()
+
+    return description
+
+
+def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
+    """
+    Generates a brief description of the crew using AI.
+
+    Args:
+        crew (Crew): The crew object.
+        chat_llm: The chat language model to use for AI calls.
+
+    Returns:
+        str: A concise description of the crew's purpose (15 words or less).
+    """
+    # Gather context from tasks and agents
+    context_texts = []
+    placeholder_pattern = re.compile(r"\{(.+?)\}")
+
+    for task in crew.tasks:
+        # Replace placeholders with input names
+        task_description = placeholder_pattern.sub(
+            lambda m: m.group(1), task.description
+        )
+        expected_output = placeholder_pattern.sub(
+            lambda m: m.group(1), task.expected_output
+        )
+        context_texts.append(f"Task Description: {task_description}")
+        context_texts.append(f"Expected Output: {expected_output}")
+    for agent in crew.agents:
+        # Replace placeholders with input names
+        agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role)
+        agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal)
+        agent_backstory = placeholder_pattern.sub(lambda m: m.group(1), agent.backstory)
+        context_texts.append(f"Agent Role: {agent_role}")
+        context_texts.append(f"Agent Goal: {agent_goal}")
+        context_texts.append(f"Agent Backstory: {agent_backstory}")
+
+    context = "\n".join(context_texts)
+    if not context:
+        raise ValueError("No context found for generating crew description.")
+
+    prompt = (
+        "Based on the following context, write a concise, action-oriented description (15 words or less) of the crew's purpose.\n"
+        "Provide only the description, without any extra text or labels. Do not include placeholders like '{topic}' in the description.\n"
+        "Context:\n"
+        f"{context}"
+    )
+    response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
+    crew_description = response.strip()
+
+    return crew_description

src/crewai/cli/templates/crew/main.py

@@ -18,7 +18,11 @@ def run():
     inputs = {
         'topic': 'AI LLMs'
     }
-    {{crew_name}}().crew().kickoff(inputs=inputs)
+    
+    try:
+        {{crew_name}}().crew().kickoff(inputs=inputs)
+    except Exception as e:
+        raise Exception(f"An error occurred while running the crew: {e}")
 
 
 def train():
@@ -55,4 +59,4 @@ def test():
         {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
 
     except Exception as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")
+        raise Exception(f"An error occurred while testing the crew: {e}")

src/crewai/crew.py

@@ -1,10 +1,11 @@
 import asyncio
 import json
+import re
 import uuid
 import warnings
 from concurrent.futures import Future
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -36,6 +37,7 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import Tool
+from crewai.types.crew_chat import ChatInputs
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINING_DATA_FILE
@@ -203,6 +205,10 @@ class Crew(BaseModel):
         default=None,
         description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.",
     )
+    chat_llm: Optional[Any] = Field(
+        default=None,
+        description="LLM used to handle chatting with the crew.",
+    )
     _knowledge: Optional[Knowledge] = PrivateAttr(
         default=None,
     )
@@ -991,6 +997,31 @@ class Crew(BaseModel):
             return self._knowledge.query(query)
         return None
 
+    def fetch_inputs(self) -> Set[str]:
+        """
+        Gathers placeholders (e.g., {something}) referenced in tasks or agents.
+        Scans each task's 'description' + 'expected_output', and each agent's
+        'role', 'goal', and 'backstory'.
+
+        Returns a set of all discovered placeholder names.
+        """
+        placeholder_pattern = re.compile(r"\{(.+?)\}")
+        required_inputs: Set[str] = set()
+
+        # Scan tasks for inputs
+        for task in self.tasks:
+            # description and expected_output might contain e.g. {topic}, {user_name}, etc.
+            text = f"{task.description or ''} {task.expected_output or ''}"
+            required_inputs.update(placeholder_pattern.findall(text))
+
+        # Scan agents for inputs
+        for agent in self.agents:
+            # role, goal, backstory might have placeholders like {role_detail}, etc.
+            text = f"{agent.role or ''} {agent.goal or ''} {agent.backstory or ''}"
+            required_inputs.update(placeholder_pattern.findall(text))
+
+        return required_inputs
+
     def copy(self):
         """Create a deep copy of the Crew."""
 
@@ -1046,7 +1077,7 @@ class Crew(BaseModel):
     def _interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
         """Interpolates the inputs in the tasks and agents."""
         [
-            task.interpolate_inputs(
+            task.interpolate_inputs_and_add_conversation_history(
                 # type: ignore # "interpolate_inputs" of "Task" does not return a value (it only ever returns None)
                 inputs
             )

src/crewai/llm.py

@@ -1,21 +1,27 @@
+import json
 import logging
 import os
 import sys
 import threading
 import warnings
 from contextlib import contextmanager
-from importlib import resources
-from typing import Any, Dict, List, Optional, Union
+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 get_supported_openai_params
+    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):
@@ -24,6 +30,7 @@ class FilteredStream:
 
     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
@@ -79,18 +86,18 @@ CONTEXT_WINDOW_USAGE_RATIO = 0.75
 def suppress_warnings():
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore")
-        warnings.filterwarnings("ignore", message="open_text is deprecated*", category=DeprecationWarning)
+        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:
-            # Restore stdout and stderr
             sys.stdout = old_stdout
             sys.stderr = old_stderr
 
@@ -111,13 +118,12 @@ class LLM:
         logit_bias: Optional[Dict[int, float]] = None,
         response_format: Optional[Dict[str, Any]] = None,
         seed: Optional[int] = None,
-        logprobs: Optional[bool] = 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] = [],
-        **kwargs,
     ):
         self.model = model
         self.timeout = timeout
@@ -139,19 +145,40 @@ class LLM:
         self.api_key = api_key
         self.callbacks = callbacks
         self.context_window_size = 0
-        self.kwargs = kwargs
 
         litellm.drop_params = True
 
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 
-    def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
+    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,
@@ -172,21 +199,58 @@ class LLM:
                     "api_version": self.api_version,
                     "api_key": self.api_key,
                     "stream": False,
-                    **self.kwargs,
+                    "tools": tools,  # pass the tool schema
                 }
 
-                # Remove None values to avoid passing unnecessary parameters
                 params = {k: v for k, v in params.items() if v is not None}
 
                 response = litellm.completion(**params)
-                return response["choices"][0]["message"]["content"]
+                response_message = cast(Choices, cast(ModelResponse, response).choices)[
+                    0
+                ].message
+                text_response = response_message.content or ""
+                tool_calls = getattr(response_message, "tool_calls", [])
+
+                # --- 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  # Re-raise the exception after logging
+                raise
 
     def supports_function_calling(self) -> bool:
         try:
@@ -205,7 +269,10 @@ class LLM:
             return False
 
     def get_context_window_size(self) -> int:
-        # Only using 75% of the context window size to avoid cutting the message in the middle
+        """
+        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
 
@@ -218,6 +285,10 @@ class LLM:
         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[:]:
@@ -254,15 +325,15 @@ class LLM:
             success_callbacks = []
             if success_callbacks_str:
                 success_callbacks = [
-                    callback.strip() for callback in success_callbacks_str.split(",")
+                    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 = [
-                    callback.strip() for callback in failure_callbacks_str.split(",")
+                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
                 ]
 
-            litellm.success_callback = success_callbacks
-            litellm.failure_callback = failure_callbacks
+                litellm.success_callback = success_callbacks
+                litellm.failure_callback = failure_callbacks

src/crewai/task.py

@@ -393,7 +393,7 @@ class Task(BaseModel):
                 self.retry_count += 1
                 context = self.i18n.errors("validation_error").format(
                     guardrail_result_error=guardrail_result.error,
-                    task_output=task_output.raw
+                    task_output=task_output.raw,
                 )
                 printer = Printer()
                 printer.print(
@@ -431,9 +431,7 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
+                else pydantic_output.model_dump_json() if pydantic_output else result
             )
             self._save_file(content)
 
@@ -453,8 +451,11 @@ class Task(BaseModel):
         tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
 
-    def interpolate_inputs(self, inputs: Dict[str, Union[str, int, float]]) -> None:
+    def interpolate_inputs_and_add_conversation_history(
+        self, inputs: Dict[str, Union[str, int, float]]
+    ) -> None:
         """Interpolate inputs into the task description, expected output, and output file path.
+           Add conversation history if present.
 
         Args:
             inputs: Dictionary mapping template variables to their values.
@@ -499,6 +500,29 @@ class Task(BaseModel):
                     f"Error interpolating output_file path: {str(e)}"
                 ) from e
 
+        if "crew_chat_messages" in inputs and inputs["crew_chat_messages"]:
+            conversation_instruction = self.i18n.slice(
+                "conversation_history_instruction"
+            )
+
+            crew_chat_messages_json = str(inputs["crew_chat_messages"])
+
+            try:
+                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
+
+            conversation_history = "\n".join(
+                f"{msg['role'].capitalize()}: {msg['content']}"
+                for msg in crew_chat_messages
+                if isinstance(msg, dict) and "role" in msg and "content" in msg
+            )
+
+            self.description += (
+                f"\n\n{conversation_instruction}\n\n{conversation_history}"
+            )
+
     def interpolate_only(
         self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]
     ) -> str:

src/crewai/translations/en.json

@@ -23,7 +23,8 @@
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
-    "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\""
+    "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"",
+    "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals."
   },
   "errors": {
     "force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}",

src/crewai/types/crew_chat.py

@@ -0,0 +1,40 @@
+from typing import List
+
+from pydantic import BaseModel, Field
+
+
+class ChatInputField(BaseModel):
+    """
+    Represents a single required input for the crew, with a name and short description.
+    Example:
+        {
+            "name": "topic",
+            "description": "The topic to focus on for the conversation"
+        }
+    """
+
+    name: str = Field(..., description="The name of the input field")
+    description: str = Field(..., description="A short description of the input field")
+
+
+class ChatInputs(BaseModel):
+    """
+    Holds a high-level crew_description plus a list of ChatInputFields.
+    Example:
+        {
+            "crew_name": "topic-based-qa",
+            "crew_description": "Use this crew for topic-based Q&A",
+            "inputs": [
+                {"name": "topic", "description": "The topic to focus on"},
+                {"name": "username", "description": "Name of the user"},
+            ]
+        }
+    """
+
+    crew_name: str = Field(..., description="The name of the crew")
+    crew_description: str = Field(
+        ..., description="A description of the crew's purpose"
+    )
+    inputs: List[ChatInputField] = Field(
+        default_factory=list, description="A list of input fields for the crew"
+    )

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