Merge branch 'main' into bugfix/add-crewai-chat-docs

add version number
add docs for crewai chat
2025-12-25 08:48:31 +00:00 · 2025-01-21 11:05:33 -05:00 · 2025-01-21 10:13:52 -05:00 · 2025-01-21 10:02:04 -05:00
46 changed files with 941 additions and 2196 deletions
--- a/docs/concepts/agents.mdx
+++ b/docs/concepts/agents.mdx
@@ -43,7 +43,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 | **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
 | **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
 | **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
-| **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
+| **Embedder Config** _(optional)_        | `embedder_config`        | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
 | **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
 | **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |

@@ -152,7 +152,7 @@ agent = Agent(
    use_system_prompt=True,  # Default: True
    tools=[SerperDevTool()],  # Optional: List of tools
    knowledge_sources=None,  # Optional: List of knowledge sources
-    embedder=None,  # Optional: Custom embedder configuration
+    embedder_config=None,  # Optional: Custom embedder configuration
    system_template=None,  # Optional: Custom system prompt template
    prompt_template=None,  # Optional: Custom prompt template
    response_template=None,  # Optional: Custom response template
--- a/docs/concepts/cli.mdx
+++ b/docs/concepts/cli.mdx
@@ -12,7 +12,7 @@ The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you

 To use the CrewAI CLI, make sure you have CrewAI installed:

-```shell Terminal
+```shell
 pip install crewai
 ```

@@ -20,7 +20,7 @@ pip install crewai

 The basic structure of a CrewAI CLI command is:

-```shell Terminal
+```shell
 crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 ```

@@ -30,7 +30,7 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]

 Create a new crew or flow.

-```shell Terminal
+```shell
 crewai create [OPTIONS] TYPE NAME
 ```

@@ -38,7 +38,7 @@ crewai create [OPTIONS] TYPE NAME
 - `NAME`: Name of the crew or flow

 Example:
-```shell Terminal
+```shell
 crewai create crew my_new_crew
 crewai create flow my_new_flow
 ```
@@ -47,14 +47,14 @@ crewai create flow my_new_flow

 Show the installed version of CrewAI.

-```shell Terminal
+```shell
 crewai version [OPTIONS]
 ```

 - `--tools`: (Optional) Show the installed version of CrewAI tools

 Example:
-```shell Terminal
+```shell
 crewai version
 crewai version --tools
 ```
@@ -63,7 +63,7 @@ crewai version --tools

 Train the crew for a specified number of iterations.

-```shell Terminal
+```shell
 crewai train [OPTIONS]
 ```

@@ -71,7 +71,7 @@ crewai train [OPTIONS]
 - `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")

 Example:
-```shell Terminal
+```shell
 crewai train -n 10 -f my_training_data.pkl
 ```

@@ -79,14 +79,14 @@ crewai train -n 10 -f my_training_data.pkl

 Replay the crew execution from a specific task.

-```shell Terminal
+```shell
 crewai replay [OPTIONS]
 ```

 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks

 Example:
-```shell Terminal    
+```shell    
 crewai replay -t task_123456
 ```

@@ -94,7 +94,7 @@ crewai replay -t task_123456

 Retrieve your latest crew.kickoff() task outputs.

-```shell Terminal
+```shell
 crewai log-tasks-outputs
 ```

@@ -102,7 +102,7 @@ crewai log-tasks-outputs

 Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).

-```shell Terminal
+```shell
 crewai reset-memories [OPTIONS]
 ```

@@ -113,7 +113,7 @@ crewai reset-memories [OPTIONS]
 - `-a, --all`: Reset ALL memories

 Example:
-```shell Terminal
+```shell
 crewai reset-memories --long --short
 crewai reset-memories --all
 ```
@@ -122,7 +122,7 @@ crewai reset-memories --all

 Test the crew and evaluate the results.

-```shell Terminal
+```shell
 crewai test [OPTIONS]
 ```

@@ -130,7 +130,7 @@ crewai test [OPTIONS]
 - `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")

 Example:
-```shell Terminal    
+```shell
 crewai test -n 5 -m gpt-3.5-turbo
 ```

@@ -138,7 +138,7 @@ crewai test -n 5 -m gpt-3.5-turbo

 Run the crew.

-```shell Terminal
+```shell
 crewai run
 ```
 <Note>
@@ -153,7 +153,7 @@ Starting in version `0.98.0`, when you run the `crewai chat` command, you start

 After receiving the results, you can continue interacting with the assistant for further instructions or questions.

-```shell Terminal
+```shell
 crewai chat
 ```
 <Note>
--- a/docs/concepts/knowledge.mdx
+++ b/docs/concepts/knowledge.mdx
@@ -324,13 +324,6 @@ agent = Agent(
    verbose=True,
    allow_delegation=False,
    llm=gemini_llm,
-    embedder={
-        "provider": "google",
-        "config": {
-            "model": "models/text-embedding-004",
-            "api_key": GEMINI_API_KEY,
-        }
-    }
 )

 task = Task(
--- a/docs/concepts/llms.mdx
+++ b/docs/concepts/llms.mdx
@@ -243,9 +243,6 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
        # llm: bedrock/amazon.titan-text-express-v1
        # llm: bedrock/meta.llama2-70b-chat-v1

-        # Amazon SageMaker Models - Enterprise-grade
-        # llm: sagemaker/<my-endpoint>
-
        # Mistral Models - Open source alternative
        # llm: mistral/mistral-large-latest
        # llm: mistral/mistral-medium-latest
@@ -509,21 +506,6 @@ Learn how to get the most out of your LLM configuration:
    )
    ```
  </Accordion>
-  
-  <Accordion title="Amazon SageMaker">
-    ```python Code
-    AWS_ACCESS_KEY_ID=<your-access-key>
-    AWS_SECRET_ACCESS_KEY=<your-secret-key>
-    AWS_DEFAULT_REGION=<your-region>
-    ```
-
-    Example usage:
-    ```python Code
-    llm = LLM(
-        model="sagemaker/<my-endpoint>"
-    )
-    ```
-  </Accordion>

  <Accordion title="Mistral">
    ```python Code
--- a/docs/installation.mdx
+++ b/docs/installation.mdx
@@ -15,48 +15,10 @@ icon: wrench
  If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
 </Note>

-# Setting Up Your Environment
-
-Before installing CrewAI, it's recommended to set up a virtual environment. This helps isolate your project dependencies and avoid conflicts.
-
-<Steps>
-    <Step title="Create a Virtual Environment">
-        Choose your preferred method to create a virtual environment:
-
-        **Using venv (Python's built-in tool):**
-        ```shell Terminal
-        python3 -m venv .venv
-        ```
-
-        **Using conda:**
-        ```shell Terminal
-        conda create -n crewai-env python=3.12
-        ```
-    </Step>
-
-    <Step title="Activate the Virtual Environment">
-        Activate your virtual environment based on your platform:
-
-        **On macOS/Linux (venv):**
-        ```shell Terminal
-        source .venv/bin/activate
-        ```
-
-        **On Windows (venv):**
-        ```shell Terminal
-        .venv\Scripts\activate
-        ```
-
-        **Using conda (all platforms):**
-        ```shell Terminal
-        conda activate crewai-env
-        ```
-    </Step>
-</Steps>
-
 # Installing CrewAI

-Now let's get you set up! 🚀
+CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. 
+Let's get you set up! 🚀

 <Steps>
    <Step title="Install CrewAI">
@@ -110,9 +72,9 @@ Now let's get you set up! 🚀

 # Creating a New Project

-<Tip>
+<Info>
  We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks.
-</Tip>
+</Info>

 <Steps>
  <Step title="Generate Project Structure">
@@ -142,18 +104,7 @@ Now let's get you set up! 🚀
                └── tasks.yaml
    ```
    </Frame>
-  </Step>
-
-  <Step title="Install Additional Tools">
-    You can install additional tools using UV:
-    ```shell Terminal
-    uv add <tool-name>
-    ```
-
-    <Tip>
-      UV is our preferred package manager as it's significantly faster than pip and provides better dependency resolution.
-    </Tip>
-  </Step>
+  </Step>   

  <Step title="Customize Your Project">
    Your project will contain these essential files:
--- a/docs/tools/composiotool.mdx
+++ b/docs/tools/composiotool.mdx
@@ -1,5 +1,5 @@
 ---
-title: Composio Tool
+title: Composio
 description: Composio provides 250+ production-ready tools for AI agents with flexible authentication management.
 icon: gear-code
 ---
@@ -75,8 +75,7 @@ filtered_action_enums = toolset.find_actions_by_use_case(
 )

 tools = toolset.get_tools(actions=filtered_action_enums)
-```
-<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
+```<Tip>Set `advanced` to True to get actions for complex use cases</Tip>

 - Using specific tools:

--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.100.0"
+version = "0.98.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -11,7 +11,7 @@ dependencies = [
    # Core Dependencies
    "pydantic>=2.4.2",
    "openai>=1.13.3",
-    "litellm==1.59.8",
+    "litellm==1.57.4",
    "instructor>=1.3.3",
    # Text Processing
    "pdfplumber>=0.11.4",
@@ -36,7 +36,6 @@ dependencies = [
    "tomli-w>=1.1.0",
    "tomli>=2.0.2",
    "blinker>=1.9.0",
-    "json5>=0.10.0",
 ]

 [project.urls]
--- a/src/crewai/init.py
+++ b/src/crewai/init.py
@@ -14,7 +14,7 @@ warnings.filterwarnings(
    category=UserWarning,
    module="pydantic.main",
 )
-__version__ = "0.100.0"
+__version__ = "0.98.0"
 __all__ = [
    "Agent",
    "Crew",
--- a/src/crewai/agent.py
+++ b/src/crewai/agent.py
@@ -1,3 +1,4 @@
+import os
 import shutil
 import subprocess
 from typing import Any, Dict, List, Literal, Optional, Union
@@ -7,6 +8,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
@@ -61,7 +63,6 @@ class Agent(BaseAgent):
            tools: Tools at agents disposal
            step_callback: Callback to be executed after each step of the agent execution.
            knowledge_sources: Knowledge sources for the agent.
-            embedder: Embedder configuration for the agent.
    """

    _times_executed: int = PrivateAttr(default=0)
@@ -123,10 +124,17 @@ class Agent(BaseAgent):
        default="safe",
        description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
    )
-    embedder: Optional[Dict[str, Any]] = Field(
+    embedder_config: Optional[Dict[str, Any]] = Field(
        default=None,
        description="Embedder configuration for the agent.",
    )
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+        default=None,
+        description="Knowledge sources for the agent.",
+    )
+    _knowledge: Optional[Knowledge] = PrivateAttr(
+        default=None,
+    )

    @model_validator(mode="after")
    def post_init_setup(self):
@@ -157,11 +165,10 @@ class Agent(BaseAgent):
                if isinstance(self.knowledge_sources, list) and all(
                    isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                ):
-                    self.knowledge = Knowledge(
+                    self._knowledge = Knowledge(
                        sources=self.knowledge_sources,
-                        embedder=self.embedder,
+                        embedder_config=self.embedder_config,
                        collection_name=knowledge_agent_name,
-                        storage=self.knowledge_storage or None,
                    )
        except (TypeError, ValueError) as e:
            raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
@@ -220,8 +227,8 @@ class Agent(BaseAgent):
            if memory.strip() != "":
                task_prompt += self.i18n.slice("memory").format(memory=memory)

-        if self.knowledge:
-            agent_knowledge_snippets = self.knowledge.query([task.prompt()])
+        if self._knowledge:
+            agent_knowledge_snippets = self._knowledge.query([task.prompt()])
            if agent_knowledge_snippets:
                agent_knowledge_context = extract_knowledge_context(
                    agent_knowledge_snippets
@@ -254,9 +261,6 @@ class Agent(BaseAgent):
                }
            )["output"]
        except Exception as e:
-            if e.__class__.__module__.startswith("litellm"):
-                # Do not retry on litellm errors
-                raise e
            self._times_executed += 1
            if self._times_executed > self.max_retry_limit:
                raise e
--- a/src/crewai/agents/agent_builder/base_agent.py
+++ b/src/crewai/agents/agent_builder/base_agent.py
@@ -18,8 +18,6 @@ from pydantic_core import PydanticCustomError
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.tools import BaseTool
 from crewai.tools.base_tool import Tool
 from crewai.utilities import I18N, Logger, RPMController
@@ -50,8 +48,6 @@ class BaseAgent(ABC, BaseModel):
        cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class.
        tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class.
        max_tokens: Maximum number of tokens for the agent to generate in a response.
-        knowledge_sources: Knowledge sources for the agent.
-        knowledge_storage: Custom knowledge storage for the agent.


    Methods:
@@ -134,17 +130,6 @@ class BaseAgent(ABC, BaseModel):
    max_tokens: Optional[int] = Field(
        default=None, description="Maximum number of tokens for the agent's execution."
    )
-    knowledge: Optional[Knowledge] = Field(
-        default=None, description="Knowledge for the agent."
-    )
-    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
-        default=None,
-        description="Knowledge sources for the agent.",
-    )
-    knowledge_storage: Optional[Any] = Field(
-        default=None,
-        description="Custom knowledge storage for the agent.",
-    )

    @model_validator(mode="before")
    @classmethod
@@ -271,44 +256,13 @@ class BaseAgent(ABC, BaseModel):
            "tools_handler",
            "cache_handler",
            "llm",
-            "knowledge_sources",
-            "knowledge_storage",
-            "knowledge",
        }

-        # Copy llm
+        # Copy llm and clear callbacks
        existing_llm = shallow_copy(self.llm)
-        copied_knowledge = shallow_copy(self.knowledge)
-        copied_knowledge_storage = shallow_copy(self.knowledge_storage)
-        # Properly copy knowledge sources if they exist
-        existing_knowledge_sources = None
-        if self.knowledge_sources:
-            # Create a shared storage instance for all knowledge sources
-            shared_storage = (
-                self.knowledge_sources[0].storage if self.knowledge_sources else None
-            )
-
-            existing_knowledge_sources = []
-            for source in self.knowledge_sources:
-                copied_source = (
-                    source.model_copy()
-                    if hasattr(source, "model_copy")
-                    else shallow_copy(source)
-                )
-                # Ensure all copied sources use the same storage instance
-                copied_source.storage = shared_storage
-                existing_knowledge_sources.append(copied_source)
-
        copied_data = self.model_dump(exclude=exclude)
        copied_data = {k: v for k, v in copied_data.items() if v is not None}
-        copied_agent = type(self)(
-            **copied_data,
-            llm=existing_llm,
-            tools=self.tools,
-            knowledge_sources=existing_knowledge_sources,
-            knowledge=copied_knowledge,
-            knowledge_storage=copied_knowledge_storage,
-        )
+        copied_agent = type(self)(**copied_data, llm=existing_llm, tools=self.tools)

        return copied_agent

--- a/src/crewai/agents/crew_agent_executor.py
+++ b/src/crewai/agents/crew_agent_executor.py
@@ -13,7 +13,6 @@ from crewai.agents.parser import (
    OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N, Printer
@@ -55,7 +54,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
        callbacks: List[Any] = [],
    ):
        self._i18n: I18N = I18N()
-        self.llm: LLM = llm
+        self.llm = llm
        self.task = task
        self.agent = agent
        self.crew = crew
@@ -81,8 +80,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
        self.tool_name_to_tool_map: Dict[str, BaseTool] = {
            tool.name: tool for tool in self.tools
        }
-        self.stop = stop_words
-        self.llm.stop = list(set(self.llm.stop + self.stop))
+        if self.llm.stop:
+            self.llm.stop = list(set(self.llm.stop + self.stop))
+        else:
+            self.llm.stop = self.stop

    def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
        if "system" in self.prompt:
@@ -97,16 +98,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
        self._show_start_logs()

        self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
-
-        try:
-            formatted_answer = self._invoke_loop()
-        except Exception as e:
-            if e.__class__.__module__.startswith("litellm"):
-                # Do not retry on litellm errors
-                raise e
-            else:
-                self._handle_unknown_error(e)
-                raise e
+        formatted_answer = self._invoke_loop()

        if self.ask_for_human_input:
            formatted_answer = self._handle_human_feedback(formatted_answer)
@@ -132,6 +124,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                self._enforce_rpm_limit()

                answer = self._get_llm_response()
+
                formatted_answer = self._process_llm_response(answer)

                if isinstance(formatted_answer, AgentAction):
@@ -149,32 +142,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                formatted_answer = self._handle_output_parser_exception(e)

            except Exception as e:
-                if e.__class__.__module__.startswith("litellm"):
-                    # Do not retry on litellm errors
-                    raise e
                if self._is_context_length_exceeded(e):
                    self._handle_context_length()
                    continue
-                else:
-                    self._handle_unknown_error(e)
-                    raise e
-            finally:
-                self.iterations += 1

        self._show_logs(formatted_answer)
        return formatted_answer

-    def _handle_unknown_error(self, exception: Exception) -> None:
-        """Handle unknown errors by informing the user."""
-        self._printer.print(
-            content="An unknown error occurred. Please check the details below.",
-            color="red",
-        )
-        self._printer.print(
-            content=f"Error details: {exception}",
-            color="red",
-        )
-
    def _has_reached_max_iterations(self) -> bool:
        """Check if the maximum number of iterations has been reached."""
        return self.iterations >= self.max_iter
@@ -186,17 +160,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):

    def _get_llm_response(self) -> str:
        """Call the LLM and return the response, handling any invalid responses."""
-        try:
-            answer = self.llm.call(
-                self.messages,
-                callbacks=self.callbacks,
-            )
-        except Exception as e:
-            self._printer.print(
-                content=f"Error during LLM call: {e}",
-                color="red",
-            )
-            raise e
+        answer = self.llm.call(
+            self.messages,
+            callbacks=self.callbacks,
+        )

        if not answer:
            self._printer.print(
@@ -217,6 +184,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
                    answer = answer.split("Observation:")[0].strip()

+        self.iterations += 1
        return self._format_answer(answer)

    def _handle_agent_action(
--- a/src/crewai/cli/cli.py
+++ b/src/crewai/cli/cli.py
@@ -350,10 +350,7 @@ def chat():
    Start a conversation with the Crew, collecting user-supplied inputs,
    and using the Chat LLM to generate responses.
    """
-    click.secho(
-        "\nStarting a conversation with the Crew\n" "Type 'exit' or Ctrl+C to quit.\n",
-    )
-
+    click.echo("Starting a conversation with the Crew")
    run_chat()


--- a/src/crewai/cli/crew_chat.py
+++ b/src/crewai/cli/crew_chat.py
@@ -1,52 +1,17 @@
 import json
-import platform
 import re
 import sys
-import threading
-import time
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Set, Tuple

 import click
 import tomli
-from packaging import version

-from crewai.cli.utils import read_toml
-from crewai.cli.version import get_crewai_version
 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

-MIN_REQUIRED_VERSION = "0.98.0"
-
-
-def check_conversational_crews_version(
-    crewai_version: str, pyproject_data: dict
-) -> bool:
-    """
-    Check if the installed crewAI version supports conversational crews.
-
-    Args:
-        crewai_version: The current version of crewAI.
-        pyproject_data: Dictionary containing pyproject.toml data.
-
-    Returns:
-        bool: True if version check passes, False otherwise.
-    """
-    try:
-        if version.parse(crewai_version) < version.parse(MIN_REQUIRED_VERSION):
-            click.secho(
-                "You are using an older version of crewAI that doesn't support conversational crews. "
-                "Run 'uv upgrade crewai' to get the latest version.",
-                fg="red",
-            )
-            return False
-    except version.InvalidVersion:
-        click.secho("Invalid crewAI version format detected.", fg="red")
-        return False
-    return True
-

 def run_chat():
    """
@@ -54,47 +19,20 @@ def run_chat():
    Incorporates crew_name, crew_description, and input fields to build a tool schema.
    Exits if crew_name or crew_description are missing.
    """
-    crewai_version = get_crewai_version()
-    pyproject_data = read_toml()
-
-    if not check_conversational_crews_version(crewai_version, pyproject_data):
-        return
-
    crew, crew_name = load_crew_and_name()
    chat_llm = initialize_chat_llm(crew)
    if not chat_llm:
        return

-    # Indicate that the crew is being analyzed
-    click.secho(
-        "\nAnalyzing crew and required inputs - this may take 3 to 30 seconds "
-        "depending on the complexity of your crew.",
-        fg="white",
+    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}]
    )
-
-    # Start loading indicator
-    loading_complete = threading.Event()
-    loading_thread = threading.Thread(target=show_loading, args=(loading_complete,))
-    loading_thread.start()
-
-    try:
-        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}]
-        )
-    finally:
-        # Stop loading indicator
-        loading_complete.set()
-        loading_thread.join()
-
-    # Indicate that the analysis is complete
-    click.secho("\nFinished analyzing crew.\n", fg="white")
-
-    click.secho(f"Assistant: {introductory_message}\n", fg="green")
+    click.secho(f"\nAssistant: {introductory_message}\n", fg="green")

    messages = [
        {"role": "system", "content": system_message},
@@ -105,17 +43,15 @@ def run_chat():
        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 show_loading(event: threading.Event):
-    """Display animated loading dots while processing."""
-    while not event.is_set():
-        print(".", end="", flush=True)
-        time.sleep(1)
-    print()
-
-
 def initialize_chat_llm(crew: Crew) -> Optional[LLM]:
    """Initializes the chat LLM and handles exceptions."""
    try:
@@ -149,7 +85,7 @@ def build_system_message(crew_chat_inputs: ChatInputs) -> str:
        "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. "
+        "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}"
@@ -166,33 +102,25 @@ def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
    return run_crew_tool_with_messages


-def flush_input():
-    """Flush any pending input from the user."""
-    if platform.system() == "Windows":
-        # Windows platform
-        import msvcrt
-
-        while msvcrt.kbhit():
-            msvcrt.getch()
-    else:
-        # Unix-like platforms (Linux, macOS)
-        import termios
-
-        termios.tcflush(sys.stdin, termios.TCIFLUSH)
-
-
 def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
    """Main chat loop for interacting with the user."""
    while True:
        try:
-            # Flush any pending input before accepting new input
-            flush_input()
+            user_input = click.prompt("You", type=str)
+            if user_input.strip().lower() in ["exit", "quit"]:
+                click.echo("Exiting chat. Goodbye!")
+                break

-            user_input = get_user_input()
-            handle_user_input(
-                user_input, chat_llm, messages, crew_tool_schema, available_functions
+            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
@@ -201,55 +129,6 @@ def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
            break


-def get_user_input() -> str:
-    """Collect multi-line user input with exit handling."""
-    click.secho(
-        "\nYou (type your message below. Press 'Enter' twice when you're done):",
-        fg="blue",
-    )
-    user_input_lines = []
-    while True:
-        line = input()
-        if line.strip().lower() == "exit":
-            return "exit"
-        if line == "":
-            break
-        user_input_lines.append(line)
-    return "\n".join(user_input_lines)
-
-
-def handle_user_input(
-    user_input: str,
-    chat_llm: LLM,
-    messages: List[Dict[str, str]],
-    crew_tool_schema: Dict[str, Any],
-    available_functions: Dict[str, Any],
-) -> None:
-    if user_input.strip().lower() == "exit":
-        click.echo("Exiting chat. Goodbye!")
-        return
-
-    if not user_input.strip():
-        click.echo("Empty message. Please provide input or type 'exit' to quit.")
-        return
-
-    messages.append({"role": "user", "content": user_input})
-
-    # Indicate that assistant is processing
-    click.echo()
-    click.secho("Assistant is processing your input. Please wait...", fg="green")
-
-    # Process assistant's response
-    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")
-
-
 def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict:
    """
    Dynamically build a Littellm 'function' schema for the given crew.
@@ -444,10 +323,10 @@ def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) ->
        ):
            # Replace placeholders with input names
            task_description = placeholder_pattern.sub(
-                lambda m: m.group(1), task.description or ""
+                lambda m: m.group(1), task.description
            )
            expected_output = placeholder_pattern.sub(
-                lambda m: m.group(1), task.expected_output or ""
+                lambda m: m.group(1), task.expected_output
            )
            context_texts.append(f"Task Description: {task_description}")
            context_texts.append(f"Expected Output: {expected_output}")
@@ -458,10 +337,10 @@ def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) ->
            or f"{{{input_name}}}" in agent.backstory
        ):
            # Replace placeholders with input names
-            agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role or "")
-            agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal or "")
+            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 or ""
+                lambda m: m.group(1), agent.backstory
            )
            context_texts.append(f"Agent Role: {agent_role}")
            context_texts.append(f"Agent Goal: {agent_goal}")
@@ -502,20 +381,18 @@ def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
    for task in crew.tasks:
        # Replace placeholders with input names
        task_description = placeholder_pattern.sub(
-            lambda m: m.group(1), task.description or ""
+            lambda m: m.group(1), task.description
        )
        expected_output = placeholder_pattern.sub(
-            lambda m: m.group(1), task.expected_output or ""
+            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 or "")
-        agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal or "")
-        agent_backstory = placeholder_pattern.sub(
-            lambda m: m.group(1), agent.backstory or ""
-        )
+        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}")
--- a/src/crewai/cli/templates/crew/.gitignore
+++ b/src/crewai/cli/templates/crew/.gitignore
@@ -1,3 +1,2 @@
 .env
 __pycache__/
-.DS_Store
--- a/src/crewai/cli/templates/crew/pyproject.toml
+++ b/src/crewai/cli/templates/crew/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.0,<1.0.0"
+    "crewai[tools]>=0.98.0,<1.0.0"
 ]

 [project.scripts]
--- a/src/crewai/cli/templates/flow/.gitignore
+++ b/src/crewai/cli/templates/flow/.gitignore
@@ -1,4 +1,3 @@
 .env
 __pycache__/
 lib/
-.DS_Store
--- a/src/crewai/cli/templates/flow/pyproject.toml
+++ b/src/crewai/cli/templates/flow/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.0,<1.0.0",
+    "crewai[tools]>=0.98.0,<1.0.0",
 ]

 [project.scripts]
--- a/src/crewai/cli/templates/tool/pyproject.toml
+++ b/src/crewai/cli/templates/tool/pyproject.toml
@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.0"
+    "crewai[tools]>=0.98.0"
 ]

 [tool.crewai]
--- a/src/crewai/crew.py
+++ b/src/crewai/crew.py
@@ -4,7 +4,6 @@ import re
 import uuid
 import warnings
 from concurrent.futures import Future
-from copy import copy as shallow_copy
 from hashlib import md5
 from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union

@@ -211,9 +210,8 @@ class Crew(BaseModel):
        default=None,
        description="LLM used to handle chatting with the crew.",
    )
-    knowledge: Optional[Knowledge] = Field(
+    _knowledge: Optional[Knowledge] = PrivateAttr(
        default=None,
-        description="Knowledge for the crew.",
    )

    @field_validator("id", mode="before")
@@ -291,7 +289,7 @@ class Crew(BaseModel):
                if isinstance(self.knowledge_sources, list) and all(
                    isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                ):
-                    self.knowledge = Knowledge(
+                    self._knowledge = Knowledge(
                        sources=self.knowledge_sources,
                        embedder_config=self.embedder,
                        collection_name="crew",
@@ -993,8 +991,8 @@ class Crew(BaseModel):
        return result

    def query_knowledge(self, query: List[str]) -> Union[List[Dict[str, Any]], None]:
-        if self.knowledge:
-            return self.knowledge.query(query)
+        if self._knowledge:
+            return self._knowledge.query(query)
        return None

    def fetch_inputs(self) -> Set[str]:
@@ -1038,8 +1036,6 @@ class Crew(BaseModel):
            "_telemetry",
            "agents",
            "tasks",
-            "knowledge_sources",
-            "knowledge",
        }

        cloned_agents = [agent.copy() for agent in self.agents]
@@ -1047,9 +1043,6 @@ class Crew(BaseModel):
        task_mapping = {}

        cloned_tasks = []
-        existing_knowledge_sources = shallow_copy(self.knowledge_sources)
-        existing_knowledge = shallow_copy(self.knowledge)
-
        for task in self.tasks:
            cloned_task = task.copy(cloned_agents, task_mapping)
            cloned_tasks.append(cloned_task)
@@ -1069,13 +1062,7 @@ class Crew(BaseModel):
        copied_data.pop("agents", None)
        copied_data.pop("tasks", None)

-        copied_crew = Crew(
-            **copied_data,
-            agents=cloned_agents,
-            tasks=cloned_tasks,
-            knowledge_sources=existing_knowledge_sources,
-            knowledge=existing_knowledge,
-        )
+        copied_crew = Crew(**copied_data, agents=cloned_agents, tasks=cloned_tasks)

        return copied_crew

--- a/src/crewai/knowledge/knowledge.py
+++ b/src/crewai/knowledge/knowledge.py
@@ -15,20 +15,20 @@ class Knowledge(BaseModel):
    Args:
        sources: List[BaseKnowledgeSource] = Field(default_factory=list)
        storage: Optional[KnowledgeStorage] = Field(default=None)
-        embedder: Optional[Dict[str, Any]] = None
+        embedder_config: Optional[Dict[str, Any]] = None
    """

    sources: List[BaseKnowledgeSource] = Field(default_factory=list)
    model_config = ConfigDict(arbitrary_types_allowed=True)
    storage: Optional[KnowledgeStorage] = Field(default=None)
-    embedder: Optional[Dict[str, Any]] = None
+    embedder_config: Optional[Dict[str, Any]] = None
    collection_name: Optional[str] = None

    def __init__(
        self,
        collection_name: str,
        sources: List[BaseKnowledgeSource],
-        embedder: Optional[Dict[str, Any]] = None,
+        embedder_config: Optional[Dict[str, Any]] = None,
        storage: Optional[KnowledgeStorage] = None,
        **data,
    ):
@@ -37,23 +37,25 @@ class Knowledge(BaseModel):
            self.storage = storage
        else:
            self.storage = KnowledgeStorage(
-                embedder=embedder, collection_name=collection_name
+                embedder_config=embedder_config, collection_name=collection_name
            )
        self.sources = sources
        self.storage.initialize_knowledge_storage()
-        self._add_sources()
+        for source in sources:
+            source.storage = self.storage
+            source.add()

    def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
        """
        Query across all knowledge sources to find the most relevant information.
        Returns the top_k most relevant chunks.
-
+        
        Raises:
            ValueError: If storage is not initialized.
        """
        if self.storage is None:
            raise ValueError("Storage is not initialized.")
-
+            
        results = self.storage.search(
            query,
            limit,
@@ -61,9 +63,6 @@ class Knowledge(BaseModel):
        return results

    def _add_sources(self):
-        try:
-            for source in self.sources:
-                source.storage = self.storage
-                source.add()
-        except Exception as e:
-            raise e
+        for source in self.sources:
+            source.storage = self.storage
+            source.add()
--- a/src/crewai/knowledge/source/base_file_knowledge_source.py
+++ b/src/crewai/knowledge/source/base_file_knowledge_source.py
@@ -29,13 +29,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
    def validate_file_path(cls, v, info):
        """Validate that at least one of file_path or file_paths is provided."""
        # Single check if both are None, O(1) instead of nested conditions
-        if (
-            v is None
-            and info.data.get(
-                "file_path" if info.field_name == "file_paths" else "file_paths"
-            )
-            is None
-        ):
+        if v is None and info.data.get("file_path" if info.field_name == "file_paths" else "file_paths") is None:
            raise ValueError("Either file_path or file_paths must be provided")
        return v

--- a/src/crewai/knowledge/storage/knowledge_storage.py
+++ b/src/crewai/knowledge/storage/knowledge_storage.py
@@ -48,11 +48,11 @@ class KnowledgeStorage(BaseKnowledgeStorage):

    def __init__(
        self,
-        embedder: Optional[Dict[str, Any]] = None,
+        embedder_config: Optional[Dict[str, Any]] = None,
        collection_name: Optional[str] = None,
    ):
        self.collection_name = collection_name
-        self._set_embedder_config(embedder)
+        self._set_embedder_config(embedder_config)

    def search(
        self,
@@ -99,7 +99,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
            )
            if self.app:
                self.collection = self.app.get_or_create_collection(
-                    name=collection_name, embedding_function=self.embedder
+                    name=collection_name, embedding_function=self.embedder_config
                )
            else:
                raise Exception("Vector Database Client not initialized")
@@ -187,15 +187,17 @@ class KnowledgeStorage(BaseKnowledgeStorage):
            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
        )

-    def _set_embedder_config(self, embedder: Optional[Dict[str, Any]] = None) -> None:
+    def _set_embedder_config(
+        self, embedder_config: Optional[Dict[str, Any]] = None
+    ) -> None:
        """Set the embedding configuration for the knowledge storage.

        Args:
            embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder.
                If None or empty, defaults to the default embedding function.
        """
-        self.embedder = (
-            EmbeddingConfigurator().configure_embedder(embedder)
-            if embedder
+        self.embedder_config = (
+            EmbeddingConfigurator().configure_embedder(embedder_config)
+            if embedder_config
            else self._create_default_embedding_function()
        )
--- a/src/crewai/llm.py
+++ b/src/crewai/llm.py
@@ -142,6 +142,7 @@ class LLM:
        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
@@ -159,63 +160,37 @@ class LLM:

        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)
        self.set_env_callbacks()

    def call(
        self,
-        messages: Union[str, List[Dict[str, str]]],
+        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 llm call method that:
-          1) Accepts either a string or a list of messages
-          2) Converts string input to the required message format
-          3) Calls litellm.completion
-          4) Handles function/tool calls if any
-          5) Returns the final text response or tool result
+        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

-        Parameters:
-        - messages (Union[str, List[Dict[str, str]]]): The input messages for the LLM.
-          - If a string is provided, it will be converted into a message list with a single entry.
-          - If a list of dictionaries is provided, each dictionary should have 'role' and 'content' keys.
-        - tools (Optional[List[dict]]): A list of tool schemas for function calling.
-        - callbacks (Optional[List[Any]]): A list of callback functions to be executed.
-        - available_functions (Optional[Dict[str, Any]]): A dictionary mapping function names to actual Python functions.
-
-        Returns:
-        - str: The final text response from the LLM or the result of a tool function call.
-
-        Examples:
-        ---------
-        # Example 1: Using a string input
-        response = llm.call("Return the name of a random city in the world.")
-        print(response)
-
-        # Example 2: Using a list of messages
-        messages = [{"role": "user", "content": "What is the capital of France?"}]
-        response = llm.call(messages)
-        print(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
        """
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-
        with suppress_warnings():
            if callbacks and len(callbacks) > 0:
                self.set_callbacks(callbacks)

            try:
-                # --- 1) Prepare the parameters for the completion call
+                # --- 1) Make the completion call
                params = {
                    "model": self.model,
                    "messages": messages,
@@ -236,21 +211,19 @@ class LLM:
                    "api_version": self.api_version,
                    "api_key": self.api_key,
                    "stream": False,
-                    "tools": tools,
+                    "tools": tools,  # pass the tool schema
                }

-                # Remove None values from params
                params = {k: v for k, v in params.items() if v is not None}

-                # --- 2) Make the completion call
                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", [])
-
-                # --- 3) Handle callbacks with usage info
+                
+                # 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"):
@@ -263,11 +236,11 @@ class LLM:
                                    end_time=0,
                                )

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

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

@@ -282,6 +255,7 @@ class LLM:
                    try:
                        # Call the actual tool function
                        result = fn(**function_args)
+
                        return result

                    except Exception as e:
--- a/src/crewai/tools/tool_usage.py
+++ b/src/crewai/tools/tool_usage.py
@@ -1,13 +1,12 @@
 import ast
 import datetime
 import json
+import re
 import time
 from difflib import SequenceMatcher
-from json import JSONDecodeError
 from textwrap import dedent
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Union

-import json5
 from json_repair import repair_json

 import crewai.utilities.events as events
@@ -408,55 +407,28 @@ class ToolUsage:
                )
            return self._tool_calling(tool_string)

-    def _validate_tool_input(self, tool_input: Optional[str]) -> Dict[str, Any]:
-        if tool_input is None:
-            return {}
-
-        if not isinstance(tool_input, str) or not tool_input.strip():
-            raise Exception(
-                "Tool input must be a valid dictionary in JSON or Python literal format"
-            )
-
-        # Attempt 1: Parse as JSON
+    def _validate_tool_input(self, tool_input: str) -> Dict[str, Any]:
        try:
+            # Replace Python literals with JSON equivalents
+            replacements = {
+                r"'": '"',
+                r"None": "null",
+                r"True": "true",
+                r"False": "false",
+            }
+            for pattern, replacement in replacements.items():
+                tool_input = re.sub(pattern, replacement, tool_input)
+
            arguments = json.loads(tool_input)
-            if isinstance(arguments, dict):
-                return arguments
-        except (JSONDecodeError, TypeError):
-            pass  # Continue to the next parsing attempt
-
-        # Attempt 2: Parse as Python literal
-        try:
-            arguments = ast.literal_eval(tool_input)
-            if isinstance(arguments, dict):
-                return arguments
-        except (ValueError, SyntaxError):
-            pass  # Continue to the next parsing attempt
-
-        # Attempt 3: Parse as JSON5
-        try:
-            arguments = json5.loads(tool_input)
-            if isinstance(arguments, dict):
-                return arguments
-        except (JSONDecodeError, ValueError, TypeError):
-            pass  # Continue to the next parsing attempt
-
-        # Attempt 4: Repair JSON
-        try:
+        except json.JSONDecodeError:
+            # Attempt to repair JSON string
            repaired_input = repair_json(tool_input)
-            self._printer.print(
-                content=f"Repaired JSON: {repaired_input}", color="blue"
-            )
-            arguments = json.loads(repaired_input)
-            if isinstance(arguments, dict):
-                return arguments
-        except Exception as e:
-            self._printer.print(content=f"Failed to repair JSON: {e}", color="red")
+            try:
+                arguments = json.loads(repaired_input)
+            except json.JSONDecodeError as e:
+                raise Exception(f"Invalid tool input JSON: {e}")

-        # If all parsing attempts fail, raise an error
-        raise Exception(
-            "Tool input must be a valid dictionary in JSON or Python literal format"
-        )
+        return arguments

    def on_tool_error(self, tool: Any, tool_calling: ToolCalling, e: Exception) -> None:
        event_data = self._prepare_event_data(tool, tool_calling)
--- a/src/crewai/utilities/embedding_configurator.py
+++ b/src/crewai/utilities/embedding_configurator.py
@@ -43,6 +43,7 @@ class EmbeddingConfigurator:
            raise Exception(
                f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
            )
+
        return self.embedding_functions[provider](config, model_name)

    @staticmethod
--- a/src/crewai/utilities/evaluators/task_evaluator.py
+++ b/src/crewai/utilities/evaluators/task_evaluator.py
@@ -96,9 +96,9 @@ class TaskEvaluator:
        final_aggregated_data = ""
        for _, data in output_training_data.items():
            final_aggregated_data += (
-                f"Initial Output:\n{data.get('initial_output', '')}\n\n"
-                f"Human Feedback:\n{data.get('human_feedback', '')}\n\n"
-                f"Improved Output:\n{data.get('improved_output', '')}\n\n"
+                f"Initial Output:\n{data['initial_output']}\n\n"
+                f"Human Feedback:\n{data['human_feedback']}\n\n"
+                f"Improved Output:\n{data['improved_output']}\n\n"
            )

        evaluation_query = (
--- a/src/crewai/utilities/llm_utils.py
+++ b/src/crewai/utilities/llm_utils.py
@@ -24,10 +24,12 @@ def create_llm(

    # 1) If llm_value is already an LLM object, return it directly
    if isinstance(llm_value, LLM):
+        print("LLM value is already an LLM object")
        return llm_value

    # 2) If llm_value is a string (model name)
    if isinstance(llm_value, str):
+        print("LLM value is a string")
        try:
            created_llm = LLM(model=llm_value)
            return created_llm
@@ -37,10 +39,12 @@ def create_llm(

    # 3) If llm_value is None, parse environment variables or use default
    if llm_value is None:
+        print("LLM value is None")
        return _llm_via_environment_or_fallback()

    # 4) Otherwise, attempt to extract relevant attributes from an unknown object
    try:
+        print("LLM value is an unknown object")
        # Extract attributes with explicit types
        model = (
            getattr(llm_value, "model_name", None)
--- a/tests/agent_test.py
+++ b/tests/agent_test.py
@@ -10,7 +10,6 @@ from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
-from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
 from crewai.tools import tool
@@ -1601,181 +1600,3 @@ def test_agent_with_knowledge_sources():

        # Assert that the agent provides the correct information
        assert "red" in result.raw.lower()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_knowledge_sources_works_with_copy():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-
-    with patch(
-        "crewai.knowledge.source.base_knowledge_source.BaseKnowledgeSource",
-        autospec=True,
-    ) as MockKnowledgeSource:
-        mock_knowledge_source_instance = MockKnowledgeSource.return_value
-        mock_knowledge_source_instance.__class__ = BaseKnowledgeSource
-        mock_knowledge_source_instance.sources = [string_source]
-
-        agent = Agent(
-            role="Information Agent",
-            goal="Provide information based on knowledge sources",
-            backstory="You have access to specific knowledge sources.",
-            llm=LLM(model="gpt-4o-mini"),
-            knowledge_sources=[string_source],
-        )
-
-        with patch(
-            "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
-        ) as MockKnowledgeStorage:
-            mock_knowledge_storage = MockKnowledgeStorage.return_value
-            agent.knowledge_storage = mock_knowledge_storage
-
-            agent_copy = agent.copy()
-
-            assert agent_copy.role == agent.role
-            assert agent_copy.goal == agent.goal
-            assert agent_copy.backstory == agent.backstory
-            assert agent_copy.knowledge_sources is not None
-            assert len(agent_copy.knowledge_sources) == 1
-            assert isinstance(agent_copy.knowledge_sources[0], StringKnowledgeSource)
-            assert agent_copy.knowledge_sources[0].content == content
-            assert isinstance(agent_copy.llm, LLM)
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_litellm_auth_error_handling():
-    """Test that LiteLLM authentication errors are handled correctly and not retried."""
-    from litellm import AuthenticationError as LiteLLMAuthenticationError
-
-    # Create an agent with a mocked LLM and max_retry_limit=0
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        llm=LLM(model="gpt-4"),
-        max_retry_limit=0,  # Disable retries for authentication errors
-    )
-
-    # Create a task
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-
-    # Mock the LLM call to raise AuthenticationError
-    with (
-        patch.object(LLM, "call") as mock_llm_call,
-        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
-    ):
-        mock_llm_call.side_effect = LiteLLMAuthenticationError(
-            message="Invalid API key", llm_provider="openai", model="gpt-4"
-        )
-        agent.execute_task(task)
-
-    # Verify the call was only made once (no retries)
-    mock_llm_call.assert_called_once()
-
-
-def test_crew_agent_executor_litellm_auth_error():
-    """Test that CrewAgentExecutor handles LiteLLM authentication errors by raising them."""
-    from litellm.exceptions import AuthenticationError
-
-    from crewai.agents.tools_handler import ToolsHandler
-    from crewai.utilities import Printer
-
-    # Create an agent and executor
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        llm=LLM(model="gpt-4", api_key="invalid_api_key"),
-    )
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-
-    # Create executor with all required parameters
-    executor = CrewAgentExecutor(
-        agent=agent,
-        task=task,
-        llm=agent.llm,
-        crew=None,
-        prompt={"system": "You are a test agent", "user": "Execute the task: {input}"},
-        max_iter=5,
-        tools=[],
-        tools_names="",
-        stop_words=[],
-        tools_description="",
-        tools_handler=ToolsHandler(),
-    )
-
-    # Mock the LLM call to raise AuthenticationError
-    with (
-        patch.object(LLM, "call") as mock_llm_call,
-        patch.object(Printer, "print") as mock_printer,
-        pytest.raises(AuthenticationError) as exc_info,
-    ):
-        mock_llm_call.side_effect = AuthenticationError(
-            message="Invalid API key", llm_provider="openai", model="gpt-4"
-        )
-        executor.invoke(
-            {
-                "input": "test input",
-                "tool_names": "",
-                "tools": "",
-            }
-        )
-
-    # Verify error handling messages
-    error_message = f"Error during LLM call: {str(mock_llm_call.side_effect)}"
-    mock_printer.assert_any_call(
-        content=error_message,
-        color="red",
-    )
-
-    # Verify the call was only made once (no retries)
-    mock_llm_call.assert_called_once()
-
-    # Assert that the exception was raised and has the expected attributes
-    assert exc_info.type is AuthenticationError
-    assert "Invalid API key".lower() in exc_info.value.message.lower()
-    assert exc_info.value.llm_provider == "openai"
-    assert exc_info.value.model == "gpt-4"
-
-
-def test_litellm_anthropic_error_handling():
-    """Test that AnthropicError from LiteLLM is handled correctly and not retried."""
-    from litellm.llms.anthropic.common_utils import AnthropicError
-
-    # Create an agent with a mocked LLM that uses an Anthropic model
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        llm=LLM(model="claude-3.5-sonnet-20240620"),
-        max_retry_limit=0,
-    )
-
-    # Create a task
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-
-    # Mock the LLM call to raise AnthropicError
-    with (
-        patch.object(LLM, "call") as mock_llm_call,
-        pytest.raises(AnthropicError, match="Test Anthropic error"),
-    ):
-        mock_llm_call.side_effect = AnthropicError(
-            status_code=500,
-            message="Test Anthropic error",
-        )
-        agent.execute_task(task)
-
-    # Verify the LLM call was only made once (no retries)
-    mock_llm_call.assert_called_once()
--- a/tests/cassettes/test_agent_error_on_parsing_tool.yaml
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+++ b/tests/cassettes/test_llm_call_with_tool_and_string_input.yaml
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--- a/tests/crew_test.py
+++ b/tests/crew_test.py
@@ -14,7 +14,6 @@ from crewai.agent import Agent
 from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
 from crewai.project import crew
@@ -556,12 +555,12 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
        _, kwargs = mock_execute_sync.call_args
        tools = kwargs["tools"]

-        assert any(isinstance(tool, TestTool) for tool in tools), (
-            "TestTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in tools
+        ), "TestTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in tools
+        ), "Delegation tool should be present"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -620,12 +619,12 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
        _, kwargs = mock_execute_sync.call_args
        tools = kwargs["tools"]

-        assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), (
-            "TestTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in new_ceo.tools
+        ), "TestTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in tools
+        ), "Delegation tool should be present"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -749,17 +748,17 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
        used_tools = kwargs["tools"]

        # Confirm AnotherTestTool is present but TestTool is not
-        assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), (
-            "AnotherTestTool should be present"
-        )
-        assert not any(isinstance(tool, TestTool) for tool in used_tools), (
-            "TestTool should not be present among used tools"
-        )
+        assert any(
+            isinstance(tool, AnotherTestTool) for tool in used_tools
+        ), "AnotherTestTool should be present"
+        assert not any(
+            isinstance(tool, TestTool) for tool in used_tools
+        ), "TestTool should not be present among used tools"

        # Confirm delegation tool(s) are present
-        assert any("delegate" in tool.name.lower() for tool in used_tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            "delegate" in tool.name.lower() for tool in used_tools
+        ), "Delegation tool should be present"

    # Finally, make sure the agent's original tools remain unchanged
    assert len(researcher_with_delegation.tools) == 1
@@ -1467,6 +1466,7 @@ def test_dont_set_agents_step_callback_if_already_set():

@pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_function_calling_llm():
+
    from crewai import LLM
    from crewai.tools import tool

@@ -1560,9 +1560,9 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():

        # Verify that exactly one tool was used and it was a CodeInterpreterTool
        assert len(used_tools) == 1, "Should have exactly one tool"
-        assert isinstance(used_tools[0], CodeInterpreterTool), (
-            "Tool should be CodeInterpreterTool"
-        )
+        assert isinstance(
+            used_tools[0], CodeInterpreterTool
+        ), "Tool should be CodeInterpreterTool"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -3107,9 +3107,9 @@ def test_fetch_inputs():
    expected_placeholders = {"role_detail", "topic", "field"}
    actual_placeholders = crew.fetch_inputs()

-    assert actual_placeholders == expected_placeholders, (
-        f"Expected {expected_placeholders}, but got {actual_placeholders}"
-    )
+    assert (
+        actual_placeholders == expected_placeholders
+    ), f"Expected {expected_placeholders}, but got {actual_placeholders}"


 def test_task_tools_preserve_code_execution_tools():
@@ -3182,20 +3182,20 @@ def test_task_tools_preserve_code_execution_tools():
        used_tools = kwargs["tools"]

        # Verify all expected tools are present
-        assert any(isinstance(tool, TestTool) for tool in used_tools), (
-            "Task's TestTool should be present"
-        )
-        assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), (
-            "CodeInterpreterTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in used_tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in used_tools
+        ), "Task's TestTool should be present"
+        assert any(
+            isinstance(tool, CodeInterpreterTool) for tool in used_tools
+        ), "CodeInterpreterTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in used_tools
+        ), "Delegation tool should be present"

        # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
-        assert len(used_tools) == 4, (
-            "Should have TestTool, CodeInterpreter, and 2 delegation tools"
-        )
+        assert (
+            len(used_tools) == 4
+        ), "Should have TestTool, CodeInterpreter, and 2 delegation tools"


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -3239,9 +3239,9 @@ def test_multimodal_flag_adds_multimodal_tools():
        used_tools = kwargs["tools"]

        # Check that the multimodal tool was added
-        assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
-            "AddImageTool should be present when agent is multimodal"
-        )
+        assert any(
+            isinstance(tool, AddImageTool) for tool in used_tools
+        ), "AddImageTool should be present when agent is multimodal"

        # Verify we have exactly one tool (just the AddImageTool)
        assert len(used_tools) == 1, "Should only have the AddImageTool"
@@ -3467,9 +3467,9 @@ def test_crew_guardrail_feedback_in_context():
    assert len(execution_contexts) > 1, "Task should have been executed multiple times"

    # Verify that the second execution included the guardrail feedback
-    assert "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1], (
-        "Guardrail feedback should be included in retry context"
-    )
+    assert (
+        "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
+    ), "Guardrail feedback should be included in retry context"

    # Verify final output meets guardrail requirements
    assert "IMPORTANT" in result.raw, "Final output should contain required keyword"
@@ -3494,6 +3494,7 @@ def test_before_kickoff_callback():

        @before_kickoff
        def modify_inputs(self, inputs):
+
            self.inputs_modified = True
            inputs["modified"] = True
            return inputs
@@ -3595,21 +3596,3 @@ def test_before_kickoff_without_inputs():
    # Verify that the inputs were initialized and modified inside the before_kickoff method
    assert test_crew_instance.received_inputs is not None
    assert test_crew_instance.received_inputs.get("modified") is True
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_with_knowledge_sources_works_with_copy():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-
-    crew = Crew(
-        agents=[researcher, writer],
-        tasks=[Task(description="test", expected_output="test", agent=researcher)],
-        knowledge_sources=[string_source],
-    )
-
-    crew_copy = crew.copy()
-
-    assert crew_copy.knowledge_sources == crew.knowledge_sources
-    assert len(crew_copy.agents) == len(crew.agents)
-    assert len(crew_copy.tasks) == len(crew.tasks)
--- a/tests/llm_test.py
+++ b/tests/llm_test.py
@@ -4,7 +4,6 @@ import pytest

 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.llm import LLM
-from crewai.tools import tool
 from crewai.utilities.token_counter_callback import TokenCalcHandler


@@ -38,119 +37,3 @@ def test_llm_callback_replacement():
    assert usage_metrics_1.successful_requests == 1
    assert usage_metrics_2.successful_requests == 1
    assert usage_metrics_1 == calc_handler_1.token_cost_process.get_summary()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_call_with_string_input():
-    llm = LLM(model="gpt-4o-mini")
-
-    # Test the call method with a string input
-    result = llm.call("Return the name of a random city in the world.")
-    assert isinstance(result, str)
-    assert len(result.strip()) > 0  # Ensure the response is not empty
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_call_with_string_input_and_callbacks():
-    llm = LLM(model="gpt-4o-mini")
-    calc_handler = TokenCalcHandler(token_cost_process=TokenProcess())
-
-    # Test the call method with a string input and callbacks
-    result = llm.call(
-        "Tell me a joke.",
-        callbacks=[calc_handler],
-    )
-    usage_metrics = calc_handler.token_cost_process.get_summary()
-
-    assert isinstance(result, str)
-    assert len(result.strip()) > 0
-    assert usage_metrics.successful_requests == 1
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_call_with_message_list():
-    llm = LLM(model="gpt-4o-mini")
-    messages = [{"role": "user", "content": "What is the capital of France?"}]
-
-    # Test the call method with a list of messages
-    result = llm.call(messages)
-    assert isinstance(result, str)
-    assert "Paris" in result
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_call_with_tool_and_string_input():
-    llm = LLM(model="gpt-4o-mini")
-
-    def get_current_year() -> str:
-        """Returns the current year as a string."""
-        from datetime import datetime
-
-        return str(datetime.now().year)
-
-    # Create tool schema
-    tool_schema = {
-        "type": "function",
-        "function": {
-            "name": "get_current_year",
-            "description": "Returns the current year as a string.",
-            "parameters": {
-                "type": "object",
-                "properties": {},
-                "required": [],
-            },
-        },
-    }
-
-    # Available functions mapping
-    available_functions = {"get_current_year": get_current_year}
-
-    # Test the call method with a string input and tool
-    result = llm.call(
-        "What is the current year?",
-        tools=[tool_schema],
-        available_functions=available_functions,
-    )
-
-    assert isinstance(result, str)
-    assert result == get_current_year()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_call_with_tool_and_message_list():
-    llm = LLM(model="gpt-4o-mini")
-
-    def square_number(number: int) -> int:
-        """Returns the square of a number."""
-        return number * number
-
-    # Create tool schema
-    tool_schema = {
-        "type": "function",
-        "function": {
-            "name": "square_number",
-            "description": "Returns the square of a number.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "number": {"type": "integer", "description": "The number to square"}
-                },
-                "required": ["number"],
-            },
-        },
-    }
-
-    # Available functions mapping
-    available_functions = {"square_number": square_number}
-
-    messages = [{"role": "user", "content": "What is the square of 5?"}]
-
-    # Test the call method with messages and tool
-    result = llm.call(
-        messages,
-        tools=[tool_schema],
-        available_functions=available_functions,
-    )
-
-    assert isinstance(result, int)
-    assert result == 25
--- a/tests/test_manager_llm_delegation.py
+++ b/tests/test_manager_llm_delegation.py
@@ -0,0 +1,51 @@
+import pytest
+
+from crewai import Agent
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+
+
+class InternalAgentTool(BaseAgentTool):
+    """Concrete implementation of BaseAgentTool for testing."""
+
+    def _run(self, *args, **kwargs):
+        """Implement required _run method."""
+        return "Test response"
+
+
+@pytest.mark.parametrize(
+    "role_name,should_match",
+    [
+        ("Futel Official Infopoint", True),  # exact match
+        ('  "Futel Official Infopoint"  ', True),  # extra quotes and spaces
+        ("Futel Official Infopoint\n", True),  # trailing newline
+        ('"Futel Official Infopoint"', True),  # embedded quotes
+        (" FUTEL\nOFFICIAL   INFOPOINT ", True),  # multiple whitespace and newline
+    ],
+)
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_tool_role_matching(role_name, should_match):
+    """Test that agent tools can match roles regardless of case, whitespace, and special characters."""
+    # Create test agent
+    test_agent = Agent(
+        role="Futel Official Infopoint",
+        goal="Answer questions about Futel",
+        backstory="Futel Football Club info",
+        allow_delegation=False,
+    )
+
+    # Create test agent tool
+    agent_tool = InternalAgentTool(
+        name="test_tool", description="Test tool", agents=[test_agent]
+    )
+
+    # Test role matching
+    result = agent_tool._execute(agent_name=role_name, task="Test task", context=None)
+
+    if should_match:
+        assert (
+            "coworker mentioned not found" not in result.lower()
+        ), f"Should find agent with role name: {role_name}"
+    else:
+        assert (
+            "coworker mentioned not found" in result.lower()
+        ), f"Should not find agent with role name: {role_name}"
--- a/tests/tools/test_tool_usage.py
+++ b/tests/tools/test_tool_usage.py
@@ -231,255 +231,3 @@ def test_validate_tool_input_with_special_characters():

    arguments = tool_usage._validate_tool_input(tool_input)
    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_none_input():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    arguments = tool_usage._validate_tool_input(None)
-    assert arguments == {}
-
-
-def test_validate_tool_input_valid_json():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    tool_input = '{"key": "value", "number": 42, "flag": true}'
-    expected_arguments = {"key": "value", "number": 42, "flag": True}
-
-    arguments = tool_usage._validate_tool_input(tool_input)
-    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_python_dict():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    tool_input = "{'key': 'value', 'number': 42, 'flag': True}"
-    expected_arguments = {"key": "value", "number": 42, "flag": True}
-
-    arguments = tool_usage._validate_tool_input(tool_input)
-    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_json5_unquoted_keys():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    tool_input = "{key: 'value', number: 42, flag: true}"
-    expected_arguments = {"key": "value", "number": 42, "flag": True}
-
-    arguments = tool_usage._validate_tool_input(tool_input)
-    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_with_trailing_commas():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    tool_input = '{"key": "value", "number": 42, "flag": true,}'
-    expected_arguments = {"key": "value", "number": 42, "flag": True}
-
-    arguments = tool_usage._validate_tool_input(tool_input)
-    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_invalid_input():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    invalid_inputs = [
-        "Just a string",
-        "['list', 'of', 'values']",
-        "12345",
-        "",
-    ]
-
-    for invalid_input in invalid_inputs:
-        with pytest.raises(Exception) as e_info:
-            tool_usage._validate_tool_input(invalid_input)
-        assert (
-            "Tool input must be a valid dictionary in JSON or Python literal format"
-            in str(e_info.value)
-        )
-
-    # Test for None input separately
-    arguments = tool_usage._validate_tool_input(None)
-    assert arguments == {}  # Expecting an empty dictionary
-
-
-def test_validate_tool_input_complex_structure():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    tool_input = """
-    {
-        "user": {
-            "name": "Alice",
-            "age": 30
-        },
-        "items": [
-            {"id": 1, "value": "Item1"},
-            {"id": 2, "value": "Item2",}
-        ],
-        "active": true,
-    }
-    """
-    expected_arguments = {
-        "user": {"name": "Alice", "age": 30},
-        "items": [
-            {"id": 1, "value": "Item1"},
-            {"id": 2, "value": "Item2"},
-        ],
-        "active": True,
-    }
-
-    arguments = tool_usage._validate_tool_input(tool_input)
-    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_code_content():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    tool_input = '{"filename": "script.py", "content": "def hello():\\n    print(\'Hello, world!\')"}'
-    expected_arguments = {
-        "filename": "script.py",
-        "content": "def hello():\n    print('Hello, world!')",
-    }
-
-    arguments = tool_usage._validate_tool_input(tool_input)
-    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_with_escaped_quotes():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    tool_input = '{"text": "He said, \\"Hello, world!\\""}'
-    expected_arguments = {"text": 'He said, "Hello, world!"'}
-
-    arguments = tool_usage._validate_tool_input(tool_input)
-    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_large_json_content():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    # Simulate a large JSON content
-    tool_input = (
-        '{"data": ' + json.dumps([{"id": i, "value": i * 2} for i in range(1000)]) + "}"
-    )
-    expected_arguments = {"data": [{"id": i, "value": i * 2} for i in range(1000)]}
-
-    arguments = tool_usage._validate_tool_input(tool_input)
-    assert arguments == expected_arguments
-
-
-def test_validate_tool_input_none_input():
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
-        function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
-    )
-
-    arguments = tool_usage._validate_tool_input(None)
-    assert arguments == {}  # Expecting an empty dictionary
--- a/uv.lock
+++ b/uv.lock
@@ -649,7 +649,7 @@ wheels = [

 [[package]]
 name = "crewai"
-version = "0.100.0"
+version = "0.98.0"
 source = { editable = "." }
 dependencies = [
    { name = "appdirs" },
@@ -659,7 +659,6 @@ dependencies = [
    { name = "click" },
    { name = "instructor" },
    { name = "json-repair" },
-    { name = "json5" },
    { name = "jsonref" },
    { name = "litellm" },
    { name = "openai" },
@@ -738,9 +737,8 @@ requires-dist = [
    { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
    { name = "instructor", specifier = ">=1.3.3" },
    { name = "json-repair", specifier = ">=0.25.2" },
-    { name = "json5", specifier = ">=0.10.0" },
    { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.59.8" },
+    { name = "litellm", specifier = "==1.57.4" },
    { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
    { name = "openai", specifier = ">=1.13.3" },
    { name = "openpyxl", specifier = ">=3.1.5" },
@@ -2079,15 +2077,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/23/38/34cb843cee4c5c27aa5c822e90e99bf96feb3dfa705713b5b6e601d17f5c/json_repair-0.30.0-py3-none-any.whl", hash = "sha256:bda4a5552dc12085c6363ff5acfcdb0c9cafc629989a2112081b7e205828228d", size = 17641 },
 ]

-[[package]]
-name = "json5"
-version = "0.10.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/85/3d/bbe62f3d0c05a689c711cff57b2e3ac3d3e526380adb7c781989f075115c/json5-0.10.0.tar.gz", hash = "sha256:e66941c8f0a02026943c52c2eb34ebeb2a6f819a0be05920a6f5243cd30fd559", size = 48202 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/aa/42/797895b952b682c3dafe23b1834507ee7f02f4d6299b65aaa61425763278/json5-0.10.0-py3-none-any.whl", hash = "sha256:19b23410220a7271e8377f81ba8aacba2fdd56947fbb137ee5977cbe1f5e8dfa", size = 34049 },
-]
-
 [[package]]
 name = "jsonlines"
 version = "3.1.0"
@@ -2374,7 +2363,7 @@ wheels = [

 [[package]]
 name = "litellm"
-version = "1.59.8"
+version = "1.57.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "aiohttp" },
@@ -2389,9 +2378,9 @@ dependencies = [
    { name = "tiktoken" },
    { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/86/b0/c8ec06bd1c87a92d6d824008982b3c82b450d7bd3be850a53913f1ac4907/litellm-1.59.8.tar.gz", hash = "sha256:9d645cc4460f6a9813061f07086648c4c3d22febc8e1f21c663f2b7750d90512", size = 6428607 }
+sdist = { url = "https://files.pythonhosted.org/packages/1a/9a/115bde058901b087e7fec1bed4be47baf8d5c78aff7dd2ffebcb922003ff/litellm-1.57.4.tar.gz", hash = "sha256:747a870ddee9c71f9560fc68ad02485bc1008fcad7d7a43e87867a59b8ed0669", size = 6304427 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b9/38/889da058f566ef9ea321aafa25e423249492cf2a508dfdc0e5acfcf04526/litellm-1.59.8-py3-none-any.whl", hash = "sha256:2473914bd2343485a185dfe7eedb12ee5fda32da3c9d9a8b73f6966b9b20cf39", size = 6716233 },
+    { url = "https://files.pythonhosted.org/packages/9f/72/35c8509cb2a37343c213b794420405cbef2e1fdf8626ee981fcbba3d7c5c/litellm-1.57.4-py3-none-any.whl", hash = "sha256:afe48924d8a36db801018970a101622fce33d117fe9c54441c0095c491511abb", size = 6592126 },
 ]

 [[package]]
Author	SHA1	Message	Date
Brandon Hancock (bhancock_ai)	b1c4c2c887	Merge branch 'main' into bugfix/add-crewai-chat-docs	2025-01-21 11:05:33 -05:00
Brandon Hancock	932537a037	add version number	2025-01-21 10:13:52 -05:00
Brandon Hancock	b3c2e4e26d	add docs for crewai chat	2025-01-21 10:02:04 -05:00