quick fix for mike

* fix issue pointed out by mike

* clean up

* Drop logger

* drop unused imports

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
+```shell Terminal
 pip install crewai
 ```
 
@@ -20,7 +20,7 @@ pip install crewai
 
 The basic structure of a CrewAI CLI command is:
 
-```shell
+```shell Terminal
 crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 ```
 
@@ -30,7 +30,7 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 
 Create a new crew or flow.
 
-```shell
+```shell Terminal
 crewai create [OPTIONS] TYPE NAME
 ```
 
@@ -38,7 +38,7 @@ crewai create [OPTIONS] TYPE NAME
 - `NAME`: Name of the crew or flow
 
 Example:
-```shell
+```shell Terminal
 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
+```shell Terminal
 crewai version [OPTIONS]
 ```
 
 - `--tools`: (Optional) Show the installed version of CrewAI tools
 
 Example:
-```shell
+```shell Terminal
 crewai version
 crewai version --tools
 ```
@@ -63,7 +63,7 @@ crewai version --tools
 
 Train the crew for a specified number of iterations.
 
-```shell
+```shell Terminal
 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
+```shell Terminal
 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
+```shell Terminal
 crewai replay [OPTIONS]
 ```
 
 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
 
 Example:
-```shell    
+```shell Terminal    
 crewai replay -t task_123456
 ```
 
@@ -94,7 +94,7 @@ crewai replay -t task_123456
 
 Retrieve your latest crew.kickoff() task outputs.
 
-```shell
+```shell Terminal
 crewai log-tasks-outputs
 ```
 
@@ -102,7 +102,7 @@ crewai log-tasks-outputs
 
 Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
 
-```shell
+```shell Terminal
 crewai reset-memories [OPTIONS]
 ```
 
@@ -113,7 +113,7 @@ crewai reset-memories [OPTIONS]
 - `-a, --all`: Reset ALL memories
 
 Example:
-```shell
+```shell Terminal
 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
+```shell Terminal
 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
+```shell Terminal    
 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
+```shell Terminal
 crewai run
 ```
 <Note>
@@ -147,7 +147,36 @@ Some commands may require additional configuration or setup within your project
 </Note>
 
 
-### 9. API Keys
+### 9. Chat
+
+Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
+
+After receiving the results, you can continue interacting with the assistant for further instructions or questions.
+
+```shell Terminal
+crewai chat
+```
+<Note>
+Ensure you execute these commands from your CrewAI project's root directory.
+</Note>
+<Note>
+IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
+
+```python
+@crew
+def crew(self) -> Crew:
+    return Crew(
+        agents=self.agents,
+        tasks=self.tasks,
+        process=Process.sequential,
+        verbose=True,
+        chat_llm="gpt-4o",  # LLM for chat orchestration
+    )
+```
+</Note>
+
+
+### 10. API Keys
 
 When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
 

docs/concepts/llms.mdx

@@ -243,6 +243,9 @@ 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
@@ -506,6 +509,21 @@ 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

docs/installation.mdx

@@ -15,10 +15,48 @@ 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
 
-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! 🚀
+Now let's get you set up! 🚀
 
 <Steps>
     <Step title="Install CrewAI">
@@ -72,9 +110,9 @@ Let's get you set up! 🚀
 
 # Creating a New Project
 
-<Info>
+<Tip>
   We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks.
-</Info>
+</Tip>
 
 <Steps>
   <Step title="Generate Project Structure">
@@ -104,7 +142,18 @@ Let's get you set up! 🚀
                 └── tasks.yaml
     ```
     </Frame>
-  </Step>   
+  </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 title="Customize Your Project">
     Your project will contain these essential files:

docs/quickstart.mdx

@@ -278,7 +278,7 @@ email_summarizer:
       Summarize emails into a concise and clear summary
     backstory: >
       You will create a 5 bullet point summary of the report
-    llm: mixtal_llm
+    llm: openai/gpt-4o
 ```
 
 <Tip>

docs/tools/composiotool.mdx

@@ -1,78 +1,118 @@
 ---
 title: Composio Tool
-description: The `ComposioTool` is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the Composio SDK.
+description: Composio provides 250+ production-ready tools for AI agents with flexible authentication management.
 icon: gear-code
 ---
 
-# `ComposioTool`
+# `ComposioToolSet`
 
 ## Description
+Composio is an integration platform that allows you to connect your AI agents to 250+ tools. Key features include:
 
-This tools is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the Composio SDK.
+- **Enterprise-Grade Authentication**: Built-in support for OAuth, API Keys, JWT with automatic token refresh
+- **Full Observability**: Detailed tool usage logs, execution timestamps, and more
 
 ## Installation
 
-To incorporate this tool into your project, follow the installation instructions below:
+To incorporate Composio tools into your project, follow the instructions below:
 
 ```shell
-pip install composio-core
-pip install 'crewai[tools]'
+pip install composio-crewai
+pip install crewai
 ```
 
-after the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`.
+After the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`. Get your Composio API key from [here](https://app.composio.dev)
 
 ## Example
 
 The following example demonstrates how to initialize the tool and execute a github action:
 
-1. Initialize Composio tools
+1. Initialize Composio toolset
 
 ```python Code
-from composio import App
-from crewai_tools import ComposioTool
-from crewai import Agent, Task
+from composio_crewai import ComposioToolSet, App, Action
+from crewai import Agent, Task, Crew
 
-
-tools = [ComposioTool.from_action(action=Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER)]
+toolset = ComposioToolSet()
 ```
 
-If you don't know what action you want to use, use `from_app` and `tags` filter to get relevant actions
-
+2. Connect your GitHub account
+<CodeGroup>
+```shell CLI
+composio add github
+```
 ```python Code
-tools = ComposioTool.from_app(App.GITHUB, tags=["important"])
+request = toolset.initiate_connection(app=App.GITHUB)
+print(f"Open this URL to authenticate: {request.redirectUrl}")
 ```
+</CodeGroup>
 
-or use `use_case` to search relevant actions
+3. Get Tools
 
+- Retrieving all the tools from an app (not recommended for production):
 ```python Code
-tools = ComposioTool.from_app(App.GITHUB, use_case="Star a github repository")
+tools = toolset.get_tools(apps=[App.GITHUB])
 ```
 
-2. Define agent
+- Filtering tools based on tags:
+```python Code
+tag = "users"
+
+filtered_action_enums = toolset.find_actions_by_tags(
+    App.GITHUB,
+    tags=[tag], 
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+
+- Filtering tools based on use case:
+```python Code
+use_case = "Star a repository on GitHub"
+
+filtered_action_enums = toolset.find_actions_by_use_case(
+    App.GITHUB, use_case=use_case, advanced=False
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
+
+- Using specific tools:
+
+In this demo, we will use the `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` action from the GitHub app.
+```python Code
+tools = toolset.get_tools(
+    actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER]
+)
+```
+Learn more about filtering actions [here](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions)
+
+4. Define agent
 
 ```python Code
 crewai_agent = Agent(
-    role="Github Agent",
-    goal="You take action on Github using Github APIs",
-    backstory=(
-        "You are AI agent that is responsible for taking actions on Github "
-        "on users behalf. You need to take action on Github using Github APIs"
-    ),
+    role="GitHub Agent",
+    goal="You take action on GitHub using GitHub APIs",
+    backstory="You are AI agent that is responsible for taking actions on GitHub on behalf of users using GitHub APIs",
     verbose=True,
     tools=tools,
+    llm= # pass an llm
 )
 ```
 
-3. Execute task
+5. Execute task
 
 ```python Code
 task = Task(
-    description="Star a repo ComposioHQ/composio on GitHub",
+    description="Star a repo composiohq/composio on GitHub",
     agent=crewai_agent,
-    expected_output="if the star happened",
+    expected_output="Status of the operation",
 )
 
-task.execute()
+crew = Crew(agents=[crewai_agent], tasks=[task])
+
+crew.kickoff()
 ```
 
-* More detailed list of tools can be found [here](https://app.composio.dev)
+* More detailed list of tools can be found [here](https://app.composio.dev)

pyproject.toml

@@ -36,6 +36,7 @@ dependencies = [
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
     "blinker>=1.9.0",
+    "json5>=0.10.0",
 ]
 
 [project.urls]

src/crewai/agent.py

@@ -1,4 +1,3 @@
-import os
 import shutil
 import subprocess
 from typing import Any, Dict, List, Literal, Optional, Union
@@ -8,7 +7,6 @@ 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
@@ -261,6 +259,9 @@ 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

src/crewai/agents/crew_agent_executor.py

@@ -13,6 +13,7 @@ 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
@@ -54,7 +55,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm = llm
+        self.llm: LLM = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -80,10 +81,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
-        if self.llm.stop:
-            self.llm.stop = list(set(self.llm.stop + self.stop))
-        else:
-            self.llm.stop = self.stop
+        self.stop = stop_words
+        self.llm.stop = list(set(self.llm.stop + self.stop))
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
@@ -98,7 +97,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_start_logs()
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
-        formatted_answer = self._invoke_loop()
+
+        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
 
         if self.ask_for_human_input:
             formatted_answer = self._handle_human_feedback(formatted_answer)
@@ -124,7 +132,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self._enforce_rpm_limit()
 
                 answer = self._get_llm_response()
-
                 formatted_answer = self._process_llm_response(answer)
 
                 if isinstance(formatted_answer, AgentAction):
@@ -142,13 +149,32 @@ 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
@@ -160,10 +186,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _get_llm_response(self) -> str:
         """Call the LLM and return the response, handling any invalid responses."""
-        answer = self.llm.call(
-            self.messages,
-            callbacks=self.callbacks,
-        )
+        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
 
         if not answer:
             self._printer.print(
@@ -184,7 +217,6 @@ 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(

src/crewai/cli/cli.py

@@ -350,7 +350,10 @@ 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")
+    click.secho(
+        "\nStarting a conversation with the Crew\n" "Type 'exit' or Ctrl+C to quit.\n",
+    )
+
     run_chat()
 
 

src/crewai/cli/crew_chat.py

@@ -1,17 +1,52 @@
 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():
     """
@@ -19,20 +54,47 @@ 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
 
-    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}]
+    # 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",
     )
-    click.secho(f"\nAssistant: {introductory_message}\n", fg="green")
+
+    # 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")
 
     messages = [
         {"role": "system", "content": system_message},
@@ -43,15 +105,17 @@ 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:
@@ -85,7 +149,7 @@ 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}"
@@ -102,25 +166,33 @@ 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:
-            user_input = click.prompt("You", type=str)
-            if user_input.strip().lower() in ["exit", "quit"]:
-                click.echo("Exiting chat. Goodbye!")
-                break
+            # Flush any pending input before accepting new input
+            flush_input()
 
-            messages.append({"role": "user", "content": user_input})
-            final_response = chat_llm.call(
-                messages=messages,
-                tools=[crew_tool_schema],
-                available_functions=available_functions,
+            user_input = get_user_input()
+            handle_user_input(
+                user_input, chat_llm, messages, crew_tool_schema, 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
@@ -129,6 +201,55 @@ 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.
@@ -323,10 +444,10 @@ 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
+                lambda m: m.group(1), task.description or ""
             )
             expected_output = placeholder_pattern.sub(
-                lambda m: m.group(1), task.expected_output
+                lambda m: m.group(1), task.expected_output or ""
             )
             context_texts.append(f"Task Description: {task_description}")
             context_texts.append(f"Expected Output: {expected_output}")
@@ -337,10 +458,10 @@ 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)
-            agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal)
+            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
+                lambda m: m.group(1), agent.backstory or ""
             )
             context_texts.append(f"Agent Role: {agent_role}")
             context_texts.append(f"Agent Goal: {agent_goal}")
@@ -381,18 +502,20 @@ 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
+            lambda m: m.group(1), task.description or ""
         )
         expected_output = placeholder_pattern.sub(
-            lambda m: m.group(1), task.expected_output
+            lambda m: m.group(1), task.expected_output or ""
         )
         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)
+        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 ""
+        )
         context_texts.append(f"Agent Role: {agent_role}")
         context_texts.append(f"Agent Goal: {agent_goal}")
         context_texts.append(f"Agent Backstory: {agent_backstory}")

src/crewai/cli/templates/crew/.gitignore

@@ -1,2 +1,3 @@
 .env
 __pycache__/
+.DS_Store

src/crewai/cli/templates/flow/.gitignore

@@ -1,3 +1,4 @@
 .env
 __pycache__/
 lib/
+.DS_Store

src/crewai/crew.py

@@ -37,7 +37,6 @@ 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
@@ -84,6 +83,7 @@ class Crew(BaseModel):
         step_callback: Callback to be executed after each step for every agents execution.
         share_crew: Whether you want to share the complete crew information and execution with crewAI to make the library better, and allow us to train models.
         planning: Plan the crew execution and add the plan to the crew.
+        chat_llm: The language model used for orchestrating chat interactions with the crew.
     """
 
     __hash__ = object.__hash__  # type: ignore

src/crewai/llm.py

@@ -142,7 +142,6 @@ 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
@@ -160,37 +159,63 @@ 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: List[Dict[str, str]],
+        messages: Union[str, 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
+        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
 
-        :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
+        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)
         """
+        if isinstance(messages, str):
+            messages = [{"role": "user", "content": messages}]
+
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
             try:
-                # --- 1) Make the completion call
+                # --- 1) Prepare the parameters for the completion call
                 params = {
                     "model": self.model,
                     "messages": messages,
@@ -211,19 +236,21 @@ class LLM:
                     "api_version": self.api_version,
                     "api_key": self.api_key,
                     "stream": False,
-                    "tools": tools,  # pass the tool schema
+                    "tools": tools,
                 }
 
+                # 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", [])
-                
-                # Ensure callbacks get the full response object with usage info
+
+                # --- 3) Handle callbacks with usage info
                 if callbacks and len(callbacks) > 0:
                     for callback in callbacks:
                         if hasattr(callback, "log_success_event"):
@@ -236,11 +263,11 @@ class LLM:
                                     end_time=0,
                                 )
 
-                # --- 2) If no tool calls, return the text response
+                # --- 4) If no tool calls, return the text response
                 if not tool_calls or not available_functions:
                     return text_response
 
-                # --- 3) Handle the tool call
+                # --- 5) Handle the tool call
                 tool_call = tool_calls[0]
                 function_name = tool_call.function.name
 
@@ -255,7 +282,6 @@ class LLM:
                     try:
                         # Call the actual tool function
                         result = fn(**function_args)
-
                         return result
 
                     except Exception as e:

src/crewai/tools/tool_usage.py

@@ -1,12 +1,13 @@
 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, Union
+from typing import Any, Dict, List, Optional, Union
 
+import json5
 from json_repair import repair_json
 
 import crewai.utilities.events as events
@@ -407,28 +408,55 @@ class ToolUsage:
                 )
             return self._tool_calling(tool_string)
 
-    def _validate_tool_input(self, tool_input: str) -> Dict[str, Any]:
+    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
         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)
-        except json.JSONDecodeError:
-            # Attempt to repair JSON string
-            repaired_input = repair_json(tool_input)
-            try:
-                arguments = json.loads(repaired_input)
-            except json.JSONDecodeError as e:
-                raise Exception(f"Invalid tool input JSON: {e}")
+            if isinstance(arguments, dict):
+                return arguments
+        except (JSONDecodeError, TypeError):
+            pass  # Continue to the next parsing attempt
 
-        return arguments
+        # 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:
+            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")
+
+        # If all parsing attempts fail, raise an error
+        raise Exception(
+            "Tool input must be a valid dictionary in JSON or Python literal format"
+        )
 
     def on_tool_error(self, tool: Any, tool_calling: ToolCalling, e: Exception) -> None:
         event_data = self._prepare_event_data(tool, tool_calling)

src/crewai/utilities/llm_utils.py

@@ -24,12 +24,10 @@ 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
@@ -39,12 +37,10 @@ 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)

tests/agent_test.py

@@ -16,7 +16,7 @@ from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.tools.tool_usage_events import ToolUsageFinished
-from crewai.utilities import RPMController
+from crewai.utilities import Printer, RPMController
 from crewai.utilities.events import Emitter
 
 
@@ -1600,3 +1600,142 @@ 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_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()

tests/cassettes/test_agent_error_on_parsing_tool.yaml

@@ -2,21 +2,21 @@ interactions:
 - request:
     body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
       personal goal is: test goal\nYou ONLY have access to the following tools, and
-      should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer(*args:
-      Any, **kwargs: Any) -> Any\nTool Description: get_final_answer() - Get the final
-      answer but don''t give it yet, just re-use this         tool non-stop. \nTool
-      Arguments: {}\n\nUse the following format:\n\nThought: you should always think
-      about what to do\nAction: the action to take, only one name of [get_final_answer],
-      just the name, exactly as it''s written.\nAction Input: the input to the action,
-      just a simple python dictionary, enclosed in curly braces, using \" to wrap
-      keys and values.\nObservation: the result of the action\n\nOnce all necessary
-      information is gathered:\n\nThought: I now know the final answer\nFinal Answer:
-      the final answer to the original input question\n"}, {"role": "user", "content":
-      "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expect criteria
-      for your final answer: The final answer\nyou MUST return the actual complete
-      content as the final answer, not a summary.\n\nBegin! This is VERY important
-      to you, use the tools available and give your best Final Answer, your job depends
-      on it!\n\nThought:"}], "model": "gpt-4o"}'
+      should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool
+      Arguments: {}\nTool Description: Get the final answer but don''t give it yet,
+      just re-use this\n        tool non-stop.\n\nIMPORTANT: Use the following format
+      in your response:\n\n```\nThought: you should always think about what to do\nAction:
+      the action to take, only one name of [get_final_answer], just the name, exactly
+      as it''s written.\nAction Input: the input to the action, just a simple JSON
+      object, enclosed in curly braces, using \" to wrap keys and values.\nObservation:
+      the result of the action\n```\n\nOnce all necessary information is gathered,
+      return the following format:\n\n```\nThought: I now know the final answer\nFinal
+      Answer: the final answer to the original input question\n```"}, {"role": "user",
+      "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expect
+      criteria for your final answer: The final answer\nyou MUST return the actual
+      complete content as the final answer, not a summary.\n\nBegin! This is VERY
+      important to you, use the tools available and give your best Final Answer, your
+      job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
     headers:
       accept:
       - application/json
@@ -25,16 +25,13 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1325'
+      - '1367'
       content-type:
       - application/json
-      cookie:
-      - _cfuvid=ePJSDFdHag2D8lj21_ijAMWjoA6xfnPNxN4uekvC728-1727226247743-0.0.1.1-604800000;
-        __cf_bm=3giyBOIM0GNudFELtsBWYXwLrpLBTNLsh81wfXgu2tg-1727226247-1.0.1.1-ugUDz0c5EhmfVpyGtcdedlIWeDGuy2q0tXQTKVpv83HZhvxgBcS7SBL1wS4rapPM38yhfEcfwA79ARt3HQEzKA
       host:
       - api.openai.com
       user-agent:
-      - OpenAI/Python 1.47.0
+      - OpenAI/Python 1.59.6
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -44,30 +41,35 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.47.0
+      - 1.59.6
       x-stainless-raw-response:
       - 'true'
+      x-stainless-retry-count:
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tests/cassettes/test_llm_call_with_tool_and_string_input.yaml

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tests/llm_test.py

@@ -4,6 +4,7 @@ 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
 
 
@@ -37,3 +38,119 @@ 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

tests/tools/test_tool_usage.py

@@ -231,3 +231,255 @@ 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

uv.lock

@@ -659,6 +659,7 @@ dependencies = [
     { name = "click" },
     { name = "instructor" },
     { name = "json-repair" },
+    { name = "json5" },
     { name = "jsonref" },
     { name = "litellm" },
     { name = "openai" },
@@ -737,6 +738,7 @@ 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 = "==1.57.4" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
@@ -2077,6 +2079,15 @@ 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"