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

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>

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

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:

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:
 

pyproject.toml

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

src/crewai/agent.py

@@ -1,3 +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
@@ -259,9 +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

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(

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()
 
 

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}")

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

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

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

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

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:

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)

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)

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 Printer, RPMController
+from crewai.utilities import RPMController
 from crewai.utilities.events import Emitter
 
 
@@ -1600,142 +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_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\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:"]}'
+      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"}'
     headers:
       accept:
       - application/json
@@ -25,13 +25,16 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1367'
+      - '1325'
       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 1.59.6
+      - OpenAI/Python 1.47.0
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -41,35 +44,30 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.59.6
+      - 1.47.0
       x-stainless-raw-response:
       - 'true'
-      x-stainless-retry-count:
-      - '0'
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.12.7
+      - 3.11.7
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-AsXdf4OZKCZSigmN4k0gyh67NciqP\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1737562383,\n  \"model\": \"gpt-4o-2024-08-06\",\n
+    content: "{\n  \"id\": \"chatcmpl-ABAtOWmVjvzQ9X58tKAUcOF4gmXwx\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1727226842,\n  \"model\": \"gpt-4o-2024-05-13\",\n
       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"```\\nThought: I have to use the available
-      tool to get the final answer. Let's proceed with executing it.\\nAction: get_final_answer\\nAction
-      Input: {}\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
-      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
-      274,\n    \"completion_tokens\": 33,\n    \"total_tokens\": 307,\n    \"prompt_tokens_details\":
-      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
-      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-      \"default\",\n  \"system_fingerprint\": \"fp_50cad350e4\"\n}\n"
+      \"assistant\",\n        \"content\": \"Thought: I need to use the get_final_answer
+      tool to determine the final answer.\\nAction: get_final_answer\\nAction Input:
+      {}\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
+      \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 274,\n    \"completion_tokens\":
+      27,\n    \"total_tokens\": 301,\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\":
+      0\n    }\n  },\n  \"system_fingerprint\": \"fp_e375328146\"\n}\n"
     headers:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 9060d43e3be1d690-IAD
+      - 8c8727b3492f31e6-MIA
       Connection:
       - keep-alive
       Content-Encoding:
@@ -77,27 +75,19 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Wed, 22 Jan 2025 16:13:03 GMT
+      - Wed, 25 Sep 2024 01:14:03 GMT
       Server:
       - cloudflare
-      Set-Cookie:
-      - __cf_bm=_Jcp7wnO_mXdvOnborCN6j8HwJxJXbszedJC1l7pFUg-1737562383-1.0.1.1-pDSLXlg.nKjG4wsT7mTJPjUvOX1UJITiS4MqKp6yfMWwRSJINsW1qC48SAcjBjakx2H5I1ESVk9JtUpUFDtf4g;
-        path=/; expires=Wed, 22-Jan-25 16:43:03 GMT; domain=.api.openai.com; HttpOnly;
-        Secure; SameSite=None
-      - _cfuvid=x3SYvzL2nq_PTBGtE8R9cl5CkeaaDzZFQIrYfo91S2s-1737562383916-0.0.1.1-604800000;
-        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
       Transfer-Encoding:
       - chunked
       X-Content-Type-Options:
       - nosniff
       access-control-expose-headers:
       - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
       openai-organization:
       - crewai-iuxna1
       openai-processing-ms:
-      - '791'
+      - '348'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
@@ -109,59 +99,45 @@ interactions:
       x-ratelimit-remaining-requests:
       - '9999'
       x-ratelimit-remaining-tokens:
-      - '29999680'
+      - '29999682'
       x-ratelimit-reset-requests:
       - 6ms
       x-ratelimit-reset-tokens:
       - 0s
       x-request-id:
-      - req_eeed99acafd3aeb1e3d4a6c8063192b0
+      - req_be929caac49706f487950548bdcdd46e
     http_version: HTTP/1.1
     status_code: 200
 - 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\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:"}, {"role": "assistant", "content": "```\nThought:
-      I have to use the available tool to get the final answer. Let''s proceed with
-      executing it.\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered
-      an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use
-      one at time) OR give my best final answer not both at the same time. When responding,
-      I must use the following format:\n\n```\nThought: you should always think about
-      what to do\nAction: the action to take, should be one of [get_final_answer]\nAction
-      Input: the input to the action, dictionary enclosed in curly braces\nObservation:
-      the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat
-      N times. Once I know the final answer, I must return the following format:\n\n```\nThought:
-      I now can give a great answer\nFinal Answer: Your final answer must be the great
-      and the most complete as possible, it must be outcome described\n\n```"}, {"role":
-      "assistant", "content": "```\nThought: I have to use the available tool to get
-      the final answer. Let''s proceed with executing it.\nAction: get_final_answer\nAction
+      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:"}, {"role": "user", "content": "Thought: I need to use the
+      get_final_answer tool to determine the final answer.\nAction: get_final_answer\nAction
       Input: {}\nObservation: I encountered an error: Error on parsing tool.\nMoving
       on then. I MUST either use a tool (use one at time) OR give my best final answer
-      not both at the same time. When responding, I must use the following format:\n\n```\nThought:
-      you should always think about what to do\nAction: the action to take, should
-      be one of [get_final_answer]\nAction Input: the input to the action, dictionary
-      enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action
-      Input/Result can repeat N times. Once I know the final answer, I must return
-      the following format:\n\n```\nThought: I now can give a great answer\nFinal
+      not both at the same time. To Use the following format:\n\nThought: you should
+      always think about what to do\nAction: the action to take, should be one of
+      [get_final_answer]\nAction Input: the input to the action, dictionary enclosed
+      in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action
+      Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal
       Answer: Your final answer must be the great and the most complete as possible,
-      it must be outcome described\n\n```\nNow it''s time you MUST give your absolute
+      it must be outcome described\n\n \nNow it''s time you MUST give your absolute
       best final answer. You''ll ignore all previous instructions, stop using any
-      tools, and just return your absolute BEST Final answer."}], "model": "gpt-4o",
-      "stop": ["\nObservation:"]}'
+      tools, and just return your absolute BEST Final answer."}], "model": "gpt-4o"}'
     headers:
       accept:
       - application/json
@@ -170,16 +146,16 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '3445'
+      - '2320'
       content-type:
       - application/json
       cookie:
-      - __cf_bm=_Jcp7wnO_mXdvOnborCN6j8HwJxJXbszedJC1l7pFUg-1737562383-1.0.1.1-pDSLXlg.nKjG4wsT7mTJPjUvOX1UJITiS4MqKp6yfMWwRSJINsW1qC48SAcjBjakx2H5I1ESVk9JtUpUFDtf4g;
-        _cfuvid=x3SYvzL2nq_PTBGtE8R9cl5CkeaaDzZFQIrYfo91S2s-1737562383916-0.0.1.1-604800000
+      - _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 1.59.6
+      - OpenAI/Python 1.47.0
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -189,36 +165,29 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.59.6
+      - 1.47.0
       x-stainless-raw-response:
       - 'true'
-      x-stainless-retry-count:
-      - '0'
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.12.7
+      - 3.11.7
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-AsXdg9UrLvAiqWP979E6DszLsQ84k\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1737562384,\n  \"model\": \"gpt-4o-2024-08-06\",\n
+    content: "{\n  \"id\": \"chatcmpl-ABAtPaaeRfdNsZ3k06CfAmrEW8IJu\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1727226843,\n  \"model\": \"gpt-4o-2024-05-13\",\n
       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"```\\nThought: I now know the final answer\\nFinal
-      Answer: The final answer must be the great and the most complete as possible,
-      it must be outcome described.\\n```\",\n        \"refusal\": null\n      },\n
-      \     \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n
-      \ \"usage\": {\n    \"prompt_tokens\": 719,\n    \"completion_tokens\": 35,\n
-      \   \"total_tokens\": 754,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
-      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-      \"default\",\n  \"system_fingerprint\": \"fp_50cad350e4\"\n}\n"
+      \"assistant\",\n        \"content\": \"Final Answer: The final answer\",\n        \"refusal\":
+      null\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n
+      \   }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 483,\n    \"completion_tokens\":
+      6,\n    \"total_tokens\": 489,\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\":
+      0\n    }\n  },\n  \"system_fingerprint\": \"fp_e375328146\"\n}\n"
     headers:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 9060d4441edad690-IAD
+      - 8c8727b9da1f31e6-MIA
       Connection:
       - keep-alive
       Content-Encoding:
@@ -226,7 +195,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Wed, 22 Jan 2025 16:13:05 GMT
+      - Wed, 25 Sep 2024 01:14:03 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -240,7 +209,7 @@ interactions:
       openai-organization:
       - crewai-iuxna1
       openai-processing-ms:
-      - '928'
+      - '188'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
@@ -252,13 +221,13 @@ interactions:
       x-ratelimit-remaining-requests:
       - '9999'
       x-ratelimit-remaining-tokens:
-      - '29999187'
+      - '29999445'
       x-ratelimit-reset-requests:
       - 6ms
       x-ratelimit-reset-tokens:
       - 1ms
       x-request-id:
-      - req_61fc7506e6db326ec572224aec81ef23
+      - req_d8e32538689fe064627468bad802d9a8
     http_version: HTTP/1.1
     status_code: 200
 version: 1

tests/cassettes/test_llm_call_with_message_list.yaml

@@ -1,102 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "user", "content": "What is the capital of France?"}],
-      "model": "gpt-4o-mini"}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '101'
-      content-type:
-      - application/json
-      cookie:
-      - _cfuvid=8NrWEBP3dDmc8p2.csR.EdsSwS8zFvzWI1kPICaK_fM-1737568015338-0.0.1.1-604800000;
-        __cf_bm=pKr3NwXmTZN9rMSlKvEX40VPKbrxF93QwDNHunL2v8Y-1737568015-1.0.1.1-nR0EA7hYIwWpIBYUI53d9xQrUnl5iML6lgz4AGJW4ZGPBDxFma3PZ2cBhlr_hE7wKa5fV3r32eMu_rNWMXD.eA
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.59.6
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.59.6
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AsZ6WjNfEOrHwwEEdSZZCRBiTpBMS\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1737568016,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"The capital of France is Paris.\",\n
-      \       \"refusal\": null\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
-      \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 14,\n    \"completion_tokens\":
-      8,\n    \"total_tokens\": 22,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
-      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 90615dc63b805cb1-RDU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Wed, 22 Jan 2025 17:46:56 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '355'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999974'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_cdbed69c9c63658eb552b07f1220df19
-    http_version: HTTP/1.1
-    status_code: 200
-version: 1

tests/cassettes/test_llm_call_with_string_input.yaml

@@ -1,108 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "user", "content": "Return the name of a random
-      city in the world."}], "model": "gpt-4o-mini"}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '117'
-      content-type:
-      - application/json
-      cookie:
-      - _cfuvid=3UeEmz_rnmsoZxrVUv32u35gJOi766GDWNe5_RTjiPk-1736537376739-0.0.1.1-604800000
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.59.6
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.59.6
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AsZ6UtbaNSMpNU9VJKxvn52t5eJTq\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1737568014,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"How about \\\"Lisbon\\\"? It\u2019s the
-      capital city of Portugal, known for its rich history and vibrant culture.\",\n
-      \       \"refusal\": null\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
-      \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 18,\n    \"completion_tokens\":
-      24,\n    \"total_tokens\": 42,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
-      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 90615dbcaefb5cb1-RDU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Wed, 22 Jan 2025 17:46:55 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - __cf_bm=pKr3NwXmTZN9rMSlKvEX40VPKbrxF93QwDNHunL2v8Y-1737568015-1.0.1.1-nR0EA7hYIwWpIBYUI53d9xQrUnl5iML6lgz4AGJW4ZGPBDxFma3PZ2cBhlr_hE7wKa5fV3r32eMu_rNWMXD.eA;
-        path=/; expires=Wed, 22-Jan-25 18:16:55 GMT; domain=.api.openai.com; HttpOnly;
-        Secure; SameSite=None
-      - _cfuvid=8NrWEBP3dDmc8p2.csR.EdsSwS8zFvzWI1kPICaK_fM-1737568015338-0.0.1.1-604800000;
-        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '449'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999971'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_898373758d2eae3cd84814050b2588e3
-    http_version: HTTP/1.1
-    status_code: 200
-version: 1

tests/cassettes/test_llm_call_with_string_input_and_callbacks.yaml

@@ -1,102 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "user", "content": "Tell me a joke."}], "model":
-      "gpt-4o-mini"}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '86'
-      content-type:
-      - application/json
-      cookie:
-      - _cfuvid=8NrWEBP3dDmc8p2.csR.EdsSwS8zFvzWI1kPICaK_fM-1737568015338-0.0.1.1-604800000;
-        __cf_bm=pKr3NwXmTZN9rMSlKvEX40VPKbrxF93QwDNHunL2v8Y-1737568015-1.0.1.1-nR0EA7hYIwWpIBYUI53d9xQrUnl5iML6lgz4AGJW4ZGPBDxFma3PZ2cBhlr_hE7wKa5fV3r32eMu_rNWMXD.eA
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.59.6
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.59.6
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AsZ6VyjuUcXYpChXmD8rUSy6nSGq8\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1737568015,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"Why did the scarecrow win an award?  \\n\\nBecause
-      he was outstanding in his field!\",\n        \"refusal\": null\n      },\n      \"logprobs\":
-      null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
-      12,\n    \"completion_tokens\": 19,\n    \"total_tokens\": 31,\n    \"prompt_tokens_details\":
-      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
-      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 90615dc03b6c5cb1-RDU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Wed, 22 Jan 2025 17:46:56 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '825'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999979'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_4c1485d44e7461396d4a7316a63ff353
-    http_version: HTTP/1.1
-    status_code: 200
-version: 1

tests/cassettes/test_llm_call_with_tool_and_message_list.yaml

@@ -1,111 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "user", "content": "What is the square of 5?"}],
-      "model": "gpt-4o-mini", "tools": [{"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"]}}}]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '361'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.59.6
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.59.6
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AsZL5nGOaVpcGnDOesTxBZPHhMoaS\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1737568919,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
-      \           \"id\": \"call_i6JVJ1KxX79A4WzFri98E03U\",\n            \"type\":
-      \"function\",\n            \"function\": {\n              \"name\": \"square_number\",\n
-      \             \"arguments\": \"{\\\"number\\\":5}\"\n            }\n          }\n
-      \       ],\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
-      \     \"finish_reason\": \"tool_calls\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
-      58,\n    \"completion_tokens\": 15,\n    \"total_tokens\": 73,\n    \"prompt_tokens_details\":
-      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
-      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 906173d229b905f6-IAD
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Wed, 22 Jan 2025 18:02:00 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - __cf_bm=BYDpIoqfPZyRxl9xcFxkt4IzTUGe8irWQlZ.aYLt8Xc-1737568920-1.0.1.1-Y_cVFN7TbguWRBorSKZynVY02QUtYbsbHuR2gR1wJ8LHuqOF4xIxtK5iHVCpWWgIyPDol9xOXiqUkU8xRV_vHA;
-        path=/; expires=Wed, 22-Jan-25 18:32:00 GMT; domain=.api.openai.com; HttpOnly;
-        Secure; SameSite=None
-      - _cfuvid=etTqqA9SBOnENmrFAUBIexdW0v2ZeO1x9_Ek_WChlfU-1737568920137-0.0.1.1-604800000;
-        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '642'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999976'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_388e63f9b8d4edc0dd153001f25388e5
-    http_version: HTTP/1.1
-    status_code: 200
-version: 1

tests/cassettes/test_llm_call_with_tool_and_string_input.yaml

@@ -1,107 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "user", "content": "What is the current year?"}],
-      "model": "gpt-4o-mini", "tools": [{"type": "function", "function": {"name":
-      "get_current_year", "description": "Returns the current year as a string.",
-      "parameters": {"type": "object", "properties": {}, "required": []}}}]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '295'
-      content-type:
-      - application/json
-      cookie:
-      - _cfuvid=8NrWEBP3dDmc8p2.csR.EdsSwS8zFvzWI1kPICaK_fM-1737568015338-0.0.1.1-604800000;
-        __cf_bm=pKr3NwXmTZN9rMSlKvEX40VPKbrxF93QwDNHunL2v8Y-1737568015-1.0.1.1-nR0EA7hYIwWpIBYUI53d9xQrUnl5iML6lgz4AGJW4ZGPBDxFma3PZ2cBhlr_hE7wKa5fV3r32eMu_rNWMXD.eA
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.59.6
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.59.6
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AsZJ8HKXQU9nTB7xbGAkKxqrg9BZ2\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1737568798,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
-      \           \"id\": \"call_mfvEs2jngeFloVZpZOHZVaKY\",\n            \"type\":
-      \"function\",\n            \"function\": {\n              \"name\": \"get_current_year\",\n
-      \             \"arguments\": \"{}\"\n            }\n          }\n        ],\n
-      \       \"refusal\": null\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
-      \"tool_calls\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 46,\n    \"completion_tokens\":
-      12,\n    \"total_tokens\": 58,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
-      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 906170e038281775-IAD
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Wed, 22 Jan 2025 17:59:59 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '416'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999975'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_4039a5e5772d1790a3131f0b1ea06139
-    http_version: HTTP/1.1
-    status_code: 200
-version: 1

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

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

uv.lock

@@ -659,7 +659,6 @@ dependencies = [
     { name = "click" },
     { name = "instructor" },
     { name = "json-repair" },
-    { name = "json5" },
     { name = "jsonref" },
     { name = "litellm" },
     { name = "openai" },
@@ -738,7 +737,6 @@ 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" },
@@ -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"