Bringing support to o1 family back + any models that don't support stop words

docs/core-concepts/Agents.md

@@ -36,7 +36,6 @@ description: What are crewAI Agents and how to use them.
 | **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
 | **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
-| **Use Stop Words** *(optional)*     | `use_stop_words`  | Adds the ability to not use stop words (to support o1 models). Default is `True`.                                                                                                                                                                  |
 | **Use System Prompt** *(optional)*     | `use_system_prompt`  | Adds the ability to not use system prompt (to support o1 models). Default is `True`.                                                                                                                                                                  |
 | **Respect Context Window** *(optional)*     | `respect_context_window`  | Summary strategy to avoid overflowing the context window. Default is `True`.                                                                                                                                                                  |
 
@@ -79,7 +78,6 @@ agent = Agent(
   callbacks=[callback1, callback2],  # Optional
   allow_code_execution=True,  # Optional
   max_retry_limit=2,  # Optional
-  use_stop_words=True,  # Optional
   use_system_prompt=True,  # Optional
   respect_context_window=True,  # Optional
 )

docs/how-to/Customizing-Agents.md

@@ -20,7 +20,6 @@ Crafting an efficient CrewAI team hinges on the ability to dynamically tailor yo
 - **System Template** *(Optional)*: `system_template` defines the system format for the agent.
 - **Prompt Template** *(Optional)*: `prompt_template` defines the prompt format for the agent.
 - **Response Template** *(Optional)*: `response_template` defines the response format for the agent.
-- **Use Stop Words** *(Optional)*: `use_stop_words` attribute controls whether the agent will use stop words during task execution. This is now supported to aid o1 models.
 - **Use System Prompt** *(Optional)*: `use_system_prompt` controls whether the agent will use a system prompt for task execution. Agents can now operate without system prompts.
 - **Respect Context Window**: `respect_context_window` renames the sliding context window attribute and enables it by default to maintain context size.
 - **Max Retry Limit**: `max_retry_limit` defines the maximum number of retries for an agent to execute a task when an error occurs.

docs/how-to/Hierarchical.md

@@ -46,7 +46,6 @@ researcher = Agent(
     verbose=False,
     # tools=[]  # This can be optionally specified; defaults to an empty list
     use_system_prompt=True,  # Enable or disable system prompts for this agent
-    use_stop_words=True,  # Enable or disable stop words for this agent
     max_rpm=30,  # Limit on the number of requests per minute
     max_iter=5  # Maximum number of iterations for a final answer
 )
@@ -58,7 +57,6 @@ writer = Agent(
     verbose=False,
     # tools=[]  # Optionally specify tools; defaults to an empty list
     use_system_prompt=True,  # Enable or disable system prompts for this agent
-    use_stop_words=True,  # Enable or disable stop words for this agent
     max_rpm=30,  # Limit on the number of requests per minute
     max_iter=5  # Maximum number of iterations for a final answer
 )

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.63.2"
+version = "0.63.4"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"

src/crewai/agent.py

@@ -74,10 +74,6 @@ class Agent(BaseAgent):
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    use_stop_words: bool = Field(
-        default=True,
-        description="Use stop words for the agent.",
-    )
     use_system_prompt: Optional[bool] = Field(
         default=True,
         description="Use system prompt for the agent.",
@@ -291,7 +287,6 @@ class Agent(BaseAgent):
             stop_words=stop_words,
             max_iter=self.max_iter,
             tools_handler=self.tools_handler,
-            use_stop_words=self.use_stop_words,
             tools_names=self.__tools_names(parsed_tools),
             tools_description=self._render_text_description_and_args(parsed_tools),
             step_callback=self.step_callback,

src/crewai/agents/crew_agent_executor.py

@@ -34,7 +34,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         max_iter: int,
         tools: List[Any],
         tools_names: str,
-        use_stop_words: bool,
         stop_words: List[str],
         tools_description: str,
         tools_handler: ToolsHandler,
@@ -60,7 +59,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tools_handler = tools_handler
         self.original_tools = original_tools
         self.step_callback = step_callback
-        self.use_stop_words = use_stop_words
+        self.use_stop_words = self.llm.supports_stop_words()
         self.tools_description = tools_description
         self.function_calling_llm = function_calling_llm
         self.respect_context_window = respect_context_window
@@ -146,6 +145,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         self.messages.append(
                             self._format_msg(formatted_answer.text, role="user")
                         )
+
         except OutputParserException as e:
             self.messages.append({"role": "user", "content": e.error})
             return self._invoke_loop(formatted_answer)

src/crewai/llm.py

@@ -78,6 +78,7 @@ class LLM:
                 "api_key": self.api_key,
                 **self.kwargs,
             }
+
             # Remove None values to avoid passing unnecessary parameters
             params = {k: v for k, v in params.items() if v is not None}
 
@@ -94,3 +95,11 @@ class LLM:
         except Exception as e:
             logging.error(f"Failed to get supported params: {str(e)}")
             return False
+
+    def supports_stop_words(self) -> bool:
+        try:
+            params = get_supported_openai_params(model=self.model)
+            return "stop" in params
+        except Exception as e:
+            logging.error(f"Failed to get supported params: {str(e)}")
+            return False

src/crewai/telemetry/telemetry.py

@@ -301,7 +301,7 @@ class Telemetry:
                 self._add_attribute(span, "tool_name", tool_name)
                 self._add_attribute(span, "attempts", attempts)
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(span, "llm", llm.model)
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -321,7 +321,7 @@ class Telemetry:
                 self._add_attribute(span, "tool_name", tool_name)
                 self._add_attribute(span, "attempts", attempts)
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(span, "llm", llm.model)
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -339,7 +339,7 @@ class Telemetry:
                     pkg_resources.get_distribution("crewai").version,
                 )
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(span, "llm", llm.model)
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:

src/crewai/tools/tool_usage.py

@@ -80,6 +80,7 @@ class ToolUsage:
 
     def parse(self, tool_string: str):
         """Parse the tool string and return the tool calling."""
+        print("tool_string", tool_string)
         return self._tool_calling(tool_string)
 
     def use(
@@ -297,59 +298,78 @@ class ToolUsage:
             )
         return "\n--\n".join(descriptions)
 
+    def _function_calling(self, tool_string: str):
+        model = (
+            InstructorToolCalling
+            if self.function_calling_llm.supports_function_calling()
+            else ToolCalling
+        )
+        converter = Converter(
+            text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
+            llm=self.function_calling_llm,
+            model=model,
+            instructions=dedent(
+                """\
+        The schema should have the following structure, only two keys:
+        - tool_name: str
+        - arguments: dict (always a dictionary, with all arguments being passed)
+
+        Example:
+        {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
+            ),
+            max_attempts=1,
+        )
+        tool_object = converter.to_pydantic()
+        calling = ToolCalling(
+            tool_name=tool_object["tool_name"],
+            arguments=tool_object["arguments"],
+            log=tool_string,  # type: ignore
+        )
+
+        if isinstance(calling, ConverterError):
+            raise calling
+
+        return calling
+
+    def _original_tool_calling(self, tool_string: str, raise_error: bool = False):
+        tool_name = self.action.tool
+        tool = self._select_tool(tool_name)
+        try:
+            tool_input = self._validate_tool_input(self.action.tool_input)
+            arguments = ast.literal_eval(tool_input)
+        except Exception:
+            if raise_error:
+                raise
+            else:
+                return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
+                    f'{self._i18n.errors("tool_arguments_error")}'
+                )
+
+        if not isinstance(arguments, dict):
+            if raise_error:
+                raise
+            else:
+                return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
+                    f'{self._i18n.errors("tool_arguments_error")}'
+                )
+
+        return ToolCalling(
+            tool_name=tool.name,
+            arguments=arguments,
+            log=tool_string,  # type: ignore
+        )
+
     def _tool_calling(
         self, tool_string: str
     ) -> Union[ToolCalling, InstructorToolCalling]:
         try:
-            if self.function_calling_llm:
+            try:
-                model = (
+                return self._original_tool_calling(tool_string, raise_error=True)
-                    InstructorToolCalling
+            except Exception:
-                    if self.function_calling_llm.supports_function_calling()
+                if self.function_calling_llm:
-                    else ToolCalling
+                    return self._function_calling(tool_string)
-                )
+                else:
-                converter = Converter(
+                    return self._original_tool_calling(tool_string)
-                    text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
-                    llm=self.function_calling_llm,
-                    model=model,
-                    instructions=dedent(
-                        """\
-              The schema should have the following structure, only two keys:
-              - tool_name: str
-              - arguments: dict (with all arguments being passed)
-
-              Example:
-              {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
-                    ),
-                    max_attempts=1,
-                )
-                tool_object = converter.to_pydantic()
-                calling = ToolCalling(
-                    tool_name=tool_object["tool_name"],
-                    arguments=tool_object["arguments"],
-                    log=tool_string,  # type: ignore
-                )
-
-                if isinstance(calling, ConverterError):
-                    raise calling
-            else:
-                tool_name = self.action.tool
-                tool = self._select_tool(tool_name)
-                try:
-                    tool_input = self._validate_tool_input(self.action.tool_input)
-                    arguments = ast.literal_eval(tool_input)
-                except Exception:
-                    return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
-                        f'{self._i18n.errors("tool_arguments_error")}'
-                    )
-                if not isinstance(arguments, dict):
-                    return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
-                        f'{self._i18n.errors("tool_arguments_error")}'
-                    )
-                calling = ToolCalling(
-                    tool_name=tool.name,
-                    arguments=arguments,
-                    log=tool_string,  # type: ignore
-                )
         except Exception as e:
             self._run_attempts += 1
             if self._run_attempts > self._max_parsing_attempts:
@@ -362,8 +382,6 @@ class ToolUsage:
                 )
             return self._tool_calling(tool_string)
 
-        return calling
-
     def _validate_tool_input(self, tool_input: str) -> str:
         try:
             ast.literal_eval(tool_input)

tests/agent_test.py

@@ -133,8 +133,8 @@ def test_agent_execute_task():
 
     assert result is not None
     assert (
-        "The area of the circle with a radius of 5 cm is approximately 78.5 square centimeters."
+        result
-        == result
+        == "The calculated area of the circle is approximately 78.5 square centimeters."
     )
     assert "square centimeters" in result.lower()
 
@@ -155,7 +155,7 @@ def test_agent_execution():
     )
 
     output = agent.execute_task(task)
-    assert output == "The result of the math operation 1 + 1 is 2."
+    assert output == "1 + 1 is 2"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -179,7 +179,7 @@ def test_agent_execution_with_tools():
         expected_output="The result of the multiplication.",
     )
     output = agent.execute_task(task)
-    assert output == "The result of 3 times 4 is 12"
+    assert output == "The result of the multiplication is 12."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -211,7 +211,7 @@ def test_logging_tool_usage():
         tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
     )
 
-    assert output == "12"
+    assert output == "The result of the multiplication is 12."
     assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
     assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
 
@@ -365,7 +365,7 @@ def test_agent_execution_with_specific_tools():
         expected_output="The result of the multiplication.",
     )
     output = agent.execute_task(task=task, tools=[multiplier])
-    assert output == "The result of 3 times 4 is 12."
+    assert output == "The result of the multiplication is 12."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -383,7 +383,6 @@ def test_agent_powered_by_new_o_model_family_that_allows_skipping_tool():
         max_iter=3,
         use_system_prompt=False,
         allow_delegation=False,
-        use_stop_words=False,
     )
 
     task = Task(
@@ -410,7 +409,6 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
         max_iter=3,
         use_system_prompt=False,
         allow_delegation=False,
-        use_stop_words=False,
     )
 
     task = Task(
@@ -419,7 +417,7 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
         expected_output="The number of customers",
     )
     output = agent.execute_task(task=task, tools=[comapny_customer_data])
-    assert output == "The company has 42 customers"
+    assert output == "42"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -549,7 +547,7 @@ def test_agent_moved_on_after_max_iterations():
         task=task,
         tools=[get_final_answer],
     )
-    assert output == "42"
+    assert output == "The final answer is 42."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -580,7 +578,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
             task=task,
             tools=[get_final_answer],
         )
-        assert output == "42"
+        assert output == "The final answer is 42."
         captured = capsys.readouterr()
         assert "Max RPM reached, waiting for next minute to start." in captured.out
         moveon.assert_called()
@@ -710,12 +708,13 @@ def test_agent_error_on_parsing_tool(capsys):
         verbose=True,
         function_calling_llm="gpt-4o",
     )
-
+    with patch.object(ToolUsage, "_original_tool_calling") as force_exception_1:
-    with patch.object(ToolUsage, "_render") as force_exception:
+        force_exception_1.side_effect = Exception("Error on parsing tool.")
-        force_exception.side_effect = Exception("Error on parsing tool.")
+        with patch.object(ToolUsage, "_render") as force_exception_2:
-        crew.kickoff()
+            force_exception_2.side_effect = Exception("Error on parsing tool.")
-        captured = capsys.readouterr()
+            crew.kickoff()
-        assert "Error on parsing tool." in captured.out
+    captured = capsys.readouterr()
+    assert "Error on parsing tool." in captured.out
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -842,12 +841,16 @@ def test_agent_function_calling_llm():
     crew = Crew(agents=[agent1], tasks=tasks)
     from unittest.mock import patch
     import instructor
+    from crewai.tools.tool_usage import ToolUsage
 
     with patch.object(
         instructor, "from_litellm", wraps=instructor.from_litellm
-    ) as mock_from_litellm:
+    ) as mock_from_litellm, patch.object(
+        ToolUsage, "_original_tool_calling", side_effect=Exception("Forced exception")
+    ) as mock_original_tool_calling:
         crew.kickoff()
         mock_from_litellm.assert_called()
+        mock_original_tool_calling.assert_called()
 
 
 def test_agent_count_formatting_error():
@@ -1520,7 +1523,7 @@ def test_agent_execute_task_with_custom_llm():
 
     result = agent.execute_task(task)
     assert result.startswith(
-        "Artificial minds,\nLearning, evolving, creating,\nFuture in circuits."
+        "Artificial minds,\nCoding thoughts in circuits bright,\nAI's silent might."
     )
 
 

tests/agent_tools/agent_tools_test.py

@@ -24,7 +24,7 @@ def test_delegate_work():
 
     assert (
         result
-        == "While I understand the concerns and skepticism surrounding AI agents, I wouldn't say that I hate them. My standpoint is more nuanced. AI agents, which are software entities that perform tasks autonomously using machine learning and other AI technologies, have tremendous potential to revolutionize various sectors.\n\nOn the positive side, AI agents can significantly enhance efficiency and productivity. For example, in customer service, AI agents can handle routine inquiries, allowing human agents to focus on more complex issues. In healthcare, they can assist in diagnosing diseases, thus speeding up the decision-making process and potentially saving lives. In finance, AI agents can automate trading, detect fraudulent activities, and provide personalized financial advice.\n\nHowever, there are legitimate concerns that need to be addressed. One major issue is the ethical implications of deploying AI agents. These include data privacy, biases in decision-making algorithms, and the lack of transparency in how these agents operate. Another concern is the potential job displacement that could result from increased automation. While AI agents can handle many tasks more efficiently than humans, this could lead to significant job losses in certain sectors.\n\nMoreover, there's the matter of reliability and accountability. AI agents, despite their advanced capabilities, are not infallible. They can make mistakes, and when they do, it can be challenging to pinpoint where things went wrong and who is responsible. This raises important questions about oversight and governance.\n\nIn summary, while I am cautious about the unchecked deployment of AI agents due to these ethical and practical concerns, I also recognize their potential to bring about significant positive changes. The key lies in finding a balanced approach that maximizes their benefits while mitigating their risks. This includes rigorous testing, continuous monitoring, and establishing clear ethical guidelines and policies to govern their use. \n\nBy addressing these challenges head-on, we can harness the power of AI agents in a way that is both innovative and responsible."
+        == "I understand why you might think I dislike AI agents, but my perspective is more nuanced. AI agents, in essence, are incredibly versatile tools designed to perform specific tasks autonomously or semi-autonomously. They harness various artificial intelligence techniques, such as machine learning, natural language processing, and computer vision, to interpret data, understand tasks, and execute them efficiently. \n\nFrom a technological standpoint, AI agents have revolutionized numerous industries. In customer service, for instance, AI agents like chatbots and virtual assistants handle customer inquiries 24/7, providing quick and efficient solutions. In healthcare, AI agents can assist in diagnosing diseases, managing patient data, and even predicting outbreaks. The automation capabilities of AI agents also enhance productivity in areas such as logistics, finance, and cybersecurity by identifying patterns and anomalies at speeds far beyond human capabilities.\n\nHowever, it's important to acknowledge the potential downsides and challenges associated with AI agents. Ethical considerations are paramount. Issues such as data privacy, security, and biases in AI algorithms need to be carefully managed. There is also the human aspect to consider—over-reliance on AI agents might lead to job displacement in certain sectors, and ensuring a fair transition for affected workers is crucial.\n\nMy concerns generally stem from these ethical and societal implications rather than from the technology itself. I advocate for responsible AI development, which includes transparency, fairness, and accountability. By addressing these concerns, we can harness the full potential of AI agents while mitigating the associated risks.\n\nSo, to clarify, I don't hate AI agents; I recognize their immense potential and the significant benefits they bring to various fields. However, I am equally aware of the challenges they present and advocate for a balanced approach to their development and deployment."
     )
 
 
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
 
     assert (
         result
-        == 'AI agents are specialized software entities that perform tasks autonomously on behalf of users. They leverage artificial intelligence to process inputs, learn from experiences, and make decisions, mimicking human-like behavior. Despite their transformative potential, I don\'t "hate" AI agents; rather, I hold a nuanced view that acknowledges both their advantages and limitations.\n\nAdvantages of AI Agents:\n1. **Efficiency and Productivity**: AI agents can handle repetitive tasks efficiently, freeing up human workers to focus on more complex and creative activities.\n2. **24/7 Operation**: Unlike humans, AI agents can work around the clock without breaks, significantly increasing productivity and service availability.\n3. **Data Processing**: They can process and analyze vast amounts of data quickly and accurately, supporting better decision-making.\n4. **Personalization**: AI agents can tailor services and recommendations based on user behavior and preferences, improving customer satisfaction.\n\nLimitations and Concerns:\n1. **Ethical Issues**: The deployment of AI agents raises concerns about data privacy, surveillance, and the potential for bias in decision-making algorithms.\n2. **Job Displacement**: There is legitimate concern about AI agents replacing human jobs, especially in industries where tasks are routine and repetitive.\n3. **Dependence on Data Quality**: AI agents\' performance hinges on the quality and quantity of data they are trained on. Poor data quality can lead to erroneous outcomes.\n4. **Complexity in Implementation**: Developing and maintaining AI agents requires significant technical expertise and resources. Problems can arise from their complexity, leading to potential failures.\n\nIn conclusion, while I don\'t "hate" AI agents, I am cautious of their broad and uncritical adoption. It’s essential to strike a balance between leveraging their capabilities and addressing the ethical, social, and technical challenges they present.'
+        == "AI agents are essentially autonomous software programs that perform tasks or provide services on behalf of humans. They're built on complex algorithms and often leverage machine learning and neural networks to adapt and improve over time. \n\nIt's important to clarify that I don't \"hate\" AI agents, but I do approach them with a critical eye for a couple of reasons. AI agents have enormous potential to transform industries, making processes more efficient, providing insightful data analytics, and even learning from user behavior to offer personalized experiences. However, this potential comes with significant challenges and risks:\n\n1. **Ethical Concerns**: AI agents operate on data, and the biases present in data can lead to unfair or unethical outcomes. Ensuring that AI operates within ethical boundaries requires rigorous oversight, which is not always in place.\n\n2. **Privacy Issues**: AI agents often need access to large amounts of data, raising questions about privacy and data security. If not managed correctly, this can lead to unauthorized data access and potential misuse of sensitive information.\n\n3. **Transparency and Accountability**: The decision-making process of AI agents can be opaque, making it difficult to understand how they arrive at specific conclusions or actions. This lack of transparency poses challenges for accountability, especially if something goes wrong.\n\n4. **Job Displacement**: As AI agents become more capable, there are valid concerns about their impact on employment. Tasks that were traditionally performed by humans are increasingly being automated, which can lead to job loss in certain sectors.\n\n5. **Reliability**: While AI agents can outperform humans in many areas, they are not infallible. They can make mistakes, sometimes with serious consequences. Continuous monitoring and regular updates are essential to maintain their performance and reliability.\n\nIn summary, while AI agents offer substantial benefits and opportunities, it's critical to approach their adoption and deployment with careful consideration of the associated risks. Balancing innovation with responsibility is key to leveraging AI agents effectively and ethically. So, rather than \"hating\" AI agents, I advocate for a balanced, cautious approach that maximizes benefits while mitigating potential downsides."
     )
 
 
@@ -52,7 +52,7 @@ def test_ask_question():
 
     assert (
         result
-        == "As a researcher specializing in technology and AI, I don't hate AI agents. In fact, I find them incredibly fascinating and beneficial. AI agents have the potential to transform various industries, improve efficiencies, and offer new solutions to complex problems. Their ability to learn, adapt, and perform tasks that were once thought to require human intelligence is remarkable. While it's important to consider ethical implications and ensure that AI systems are designed and deployed responsibly, I believe their overall positive impact on society and technology is significant. So to clarify, I don't hate AI agents; rather, I am quite enthusiastic about their potential and the advancements they bring to the field of technology."
+        == "As an expert researcher specialized in technology, I don't harbor emotions such as hate towards AI agents. Instead, my focus is on understanding, analyzing, and leveraging their potential to advance various fields. AI agents, when designed and implemented effectively, can greatly augment human capabilities, streamline processes, and provide valuable insights that might otherwise be overlooked. My enthusiasm for AI agents stems from their ability to transform industries and improve everyday life, making complex tasks more manageable and enhancing overall efficiency. This passion drives my research and commitment to making meaningful contributions in the realm of AI and AI agents."
     )
 
 
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
 
     assert (
         result
-        == "As an expert researcher specialized in technology and AI, my perspective on AI agents is shaped by both their potential and limitations. AI agents are tools designed to perform tasks, analyze data, and assist in various domains efficiently and accurately. They have the capability to revolutionize industries by automating complex processes, enhancing decision-making, and providing personalized experiences. For instance, in healthcare, AI agents can help in diagnosing diseases with high precision, while in finance, they can predict market trends and prevent fraud.\n\nHowever, my appreciation for AI agents does not mean I am blind to their challenges. There are valid concerns related to privacy, ethical use, and the potential displacement of jobs. The development and deployment of AI should be approached with caution, ensuring transparency, fairness, and accountability.\n\nIn conclusion, I value the advancements AI agents bring to the table and acknowledge their profound impact on society. My interest lies in leveraging their potential responsibly while addressing the associated ethical and societal challenges. So, while I love the capabilities and innovations brought forth by AI agents, I remain critically aware of the need for responsible development and use."
+        == "I don't hate AI agents; on the contrary, I find them fascinating and incredibly useful. Considering the rapid advancements in AI technology, these agents have the potential to revolutionize various industries by automating tasks, improving efficiency, and providing insights that were previously unattainable. My expertise in researching and analyzing AI and AI agents has allowed me to appreciate the intricate design and the vast possibilities they offer. Therefore, it's more accurate to say that I love AI agents for their potential to drive innovation and improve our daily lives."
     )
 
 
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
 
     assert (
         result
-        == "It's interesting that you've heard I dislike AI agents; I suspect there may have been a miscommunication. My thoughts on AI agents are more nuanced than a simple like or dislike.\n\nAI agents can be incredibly powerful tools with the potential to drastically transform various industries. Their ability to automate tasks, analyze vast amounts of data, and make predictions can lead to significant improvements in efficiency and innovation. For instance, in healthcare, AI agents can assist in diagnosing diseases by quickly analyzing medical images. In finance, they can help in fraud detection by swiftly recognizing suspicious patterns in transactions. The applications are virtually limitless and continually expanding.\n\nHowever, there are concerns that need to be addressed, which might have led to a perception that I \"hate\" AI agents. One concern is the ethical implications surrounding their deployment. Issues such as data privacy, algorithmic bias, and the potential for job displacement are significant. For example, if an AI system is trained on biased data, it may make unfair or discriminatory decisions, perpetuating existing societal inequalities. Moreover, as AI agents take over repetitive tasks, there's a real risk that many jobs could become obsolete, causing economic disruption.\n\nAdditionally, there's the matter of accountability. When an AI agent makes a decision, it's not always clear who is responsible if something goes wrong. This opacity poses challenges for regulatory frameworks and trust in these systems. \n\nBalancing the tremendous benefits AI agents can provide with the ethical and practical challenges they introduce is crucial. Rather than viewing AI agents as something to be liked or disliked, I see them as tools that need thoughtful integration and rigorous oversight to maximize their positive impact and minimize their risks. Therefore, while I am enthusiastic about the potential of AI agents, I advocate for a cautious and responsible approach to their development and deployment."
+        == "My perspective on AI agents is quite nuanced and not a matter of simple like or dislike. AI agents, depending on their design, deployment, and use cases, can bring about both significant benefits and substantial challenges.\n\nOn the positive side, AI agents have the potential to automate mundane tasks, enhance productivity, and provide personalized services in ways that were previously unimaginable. For instance, in customer service, AI agents can handle inquiries 24/7, reducing waiting times and improving user satisfaction. In healthcare, they can assist in diagnosing diseases by analyzing vast datasets much faster than humans. These applications demonstrate the transformative power of AI in improving efficiency and delivering better outcomes across various industries.\n\nHowever, my reservations stem from several critical concerns. Firstly, there's the issue of reliability and accuracy. Mismanaged or poorly designed AI systems can lead to significant errors, which could be particularly detrimental in high-stakes environments like healthcare or autonomous vehicles. Second, there's a risk of job displacement as AI agents become capable of performing tasks traditionally done by humans. This raises socio-economic concerns that need to be addressed through effective policy-making and upskilling programs.\n\nAdditionally, there are ethical and privacy considerations. AI agents often require large amounts of data to function effectively, which can lead to issues concerning consent, data security, and individual privacy rights. The lack of transparency in how these agents make decisions can also pose challenges—this is often referred to as the \"black box\" problem, where even the developers may not fully understand how specific AI outputs are generated.\n\nFinally, the deployment of AI agents by bad actors for malicious purposes, such as deepfakes, misinformation, and hacking, remains a pertinent concern. These potential downsides imply that while AI technology is extremely powerful and promising, it must be developed and implemented with care, consideration, and robust ethical guidelines.\n\nSo, in summary, I don't hate AI agents—rather, I approach them critically with a balanced perspective, recognizing both their profound potential and the significant challenges they present. Thoughtful development, responsible deployment, and ethical governance are crucial to harness the benefits while mitigating the risks associated with AI agents."
     )
 
 
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
 
     assert (
         result
-        == "As an expert researcher in technology with a specialization in AI and AI agents, my perspective is rooted in my deep understanding of their capabilities and potential. AI agents, like any technology, are tools that can be used for both beneficial and harmful purposes. Personally, I do not hate AI agents; rather, I recognize their immense potential to transform industries, improve efficiencies, and solve complex problems. However, I also acknowledge that they come with challenges that need to be carefully managed, such as ethical considerations, privacy concerns, and the potential for job displacement.\n\nThe reason you might have heard that I love them is likely because I am passionate about the potential that AI agents hold for advancing technology and aiding humanity. I believe that with responsible development, transparent governance, and thoughtful integration, AI agents can indeed bring about positive change. My enthusiasm should not be misconstrued as blind love but rather as a measured appreciation for their capabilities and a commitment to navigating their complexities responsibly."
+        == "As an expert researcher specializing in technology and AI, I have a deep appreciation for AI agents. These advanced tools have the potential to revolutionize countless industries by improving efficiency, accuracy, and decision-making processes. They can augment human capabilities, handle mundane and repetitive tasks, and even offer insights that might be beyond human reach. While it's crucial to approach AI with a balanced perspective, understanding both its capabilities and limitations, my stance is one of optimism and fascination. Properly developed and ethically managed, AI agents hold immense promise for driving innovation and solving complex problems. So yes, I do love AI agents for their transformative potential and the positive impact they can have on society."
     )
 
 

tests/agent_tools/cassettes/test_ask_question.yaml

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   response:
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-      Answer: As a researcher specializing in technology and AI, I don't hate AI agents.
+      Answer: As an expert researcher specialized in technology, I don't harbor emotions
-      In fact, I find them incredibly fascinating and beneficial. AI agents have the
+      such as hate towards AI agents. Instead, my focus is on understanding, analyzing,
-      potential to transform various industries, improve efficiencies, and offer new
+      and leveraging their potential to advance various fields. AI agents, when designed
-      solutions to complex problems. Their ability to learn, adapt, and perform tasks
+      and implemented effectively, can greatly augment human capabilities, streamline
-      that were once thought to require human intelligence is remarkable. While it's
+      processes, and provide valuable insights that might otherwise be overlooked.
-      important to consider ethical implications and ensure that AI systems are designed
+      My enthusiasm for AI agents stems from their ability to transform industries
-      and deployed responsibly, I believe their overall positive impact on society
+      and improve everyday life, making complex tasks more manageable and enhancing
-      and technology is significant. So to clarify, I don't hate AI agents; rather,
+      overall efficiency. This passion drives my research and commitment to making
-      I am quite enthusiastic about their potential and the advancements they bring
+      meaningful contributions in the realm of AI and AI agents.\",\n        \"refusal\":
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+      crucial to approach AI with a balanced perspective, understanding both its capabilities
-      challenges that need to be carefully managed, such as ethical considerations,
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tests/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml

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+      Answer: I understand why you might think I dislike AI agents, but my perspective
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+      is more nuanced. AI agents, in essence, are incredibly versatile tools designed
-      are software entities that perform tasks autonomously using machine learning
+      to perform specific tasks autonomously or semi-autonomously. They harness various
-      and other AI technologies, have tremendous potential to revolutionize various
+      artificial intelligence techniques, such as machine learning, natural language
-      sectors.\\n\\nOn the positive side, AI agents can significantly enhance efficiency
+      processing, and computer vision, to interpret data, understand tasks, and execute
-      and productivity. For example, in customer service, AI agents can handle routine
+      them efficiently. \\n\\nFrom a technological standpoint, AI agents have revolutionized
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+      numerous industries. In customer service, for instance, AI agents like chatbots
-      they can assist in diagnosing diseases, thus speeding up the decision-making
+      and virtual assistants handle customer inquiries 24/7, providing quick and efficient
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+      solutions. In healthcare, AI agents can assist in diagnosing diseases, managing
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+      goes wrong.\\n\\n4. **Job Displacement**: As AI agents become more capable,
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       - '10000'
       x-ratelimit-limit-tokens:
@@ -199,7 +199,7 @@ interactions:
       x-ratelimit-reset-tokens:
       - 0s
       x-request-id:
-      - req_ac45ff9e39dc065470ae2220b7294377
+      - req_4fb7c6a4aee0c29431cc41faf56b6e6b
     http_version: HTTP/1.1
     status_code: 200
 - request:
@@ -226,8 +226,8 @@ interactions:
       content-type:
       - application/json
       cookie:
-      - __cf_bm=4rzJDR3F8S3Dp4B_qwylZU0mdm_WUwmv8vNHHp3IplM-1727119501-1.0.1.1-qobO_Sf88yG1qtXFnLgykvyc9YXR_fm1J7ZpXIhvtynVnsz67Uwcf4122PgHs4GMrlvZMaL6z_UVcVYSYUJOKQ;
+      - __cf_bm=rb61BZH2ejzD5YPmLaEJqI7km71QqyNJGTVdNxBq6qk-1727213194-1.0.1.1-pJ49onmgX9IugEMuYQMralzD7oj_6W.CHbSu4Su1z3NyjTGYg.rhgJZWng8feFYah._oSnoYlkTjpK1Wd2C9FA;
-        _cfuvid=9K_ipT1uTVyDTneB1W5OZHPxCh36aOsDZCBnsQTONqQ-1727119501207-0.0.1.1-604800000
+        _cfuvid=lbRdAddVWV6W3f5Dm9SaOPWDUOxqtZBSPr_fTW26nEA-1727213194587-0.0.1.1-604800000
       host:
       - api.openai.com
       user-agent:
@@ -251,8 +251,8 @@ interactions:
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-AAj0sTSWVoT8kIwcrph0s6riGjHP2\",\n  \"object\":
+    content: "{\n  \"id\": \"chatcmpl-AB7OK8oHq66mHii53aw3gUNsAZLow\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1727119674,\n  \"model\": \"gpt-4o-2024-05-13\",\n
+      \"chat.completion\",\n  \"created\": 1727213384,\n  \"model\": \"gpt-4o-2024-05-13\",\n
       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
       \"assistant\",\n        \"content\": \"Thought: I now can give a great answer\\nFinal
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@@ -263,7 +263,7 @@ interactions:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 8c7cef49bf6c228a-MIA
+      - 8c85df25383c1cf3-GRU
       Connection:
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       Content-Encoding:
@@ -271,7 +271,7 @@ interactions:
       Content-Type:
       - application/json
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-      - Mon, 23 Sep 2024 19:27:54 GMT
+      - Tue, 24 Sep 2024 21:29:45 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -280,16 +280,14 @@ interactions:
       - nosniff
       access-control-expose-headers:
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-      alt-svc:
-      - h3=":443"; ma=86400
       openai-organization:
       - crewai-iuxna1
       openai-processing-ms:
-      - '256'
+      - '335'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
-      - max-age=15552000; includeSubDomains; preload
+      - max-age=31536000; includeSubDomains; preload
       x-ratelimit-limit-requests:
       - '10000'
       x-ratelimit-limit-tokens:
@@ -303,7 +301,7 @@ interactions:
       x-ratelimit-reset-tokens:
       - 0s
       x-request-id:
-      - req_417d17a8ce79824439a95013abc75202
+      - req_0e03176bfa219d7bf47910ebd0041e1e
     http_version: HTTP/1.1
     status_code: 200
 version: 1

tests/cassettes/test_agent_with_ollama_gemma.yaml

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 interactions:
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tests/cassettes/test_agent_without_max_rpm_respet_crew_rpm.yaml

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     headers:
       Accept:
       - '*/*'
@@ -220,7 +220,7 @@ interactions:
       Content-Type:
       - application/x-protobuf
       Date:
-      - Mon, 23 Sep 2024 19:45:08 GMT
+      - Tue, 24 Sep 2024 21:45:21 GMT
     status:
       code: 200
       message: OK
@@ -254,8 +254,8 @@ interactions:
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tests/cassettes/test_crew_log_file_output.yaml

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tests/cassettes/test_llm_call.yaml

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tests/cassettes/test_llm_call_with_ollama_gemma.yaml

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tests/cassettes/test_save_task_output.yaml

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+      to explore for an article, along with what makes them compelling:\n\n1. **AI
-      This article could explore significant real-world applications of AI agents
+      in Healthcare: Advances and Ethical Implications**\n   - **Unique Aspect:**
-      in various industries such as healthcare, finance, retail, and transportation.
+      This topic explores how AI is revolutionizing healthcare through advancements
-      Highlighting successful case studies will provide readers with a clear understanding
+      in diagnostics, personalized treatment plans, and predictive analytics. It also
-      of how AI agents are currently revolutionizing these fields and what future
+      delves into ethical concerns such as patient privacy, data security, and the
-      advancements might look like.\n\n2. **Ethics and AI: Navigating the Moral Dilemmas
+      potential for biased algorithms.\n   - **Interest Factor:** The intersection
-      of Autonomous Agents**\n   - *What makes it unique and interesting:* This article
+      of cutting-edge technology and critical ethical discussions makes this a highly
-      delves into the ethical concerns and moral dilemmas associated with deploying
+      relevant and thought-provoking topic.\n\n2. **The Rise of AI Agents and their
-      AI agents. By discussing topics like bias in AI, decision-making accountability,
+      Impact on the Workforce**\n   - **Unique Aspect:** This article could investigate
-      and the potential societal impacts, the article can engage readers in a crucial
+      how AI agents, such as virtual assistants and autonomous systems, are transforming
-      conversation about responsible AI development.\n\n3. **The Evolution of AI Agents:
+      various industries. It can cover both the positive impacts, like increased efficiency,
-      From Rule-Based Systems to Reinforcement Learning**\n   - *What makes it unique
+      and the challenges, such as job displacement.\n   - **Interest Factor:** Given
-      and interesting:* Tracing the historical development of AI agents offers readers
+      the widespread concern about AI''s impact on employment, this topic offers a
-      a comprehensive view of how these technologies have evolved. Comparing early
+      balanced view of both opportunities and obstacles, making it highly pertinent
-      rule-based systems with modern reinforcement learning techniques can highlight
+      and engaging.\n\n3. **AI and Climate Change: Innovative Solutions for a Global
-      significant advancements and provide context for current trends and future possibilities
+      Crisis**\n   - **Unique Aspect:** Exploring the role of AI in addressing climate
-      in AI research.\n\n4. **AI in Personal Assistants: How Intelligent Agents are
+      change, from improving renewable energy sources to predicting environmental
-      Changing Daily Life**\n   - *What makes it unique and interesting:* This article
+      changes and optimizing resource use.\n   - **Interest Factor:** The urgency
-      can focus on how AI-powered personal assistants like Siri, Alexa, and Google
+      of the climate crisis and the innovative use of AI to tackle these challenges
-      Assistant are impacting everyday life. Covering aspects such as efficiency in
+      make this an inspiring and crucial topic to explore in depth.\n\n4. **Ethical
-      time management, accessibility, and improving quality of life, the article can
+      AI: Developing Responsible and Fair Artificial Intelligence**\n   - **Unique
-      show readers the practical benefits and challenges associated with these ubiquitous
+      Aspect:** This article focuses on the ethical development of AI systems, discussing
-      technologies.\n\n5. **The Future of Work: AI Agents and Job Transformation**\n   -
+      frameworks and guidelines for ensuring AI is used responsibly. Topics could
-      *What makes it unique and interesting:* Focusing on the profound ways AI agents
+      include transparency, accountability, and fairness in AI algorithms.\n   - **Interest
-      are expected to transform the job market, this article can examine both the
+      Factor:** With growing awareness and concern about AI ethics, this article would
-      opportunities and challenges. Discussing topics like job displacement, the creation
+      resonate with readers interested in the moral implications of technological
-      of new job categories, and the skills workers will need to thrive in an AI-driven
+      advancements.\n\n5. **The Future of AI in Creative Industries: Art, Music, and
-      economy will provide valuable insights for readers concerned about the future
+      Literature**\n   - **Unique Aspect:** Investigating how AI is being used to
-      of work.\n\n----------\n\n1. **Turing Test (1950)**\n   - Alan Turing published
+      create new forms of artistic expression and how it collaborates with human creativity.
-      a paper titled \"Computing Machinery and Intelligence\" in which he proposed
+      This can include AI-generated art, music composition algorithms, and storytelling
-      the idea of the Turing Test. This test was designed to evaluate whether a machine
+      bots.\n   - **Interest Factor:** The blend of technology and creativity captivates
-      could exhibit intelligent behavior equivalent to, or indistinguishable from,
+      audiences and raises intriguing questions about the nature of art and the future
-      that of a human. The Turing Test has had a lasting impact on AI research and
+      role of human creativity.\n\nThese topics are not only contemporary and significant
-      is often considered the cornerstone of AI philosophy.\n\n2. **Dartmouth Workshop
+      but also offer a depth of exploration that can yield insightful and engaging
-      (1956)**\n   - The Dartmouth Conference, held in the summer of 1956, is widely
+      articles.\n\n----------\n\n1. **Dartmouth Conference (1956): The Birth of AI**\n   -
-      considered the birth of AI as a field of study. During this workshop, the term
+      **Event:** The Dartmouth Summer Research Project on Artificial Intelligence.\n   -
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+      **Impact:** Regarded as the founding moment of AI as a field of study, this
-      together the leading minds in computing and laid the foundational goals and
+      conference brought together key figures such as John McCarthy (who coined the
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-      The development of expert systems represented a major leap in AI technology.
+      Shannon to discuss and explore the potential of \"thinking machines.\"\n\n2.
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+      creation of DENDRAL, one of the first expert systems, by Edward Feigenbaum,
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-      interests in AI.\n\n4. **Deep Learning Breakthrough (2012)**\n   - A significant
+      of organic molecules. This project demonstrated the practical applications of
-      milestone in AI was the advent of deep learning, particularly marked by AlexNet''s
+      AI in solving complex scientific problems and paved the way for future expert
-      victory in the ImageNet Large Scale Visual Recognition Challenge in 2012. Developed
+      systems in various domains.\n\n3. **IBM''s Deep Blue Defeats Garry Kasparov
-      by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, AlexNet significantly
+      (1997)**\n   - **Event:** IBM''s Deep Blue, a chess-playing computer, defeated
-      outperformed other approaches in image recognition tasks. This success spurred
+      world chess champion Garry Kasparov.\n   - **Impact:** This historic victory
-      widespread interest and investment in deep learning, leading to major advancements
+      illustrated the potential of AI to perform at superhuman levels in specific
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+      power and problem-solving capabilities.\n\n4. **Google DeepMind''s AlphaGo Defeats
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