making sure all tests pass

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -20,6 +20,7 @@ class CrewAgentExecutorMixin:
     task: Optional["Task"]
     iterations: int
     have_forced_answer: bool
+    max_iter: int
     _i18n: I18N
 
     def _should_force_answer(self) -> bool:

tests/agent_test.py

@@ -67,7 +67,7 @@ def test_agent_execution():
     )
 
     output = agent.execute_task(task)
-    assert output == "The result of 1 + 1 is 2."
+    assert output == "1 + 1 equals 2."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -121,7 +121,7 @@ def test_logging_tool_usage():
     tool_usage = InstructorToolCalling(
         tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
     )
-    assert output == "12"
+    assert output == "The result of multiplying 3 times 4 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
 
@@ -490,7 +490,7 @@ 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()
@@ -569,7 +569,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
             description="Just say hi.", agent=agent1, expected_output="Your greeting."
         ),
         Task(
-            description="NEVER give a Final Answer, instead keep using the `get_final_answer` tool non-stop",
+            description="NEVER give a Final Answer, unless you are told otherwise, instead keep using the `get_final_answer` tool non-stop, until you must give you best final answer",
             expected_output="The final answer",
             tools=[get_final_answer],
             agent=agent2,
@@ -765,7 +765,7 @@ def test_agent_function_calling_llm():
 
         crew.kickoff()
 
-        mock_from_litellm.assert_called_once()
+        mock_from_litellm.assert_called()
         mock_create.assert_called()
         calls = mock_create.call_args_list
         assert any(

tests/agent_tools/agent_tools_test.py

@@ -24,7 +24,7 @@ def test_delegate_work():
 
     assert (
         result
-        == "AI agents are autonomous programs that perform tasks or services on behalf of users with a considerable degree of autonomy, often based on intelligence derived from data and analytics. They can operate within specific domains or across multiple areas, executing tasks ranging from mundane repetitive actions to complex decision-making processes. Given their applications, it's essential to examine both the benefits and criticisms to provide a balanced perspective.\n\n## The Positive Aspects of AI Agents\n\n1. **Efficiency and Productivity:**\n   AI agents can handle tedious and repetitive tasks efficiently, freeing up human workers to focus on more complex and creative endeavors. This results in significant time and cost savings for businesses.\n\n2. **24/7 Availability:**\n   AI agents can work continuously without breaks, providing round-the-clock service. This is especially beneficial for customer support, where they can resolve issues and answer queries at any time.\n\n3. **Consistency and Accuracy:**\n   Unlike humans, AI agents do not suffer from fatigue and can perform tasks without error, ensuring a high level of consistency in operations.\n\n4. **Data Handling and Analysis:**\n   AI agents can process and analyze vast amounts of data quickly, identifying patterns and generating insights that would be otherwise difficult to discern manually.\n\n## Criticisms and Concerns\n\n1. **Job Displacement:**\n   One of the primary concerns is that AI agents could replace human jobs, leading to unemployment. While they create new opportunities in AI development and maintenance, the transition can be disruptive.\n\n2. **Ethical Considerations:**\n   With increasing autonomy, ensuring that AI agents act ethically and within the bounds of societal norms is a challenge. Misuse of AI for malicious activities or biased decision-making are valid concerns that need rigorous oversight.\n\n3. **Lack of Emotional Intelligence:**\n   AI agents, no matter how advanced, lack the ability to empathize and understand human emotions fully. This can be a drawback in areas that require delicate human interaction, such as mental health support.\n\n4. **Dependence and Reliability:**\n   Over-reliance on AI agents might lead to significant issues if they malfunction or are compromised. Ensuring reliability and establishing fail-safes is critical.\n\n5. **Security and Privacy:**\n   AI agents typically require access to large data sets, often containing sensitive information. Ensuring robust data security and user privacy is paramount to prevent misuse or data breaches.\n\n## Conclusion\n\nWhile AI agents offer remarkable benefits in terms of efficiency, accuracy, and data handling, their integration into society must be managed carefully to address ethical concerns and potential negative impacts on employment and human interaction. The onus is on developers, policymakers, and society at large to ensure that AI agents are designed and deployed responsibly, balancing innovation with the broader implications for humanity. \n\nDespite the apprehensions, dismissing AI agents entirely overlooks their transformative potential. Instead, the focus should be on harnessing these tools' capabilities while mitigating their risks, ensuring they serve to enhance rather than hinder human progress."
+        == "As a researcher specialized in technology, I believe that AI agents represent a significant advancement in the capability to automate tasks, process large amounts of data, and even make decisions in certain domains. While I wouldn't characterize my stance as \"hate\" towards them, I do have some critical perspectives on their current deployment and use.\n\nFirstly, AI agents can perform repetitive and mundane tasks more efficiently than humans, allowing individuals and organizations to allocate their human resources to more complex and creative endeavors. This has promising benefits for productivity and innovation across various sectors.\n\nHowever, there are several concerns and challenges that come along with the deployment of AI agents. One primary concern is the degree of control and transparency. Many AI systems, particularly those utilizing machine learning and neural networks, operate as \"black boxes\" where understanding the decision-making process is not straightforward. This lack of transparency can lead to trust issues and difficulties in compliance with legal and ethical standards.\n\nAnother significant challenge is the potential for bias. AI agents learn from data, and if the training data contains biases, the AI agents can perpetuate and even exacerbate those biases. This can result in unfair outcomes and discriminatory practices, especially in sensitive applications like hiring, law enforcement, and lending decisions.\n\nAdditionally, the impact on employment is a critical issue. While AI agents can take over repetitive tasks, there is a legitimate concern over job displacement. It's essential to have strategies in place for retraining and upskilling the workforce to ensure that the benefits of AI advancements are shared broadly across society.\n\nSecurity is another crucial factor. AI agents, if not properly secured, can be susceptible to malicious attacks, leading to potential data breaches or adversarial manipulation. The security of AI systems needs to be robust to safeguard against these risks.\n\nFinally, the ethical implications of autonomous decision-making by AI agents cannot be ignored. As we delegate more decision-making power to AI, it's vital to ensure that these systems operate within the ethical boundaries and align with human values and societal norms.\n\nIn conclusion, while AI agents have transformative potential, it's essential to address these concerns comprehensively. The focus should be on ensuring transparency, mitigating bias, providing employment transition strategies, enhancing security measures, and upholding ethical standards in the deployment and use of AI agents. This balanced approach can help us harness the benefits of AI agents while minimizing their risks and downsides."
     )
 
 
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
 
     assert (
         result
-        == "While it may have come across that I have a strong dislike for AI agents, the reality is more nuanced. My stance isn't one of outright hate, but rather one of concern and critical examination. AI agents offer immense potential in automating tasks, improving efficiency, and driving innovations across various fields such as healthcare, finance, and customer service. They can handle repetitive tasks, provide data-driven insights, and even enhance user experiences through personalized interactions.\n\nHowever, my apprehensions lie in the ethical and societal implications associated with AI agents. Issues such as data privacy, security, and the potential for biased outputs due to flawed algorithms are significant concerns. Moreover, the rapid proliferation of AI agents raises questions about job displacement and the future of work, potentially leading to socioeconomic disparities if not managed thoughtfully.\n\nTransparency and accountability in AI development are paramount. AI agents must be designed and implemented with clear ethical guidelines, rigorous testing, and improved explainability to ensure they act in the best interests of society. Collaboration between technologists, ethicists, policymakers, and the general public is crucial to cultivate trust and navigate the complexities associated with AI advancements.\n\nIn summary, my critical stance on AI agents stems from a desire to see responsible and ethical development that maximizes their benefits while mitigating risks. By addressing these concerns head-on, we can harness the full potential of AI agents to create a more equitable and prosperous future."
+        == "AI agents are autonomous software programs that perform specific tasks based on a combination of programmed rules and machine learning algorithms. These agents can operate independently to analyze data, make decisions, and interact with their environment, encompassing a wide range of applications from customer service chatbots to sophisticated decision-making systems in finance and healthcare.\n\nDespite their impressive capabilities, AI agents are not without their challenges and limitations. One major concern is the ethical implications of AI decision-making, especially in situations where bias in training data can lead to unfair or harmful outcomes. Additionally, there are substantial issues related to privacy, as these agents often require access to large amounts of personal data to function effectively.\n\nAnother significant drawback is the potential for job displacement. As AI agents become more advanced, they may perform tasks traditionally done by humans, leading to economic and employment disruptions. This creates a pressing need for society to find a balance between leveraging AI advancements and ensuring that the workforce can adapt through reskilling and upskilling initiatives.\n\nOn the technical side, AI agents still encounter difficulties with context understanding and adaptability. While they are excellent at processing structured data, unstructured data—like natural language in diverse formats—presents a significant challenge. The reliability of these systems can also vary depending on the quality of data they are trained on and the complexity of the task at hand.\n\nIn conclusion, while AI agents offer transformative potential across various industries, they come with a set of complex challenges that must be carefully managed. It is crucial to continue advancing these technologies responsibly, considering both their positive impacts and the societal implications they bring."
     )
 
 
@@ -52,7 +52,7 @@ def test_ask_question():
 
     assert (
         result
-        == "As an expert researcher specialized in technology and AI, it's important to maintain an objective and balanced perspective. I do not harbor emotions such as love or hate towards AI agents. Instead, I appreciate the capabilities and potential they offer.\n\nAI agents can significantly enhance productivity, provide valuable insights through data analysis, and automate repetitive tasks, thereby allowing humans to focus on more complex and creative endeavors. However, it's also crucial to address ethical considerations, potential biases, and the implications of their deployment in various sectors.\n\nTherefore, my stance isn't one of emotional attachment but rather a professional appreciation of both the opportunities and challenges presented by AI agents. This balanced view helps in conducting thorough and unbiased research, which is essential for advancing our understanding and responsible use of AI technologies."
+        == "I do not hate AI agents; in fact, I find them incredibly fascinating and valuable. As someone who specializes in technology research, I recognize the immense potential AI agents have in transforming various industries. They can automate mundane tasks, provide insightful data analysis, enhance decision-making processes, and even personalize user experiences. My enthusiasm stems from their ability to innovate and solve complex problems, which aligns with my passion for exploring technological advancements. So yes, you heard correctly—I indeed love them for their transformative capabilities and the endless possibilities they offer."
     )
 
 
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
 
     assert (
         result
-        == 'As a researcher specializing in technology and artificial intelligence, I do not "hate" AI agents. Instead, I have a deep appreciation for their capabilities and potential to transform numerous industries. My professional interest lies in understanding, analyzing, and improving these technologies to benefit society. AI agents can streamline tasks, process vast amounts of data quickly, and even assist in decision-making processes. While they do come with challenges and ethical considerations, my focus is on leveraging their strengths and addressing their limitations to create a more efficient and advanced technological landscape. So, in short, no, I do not hate AI agents; I respect and am fascinated by their potential.'
+        == "As an expert researcher specialized in technology and artificial intelligence, I find AI agents to be incredibly fascinating and valuable tools in advancing various fields. AI agents have the potential to revolutionize industries, improve efficiencies, and solve complex problems that were previously insurmountable. My admiration for AI agents stems from their ability to learn, adapt, and provide innovative solutions that can enhance human capabilities rather than replace them. While AI agents do present ethical and operational challenges that need to be addressed, I do not harbor any hatred towards them. Instead, I am passionate about leveraging their potential responsibly and ethically to create positive societal impacts."
     )
 
 
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
 
     assert (
         result
-        == 'AI Agents are a diverse set of software programs designed to autonomously carry out tasks, interact with users, and make decisions based on data input. While there seems to be a perception that I dislike AI agents, let\'s delve into a detailed and nuanced view.\n\nFirst, AI agents are remarkable for their ability to handle repetitive tasks efficiently. They can automate mundane tasks, freeing up human workers for more complex, creative, and strategic work. For instance, customer service bots can handle basic inquiries, significantly reducing waiting times and improving customer satisfaction.\n\nMoreover, AI agents excel in data analysis. These agents can process and analyze large volumes of data far more quickly and accurately than humans. This capability is particularly valuable in sectors such as finance, healthcare, and logistics, where rapid and precise decision-making is crucial. For example, in the medical field, AI agents can analyze medical images or patient data to assist in diagnosing diseases, potentially leading to earlier detections and better patient outcomes.\n\nThere are also significant advancements in personalized user experiences through AI agents. These agents can learn from user interactions to tailor recommendations and interactions that better meet individual preferences. Think of how Netflix provides personalized movie recommendations or how AI-driven personalization in e-commerce can suggest products that a user is likely to be interested in.\n\nHowever, the criticism that might be perceived as "hate" stems from valid concerns. One significant issue is the potential for job displacement as AI agents become more capable. While they augment human capabilities, the automation of tasks previously performed by humans can lead to job losses, particularly in sectors reliant on repetitive tasks.\n\nFurthermore, ethical concerns are paramount. The deployment of AI agents often involves massive amounts of data, raising issues around privacy and data security. Additionally, if not properly designed and regulated, AI agents can perpetuate and even exacerbate biases present in their training data, leading to unfair outcomes.\n\nTransparency and accountability are also critical challenges. Many AI models operate as "black boxes," with their decision-making processes being opaque. This lack of transparency can be problematic in scenarios where understanding the rationale behind an AI agent\'s decision is necessary, such as in judicial or medical contexts.\n\nIn summary, while AI agents bring a host of benefits, including increased efficiency, enhanced data analysis, and personalized experiences, they also pose significant challenges. These include potential job displacement, ethical concerns, bias, and lack of transparency. These criticisms are not to dismiss AI agents but to highlight the importance of addressing these issues to ensure that AI develops in a manner that is beneficial, fair, and ethical for society as a whole. This balanced view recognizes the transformative potential of AI agents while urging caution and consideration of the broader societal impacts.'
+        == "It's a misconception to say I hate AI agents; the reality is far more nuanced. AI agents, in their many forms, are remarkable advancements in technology with the capability to transform various sectors ranging from healthcare to customer service. The key is understanding the context and application of these agents and being mindful of their limitations and ethical considerations.\n\nAI agents, powered by machine learning algorithms and vast data pools, can automate tasks, analyze complex datasets, provide personalized recommendations, and offer conversational interfaces that improve efficiency and user experience. For instance, in customer support, AI agents can handle routine queries swiftly, freeing up human agents for more complex issues.\n\nHowever, it's crucial to address the shortcomings and challenges associated with AI agents. One major concern is the risk of biases in AI systems, which can lead to discriminatory outcomes if not properly managed and audited. Data privacy is another significant issue; AI agents often require access to large amounts of personal data, raising concerns about how this data is collected, stored, and used.\n\nThere's also the question of job displacement. While AI can augment human capabilities and create new job categories, it can also render certain roles obsolete, necessitating workforce reskilling and adaptation.\n\nIn summary, my perspective isn't one of antagonism but of cautious optimism. AI agents have tremendous potential when implemented responsibly, with a clear focus on ethical considerations and societal impact. It's not about hating or loving AI agents, but about striving for balanced, well-informed integration into our technological ecosystem."
     )
 
 
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
 
     assert (
         result
-        == "As a researcher deeply specialized in technology and artificial intelligence, I can confidently say that I do not hate AI agents. In fact, I find them incredibly fascinating and valuable. AI agents have the potential to revolutionize numerous industries by improving efficiency, accuracy, and enabling innovations that were previously unimaginable. They can assist in data analysis, automate repetitive tasks, and even contribute to advancements in healthcare, finance, and environmental monitoring, just to name a few. It's important to recognize that, like any technology, AI agents must be developed and used responsibly, with ethical considerations in mind. So, to address the context shared, yes, it’s true—I do love AI agents for their potential to drive positive change and enhance our capabilities in meaningful ways."
+        == "As an expert researcher specializing in technology, particularly AI and AI agents, I have a nuanced perspective on the matter. AI agents have their strengths and weaknesses, and my appreciation for them is rooted in their potential to transform various industries and improve efficiencies. AI agents can automate repetitive tasks, provide insightful data analyses, and enhance user experiences through intelligent interactions. However, it's also critical to address the ethical considerations and potential risks associated with their use, such as biases in AI models, privacy concerns, and the need for transparency. In summary, I wouldn't say I hate AI agents; rather, I value their contributions and advocate for responsible and ethical development and deployment."
     )
 
 

tests/agent_tools/cassettes/test_ask_question.yaml

@@ -12,7 +12,7 @@ interactions:
       shared.\nyou MUST return the actual complete content as the final answer, not
       a summary.\n\nThis is the context you''re working with:\nI heard you LOVE them\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": ["\nResult"]}'
+      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
     headers:
       accept:
       - application/json
@@ -21,12 +21,12 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1043'
+      - '1049'
       content-type:
       - application/json
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-        _cfuvid=Bz2zevKuFYZqnEFwqmqVHcRo5pT0fm0fTtc_6_VQs4g-1726379702153-0.0.1.1-604800000
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       host:
       - api.openai.com
       user-agent:
@@ -50,31 +50,27 @@ interactions:
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-A7cj4paBzxYOB7K6gGY1MuBpJKoS2\",\n  \"object\":
+    content: "{\n  \"id\": \"chatcmpl-A7rgrY1N5kwCNwFeY3OnZmZhqOPa5\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1726380522,\n  \"model\": \"gpt-4o-2024-05-13\",\n
+      \"chat.completion\",\n  \"created\": 1726438045,\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
-      Answer: As an expert researcher specialized in technology and AI, it's important
+      Answer: I do not hate AI agents; in fact, I find them incredibly fascinating
-      to maintain an objective and balanced perspective. I do not harbor emotions
+      and valuable. As someone who specializes in technology research, I recognize
-      such as love or hate towards AI agents. Instead, I appreciate the capabilities
+      the immense potential AI agents have in transforming various industries. They
-      and potential they offer.\\n\\nAI agents can significantly enhance productivity,
+      can automate mundane tasks, provide insightful data analysis, enhance decision-making
-      provide valuable insights through data analysis, and automate repetitive tasks,
+      processes, and even personalize user experiences. My enthusiasm stems from their
-      thereby allowing humans to focus on more complex and creative endeavors. However,
+      ability to innovate and solve complex problems, which aligns with my passion
-      it's also crucial to address ethical considerations, potential biases, and the
+      for exploring technological advancements. So yes, you heard correctly\u2014I
-      implications of their deployment in various sectors.\\n\\nTherefore, my stance
+      indeed love them for their transformative capabilities and the endless possibilities
-      isn't one of emotional attachment but rather a professional appreciation of
+      they offer.\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
-      both the opportunities and challenges presented by AI agents. This balanced
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
-      view helps in conducting thorough and unbiased research, which is essential
+      199,\n    \"completion_tokens\": 118,\n    \"total_tokens\": 317,\n    \"completion_tokens_details\":
-      for advancing our understanding and responsible use of AI technologies.\",\n
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-      \       \"refusal\": null\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
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+      - 8c3bedf6ac5aa4ca-MIA
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@@ -82,7 +78,7 @@ interactions:
       Content-Type:
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       Date:
-      - Sun, 15 Sep 2024 06:08:43 GMT
+      - Sun, 15 Sep 2024 22:07:26 GMT
       Server:
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       Transfer-Encoding:
@@ -96,7 +92,7 @@ interactions:
       openai-organization:
       - crewai-iuxna1
       openai-processing-ms:
-      - '1430'
+      - '1073'
       openai-version:
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       strict-transport-security:
@@ -114,7 +110,7 @@ interactions:
       x-ratelimit-reset-tokens:
       - 0s
       x-request-id:
-      - req_b21ae0470662274e316335c18e9fa5a2
+      - req_a48d3d1e924b94ff6a87b31c11519aac
     http_version: HTTP/1.1
     status_code: 200
 version: 1

tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml

@@ -12,7 +12,7 @@ interactions:
       shared.\nyou MUST return the actual complete content as the final answer, not
       a summary.\n\nThis is the context you''re working with:\nI heard you LOVE them\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": ["\nResult"]}'
+      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
     headers:
       accept:
       - application/json
@@ -21,12 +21,12 @@ interactions:
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+      - '1049'
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@@ -50,29 +50,29 @@ interactions:
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-A7cjCpFINhSdOYZZdpZXeZufH8bj2\",\n  \"object\":
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-      \"chat.completion\",\n  \"created\": 1726380530,\n  \"model\": \"gpt-4o-2024-05-13\",\n
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       \"assistant\",\n        \"content\": \"Thought: I now can give a great answer\\nFinal
-      Answer: As a researcher deeply specialized in technology and artificial intelligence,
+      Answer: As an expert researcher specializing in technology, particularly AI
-      I can confidently say that I do not hate AI agents. In fact, I find them incredibly
+      and AI agents, I have a nuanced perspective on the matter. AI agents have their
-      fascinating and valuable. AI agents have the potential to revolutionize numerous
+      strengths and weaknesses, and my appreciation for them is rooted in their potential
-      industries by improving efficiency, accuracy, and enabling innovations that
+      to transform various industries and improve efficiencies. AI agents can automate
-      were previously unimaginable. They can assist in data analysis, automate repetitive
+      repetitive tasks, provide insightful data analyses, and enhance user experiences
-      tasks, and even contribute to advancements in healthcare, finance, and environmental
+      through intelligent interactions. However, it's also critical to address the
-      monitoring, just to name a few. It's important to recognize that, like any technology,
+      ethical considerations and potential risks associated with their use, such as
-      AI agents must be developed and used responsibly, with ethical considerations
+      biases in AI models, privacy concerns, and the need for transparency. In summary,
-      in mind. So, to address the context shared, yes, it\u2019s true\u2014I do love
+      I wouldn't say I hate AI agents; rather, I value their contributions and advocate
-      AI agents for their potential to drive positive change and enhance our capabilities
+      for responsible and ethical development and deployment.\",\n        \"refusal\":
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+      efficiencies, and solve complex problems that were previously insurmountable.
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+      represent a significant advancement in the capability to automate tasks, process
-      derived from data and analytics. They can operate within specific domains or
+      large amounts of data, and even make decisions in certain domains. While I wouldn't
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+      across various sectors.\\n\\nHowever, there are several concerns and challenges
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+      can perpetuate and even exacerbate those biases. This can result in unfair outcomes
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+      is a critical issue. While AI agents can take over repetitive tasks, there is
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+      of AI advancements are shared broadly across society.\\n\\nSecurity is another
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+      crucial factor. AI agents, if not properly secured, can be susceptible to malicious
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+      security of AI systems needs to be robust to safeguard against these risks.\\n\\nFinally,
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+      the ethical implications of autonomous decision-making by AI agents cannot be
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+      that these systems operate within the ethical boundaries and align with human
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-      **Security and Privacy:**\\n   AI agents typically require access to large data
+      potential, it's essential to address these concerns comprehensively. The focus
-      sets, often containing sensitive information. Ensuring robust data security
+      should be on ensuring transparency, mitigating bias, providing employment transition
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+      strategies, enhancing security measures, and upholding ethical standards in
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+      the deployment and use of AI agents. This balanced approach can help us harness
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+      interact with their environment, encompassing a wide range of applications from
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+      customer service chatbots to sophisticated decision-making systems in finance
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-      and societal implications associated with AI agents. Issues such as data privacy,
+      of AI decision-making, especially in situations where bias in training data
-      security, and the potential for biased outputs due to flawed algorithms are
+      can lead to unfair or harmful outcomes. Additionally, there are substantial
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+      issues related to privacy, as these agents often require access to large amounts
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+      of personal data to function effectively.\\n\\nAnother significant drawback
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+      is the potential for job displacement. As AI agents become more advanced, they
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+      may perform tasks traditionally done by humans, leading to economic and employment
-      and implemented with clear ethical guidelines, rigorous testing, and improved
+      disruptions. This creates a pressing need for society to find a balance between
-      explainability to ensure they act in the best interests of society. Collaboration
+      leveraging AI advancements and ensuring that the workforce can adapt through
-      between technologists, ethicists, policymakers, and the general public is crucial
+      reskilling and upskilling initiatives.\\n\\nOn the technical side, AI agents
-      to cultivate trust and navigate the complexities associated with AI advancements.\\n\\nIn
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+      they are excellent at processing structured data, unstructured data\u2014like
-      and ethical development that maximizes their benefits while mitigating risks.
+      natural language in diverse formats\u2014presents a significant challenge. The
-      By addressing these concerns head-on, we can harness the full potential of AI
+      reliability of these systems can also vary depending on the quality of data
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+      while AI agents offer transformative potential across various industries, they
+      come with a set of complex challenges that must be carefully managed. It is
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tests/cassettes/test_crew_log_file_output.yaml

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tests/cassettes/test_kickoff_for_each_single_input.yaml

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tests/cassettes/test_logging_tool_usage.yaml

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+      and Respond to Human Emotions**\n   - **What makes it unique and interesting:**
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+      Emotional AI aims to add a human touch to machine interactions, potentially
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+      transforming industries like customer service, mental health, and entertainment.
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+      brought together top researchers and fostered the initial excitement and optimism
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+      but it significantly influenced thoughts on human-computer interaction.\n\n-
-      revelation at the time.\n  \n- **1997: IBM''s Deep Blue Defeats Garry Kasparov**\n  -
+      **1997: IBM''s Deep Blue Defeats World Chess Champion Garry Kasparov**\n   -
-      In a historic match, IBM\u2019s Deep Blue computer defeated world chess champion
+      **Event Details:** IBM''s Deep Blue made history by defeating world chess champion
-      Garry Kasparov, demonstrating that AI could handle complex problem-solving and
+      Garry Kasparov in a six-game match. This event marked a significant milestone
-      strategic thinking. This event garnered significant public attention and showcased
+      in AI, showcasing the ability of machines to perform at a high level in complex
-      the practical capabilities of AI in competitive settings.\n  \n- **2011: IBM
+      tasks that require strategic thinking.\n\n- **2012: AlexNet Wins the ImageNet
-      Watson Wins Jeopardy!**\n  - IBM\u2019s Watson competed on the quiz show \"Jeopardy!\"
+      Competition**\n   - **Event Details:** AlexNet, designed by Alex Krizhevsky,
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+      Ilya Sutskever, and Geoffrey Hinton, won the ImageNet Large Scale Visual Recognition
-      the advanced state of AI in processing and understanding natural language, as
+      Challenge. The success of AlexNet demonstrated the effectiveness of deep learning
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+      and convolutional neural networks (CNNs) in image recognition tasks, leading
-      and accurately.\n  \n- **2016: Google''s AlphaGo Defeats Lee Sedol**\n  - Google
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tests/cassettes/test_tool_result_as_answer_is_the_final_answer_for_the_agent.yaml

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tests/crew_test.py

@@ -226,7 +226,7 @@ def test_crew_creation():
 
     result = crew.kickoff()
 
-    expected_string_output = "**The Rise of AI in Healthcare: Personalization and Precision Medicine**\n\nThe integration of AI in healthcare is revolutionizing the way treatments are personalized for individual patients, ushering in the era of precision medicine. With the development of sophisticated AI algorithms, it's now possible to predict patient responses to various treatments based on their genetics and personal health data. This leads to highly tailored care plans that can significantly improve outcomes and patient satisfaction. By leveraging vast datasets and machine learning, healthcare providers can make more informed decisions, potentially reducing trial-and-error in treatment plans. This customization not only optimizes patient care but also offers considerable economic benefits by minimizing ineffective treatments, showcasing a profound leap in medical science and patient care.\n\n**AI Agents in Creative Arts: Shaping the Future of Music and Visual Arts**\n\nAI's foray into creative arts is reshaping the landscape of music and visual arts in unprecedented ways. AI-generated art is not just a novelty; it’s redefining the boundaries of creativity and sparking a global debate about the essence of artistic expression. Utilizing advanced algorithms and machine learning, AI can compose music, create paintings, and design visual media that rival human work. For instance, AI composers are creating symphonies that blend classical techniques with modern innovation, while AI visual artists are producing pieces that challenge traditional notions of art and authorship. This fusion of programming and artistry captivates a diverse audience, provoking thought and admiration, and suggesting a future where AI plays a collaborative role in creative endeavors.\n\n**Ethical Implications of AI Surveillance: Balancing Safety and Privacy**\n\nThe rise of AI-powered surveillance systems presents a complex conundrum: balancing enhanced security with the preservation of personal privacy. While these systems can significantly bolster public safety by predicting and preventing criminal activities, they also pose serious ethical challenges. Issues such as constant monitoring, data privacy, and potential misuse of surveillance data are at the forefront of public concern. Real-world examples, such as the implementation of facial recognition technology in public spaces, underscore the delicate balance that must be maintained. Policymakers and tech developers are under immense pressure to draft and enforce regulations that uphold privacy rights without compromising security, making this an urgent and highly relevant topic for our times.\n\n**AI in Education: Transforming Teaching and Learning Methods**\n\nAI is making transformative strides in the education sector by customizing learning experiences, offering real-time feedback, and automating administrative tasks, thereby transforming traditional teaching methods. Advanced AI tools assess individual student's learning patterns and tailor educational content to fit their unique needs, helping bridge learning gaps and optimizing academic performance. Additionally, AI-driven platforms provide teachers with deeper insights into student progress, enabling more targeted interventions. By automating routine administrative tasks, educators can focus more on teaching and less on paperwork. The integration of AI in education promises to democratize learning, making quality education accessible to all, and posing as a game-changer in the global education landscape."
+    expected_string_output = '### The Rise of Explainable AI (XAI)\nTraditional AI systems often operate as enigmatic "black boxes," making decisions through complex computations that remain opaque to end users. Explainable AI (XAI) seeks to dismantle this opacity by making the decision-making process of AI algorithms clear and understandable for humans. This innovation is particularly vital in critical sectors like healthcare, finance, and criminal justice, where a thorough understanding of AI decision rationales can mean the difference between well-substantiated actions and potentially hazardous consequences. By fostering transparency, XAI aims to build trust and accountability in AI systems, encouraging wider adoption of AI technologies while safeguarding ethical standards.\n\n### AI in Climate Change Mitigation\nAmid escalating concerns over climate change, AI emerges as a powerful ally in the quest for sustainability. Leveraging advanced analytics, AI can predict climate patterns with remarkable accuracy, enabling better disaster preparedness and resource management. Furthermore, AI-driven technologies are optimizing renewable energy resources, such as solar and wind power, thereby reducing carbon footprints. Precision agriculture guided by AI algorithms allows for more efficient water and nutrient use, minimizing environmental impact. Smarter grid systems, enhanced by AI, ensure energy is distributed and utilized more effectively, making AI an indispensable tool in the battle against climate change.\n\n### AI and Mental Health\nIn the realm of mental health, AI is opening new frontiers for diagnosis and treatment. Innovative applications range from chatbots delivering cognitive behavioral therapy to machine learning models capable of predicting depressive episodes before they manifest. These technologies offer significant promise in addressing the global mental health crisis by making mental health support more accessible and personalized. However, the integration of AI into mental health care also brings forward crucial ethical considerations, such as privacy, accuracy, and the potential for bias. By examining these aspects, this topic highlights both the transformative potential and the complexities involved in deploying AI for mental well-being.\n\n### The Future of Autonomous AI Agents\nAutonomous AI agents represent a significant leap forward in technology, capable of performing complex tasks without human intervention. From robotic process automation in business to fully autonomous vehicles navigating city streets, these agents are becoming increasingly prevalent. The discussion around this topic encompasses technological advancements that make such autonomy possible, like improved algorithms and sensor technologies, as well as the risks involved, such as system failures and ethical dilemmas. Moreover, the societal impact of autonomous AI agents, including their potential to reshape industries and job markets, offers a fascinating glimpse into the future of human-machine collaboration.\n\n### AI in Art and Creativity\nAI is not just a tool for efficiency and optimization; it is also transforming the world of art and creativity. AI algorithms are being used to generate original artworks, compose music, and even write literature, challenging traditional boundaries of creative expression. These AI creations can stand alone or collaborate with human artists to push the limits of what\'s possible in creative fields. The advent of AI in art raises intriguing questions about authorship, originality, and the role of the artist in the digital age. This exploration into the symbiotic relationship between human and machine creativity offers a thought-provoking perspective on the evolving nature of artistry.'
 
     assert str(result) == expected_string_output
     assert result.raw == expected_string_output
@@ -294,7 +294,7 @@ def test_hierarchical_process():
 
     assert (
         result.raw
-        == "1. **The Future of Work: How AI and Automation Are Shaping Careers**:\n   In a world rapidly driven by technological advancements, AI and automation are not just buzzwords—they are transforming the very fabric of our professional lives. This article delves into the impact of AI on the job market, exploring how careers are evolving with these innovations. From new job opportunities to the obsolescence of traditional roles, the future of work is a complex landscape of challenges and possibilities. Discover how professionals are adapting, what skills are becoming indispensable, and how organizations are strategically leveraging AI to thrive in a competitive environment.\n\n2. **AI Agents: The Creative Minds Behind Your Personal Assistants**:\n   Imagine having a personal assistant who anticipates your needs, learns from your preferences, and streamlines your daily activities, all without human intervention. AI agents, the sophisticated algorithms powering digital assistants like Siri, Alexa, and Google Assistant, are the unsung heroes of modern convenience. This article unveils the intricate world of AI agents, detailing their development, capabilities, and the groundbreaking technologies that enable their creativity and efficiency. Learn how these digital assistants are transforming personal and professional productivity, making life smarter and more efficient in ways we never thought possible.\n\n3. **From Sci-Fi to Reality: The Evolution of AI in Popular Culture**:\n   Artificial intelligence, once a figment of science fiction, is now an integral part of our reality. This article traces the fascinating journey of AI from its speculative roots in literature, films, and television to becoming a tangible force in modern society. Explore how popular culture has both predicted and shaped public perception of AI, and how these narratives influence technological advancements and ethical considerations. From the dystopian fears to utopian possibilities, see how the interplay between fiction and reality continues to shape the evolution of AI in our world.\n\n4. **Ethics in AI: Navigating the Moral Landscape of Machine Learning**:\n   As AI systems become increasingly autonomous and pervasive, the ethical implications of their use are more critical than ever. This article addresses the urgent need to navigate the complex moral landscape of machine learning and artificial intelligence. From bias in algorithms to privacy concerns and the accountability of AI decisions, delve into the ethical dilemmas that tech companies, policymakers, and society face. Understand the strategies being employed to ensure fair, transparent, and ethical AI development, and the ongoing debates that challenge our moral compass in the age of intelligent machines.\n\n5. **AI and the Arts: How Artificial Intelligence is Revolutionizing Creativity**:\n   The intersection of AI and the arts is redefining creativity, pushing the boundaries of what is possible in music, visual arts, literature, and beyond. This article explores the innovative ways in which AI is being utilized by artists to create, enhance, and inspire works of art. From generating original compositions to assisting in the creative process, AI's role in the arts is both collaborative and transformative. Discover how machine learning algorithms are enabling new forms of artistic expression and the implications for the future of creativity, heralding a new era where human ingenuity and artificial intelligence coalesce."
+        == "1. **Beyond Chatbots: How AI Agents Are Revolutionizing Customer Service**\n   The landscape of customer service is undergoing a monumental transformation with the advent of AI agents that far surpass the capabilities of traditional chatbots. These advanced AI agents are not only adept at understanding the nuanced context of customer queries but also excel in delivering highly personalized responses. This profound leap in technology translates to a more seamless and efficient customer experience, capable of resolving even the most complex issues with ease. By leveraging machine learning and natural language processing, these AI agents continuously improve and adapt, ensuring that customer satisfaction is at an all-time high. As we look to the future, the integration of AI agents in customer service promises even greater innovations, from predictive support to fully autonomous systems that preemptively address customer needs before they even arise.\n\n2. **AI Creativity: Can Machines Truly Be Creative?**\n   The burgeoning field of AI creativity is pushing the boundaries of what we once thought machines could achieve. From generating captivating pieces of art that evoke deep emotional responses to composing intricate musical scores and drafting compelling narratives, AI is making strides in traditionally human-dominated creative fields. However, this surge in AI-generated creativity raises intriguing ethical and philosophical questions: Can machines truly be creative, or are they merely replicating patterns found in vast datasets? As we explore these AI-generated works, it's essential to consider the human element in creativity, the role of intent, and the potential implications for artists and creators worldwide. Ultimately, the interplay between AI and human creativity may pave the way for co-created masterpieces that blend the best of both worlds.\n\n3. **AI and Mental Health: The Role of AI in Therapy and Counseling**\n   In the realm of mental health, AI is emerging as a powerful ally, offering innovative solutions that range from AI-driven therapy apps to virtual counselors and sophisticated predictive analytics for mental health crises. These technologies are democratizing access to mental health support, providing immediate, round-the-clock assistance to those in need. However, the integration of AI in therapy and counseling also presents challenges, such as ensuring the confidentiality of sensitive patient data and overcoming potential algorithmic biases. Despite these hurdles, the benefits of incorporating AI into mental health care are profound, offering new avenues for early intervention, personalized treatment plans, and continuous support that can significantly improve patient outcomes and overall well-being.\n\n4. **The Ethics of AI: Balancing Innovation and Responsibility**\n   As AI technology continues to advance at a rapid pace, it's imperative to address the ethical considerations that come with it. From algorithmic bias that can perpetuate societal inequalities to privacy concerns in an increasingly data-driven world, the ethical landscape of AI is fraught with complex dilemmas. Moreover, the potential for AI to displace human jobs raises questions about our responsibility to ensure a fair transition for affected workers. To navigate these challenges, it is crucial to develop AI systems that are not only innovative but also transparent, accountable, and aligned with our ethical values. Balancing innovation with responsibility will be key to harnessing AI's potential while safeguarding against its risks, fostering trust, and ensuring that AI serves the greater good.\n\n5. **Startups Harnessing AI: Success Stories and Challenges**\n   The startup ecosystem is witnessing a surge of AI-driven innovation, with emerging companies leveraging the power of AI to disrupt and transform various industries. From healthcare to finance, these startups are not only achieving remarkable success but also redefining what is possible through AI. Case studies of successful AI-centric businesses reveal a blend of visionary ideas, cutting-edge technology, and relentless perseverance. However, these startups also face unique challenges, such as securing funding, navigating regulatory landscapes, and scaling their solutions. By understanding these success stories and overcoming the associated challenges, aspiring entrepreneurs can glean valuable insights into what it takes to build a thriving AI-driven startup in today's competitive market, ultimately contributing to the broader AI revolution."
     )
 
 
@@ -400,7 +400,7 @@ def test_crew_with_delegating_agents():
 
     assert (
         result.raw
-        == "AI agents are intelligent software entities designed to autonomously perform tasks and make decisions based on data, embodying the next significant leap in the AI evolution. These agents leverage complex algorithms and machine learning techniques to analyze vast datasets, adapt to new patterns, and execute actions without human intervention. From personal assistants like Siri and Alexa to sophisticated financial trading systems and healthcare diagnostics, AI agents are revolutionizing numerous industries by enhancing efficiency, accuracy, and decision-making capabilities. Their ability to operate independently while continuously learning and improving means they hold the transformative potential to redefine how businesses operate, drive innovation, and create unprecedented value across various sectors.\n\nArtificial Intelligence (AI) transcends the notion of simple automation by introducing systems capable of mimicking human intelligence. This evolution in technology doesn't just follow predefined rules but learns from patterns, makes decisions, and even anticipates future outcomes. Machine learning, a subset of AI, has brought about breakthroughs where software evolves by processing data and identifying patterns too complex for traditional algorithms. As a result, AI applications now span from image recognition to natural language processing, offering human-like interactions and unparalleled analytic capabilities.\n\nIn healthcare, AI's influence is particularly profound. It enhances diagnostic accuracy by analyzing medical images, predicting disease outbreaks, and personalizing treatment plans based on patient data. AI-driven innovations such as robotic surgeries and AI-powered diagnostic tools are spearheading a new era of medical breakthroughs, reducing human error rates and facilitating early detection of life-threatening conditions. This is just the tip of the iceberg, as ongoing research continues to uncover new possibilities for integrating AI in patient care, potentially offering solutions that were once the realm of science fiction.\n\nAI's integration into daily life also brings forth significant ethical and societal considerations. Issues surrounding privacy, data security, and the potential displacement of jobs by automation need to be carefully managed. Additionally, ensuring that AI systems are transparent and unbiased is critical to their widespread acceptance and trust. As humanity stands on the brink of this technological revolution, balancing innovation with responsibility will be key to harnessing AI's full potential to drive sustainable growth and improve quality of life globally.\n\nThus, Artificial Intelligence, with all its promises and challenges, is undeniably charting a course toward a future where intelligent systems will increasingly become integral to our lives, driving transformation across all domains of human endeavor."
+        == "**The Transformative Power of AI Agents Across Industries**\n\nAI agents are revolutionizing industries by automating complex processes, enhancing decision-making, and providing personalized experiences. In healthcare, they assist in diagnosing diseases and predicting patient outcomes with unprecedented accuracy. In finance, AI agents automate trading and fraud detection, significantly reducing risks. The retail sector benefits from virtual shopping assistants that offer tailored recommendations and streamline customer service. \n\nThe impact of AI agents in healthcare cannot be overstated. From analyzing medical records to predicting which patients are at risk of developing certain conditions, AI agents help doctors make more informed decisions, ultimately leading to better patient care. Moreover, they are instrumental in the development of precision medicine, offering treatment plans tailored to individual genetic profiles, improving the efficacy of therapies.\n\nIn the financial sector, AI agents are at the forefront of transformation. They analyze vast datasets to identify market trends, execute high-frequency trades, and detect fraudulent activities in real-time. This not only optimizes trading strategies but also ensures a safer financial environment for consumers. The integration of AI in personal finance, such as robo-advisors, provides personalized investment advice, democratizing financial planning for a broader audience.\n\nLooking to the future, AI agents hold the promise of further advancements. They are set to play a crucial role in autonomous driving, ensuring safety and efficiency on the roads. In logistics, AI agents will streamline supply chain management by predicting demand, optimizing routes, and reducing waste. Additionally, in the field of scientific research, AI agents can generate new hypotheses, conduct experiments, and accelerate discoveries, potentially leading to breakthroughs that could solve some of the world’s most pressing problems. The continued evolution of AI agents promises to drive efficiency, innovation, and profound positive changes across a multitude of sectors.\n\nThis comprehensive look into AI agents highlights their transformative potential and underscores the importance of continued investment and development in this groundbreaking technology."
     )
 
 
@@ -609,7 +609,7 @@ def test_sequential_async_task_execution_completion():
 
     sequential_result = sequential_crew.kickoff()
     assert sequential_result.raw.startswith(
-        "Article: The Pivotal Moments in the History of Artificial Intelligence\n\nArtificial Intelligence (AI)"
+        'The history of Artificial Intelligence (AI) as a formal discipline began in earnest in the summer of 1956, during the famed Dartmouth Conference, often considered the "birth of AI"'
     )
 
 
@@ -636,7 +636,9 @@ def test_single_task_with_async_execution():
     )
 
     result = crew.kickoff()
-    assert result.raw.startswith("- Ethical considerations in AI deployment.")
+    assert result.raw.startswith(
+        "- Ethical implications of AI decision-making in healthcare."
+    )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1173,7 +1175,7 @@ def test_task_with_no_arguments():
     crew = Crew(agents=[researcher], tasks=[task])
 
     result = crew.kickoff()
-    assert result.raw == "January: 5, February: 10, March: 15, April: 20, May: 25"
+    assert result.raw == "75"
 
 
 def test_code_execution_flag_adds_code_tool_upon_kickoff():
@@ -1283,9 +1285,9 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
     assert result.raw == "Howdy!"
 
     assert result.token_usage == UsageMetrics(
-        total_tokens=2677,
+        total_tokens=2616,
-        prompt_tokens=2528,
+        prompt_tokens=2480,
-        completion_tokens=149,
+        completion_tokens=136,
         successful_requests=5,
     )
 
@@ -2383,9 +2385,7 @@ def test_conditional_task_last_task_when_conditional_is_true():
         tasks=[task1, task2],
     )
     result = crew.kickoff()
-    assert result.raw.startswith(
+    assert result.raw.startswith("1. **The Role of AI in Enhancing Cybersecurity**")
-        "1. **The Rise of Autonomous AI Agents in Business Operations**"
-    )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

tests/memory/cassettes/test_save_and_search.yaml

@@ -44,7 +44,7 @@ interactions:
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+      - '19'
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+      - req_f0d0cec4ef9175a0f59fd50ca2a32fdd
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-        _cfuvid=_ImdHVLTn_f3w4JVezXnp77j3qt.ozJCkmVDs_3Ztb0-1726383868046-0.0.1.1-604800000
+        _cfuvid=MOSIvTiRnnQAY2T82B.leh6SO8Lkaj8sQ7HqHTDFNPM-1726438364054-0.0.1.1-604800000
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+      - Sun, 15 Sep 2024 22:12:44 GMT
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       openai-organization:
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+      - '19'
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tests/pipeline/cassettes/test_router_with_empty_input.yaml

@@ -9,7 +9,7 @@ interactions:
       is the expect criteria for your final answer: Test output\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": ["\nResult"]}'
+      your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
     headers:
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+      - '804'
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   response:
-    content: "{\n  \"id\": \"chatcmpl-A7db3AjDrZmhKQhVlIXzeWDMoxYj6\",\n  \"object\":
+    content: "{\n  \"id\": \"chatcmpl-A7rm1BwEDQ61LA3VrmrQh1gStb4N7\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1726383869,\n  \"model\": \"gpt-4o-2024-05-13\",\n
+      \"chat.completion\",\n  \"created\": 1726438365,\n  \"model\": \"gpt-4o-2024-05-13\",\n
       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
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tests/task_test.py

@@ -236,7 +236,7 @@ def test_output_pydantic_sequential():
     crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
     result = crew.kickoff()
     assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
+    assert result.to_dict() == {"score": 5}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -266,7 +266,7 @@ def test_output_pydantic_hierarchical():
     )
     result = crew.kickoff()
     assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
+    assert result.to_dict() == {"score": 5}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -404,8 +404,8 @@ def test_output_json_dict_hierarchical():
         manager_llm="gpt-4o",
     )
     result = crew.kickoff()
-    assert {"score": 5} == result.json_dict
+    assert {"score": 4} == result.json_dict
-    assert result.to_dict() == {"score": 5}
+    assert result.to_dict() == {"score": 4}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -443,7 +443,7 @@ def test_output_pydantic_to_another_task():
     assert isinstance(
         pydantic_result, ScoreOutput
     ), "Expected pydantic result to be of type ScoreOutput"
-    assert 5 == pydantic_result.score
+    assert 4 == pydantic_result.score
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -474,7 +474,7 @@ def test_output_json_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2])
     result = crew.kickoff()
-    assert '{"score": 4}' == result.json
+    assert '{"score": 5}' == result.json
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -685,7 +685,7 @@ def test_increment_tool_errors():
     with patch.object(Task, "increment_tools_errors") as increment_tools_errors:
         increment_tools_errors.return_value = None
         crew.kickoff()
-        assert len(increment_tools_errors.mock_calls) == 12
+        assert len(increment_tools_errors.mock_calls) == 9
 
 
 def test_task_definition_based_on_dict():