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updating tests

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João Moura
2024-09-23 03:58:41 -03:00
parent 7d981ba8ce
commit 59e51f18fd
98 changed files with 36353 additions and 16646 deletions
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93
tests/agent_test.py
View File

@@ -3,6 +3,7 @@
from unittest import mock
from unittest.mock import patch
import os
import pytest
from crewai import Agent, Crew, Task
from crewai.agents.cache import CacheHandler
@@ -16,6 +17,49 @@ from crewai_tools import tool
from crewai.agents.parser import AgentAction
def test_agent_llm_creation_with_env_vars():
# Store original environment variables
original_api_key = os.environ.get("OPENAI_API_KEY")
original_api_base = os.environ.get("OPENAI_API_BASE")
original_model_name = os.environ.get("OPENAI_MODEL_NAME")
# Set up environment variables
os.environ["OPENAI_API_KEY"] = "test_api_key"
os.environ["OPENAI_API_BASE"] = "https://test-api-base.com"
os.environ["OPENAI_MODEL_NAME"] = "gpt-4-turbo"
# Create an agent without specifying LLM
agent = Agent(role="test role", goal="test goal", backstory="test backstory")
# Check if LLM is created correctly
assert isinstance(agent.llm, LLM)
assert agent.llm.model == "gpt-4-turbo"
assert agent.llm.api_key == "test_api_key"
assert agent.llm.base_url == "https://test-api-base.com"
# Clean up environment variables
del os.environ["OPENAI_API_KEY"]
del os.environ["OPENAI_API_BASE"]
del os.environ["OPENAI_MODEL_NAME"]
# Create an agent without specifying LLM
agent = Agent(role="test role", goal="test goal", backstory="test backstory")
# Check if LLM is created correctly
assert isinstance(agent.llm, LLM)
assert agent.llm.model != "gpt-4-turbo"
assert agent.llm.api_key != "test_api_key"
assert agent.llm.base_url != "https://test-api-base.com"
# Restore original environment variables
if original_api_key:
os.environ["OPENAI_API_KEY"] = original_api_key
if original_api_base:
os.environ["OPENAI_API_BASE"] = original_api_base
if original_model_name:
os.environ["OPENAI_MODEL_NAME"] = original_model_name
def test_agent_creation():
agent = Agent(role="test role", goal="test goal", backstory="test backstory")
@@ -27,7 +71,7 @@ def test_agent_creation():
def test_agent_default_values():
agent = Agent(role="test role", goal="test goal", backstory="test backstory")
assert agent.llm == "gpt-4o-mini"
assert agent.llm.model == "gpt-4o"
assert agent.allow_delegation is False
@@ -35,7 +79,7 @@ def test_custom_llm():
agent = Agent(
role="test role", goal="test goal", backstory="test backstory", llm="gpt-4"
)
assert agent.llm == "gpt-4"
assert agent.llm.model == "gpt-4"
def test_custom_llm_with_langchain():
@@ -48,7 +92,7 @@ def test_custom_llm_with_langchain():
llm=ChatOpenAI(temperature=0, model="gpt-4"),
)
assert agent.llm == "gpt-4"
assert agent.llm.model == "gpt-4"
def test_custom_llm_temperature_preservation():
@@ -89,8 +133,8 @@ def test_agent_execute_task():
assert result is not None
assert (
"The area of a circle with a radius of 5 cm is calculated using the formula A = πr^2, where A is the area and r is the...in the values, we get A = π*5^2 = 25π square centimeters. Therefore, the area of the circle is 25π square centimeters."
in result
"The calculated area of the circle is approximately 78.5 square centimeters."
== result
)
assert "square centimeters" in result.lower()
@@ -111,7 +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 == "The result of 1 + 1 is 2."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -153,6 +197,7 @@ def test_logging_tool_usage():
verbose=True,
)
assert agent.llm.model == "gpt-4o"
assert agent.tools_handler.last_used_tool == {}
task = Task(
description="What is 3 times 4?",
@@ -165,7 +210,8 @@ def test_logging_tool_usage():
tool_usage = InstructorToolCalling(
tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
)
assert output == "The result of the multiplication is 12."
assert output == "The result of multiplying 3 by 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
@@ -319,7 +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 the multiplication is 12."
assert output == "The result of multiplying 3 and 4 is 12."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -373,7 +419,7 @@ 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"])
@@ -503,7 +549,7 @@ def test_agent_moved_on_after_max_iterations():
task=task,
tools=[get_final_answer],
)
assert output == "The final answer is 42."
assert output == "42"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -770,7 +816,7 @@ def test_agent_step_callback():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_function_calling_llm():
llm = "gpt-4o"
llm = "gpt-4"
@tool
def learn_about_AI() -> str:
@@ -794,27 +840,14 @@ def test_agent_function_calling_llm():
)
tasks = [essay]
crew = Crew(agents=[agent1], tasks=tasks)
from unittest.mock import patch, Mock
from unittest.mock import patch
import instructor
with patch.object(instructor, "from_litellm") as mock_from_litellm:
mock_client = Mock()
mock_from_litellm.return_value = mock_client
mock_chat = Mock()
mock_client.chat = mock_chat
mock_completions = Mock()
mock_chat.completions = mock_completions
mock_create = Mock()
mock_completions.create = mock_create
with patch.object(
instructor, "from_litellm", wraps=instructor.from_litellm
) as mock_from_litellm:
crew.kickoff()
mock_from_litellm.assert_called()
mock_create.assert_called()
calls = mock_create.call_args_list
assert any(
call.kwargs.get("model") == "gpt-4o" for call in calls
), "Instructor was not created with the expected model"
def test_agent_count_formatting_error():
@@ -1487,7 +1520,9 @@ def test_agent_execute_task_with_custom_llm():
result = agent.execute_task(task)
assert len(result.split("\n")) == 3
assert "AI" in result or "artificial intelligence" in result.lower()
assert result.startswith(
"Artificial minds,\nSilent, yet they learn and grow,\nFuture in their code."
)
@pytest.mark.vcr(filter_headers=["authorization"])

12
tests/agent_tools/agent_tools_test.py
View File

@@ -24,7 +24,7 @@ def test_delegate_work():
assert (
result
== "While it's a common misconception that I might \"hate\" AI agents, the reality is much more nuanced. As an expert in technology research, especially in the realm of AI, I have a deep appreciation for both the potential and the challenges that AI agents present.\n\nAI agents, which can be broadly defined as autonomous software entities that perform tasks on behalf of users or other programs, are transforming numerous aspects of our daily lives and industries. Here are a few key points to consider:\n\n1. **Advantages of AI Agents**:\n - **Automation and Efficiency**: AI agents can handle repetitive and time-consuming tasks efficiently, freeing up human workers to focus on more complex and creative activities.\n - **Availability**: Unlike humans, AI agents can operate 24/7 without breaks, providing continuous service and support.\n - **Scalability**: AI agents can be deployed across different platforms and industries, scaling solutions quickly and effectively.\n - **Data Analysis and Insights**: They can process vast amounts of data rapidly, providing insights that would be difficult, if not impossible, for humans to derive on their own.\n\n2. **Challenges and Concerns**:\n - **Ethical Implications**: There are significant concerns regarding the ethical use of AI agents, particularly related to privacy, bias, and decision-making transparency.\n - **Job Displacement**: While AI agents increase efficiency, they also raise concerns about potential job displacement in certain sectors.\n - **Dependence and Reliability**: Over-reliance on AI agents could lead to vulnerabilities if the technology fails or is compromised.\n\n3. **Looking Forward**:\n - **Collaboration Between Humans and AI**: The best outcomes are likely to come from a hybrid approach where AI agents augment human capabilities rather than replace them outright.\n - **Regulation and Standards**: There is a growing need for clear regulations and standards to ensure the ethical development and deployment of AI agents.\n - **Continuous Improvement**: The technology behind AI agents is constantly evolving. Continuous research and development are essential to address current limitations and maximize benefits.\n\nIn conclusion, my perspective on AI agents is not one of disdain but rather a cautious optimism. They hold incredible promise for transforming various sectors and improving efficiencies, but this potential comes with significant responsibilities. It's essential to address the ethical, social, and technical challenges to harness the full benefits of AI agents while mitigating potential downsides. Thus, thoughtful consideration and ongoing dialogue about the role of AI agents in society are crucial."
== 'AI agents are a significant advancement in the field of artificial intelligence, offering immense potential and diverse applications across various sectors. However, the perception that I "hate" them is not entirely accurate. My stance on AI agents is more nuanced and analytical rather than emotional. Here\'s a comprehensive take:\n\n1. **Capabilities and Applications**:\n - **Automation**: AI agents excel in performing repetitive and mundane tasks, allowing human workers to focus on more complex and creative activities.\n - **Efficiency**: They can process vast amounts of data at speeds incomprehensible to humans, leading to quicker decision-making and problem-solving.\n - **Personalization**: In customer service, AI agents provide personalized experiences by analyzing customer data and predicting preferences.\n - **Healthcare**: AI agents assist in diagnosing diseases, recommending treatments, and even predicting outbreaks by analyzing medical data.\n\n2. **Benefits**:\n - **Increased Productivity**: By automating routine tasks, businesses can significantly improve productivity and reduce operational costs.\n - **24/7 Availability**: Unlike human workers, AI agents can operate continuously without breaks, providing constant support and monitoring.\n - **Data-Driven Insights**: AI agents can uncover patterns and insights from data that might be missed by human analysts.\n\n3. **Challenges and Concerns**:\n - **Job Displacement**: There is a legitimate concern about AI agents replacing human jobs, particularly in roles involving routine and predictable tasks.\n - **Bias and Fairness**: AI agents can perpetuate and even amplify existing biases found in the data they are trained on, leading to unfair or discriminatory outcomes.\n - **Transparency**: The decision-making process of AI agents can sometimes be opaque, making it difficult to understand how they arrive at certain conclusions (known as the "black box" problem).\n - **Security**: AI systems can be vulnerable to hacking and other malicious activities, posing significant security risks.\n\n4. **Ethical Considerations**:\n - **Accountability**: Who is responsible when an AI agent makes a mistake? This question remains complex and is a major ethical consideration.\n - **Privacy**: The extensive use of data by AI agents raises concerns about user privacy and the potential for misuse of sensitive information.\n - **Consent**: Users must be informed and provide consent regarding how their data is used by AI agents.\n\n5. **Future Directions**:\n - **Regulation and Governance**: Developing robust regulatory frameworks to oversee the deployment and use of AI agents is crucial.\n - **Continuous Improvement**: Ongoing research is needed to enhance the capabilities of AI agents while addressing their current limitations and ethical concerns.\n - **Collaboration**: Encouraging collaboration between AI agents and human workers can lead to hybrid systems that leverage the strengths of both.\n\nIn summary, while AI agents offer numerous benefits and transformative potential, they also come with a set of challenges and ethical questions that must be carefully managed. My analysis focuses on a balanced view, recognizing both the opportunities and the complexities associated with AI agents. \n\nThus, it\'s not about hating AI agents, but rather critically evaluating their impact and advocating for responsible and fair implementation. This ensures that while we harness their capabilities, we also address and mitigate any negative repercussions.'
)
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
assert (
result
== "It's not accurate to say that I hate AI agents. My stance is more nuanced and rooted in a deep understanding of their capabilities, limitations, and the potential impact on various sectors.\n\nAI agents are software entities that perform tasks autonomously on behalf of users. They are designed to mimic human decision-making and problem-solving abilities. The advances in machine learning, natural language processing, and data analytics have significantly improved the performance of AI agents, making them valuable tools for automation, customer service, data analysis, and more.\n\nHowever, my concerns about AI agents stem from several key points:\n\n1. **Ethical Considerations**: AI agents can perpetuate biases present in their training data. If the data used to train these agents contain biases, whether related to gender, race, or other factors, the AI can replicate and even amplify these biases in its operations. This has serious ethical implications for fairness and equality.\n\n2. **Job Displacement**: While AI agents can enhance efficiency and productivity, they can also displace human workers. Many routine and repetitive tasks previously performed by humans are now automated, leading to job losses in certain sectors. This aspect calls for a balanced approach where human roles are redefined rather than completely eliminated.\n\n3. **Transparency and Accountability**: AI agents often operate as \"black boxes,\" meaning their decision-making processes are not transparent. If an AI agent makes an error—whether in financial transactions, medical diagnoses, or legal decisions—it can be challenging to understand why the error occurred and who is responsible. Ensuring transparency and accountability is crucial for trust and reliability.\n\n4. **Security and Privacy**: AI agents often deal with vast amounts of personal data. Ensuring the security and privacy of this data is paramount. There are risks associated with data breaches and the misuse of personal information, which can have significant repercussions for individuals and organizations.\n\nDespite these concerns, I also recognize the immense potential of AI agents to drive innovation, improve efficiency, and provide solutions to complex problems. The key is to develop and deploy AI responsibly, with robust regulatory frameworks and ethical guidelines.\n\nIn conclusion, I don't hate AI agents. Instead, I advocate for a balanced perspective that acknowledges their benefits while addressing their challenges. By doing so, we can harness the power of AI agents for the greater good, ensuring they contribute positively to society."
== "While it might have circulated that I \"hate\" AI agents, I think it's more nuanced than that. AI agents are incredibly sophisticated pieces of technology that carry immense potential for transforming multiple sectors like healthcare, finance, and customer service.\n\nFirst, let's define what AI agents are. AI agents are autonomous entities designed to perform specific tasks, often mimicking human decision-making and problem-solving abilities. They can range from simple chatbots to complex systems capable of managing large-scale operations.\n\nThe Pros:\n1. Efficiency: AI agents can complete tasks faster and more accurately than humans. For instance, they can instantaneously process large datasets to provide real-time insights.\n2. Scalability: With AI agents, services can be scaled without the proportional increase in cost, which is particularly beneficial for businesses looking to grow without extensive human resource expenditures.\n3. 24/7 Availability: Unlike human workers, AI agents can operate around the clock, increasing productivity and availability, particularly in customer service domains.\n4. Data Handling: AI agents can handle and process data at speeds and volumes that far exceed human capabilities, enabling smarter business decisions and innovations.\n\nHowever, there are some drawbacks and concerns:\n1. Ethical Issues: AI agents often face ethical dilemmas, especially when their decision-making processes are opaque. Bias in AI systems can perpetuate existing inequalities, making transparency and fairness critical issues.\n2. Job Displacement: One significant concern is the potential for AI agents to displace human workers, particularly in industries reliant on routine, repetitive tasks.\n3. Dependency: Over-reliance on AI can lead to skill erosion among human employees and vulnerabilities in cases where technology fails or is compromised.\n4. Security: Given their access to sensitive data, AI agents can be attractive targets for cyber-attacks. Ensuring robust security measures is essential.\n\nSo, while I recognize the transformative power and benefits of AI agents, it's crucial to approach them with a balanced perspective. Adequate governance, ethical considerations, and transparency are needed to harness their potential responsibly. It's not a matter of hating them, but rather advocating for their responsible and fair use in society."
)
@@ -52,7 +52,7 @@ def test_ask_question():
assert (
result
== "As a researcher specialized in technology with a particular focus on AI and AI agents, my stance on AI agents isn't rooted in emotional responses like hate or love. Rather, it is grounded in objective analysis and thoughtful consideration of their capabilities, ethics, and impact on society.\n\nAI agents are powerful tools that can transform various industries, from healthcare and finance to customer service and manufacturing. They have the potential to increase efficiency, improve decision-making, and even solve complex problems that were previously insurmountable. For instance, AI agents can analyze vast amounts of data far more quickly and accurately than humans, leading to advancements in medical research and diagnostics.\n\nHowever, it is equally important to recognize the ethical and societal implications of AI agents. Issues such as data privacy, algorithmic bias, and the potential displacement of jobs need to be carefully managed. As a researcher, my role is to study these aspects comprehensively, provide balanced insights, and advocate for responsible development and deployment of AI technologies.\n\nSo, to address your question directly: I don't hate AI agents. I appreciate their potential and am keenly aware of their challenges. My goal is to contribute to a future where AI agents are designed and used ethically and effectively to benefit humanity."
== 'As a researcher specialized in technology, particularly in artificial intelligence (AI) and AI agents, my perspective is shaped by the extensive analysis and study I engage in daily. AI agents are neither inherently good nor bad; they are tools with the potential for both positive and negative impacts depending on how they are designed, implemented, and used.\n\nI do not "hate" AI agents. In fact, I find them fascinating and hold an appreciation for their capabilities and the potential they have to transform various sectors, from healthcare and education to finance and entertainment. AI agents can offer significant benefits, such as improving efficiency, personalizing user experiences, and even tackling complex problems that are beyond human capacity to solve alone.\n\nHowever, it is essential to approach AI with a critical eye. There are legitimate concerns around privacy, security, ethical use, and the socio-economic implications of AI deployment. Ensuring that AI is developed responsibly and is aligned with ethical standards is crucial for minimizing potential negatives.\n\nIn summary, my stance on AI agents is informed by a balanced view that recognizes both their potential and the necessity for responsible stewardship. The enthusiasm I have for their capabilities is tempered by a commitment to understanding and addressing the challenges they present.'
)
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
assert (
result
== "As an expert researcher specialized in technology, I have a profound appreciation for AI and AI agents. They represent a pinnacle of human innovation and have the potential to greatly enhance various aspects of our lives. AI agents can assist in automating mundane tasks, providing insights through data analysis, and even offering companionship to those in need. However, it's also essential to approach AI with a balanced perspective, acknowledging both its strengths and the ethical considerations it raises. In short, I don't hate AI agents; I recognize their immense potential and value while being mindful of the responsibilities that come with their development and deployment."
== "As an expert researcher specialized in technology and AI, I do not harbor feelings of hate towards AI agents. In fact, the notion of hate or love for a technology is somewhat misplaced. AI agents are tools created to solve specific problems and enhance our capabilities. They can transform industries, improve efficiencies, and even save lives in fields such as healthcare. My enthusiasm and interest in AI agents come from their potential to innovate and drive progress. While it is essential to be mindful of ethical considerations and responsible use, it is equally important to remain objective. My goal as a researcher is to contribute to balanced and insightful analysis, exploring both the benefits and challenges of AI, and not to personalize or emotionally charge the discussion about technological tools."
)
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
assert (
result
== "In addressing your query about AI agents, it's crucial to clarify my stance. While I don't \"hate\" AI agents, I have a nuanced perspective that balances both their potential benefits and inherent drawbacks. This balanced view is essential for understanding the complex role that AI agents play in today's technology landscape.\n\nAI agents are remarkable tools that can automate tasks, provide intelligent recommendations, and enhance user experiences. They harness advancements in machine learning, natural language processing, and big data analytics to perform a wide range of functions, from personal assistants like Siri and Alexa to sophisticated customer service bots and autonomous systems in various industries.\n\nThe Potential Benefits of AI Agents:\n1. **Automation and Efficiency**: AI agents can handle repetitive and mundane tasks, freeing up human workers to focus on more creative and high-value activities. This can lead to increased productivity and operational efficiency in businesses.\n2. **Data-Driven Insights**: By analyzing vast amounts of data, AI agents can provide actionable insights that inform decision-making processes in areas like finance, healthcare, and marketing.\n3. **Personalization**: AI agents can tailor recommendations and services based on individual user preferences and behaviors, enhancing user satisfaction and engagement.\n4. **24/7 Availability**: Unlike human workers, AI agents can operate continuously without the need for breaks, providing round-the-clock support and services.\n\nHowever, there are also several challenges and concerns associated with AI agents that cannot be overlooked:\n\n1. **Ethical Considerations**: The deployment of AI agents raises ethical questions about privacy, surveillance, and the potential for bias in their algorithms. Ensuring that AI systems are transparent and fair is a critical issue.\n2. **Job Displacement**: While AI agents can increase efficiency, they also have the potential to displace human workers, leading to concerns about unemployment and the need for workforce reskilling.\n3. **Trust and Reliability**: Building trust in AI systems is essential. Users need to be confident that AI agents will perform their tasks accurately and reliably, without unexpected failures or errors.\n4. **Control and Accountability**: Determining who is accountable when AI agents make errors or cause harm is a complex issue, especially when these systems operate autonomously or with minimal human oversight.\n\nIn summary, my view on AI agents is not one of antagonism but of cautious optimism. I recognize the transformative potential of AI agents in various domains, but I also emphasize the importance of addressing the ethical, economic, and technical challenges they present. Our goal should be to develop and deploy AI agents in a way that maximizes their benefits while mitigating their risks. This balanced approach will ensure that AI agents can be a positive force in society, driving innovation and improving quality of life without compromising ethical standards or societal values."
== "AI agents, like any technology, have both significant advantages and notable challenges. Initially, the skepticism surrounding AI agents might stem from concerns about their implementation, ethical considerations, or potential impact on jobs. However, let's delve into a comprehensive analysis of their roles and implications.\n\nFirstly, AI agents are software entities that perform tasks autonomously on behalf of a user or another program with some degree of intelligence and learning capability. These agents can interpret data, learn from patterns, and even make decisions based on the information they process. They are essential across various sectors, including healthcare, finance, customer service, and logistics.\n\nPositive Aspects:\n1. **Efficiency and Productivity:** AI agents can process large volumes of data faster than humans, enabling quicker decision-making. This efficiency can significantly boost productivity in organizations by automating repetitive tasks, allowing human workers to focus on more strategic activities.\n\n2. **24/7 Operation:** Unlike humans, AI agents can work around the clock without fatigue. This capability is crucial for industries that require continuous operation, such as monitoring systems in cybersecurity or customer service through chatbots.\n\n3. **Personalization:** AI agents can tailor experiences to individual users by learning from their behavior. For instance, recommendation systems used by services like Netflix or Amazon provide personalized content, enhancing user satisfaction and engagement.\n\n4. **Accuracy and Precision:** AI agents, when properly trained, can perform tasks with a high degree of accuracy, reducing the likelihood of human error. This precision is especially valuable in fields such as medical diagnostics, where accurate analysis can be life-saving.\n\nChallenges and Concerns:\n1. **Ethical Issues:** The deployment of AI agents raises ethical questions regarding privacy, data security, and potential biases in decision-making. It is essential to ensure that AI systems are transparent, fair, and respect user privacy.\n\n2. **Job Displacement:** As AI agents automate more tasks, there is a genuine concern about job loss, particularly in roles that involve routine and repetitive tasks. It is critical to address this issue by reskilling and upskilling the workforce to adapt to the changing job landscape.\n\n3. **Dependence on Data:** AI agents rely heavily on large datasets to learn and make decisions. If the data is biased or incomplete, the AI’s performance and reliability can be compromised. This dependence also raises questions about data ownership and governance.\n\n4. **Complexity and Accountability:** Understanding and managing AI agents can be complex, and determining accountability in the event of failures or erroneous decisions can be challenging. Developing robust frameworks for monitoring and accountability is crucial.\n\nIn conclusion, while there are valid concerns associated with AI agents, they also offer transformative potential in improving efficiency, personalization, and accuracy in various applications. It is important to engage in balanced discussions, address ethical considerations, and develop policies to mitigate potential negative impacts while leveraging the benefits of AI technology. Hence, my stance is not of outright hatred, but one of cautious optimism, advocating for responsible development and deployment of AI agents."
)
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
assert (
result
== "No, I do not hate AI agents. In fact, I appreciate their potential and the positive impact they can have in various fields. AI agents can perform tasks that are either too mundane or too complex for humans, thereby increasing productivity and allowing us to focus on more creative and strategic activities. Moreover, they can process and analyze vast amounts of data much more quickly and accurately than humans, leading to better decision-making and innovations. As a researcher specialized in technology, I am always excited to see advancements in AI and AI agents, as they push the boundaries of what technology can achieve. So, yes, I do love the possibilities they bring to our world."
== "While I understand there are mixed feelings about AI agents, I do not hate them. In fact, I see significant potential in AI and AI agents to transform various industries and improve our daily lives. My enthusiasm for AI comes from appreciating the advancements it's bringing, such as personalized recommendations, improved diagnostics in healthcare, and more efficient business processes.\n\nHowever, it's essential to approach AI with a balanced perspective. Ethical considerations, privacy concerns, and the potential for job displacement are critical issues that need to be addressed. I believe in responsible AI development, where the focus is on maximizing benefits while minimizing potential harms.\n\nSo, in short, I love the potential and capabilities of AI agents when developed and implemented responsibly and ethically."
)

69
tests/agent_tools/cassettes/test_ask_question.yaml
View File

@@ -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": ["\nObservation:"]}'
Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o"}'
headers:
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@@ -21,16 +21,16 @@ interactions:
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host:
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user-agent:
- OpenAI/Python 1.46.0
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x-stainless-arch:
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x-stainless-async:
@@ -40,7 +40,7 @@ interactions:
x-stainless-os:
- MacOS
x-stainless-package-version:
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x-stainless-raw-response:
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x-stainless-runtime:
@@ -50,37 +50,36 @@ interactions:
method: POST
uri: https://api.openai.com/v1/chat/completions
response:
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headers:
Accept:
- '*/*'
@@ -223,7 +83,7 @@ interactions:
Connection:
- keep-alive
Content-Length:
- '4210'
- '4204'
Content-Type:
- application/x-protobuf
User-Agent:
@@ -239,27 +99,50 @@ interactions:
Content-Type:
- application/x-protobuf
Date:
- Wed, 18 Sep 2024 07:01:53 GMT
- Mon, 23 Sep 2024 06:30:06 GMT
status:
code: 200
message: OK
- request:
body: '{"messages": [{"role": "system", "content": "You are Scorer. You''re an
expert scorer, specialized in scoring titles.\nYour personal goal is: Score
the title\nTo give my best complete final answer to the task use the exact following
format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
answer must be the great and the most complete as possible, it must be outcome
described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
"content": "\nCurrent Task: Give an integer score between 1-5 for the title
body: '{"messages": [{"role": "system", "content": "You are Crew Manager. You
are a seasoned manager with a knack for getting the best out of your team.\nYou
are also known for your ability to delegate work to the right people, and to
ask the right questions to get the best out of your team.\nEven though you don''t
perform tasks by yourself, you have a lot of experience in the field, which
allows you to properly evaluate the work of your team members.\nYour personal
goal is: Manage the team to complete the task in the best way possible.\nYou
ONLY have access to the following tools, and should NEVER make up tools that
are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context:
str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a
specific task to one of the following coworkers: Scorer\nThe input to this tool
should be the coworker, the task you want them to do, and ALL necessary context
to execute the task, they know nothing about the task, so share absolute everything
you know, don''t reference things but instead explain them.\nTool Arguments:
{''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': {''title'':
''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'',
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Diagnostics and Treatment**\n - What Makes It Unique: The integration of AI
into healthcare is transforming how diseases are diagnosed and treated. Machine
learning algorithms can analyze vast amounts of medical data much faster than
humans can, leading to earlier and more accurate diagnoses. Additionally, AI
can personalize treatment plans based on individual patient data, improving
outcomes.\n - Why It''s Interesting: The potential to save lives and improve
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What Makes It Unique: AI agents, such as chatbots and virtual assistants, are
increasingly being used to handle customer inquiries and support. These agents
can operate 24/7, provide instant responses, and learn from interactions to
improve over time.\n - Why It''s Interesting: This topic explores the balance
between automation and human touch in customer service. It also delves into
the future of employment in customer support and the ethical use of AI in interacting
with customers.\n\n- **Autonomous Vehicles: The Road Ahead**\n - What Makes
It Unique: Autonomous vehicles are not just a technological advancement but
a societal shift. They promise to reduce accidents, lower emissions, and provide
new mobility solutions for people who cannot drive.\n - Why It''s Interesting:
The article could explore current advancements, challenges such as regulatory
hurdles, and the potential impact on industries like insurance, public transport,
and urban planning.\n\n- **AI in Environmental Conservation: Fighting Climate
Change**\n - What Makes It Unique: AI technologies are being used to tackle
some of the world''s most pressing environmental issues. From predicting natural
disasters to optimizing energy use, AI can play a significant role in mitigating
climate change.\n - Why It''s Interesting: This topic bridges technology and
global conservation efforts, making it relevant and urgent. It also looks at
how AI can be a tool for policy-making and advocacy.\n\n- **Ethical AI: Navigating
the Moral Implications**\n - What Makes It Unique: The rise of AI brings with
it significant ethical considerations, from data privacy to algorithmic bias.
Ensuring that AI systems are fair, transparent, and accountable is a growing
concern.\n - Why It''s Interesting: The ethical implications of AI are a hotly
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ethical AI systems.\n\n----------\n\n1. **Dartmouth Conference (1956)**\n -
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and set the stage for future developments in the field.\n\n2. **Development
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to determine whether a machine could exhibit intelligent behavior indistinguishable
from that of a human.\n - **Why It''s Important:** The Turing Test provided
a foundational benchmark for AI research and remains a key philosophical touchstone
in discussions about machine intelligence and consciousness.\n\n3. **Creation
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backpropagation algorithm, revolutionizing neural networks. The technique allowed
multi-layer neural networks to adjust their weights based on their errors, significantly
improving their ability to learn from data.\n - **Why It''s Important:** This
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in the development of modern AI applications.\n\n4. **IBM''s Deep Blue Defeats
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computer Deep Blue defeated the reigning world chess champion, Garry Kasparov,
in a six-game match. This marked the first time a computer had triumphed over
a world champion in a classical chess match.\n - **Why It''s Important:**
Deep Blue''s victory was a monumental milestone showcasing the capabilities
of AI in strategic decision-making and problem-solving. It illustrated the practical
applications of AI and captured the public\u2019s imagination about the technology''s
potential.\n\n5. **Advent of Deep Learning and AlexNet (2012)**\n - **What
Happened:** In 2012, AlexNet, a deep convolutional neural network designed by
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Scale Visual Recognition Challenge by a significant margin. The network''s performance
in image recognition and classification underscored the power of deep learning.\n -
**Why It''s Important:** AlexNet\u2019s success demonstrated the effectiveness
of deep neural networks and accelerated a boom in AI research and applications.
It was a catalyst for widespread adoption of deep learning techniques in areas
such as computer vision, natural language processing, and beyond.\n\nBegin!
is the context you''re working with:\n- Autonomous AI Agents in Creative Industries:
An exploration of how AI is transforming the creative sector, from automated
content generation to AI-driven design tools. Unique aspects include the ethical
implications and the collaboration between human creativity and machine learning.\n\n-
AI in Predictive Healthcare: A look at how artificial intelligence is being
used to predict diseases before they manifest, optimize treatment plans, and
improve patient outcomes. This is interesting because it integrates big data,
machine learning, and real-time analytics to revolutionize medical science.\n\n-
AI and Ethical Decision Making: An examination of how AI can be programmed to
make ethical decisions, particularly in autonomous systems like self-driving
cars and military applications. This topic is compelling due to its philosophical,
social, and technical challenges.\n\n- AI for Sustainable Development: Investigating
how AI technologies are being leveraged to address environmental challenges,
such as climate change, resource management, and wildlife conservation. This
is significant due to the pressing need for sustainable solutions and the innovative
use of technology.\n\n- AI in Personal Finance: How AI-driven applications are
reshaping personal finance management, from robo-advisors to fraud detection
and personalized financial planning. The unique angle here is the blend of advanced
analytics, user behavior insights, and financial technology (fintech).\n\n----------\n\n1.
**The Dartmouth Conference (1956):**\n - **Event Description:** The Dartmouth
Conference, held in the summer of 1956, is widely considered the birthplace
of artificial intelligence as a field. Organized by John McCarthy, Marvin Minsky,
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of pattern recognition and learning from data.\n - **Impact:** This development
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AI subfields.\n\n5. **The Rise of Transformer Models (2017):**\n - **Event
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natural language processing (NLP) by enabling more efficient and effective handling
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advanced language understanding, translation, and generation tasks, paving the
way for more sophisticated AI applications in diverse industries, including
creative content generation and personal finance.\n\nEach of these events has
played a critical role in shaping the evolution of AI technology, influencing
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tests/crew_test.py
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from unittest import mock
from unittest.mock import MagicMock, patch
import instructor
import pydantic_core
import pytest
from crewai.agent import Agent
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expected_string_output = "### Exploring Artificial Intelligence: The Game Changer Across Fields\n\nArtificial Intelligence (AI) holds immense promise in tackling one of humanity’s gravest challenges: climate change. By leveraging sophisticated machine learning algorithms, AI can predict climate patterns with greater accuracy, aiding in the timely and efficient mitigation of extreme weather events. Moreover, AI can optimize renewable energy production by fine-tuning wind turbines and solar panels for maximum efficiency. These technologies not only contribute to sustainable energy practices but also significantly reduce carbon footprints. Case studies like Google's project on improving energy efficiency through DeepMind's AI are stellar examples showcasing AI's potential in environmental sustainability.\n\nThe ethical dimensions of AI, particularly concerning bias and fairness, are becoming increasingly significant. The dialogue surrounding AI ethics addresses how biased data inputs can lead to discriminatory outcomes in various sectors, including law enforcement and hiring practices. For instance, there have been cases where facial recognition software has shown prejudiced behavior, leading to wrongful identifications and arrests predominantly affecting racial minorities. By discussing the importance of developing unbiased AI systems and the recent regulatory measures aimed at mitigating these issues, this topic highlights the societal implications of AI's expansive reach.\n\nIn the realm of healthcare, AI is revolutionizing personalized medicine by tailoring treatments and predicting patient outcomes with unparalleled precision. Through the analysis of genomic data, AI can suggest individualized treatment plans that are more effective than generalized approaches. This not only enhances patient care but also streamlines healthcare systems by focusing resources where they are needed most. Notable case studies include IBM Watson's application in oncology, which helps in formulating personalized treatment protocols based on the latest medical research and patient history.\n\nOne of the visible impacts of AI on everyday life is its transformation of customer service through AI agents and chatbots. While early iterations were limited to basic query handling, modern-day AI agents utilize advanced Natural Language Processing (NLP) techniques to understand and respond to complex customer queries efficiently. This advancement has led to significant improvements in customer satisfaction and reduced operational costs. Emerging trends such as emotion-detection AI agents suggest a future where customer service will be even more personalized and intuitive, highlighting how AI is becoming an indispensable tool in business operations.\n\nBy delving into these compelling areas, an article on AI can cover a range of topics from climate change to ethics, healthcare, and customer service. Each section promises a deep dive into how AI is not just a technological advancement but a crucial player in shaping a sustainable, fair, and efficient future."
expected_string_output = "**1. The Rise of Autonomous AI Agents in the Business Sector**\n\nThe business sector is experiencing a radical transformation with the rise of autonomous AI agents taking on roles traditionally filled by human employees. These intelligent systems are revolutionizing customer service, sales, and administrative tasks by offering unmatched efficiency and cost savings. Yet, this shift brings forward ethical and practical challenges that need careful navigation. Companies like IPsoft have pioneered the deployment of AI agents, demonstrating both the immense potential and the hurdles encountered. By automating routine tasks, businesses can reallocate human talent towards more strategic initiatives, driving innovation while maintaining operational excellence.\n\n**2. AI and Creativity: Can Machines Be Truly Creative?**\n\nThe debate over whether machines can truly be creative is gaining momentum as AI algorithms begin to compose music, create visual art, and author literature. This exploration into AI's creative potential raises profound questions about the nature of creativity and consciousness. AI systems like OpenAI's GPT-3 have crafted compelling narratives and artistic expressions, showcasing glimpses of what the future holds for the fusion of technology and creativity. Highlighting such AI-generated works provides a tantalizing view into a future where humans and machines could collaborate in groundbreaking artistic ventures, reshaping our understanding of creativity itself.\n\n**3. Ethical AI: Navigating the Moral Landscape of Artificial Intelligence**\n\nAs AI technologies advance, the need for ethical considerations becomes increasingly critical. This topic delves into the frameworks and guidelines being developed to ensure that AI systems align with human values and morals. The ethical landscape of AI intersects with various fields including law, philosophy, and social justice, prompting a multidisciplinary approach to responsible AI development. Featuring insights from AI ethicists, policymakers, and technologists, the article explores the balance between innovation and ethical responsibility. By navigating these moral complexities, society can harness the benefits of AI while mitigating potential risks and ensuring equitable outcomes.\n\n**4. AI in Healthcare: Transforming Diagnosis and Treatment**\n\nAI is on the brink of revolutionizing healthcare, offering transformative changes in diagnosis and treatment. From predictive diagnostics to personalized treatment plans, and even robotic surgery, AI technologies are improving patient outcomes and reducing healthcare costs. Virtual health assistants powered by AI are broadening access to care, making healthcare more inclusive and efficient. Real-world examples, such as IBM Watson's ability to diagnose complex medical conditions, underscore the profound impact AI can have in the medical field. By examining these advancements, the article provides a comprehensive look at how AI is poised to redefine the future of healthcare.\n\n**5. AI and Cybersecurity: The Arms Race of the Future**\n\nIn the ever-evolving world of cybersecurity, AI is emerging as both a potent defender and a formidable adversary. AI technologies are now capable of identifying and responding to threats in real-time, offering robust protection against cyber-attacks. However, malicious actors are also harnessing AI to launch more sophisticated and stealthy attacks, leading to an ongoing arms race in the digital landscape. The relevance of this topic is underscored by the multitude of high-profile security breaches, driving public and private sectors to invest heavily in AI-driven cybersecurity measures. Through expert insights and case studies, this article will explore the capabilities of AI in safeguarding sensitive data and anticipating future threats, painting a vivid picture of the cybersecurity landscape of tomorrow."
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assert (
result.raw
== '1. **"The Rise of Autonomous AI Agents: Transforming Modern Workflows"**\n Autonomous AI agents are rapidly revolutionizing modern workflows across diverse industries. By automating repetitive tasks, optimizing resource allocation, and minimizing human error, these intelligent systems are paving the way for unprecedented efficiency and productivity. Case studies from leading enterprises demonstrate the transformative impact of incorporating AI agents, showcasing examples where routine administrative tasks are seamlessly handled by AI, freeing human employees to focus on strategic and creative endeavors. This article will delve into these success stories, explore the cutting-edge technologies enabling these advancements, and forecast future implications—an essential read for anyone interested in the frontier of automation.\n\n2. **"Ethics in AI: Balancing Innovation and Responsibility"**\n As artificial intelligence continues to evolve, the ethical landscape surrounding its development and deployment demands rigorous examination. This article will tackle critical issues such as algorithmic bias, privacy concerns, and the accountability of AI-driven decisions. By interviewing leading ethicists and AI developers, the piece aims to provide a multidimensional perspective on how society can harness the benefits of AI while mitigating potential harms. Readers will gain insights into the frameworks for responsible AI innovation, the importance of transparency, and the role of regulatory policies in ensuring ethical compliance in AI applications—a comprehensive guide for stakeholders committed to fostering ethical AI ecosystems.\n\n3. **"AI in Mental Health: Opportunities and Challenges"**\n Artificial intelligence is making significant inroads in the field of mental health, offering innovative solutions such as virtual therapy sessions, AI-driven diagnostics, and personalized mental health recommendations. This article will explore the profound opportunities AI presents in enhancing mental health care accessibility and efficacy, especially in underserved areas. It will also address the formidable challenges, including the reliability of AI assessments, ethical concerns about data privacy, and the importance of human oversight. By presenting a balanced view, complete with expert opinions and case studies, this piece aims to illuminate the potentials and pitfalls of integrating AI into mental health services.\n\n4. **"From Silicon Valley to Global Hubs: How AI Startups are Shaping International Innovation"**\n While Silicon Valley remains a beacon of technological innovation, AI startups are emerging as transformative forces in global hubs from Shenzhen to Tel Aviv and beyond. This article will highlight the dynamic ecosystems fostering AI advancements outside traditional tech epicenters, showcasing unique innovations and the local factors driving their success. Readers will discover compelling narratives of entrepreneurial ventures, pioneering research, and supportive public policies that contribute to these thriving AI landscapes. By painting a global picture of AI innovation, this piece will inspire investors, technologists, and policymakers to recognize and support the burgeoning potential worldwide.\n\n5. **"The Future of Human-AI Collaboration: Co-working with Intelligent Systems"**\n Advancements in AI are redefining the boundaries of human-AI collaboration, promising to transform professional environments in remarkable ways. This article will delve into current applications where intelligent systems enhance human capabilities, from decision-making processes to creative problem-solving. It will explore the tangible benefits of such collaborations, including increased efficiency and innovation. Additionally, the piece will address essential considerations like workforce training, skill development, and the psychological impact of working alongside AI. By providing a forward-looking analysis, this article aims to equip readers with a comprehensive understanding of how to navigate and thrive in the evolving landscape of human-AI synergy.'
== "1. **The Rise of AI in Healthcare: Transformations and Challenges**\n The advent of artificial intelligence is profoundly transforming the healthcare landscape, ushering in a new era of diagnostics and personalized treatment plans. AI is not only enhancing the accuracy and speed of diagnostics through advanced medical imaging and predictive analytics but also empowering physicians to devise highly tailored treatment plans based on individual patient data. Despite these substantial benefits, the integration of AI into healthcare systems is not without its challenges, including issues related to data privacy, the need for substantial infrastructural changes, and the necessity of re-skilling healthcare professionals to work alongside AI technologies. As we navigate these transformations, it is crucial to address these challenges comprehensively to fully harness the potential of AI in improving patient outcomes.\n\n2. **From Chatbots to Autonomous Agents: The Evolution of AI-driven Customer Service**\n Customer service has undergone a significant evolution with the advent of AI, transitioning from basic chatbots to sophisticated autonomous agents. Early chatbots provided rudimentary assistance, often limited to scripted responses. However, the advent of advanced natural language processing and machine learning algorithms has birthed autonomous agents capable of understanding context, generating human-like responses, and even predicting customer needs. These intelligent agents are now handling complex customer inquiries, providing personalized interactions, and operating across multiple channels seamlessly. Companies deploying these technologies are witnessing increased customer satisfaction and operational efficiency, marking a pivotal shift in the realm of customer service.\n\n3. **AI for Good: How Artificial Intelligence is Addressing Global Challenges**\n Artificial intelligence is increasingly becoming a powerful tool in addressing some of the world's most pressing challenges, from climate change and poverty to healthcare disparities. One transformative example is AI’s capability to analyze vast datasets to predict natural disasters, enabling timely interventions and life-saving measures. Projects like AI-driven agricultural systems are optimizing crop yields to combat hunger, while intelligent algorithms are being used to identify and bridge gaps in healthcare delivery in underserved regions. Through these innovative applications, AI is not just a technological advancement but a catalyst for global good, proving that technology can indeed play a pivotal role in solving critical human issues.\n\n4. **The Future of Work: AI and its Implications on Employment and Skillsets**\n As artificial intelligence continues to develop, its implications on the job market are profound and multifaceted. AI is automating routine and repetitive tasks, prompting a shift towards jobs that require more complex problem-solving, creativity, and emotional intelligence. This evolution is creating an imperative for a new set of skills, including proficiency in AI tools, data analysis, and adaptability to new technologies. The role of education and policy cannot be overstated in this transition, as they must adapt to prepare the workforce for these emerging requirements. Lifelong learning and reskilling are becoming essential as AI redefines existing job roles and creates entirely new ones.\n\n5. **Ethical AI: Navigating the Principles and Practices of Responsible AI Development**\n The development and deployment of artificial intelligence bring a suite of ethical considerations that are critical to its responsible use. Foundational principles such as fairness, accountability, and transparency must guide AI development to prevent biases and ensure equitable outcomes. For instance, algorithmic transparency is crucial to understanding and mitigating biases that may arise from training data. Furthermore, accountability measures are necessary to address the impacts of AI decisions, particularly in high-stakes areas like criminal justice and employment. Current guidelines and case studies highlight the importance of these ethical frameworks, urging developers and policymakers to adopt practices that promote responsible AI usage, thereby fostering public trust and maximizing societal benefits."
)
@@ -400,7 +401,7 @@ def test_crew_with_delegating_agents():
assert (
result.raw
== "Artificial Intelligence (AI) Agents are autonomous systems capable of performing tasks that typically require human intelligence. These tasks range from simple automation to complex decision-making processes. AI Agents leverage various technologies, such as machine learning, natural language processing, and computer vision, to analyze data, learn from it, and make informed decisions. Their significance lies in their ability to operate independently, thus transforming industries by increasing efficiency, reducing error rates, and enabling new capabilities previously thought to be the realm of science fiction.\n\nAI Agents are already making significant impacts in a variety of real-world applications. In healthcare, for instance, AI Agents assist doctors by analyzing patient data to predict disease outbreaks, personalize treatment plans, and even perform robotic surgeries with precision. In the financial sector, AI Agents are employed to detect fraudulent transactions, manage trading strategies, and offer personalized financial advice. Their ability to process vast amounts of data in real-time means they can identify patterns and insights that humans might overlook, thereby enhancing operational efficiency and security.\n\nLooking ahead, the potential of AI Agents extends to numerous other sectors. In education, AI Agents can provide personalized learning experiences, adapting content to fit the individual needs of each student and offering real-time feedback. Smart cities stand to benefit from AI Agents through improved traffic management, energy efficiency, and enhanced public safety services. Furthermore, the development of autonomous vehicles is heavily reliant on advanced AI Agents to navigate complex environments, making transportation safer and more reliable. The versatility and adaptability of AI Agents make them crucial for future advancements across various domains.\n\nIn summary, AI Agents are not just a technological trend but a cornerstone for future innovation. Their ability to learn, adapt, and make decisions autonomously plays a pivotal role in various sectors, leading to increased connectivity and efficiency. As technology continues to advance, the impact of AI Agents will only grow, ushering in a new era of smart and interconnected systems. Ultimately, AI Agents represent the building blocks of a more intelligent and connected world, promising to revolutionize how we live, work, and interact with technology."
== "In today's rapidly evolving technological landscape, AI agents stand at the forefront of innovation, reshaping industries with their unparalleled capabilities. These intelligent systems, capable of performing complex tasks autonomously, are revolutionizing a myriad of sectors—from healthcare, where they assist in diagnosing and personalizing treatment plans, to finance, where they enhance trading strategies and fraud detection. The significance of AI agents lies in their ability to learn and adapt, continuously improving their performance through advanced machine learning algorithms. As businesses strive for optimization and efficiency, AI agents emerge as indispensable allies, driving progress and heralding a new era of intelligent automation."
)
@@ -611,7 +612,7 @@ def test_sequential_async_task_execution_completion():
sequential_result = sequential_crew.kickoff()
assert sequential_result.raw.startswith(
"The fascinating history of artificial intelligence (AI) is marked by a series of pivotal events and breakthroughs that have significantly shaped its development."
"The history of artificial intelligence (AI) is marked by a series of pivotal events that have shaped its progress and applications across diverse sectors."
)
@@ -638,7 +639,7 @@ def test_single_task_with_async_execution():
)
result = crew.kickoff()
assert result.raw.startswith("- AI in climate change: strategies and solutions.")
assert result.raw.startswith("- The rise of AI in healthcare diagnostics.")
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1101,9 +1102,8 @@ def test_dont_set_agents_step_callback_if_already_set():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_crew_function_calling_llm():
from unittest.mock import patch, Mock
from unittest.mock import patch
from crewai_tools import tool
import instructor
llm = "gpt-4o"
@@ -1129,24 +1129,11 @@ def test_crew_function_calling_llm():
tasks = [essay]
crew = Crew(agents=[agent1], tasks=tasks)
with patch.object(instructor, "from_litellm") as mock_from_litellm:
mock_client = Mock()
mock_from_litellm.return_value = mock_client
mock_chat = Mock()
mock_client.chat = mock_chat
mock_completions = Mock()
mock_chat.completions = mock_completions
mock_create = Mock()
mock_completions.create = mock_create
with patch.object(
instructor, "from_litellm", wraps=instructor.from_litellm
) as mock_from_litellm:
crew.kickoff()
mock_from_litellm.assert_called()
mock_create.assert_called()
calls = mock_create.call_args_list
assert any(
call.kwargs.get("model") == "gpt-4o" for call in calls
), "Instructor was not created with the expected model"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1285,9 +1272,9 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
assert result.raw == "Howdy!"
assert result.token_usage == UsageMetrics(
total_tokens=2685,
prompt_tokens=2534,
completion_tokens=151,
total_tokens=2634,
prompt_tokens=2492,
completion_tokens=142,
successful_requests=5,
)
@@ -2386,7 +2373,7 @@ def test_conditional_task_last_task_when_conditional_is_true():
)
result = crew.kickoff()
assert result.raw.startswith(
"1. **The Evolution of AI and Its Impact on Modern Businesses**"
"1. **The Rise of AI Agents in Customer Service: Enhancing User Experience Through Intelligent Automation**"
)

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tests/pipeline/cassettes/test_router_with_empty_input.yaml
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10
tests/task_test.py
View File

@@ -321,8 +321,8 @@ def test_output_json_hierarchical():
manager_llm="gpt-4o",
)
result = crew.kickoff()
assert '{"score": 5}' == result.json
assert result.to_dict() == {"score": 5}
assert '{"score": 4}' == result.json
assert result.to_dict() == {"score": 4}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -526,7 +526,7 @@ def test_save_task_json_output():
output_file_exists = os.path.exists("score.json")
assert output_file_exists
assert {"score": 5} == json.loads(open("score.json").read())
assert {"score": 4} == json.loads(open("score.json").read())
if output_file_exists:
os.remove("score.json")
@@ -556,7 +556,7 @@ def test_save_task_pydantic_output():
output_file_exists = os.path.exists("score.json")
assert output_file_exists
assert {"score": 5} == json.loads(open("score.json").read())
assert {"score": 4} == json.loads(open("score.json").read())
if output_file_exists:
os.remove("score.json")
@@ -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():

2
tests/utilities/evaluators/test_crew_evaluator_handler.py
View File

@@ -44,7 +44,7 @@ class TestCrewEvaluator:
== "Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed"
)
assert agent.verbose is False
assert agent.llm == "gpt-4o-mini"
assert agent.llm.model == "gpt-4o-mini"
def test_evaluation_task(self, crew_planner):
evaluator_agent = Agent(

7
tests/utilities/test_converter.py
View File

@@ -2,6 +2,7 @@ import json
from unittest.mock import MagicMock, Mock, patch
import pytest
from crewai.llm import LLM
from crewai.utilities.converter import (
Converter,
ConverterError,
@@ -213,11 +214,13 @@ def test_get_conversion_instructions_non_gpt():
# Tests for is_gpt
def test_is_gpt_true():
assert is_gpt("gpt-4") is True
llm = LLM(model="gpt-4")
assert is_gpt(llm) is True
def test_is_gpt_false():
assert is_gpt("lol-4") is False
llm = LLM(model="lol-4")
assert is_gpt(llm) is False
class CustomConverter(Converter):