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* feat: implement tool usage limit exception handling - Introduced `ToolUsageLimitExceeded` exception to manage maximum usage limits for tools. - Enhanced `CrewStructuredTool` to check and raise this exception when the usage limit is reached. - Updated `_run` and `_execute` methods to include usage limit checks and handle exceptions appropriately, improving reliability and user feedback. * feat: enhance PlusAPI and ToolUsage with task metadata - Removed the `send_trace_batch` method from PlusAPI to streamline the API. - Added timeout parameters to trace event methods in PlusAPI for improved reliability. - Updated ToolUsage to include task metadata (task name and ID) in event emissions, enhancing traceability and context during tool usage. - Refactored event handling in LLM and ToolUsage events to ensure task information is consistently captured. * feat: enhance memory and event handling with task and agent metadata - Added task and agent metadata to various memory and event classes, improving traceability and context during memory operations. - Updated the `ContextualMemory` and `Memory` classes to associate tasks and agents, allowing for better context management. - Enhanced event emissions in `LLM`, `ToolUsage`, and memory events to include task and agent information, facilitating improved debugging and monitoring. - Refactored event handling to ensure consistent capture of task and agent details across the system. * drop * refactor: clean up unused imports in memory and event modules - Removed unused TYPE_CHECKING imports from long_term_memory.py to streamline the code. - Eliminated unnecessary import from memory_events.py, enhancing clarity and maintainability. * fix memory tests * fix task_completed payload * fix: remove unused test agent variable in external memory tests * refactor: remove unused agent parameter from Memory class save method - Eliminated the agent parameter from the save method in the Memory class to streamline the code and improve clarity. - Updated the TraceBatchManager class by moving initialization of attributes into the constructor for better organization and readability. * refactor: enhance ExecutionState and ReasoningEvent classes with optional task and agent identifiers - Added optional `current_agent_id` and `current_task_id` attributes to the `ExecutionState` class for better tracking of agent and task states. - Updated the `from_task` attribute in the `ReasoningEvent` class to use `Optional[Any]` instead of a specific type, improving flexibility in event handling. * refactor: update ExecutionState class by removing unused agent and task identifiers - Removed the `current_agent_id` and `current_task_id` attributes from the `ExecutionState` class to simplify the code and enhance clarity. - Adjusted the import statements to include `Optional` for better type handling. * refactor: streamline LLM event handling in LiteAgent - Removed unused LLM event emissions (LLMCallStartedEvent, LLMCallCompletedEvent, LLMCallFailedEvent) from the LiteAgent class to simplify the code and improve performance. - Adjusted the flow of LLM response handling by eliminating unnecessary event bus interactions, enhancing clarity and maintainability. * flow ownership and not emitting events when a crew is done * refactor: remove unused agent parameter from ShortTermMemory save method - Eliminated the agent parameter from the save method in the ShortTermMemory class to streamline the code and improve clarity. - This change enhances the maintainability of the memory management system by reducing unnecessary complexity. * runtype check fix * fixing tests * fix lints * fix: update event assertions in test_llm_emits_event_with_lite_agent - Adjusted the expected counts for completed and started events in the test to reflect the correct behavior of the LiteAgent. - Updated assertions for agent roles and IDs to match the expected values after recent changes in event handling. * fix: update task name assertions in event tests - Modified assertions in `test_stream_llm_emits_event_with_task_and_agent_info` and `test_llm_emits_event_with_task_and_agent_info` to use `task.description` as a fallback for `task.name`. This ensures that the tests correctly validate the task name even when it is not explicitly set. * fix: update test assertions for output values and improve readability - Updated assertions in `test_output_json_dict_hierarchical` to reflect the correct expected score value. - Enhanced readability of assertions in `test_output_pydantic_to_another_task` and `test_key` by formatting the error messages for clarity. - These changes ensure that the tests accurately validate the expected outputs and improve overall code quality. * test fixes * fix crew_test * added another fixture * fix: ensure agent and task assignments in contextual memory are conditional - Updated the ContextualMemory class to check for the existence of short-term, long-term, external, and extended memory before assigning agent and task attributes. This prevents potential attribute errors when memory types are not initialized.
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{''description'': ''The role/name of the coworker to delegate to'', ''type'':
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''str''}}\nTool Description: Delegate a specific task to one of the following
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|
coworkers: Researcher, Senior Writer\nThe input to this tool should be the coworker,
|
|
the task you want them to do, and ALL necessary context to execute the task,
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they know nothing about the task, so share absolutely everything you know, don''t
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reference things but instead explain them.\nTool Name: Ask question to coworker\nTool
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Arguments: {''question'': {''description'': ''The question to ask'', ''type'':
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''str''}, ''context'': {''description'': ''The context for the question'', ''type'':
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|
''str''}, ''coworker'': {''description'': ''The role/name of the coworker to
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ask'', ''type'': ''str''}}\nTool Description: Ask a specific question to one
|
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of the following coworkers: Researcher, Senior Writer\nThe input to this tool
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should be the coworker, the question you have for them, and ALL necessary context
|
|
to ask the question properly, they know nothing about the question, so share
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absolutely everything you know, don''t reference things but instead explain
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them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
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you should always think about what to do\nAction: the action to take, only one
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name of [Delegate work to coworker, Ask question to coworker], just the name,
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exactly as it''s written.\nAction Input: the input to the action, just a simple
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JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation:
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Answer: the final answer to the original input question\n```"}, {"role": "user",
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|
"content": "\nCurrent Task: Come up with a list of 5 interesting ideas to explore
|
|
for an article, then write one amazing paragraph highlight for each idea that
|
|
showcases how good an article about this topic could be. Return the list of
|
|
ideas with their paragraph and your notes.\n\nThis is the expected criteria
|
|
for your final answer: 5 bullet points with a paragraph for each idea.\nyou
|
|
MUST return the actual complete content as the final answer, not a summary.\n\nBegin!
|
|
This is VERY important to you, use the tools available and give your best Final
|
|
Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content":
|
|
"Thought: The first step is to brainstorm and research interesting ideas for
|
|
the article. Since the task involves both research and writing, I will need
|
|
to delegate the brainstorming of ideas to the Researcher and the writing of
|
|
the paragraphs to the Senior Writer. \n\nAction: Delegate work to coworker\nAction
|
|
Input: {\"task\": \"Come up with a list of 5 interesting ideas to explore for
|
|
an article.\", \"context\": \"We need a list of 5 interesting ideas that would
|
|
make compelling articles. The topics should be relevant, timely, and engaging
|
|
for a broad audience.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The
|
|
Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n \"Imagine
|
|
a world where routine check-ups and complex diagnostics are seamlessly conducted
|
|
by intelligent systems. AI in healthcare is rapidly transforming this vision
|
|
into reality, enhancing patient care through early and accurate disease detection,
|
|
personalized treatment plans, and continuous health monitoring. This revolution
|
|
is not just a futuristic concept; it''s already happening with AI-driven technologies
|
|
enabling doctors to make more informed decisions and offering patients a more
|
|
proactive approach to their health. The ethical and privacy concerns, however,
|
|
pose new challenges that the industry must navigate to ensure responsible advancement.\"\n\n2.
|
|
**AI Agents in the Workplace: Boosting Productivity and Shaping the Future of
|
|
Jobs**\n \"The modern workplace is being redefined by the integration of AI
|
|
agents, intelligent systems designed to handle repetitive tasks, analyze vast
|
|
amounts of data, and assist in complex decision-making processes. These innovations
|
|
are boosting productivity, allowing employees to focus on creativity and strategic
|
|
thinking. The rise of AI in the workspace heralds a future where human and machine
|
|
collaboration leads to unparalleled efficiencies and novel job opportunities.
|
|
However, this shift also brings with it challenges related to job displacement
|
|
and the urgent need for reskilling the workforce to thrive in an AI-augmented
|
|
environment.\"\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n \"As
|
|
AI technology becomes more pervasive, the quest for ethical AI has become critical.
|
|
Ensuring fairness, accountability, and transparency in AI systems is essential
|
|
to prevent biases and promote trust. This involves creating robust frameworks
|
|
and guidelines that govern AI development and deployment. Real-world case studies
|
|
highlight both the triumphs and the tribulations of tech companies as they navigate
|
|
the complex moral landscape that AI presents. As society grapples with these
|
|
challenges, the push for ethical AI continues to drive innovation and policy-making
|
|
in the tech industry.\"\n\n4. **AI-Powered Startups: Innovators Changing the
|
|
Tech Landscape**\n \"In the bustling world of tech startups, AI-powered companies
|
|
are emerging as the vanguards of innovation, disrupting traditional business
|
|
models and reshaping entire industries. From revolutionizing financial services
|
|
to pioneering advances in healthcare and beyond, these startups are at the forefront
|
|
of technological progress. By leveraging machine learning and AI, they are solving
|
|
complex problems and bringing novel solutions to market at an unprecedented
|
|
pace. Investors are taking notice, pouring capital into these ventures, and
|
|
fueling a new wave of growth and competition in the AI ecosystem.\"\n\n5. **The
|
|
Future of AI in Entertainment: From Movies to Video Games**\n \"The entertainment
|
|
industry is undergoing a seismic shift driven by AI technology. In movies, AI
|
|
is revolutionizing special effects, enabling filmmakers to create stunning,
|
|
hyper-realistic worlds. In video games, AI is enhancing player experiences by
|
|
creating adaptive, intelligent characters and dynamic storylines that respond
|
|
to player choices. This transformative power of AI promises to push the boundaries
|
|
of creativity, offering audiences more immersive and interactive entertainment
|
|
experiences. As the technology continues to evolve, the future of entertainment
|
|
looks set to be more exciting and unpredictable than ever before.\""}, {"role":
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"assistant", "content": "Thought: I''ll proceed to delegate the writing of one
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|
amazing paragraph highlight for each of the identified ideas to the Senior Writer,
|
|
ensuring each topic is adequately detailed to showcase its potential.\n\nAction:
|
|
Delegate work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph
|
|
highlight for each of these 5 ideas that showcases how good an article about
|
|
these topics could be.\", \"context\": \"Here are the 5 ideas:\\n1. The Rise
|
|
of AI in Healthcare: Transforming Patient Care and Diagnostics - Explore how
|
|
AI is revolutionizing healthcare through enhanced diagnostic tools, personalized
|
|
treatment plans, and post-operative care. Discuss current achievements, potential
|
|
future applications, and ethical considerations.\\n2. AI Agents in the Workplace:
|
|
Boosting Productivity and Shaping the Future of Jobs - Examine the integration
|
|
of AI agents in various industries. Highlight how these intelligent agents are
|
|
improving efficiency, automating mundane tasks, and assisting in decision-making
|
|
processes. Address potential job displacement and the need for upskilling the
|
|
workforce.\\n3. Ethical AI: Ensuring Fairness, Accountability, and Transparency
|
|
- Delve into the ethical implications of AI deployment. Analyze the frameworks
|
|
and guidelines being proposed to ensure AI systems are fair, transparent, and
|
|
accountable. Include case studies of ethical dilemmas faced by companies using
|
|
AI.\\n4. AI-Powered Startups: Innovators Changing the Tech Landscape - Provide
|
|
an overview of how AI-driven startups are disrupting traditional business models
|
|
and industries. Profile some of the most innovative startups, their business
|
|
models, and the unique challenges they face. Discuss the role of venture capital
|
|
in fostering AI innovation.\\n5. The Future of AI in Entertainment: From Movies
|
|
to Video Games - Investigate how AI is transforming the entertainment industry,
|
|
from scriptwriting and special effects in movies to creating more immersive
|
|
and intelligent experiences in video games. Offer predictions about future trends
|
|
and possible impacts on content creation and consumption.\", \"coworker\": \"Senior
|
|
Writer\"}\nObservation:\nObservation: 1. **The Rise of AI in Healthcare: Transforming
|
|
Patient Care and Diagnostics**\n AI is undeniably revolutionizing the healthcare
|
|
sector, introducing transformative changes in patient care and diagnostics.
|
|
With the advent of advanced diagnostic tools powered by AI, healthcare professionals
|
|
can now detect and monitor diseases with unprecedented accuracy and speed. Personalized
|
|
treatment plans, which were once a distant dream, are becoming a reality as
|
|
AI algorithms analyze vast amounts of patient data, tailoring treatments that
|
|
are specific to individual needs. Post-operative care has also seen significant
|
|
improvements, with AI-driven monitoring systems ensuring that patients receive
|
|
timely and effective care. Current achievements like IBM Watson''s oncology
|
|
platform or Google''s DeepMind in eye care are just the beginning, with future
|
|
applications promising to further enhance surgical robotics, predictive analytics,
|
|
and remote patient monitoring. However, alongside these advancements, ethical
|
|
considerations such as patient privacy, data security, and decision-making transparency
|
|
must be rigorously addressed to fully harness AI\u2019s potential in healthcare.\n\n2.
|
|
**AI Agents in the Workplace: Boosting Productivity and Shaping the Future of
|
|
Jobs**\n The integration of AI agents in the workplace marks a pivotal shift
|
|
in how businesses operate and compete. These intelligent agents are revolutionizing
|
|
various industries by automating mundane tasks, optimizing workflows, and providing
|
|
actionable insights from data analysis. For instance, AI chatbots in customer
|
|
service can handle routine inquiries, freeing human agents to tackle more complex
|
|
issues. In manufacturing, AI systems predict equipment failures, reducing downtime
|
|
and maintaining peak productivity. By enhancing operational efficiency, AI agents
|
|
not only boost productivity but also reshape the future of jobs. While the risk
|
|
of job displacement is a valid concern, it concurrently heralds opportunities
|
|
for upskilling and creating new roles that harness human creativity and strategic
|
|
thinking. Preparing the workforce for these changes is crucial, fostering a
|
|
symbiotic relationship between AI and human labor to drive innovation and economic
|
|
growth.\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n As
|
|
AI technologies become increasingly pervasive, ensuring their ethical deployment
|
|
is paramount. Ethical AI demands the formulation and implementation of robust
|
|
frameworks that enshrine fairness, accountability, and transparency. Such frameworks
|
|
are essential in mitigating biases embedded within AI systems, which can perpetuate
|
|
and even exacerbate inequality. Case studies, such as the controversy surrounding
|
|
facial recognition systems and their racial biases, highlight the pressing need
|
|
for ethical oversight. Transparency in how AI models make decisions, coupled
|
|
with accountability measures for their outcomes, establishes trust and integrity
|
|
in AI applications. Legislative frameworks from the EU\u2019s GDPR to the IEEE\u2019s
|
|
global initiatives for ethical AI provide the scaffolding for these principles.
|
|
Companies are now faced with the challenge of integrating these ethical considerations
|
|
into their development processes, ensuring that AI advancements benefit all
|
|
of society equitably.\n\n4. **AI-Powered Startups: Innovators Changing the Tech
|
|
Landscape**\n AI-powered startups are at the forefront of a technological
|
|
renaissance, disrupting traditional business models and reshaping industries.
|
|
These startups leverage AI to offer innovative solutions, whether it''s automating
|
|
financial trading with precision algorithms or enhancing cybersecurity through
|
|
predictive analytics. High-profile examples like OpenAI and UiPath illustrate
|
|
the transformative potential of AI-driven innovation. These companies face unique
|
|
challenges, from securing sufficient funding to navigating the complexities
|
|
of large-scale implementation. Venture capital plays a critical role in this
|
|
ecosystem, providing the necessary resources and support to turn bold ideas
|
|
into impactful realities. The synergy between AI innovation and entrepreneurial
|
|
spirit is fostering a dynamic landscape where the boundaries of technology and
|
|
commerce are continually expanding.\n\n5. **The Future of AI in Entertainment:
|
|
From Movies to Video Games**\n The entertainment industry is undergoing a
|
|
metamorphic change with the integration of AI technologies, paving the way for
|
|
more immersive and engaging experiences. In movies, AI streamlines the scriptwriting
|
|
process, enhances special effects, and even predicts box office success. Machine
|
|
learning models analyze vast datasets to create compelling narratives and optimize
|
|
marketing strategies. Video games have also been revolutionized by AI, with
|
|
non-player characters (NPCs) displaying more lifelike behaviors and adaptive
|
|
learning algorithms personalizing gaming experiences. Future trends indicate
|
|
a seamless blend of AI with augmented reality (AR) and virtual reality (VR),
|
|
creating interactive environments where the line between virtual and real worlds
|
|
blur. These advancements are not only transforming content creation but also
|
|
revolutionizing how audiences consume and interact with entertainment, predicting
|
|
a future where AI is an indispensable tool in the creative arsenal."}], "model":
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