RuntimeError

[Balogunolalere]

[joaomdmoura]

hey @Balogunolalere can you share the whole error log?

[Balogunolalere]

Finished chain. The next big trend in AI in healthcare 2024 will be Personalized Medicine and Predictive Analytics. This trend combines advancements in machine learning and data analysis to provide tailored treatment plans based on individual patient needs, genetic makeup, and medical history. By analyzing vast amounts of medical data, AI can identify patterns and risk factors for various diseases, allowing healthcare professionals to make more informed decisions about prevention, diagnosis, and treatment.

Pros:

1. Improved Patient Outcomes: Personalized Medicine and Predictive Analytics can lead to better patient outcomes by providing customized care that addresses individual needs and risk factors. This can result in reduced complications, faster recovery times, and improved overall health.
2. Cost-Effectiveness: By identifying high-risk patients early on, AI can help prevent the need for more expensive treatments down the line. Additionally, personalized treatment plans may reduce the number of unnecessary procedures or medications, further lowering healthcare costs.
3. Enhanced Efficiency: AI algorithms can quickly analyze large amounts of data, enabling healthcare professionals to make informed decisions in real-time. This can result in reduced wait times and more efficient use of resources within the healthcare system.

Cons:

1. Data Privacy Concerns: The use of Personalized Medicine and Predictive Analytics relies heavily on the collection and analysis of vast amounts of personal health data. This raises concerns about data privacy and security, as well as questions about who owns and controls this information.
2. Technical Limitations: AI algorithms are only as good as the data they are trained on, and there may be limitations in the quality or availability of certain types of medical data. Additionally, AI models may not account for human variability, leading to potential errors in treatment recommendations.
3. Socioeconomic Inequity: Personalized Medicine and Predictive Analytics may exacerbate existing healthcare disparities by prioritizing those with access to high-quality medical care and data. This could result in a widening gap between those who can benefit from AI-driven personalized medicine and those who cannot.

Market Opportunities: The market for Personalized Medicine and Predictive Analytics is expected to grow significantly over the next decade, driven by advancements in AI technology, increased demand for personalized care, and a growing focus on preventative healthcare. Key players in this space include tech giants like Google, Apple, and IBM, as well as startups focused on specific applications of AI in healthcare.

Potential Risks: While Personalized Medicine and Predictive Analytics have the potential to revolutionize healthcare, there are significant risks associated with their widespread adoption. Data privacy concerns, technical limitations, and socioeconomic inequity could all hinder progress in this field, leading to a slowdown or even backlash against AI-driven personalized medicine.

In conclusion, Personalized Medicine and Predictive Analytics represent the next big trend in AI in healthcare 2024. By combining machine learning with data analysis, this approach has the potential to transform patient outcomes, increase efficiency within the healthcare system, and drive significant market growth. However, it is crucial that we address the potential risks and challenges associated with this trend to ensure equitable access to personalized care and maintain public trust in AI-driven healthcare solutions.

Entering new CrewAgentExecutor chain... Exception in thread Thread-1 (_execute): Traceback (most recent call last): File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner self.run() File "/usr/lib/python3.10/threading.py", line 953, in run self._target(*self._args, self.kwargs) File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/crewai/task.py", line 142, in _execute result = agent.executetask( File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/crewai/agent.py", line 168, in execute_task result = self.agentexecutor.invoke( File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain/chains/base.py", line 163, in invoke raise e File "/home/doombuggy_/Projects/crewProj/env/lib/python3.10/site-packages/langchain/chains/base.py", line 153, in invoke self._call(inputs, run_manager=runmanager) File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/crewai/agents/executor.py", line 61, in _call next_step_output = self._take_nextstep( File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain/agents/agent.py", line 1097, in _take_nextstep [ File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain/agents/agent.py", line 1097, in [ File "/home/doombuggy_/Projects/crewProj/env/lib/python3.10/site-packages/crewai/agents/executor.py", line 108, in _iter_nextstep output = self.agent.plan( File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain/agents/agent.py", line 387, in plan for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}): File "/home/doombuggy_/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2427, in stream yield from self.transform(iter([input]), config, kwargs) File "/home/doombuggy_/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2414, in transform yield from self._transform_stream_withconfig( File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 1494, in _transform_stream_withconfig chunk: Output = context.run(next, iterator) # type: ignore File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2378, in _transform for output in finalpipeline: File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchaincore/runnables/base.py", line 1032, in transform for chunk in input: File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchaincore/runnables/base.py", line 4164, in transform yield from self.bound.transform( File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchaincore/runnables/base.py", line 1032, in transform for chunk in input: File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchaincore/runnables/base.py", line 1032, in transform for chunk in input: File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2794, in transform yield from self._transform_stream_withconfig( File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 1494, in _transform_stream_withconfig chunk: Output = context.run(next, iterator) # type: ignore File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2767, in transform futures = { File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2768, in executor.submit(next, generator): (stepname, generator) File "/home/doombuggy/Projects/crewProj/env/lib/python3.10/site-packages/langchain_core/runnables/config.py", line 431, in submit return super().submit( File "/usr/lib/python3.10/concurrent/futures/thread.py", line 169, in submit raise RuntimeError('cannot schedule new futures after ' RuntimeError: cannot schedule new futures after interpreter shutdown

[KurtKobalt]

+1

I receive the same error in 0.19.0.

[haraldini]

+1 Just started running into this all of a sudden.

[haraldini]

Little update, I think it's related to using the search_toolin combination with async_execution=True, setting async to False helped in my case.

[rkkoszewski]

Little update, I think it's related to using the search_toolin combination with async_execution=True, setting async to False helped in my case.

I concur, same issue here, using async_execution=True with a search_tool. To be specific the "DuckDuckGoSearchRun()". Definining it multiple times for each task does not fix the issue.

[AmoghM]

+1 getting the same error

[theCyberTech]

Please make sure you are on the latest version of CrewAI which as of now is

crewai 0.28.8 crewai-tools 0.1.7

[jitendra-koodo]

+1 getting the same error

[xixas]

This happens since we have the option async_execution=True. Though not sure why.

[yuragorlo]

I have the same problem without using search_tool or any other tools. I think it happens because some threads did not finish before the main thread finished. I'm not sure if this is the best way, but in my case it helped:

import threading

Kick off the crew

starttime = time.time() = crew.kickoff()

while threading.active_count() > 2: time.sleep(1)

end_time = time.time() elapsed_time = round(end_time - start_time, 2) print(f"Total time: {elapsed_time} seconds")

[rkkoszewski]

This happens since we have the option async_execution=True. Though not sure why.

Looks to me like the Agent thread does not like to have the tools initialized form another thread. Perhaps @joaomdmoura can shed some light?

[berwinsingh]

I was facing the same issue. But turning of async_execution=False fixed the issue for me.

[DanielM-oz]

I have the same issue - is there a proper fix as setting async_execution=False limits one of the features of CrewAI? I have done the DeepLearning course and get this issue when trying to run the Event Planning code. Running the code in the provided jupyter window works fine (so there must be some set-up that is ok), but running the code locally gives this runtime error.

I have tried this with version 0.1.6 of crewai-tools (as per the course), and version 0.1.7 as suggested by TheCyberTech and version 0.2.6 (the latest version as I write this). Same error each time.

Perhaps the issue is with the Search tool itself - maybe my API key does not permit simultaneous requests while the API key used in the DeepLearning course does?

[alexnodeland]

@DanielM-oz +1 I've had the same experience. Setting async_execution=False works, but it'd be nice to be able to run tasks in parallel

[joaomdmoura]

@alexnodeland is this on the more recent versions? I'll pump this to the top of the list

[alexnodeland]

@alexnodeland is this on the more recent versions? I'll pump this to the top of the list

@joaomdmoura this was for crewai==0.28.8 & crewai_tools==0.1.6 specifically (as per the course). I also tried the other versions mentioned by @DanielM-oz, but no luck there either.

The notebook in the deeplearning.ai course worked, but I couldn't replicate it locally

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