Hi there,

After I upgraded to the latest version of langchain_google_genai, crewAI stopped working with vertexAI and Gemini.

I'm using the following code:

`import os from crewai import Agent, Task, Crew, Process from langchain.agents import Tool from langchain_google_genai import ChatGoogleGenerativeAI from langchain_community.utilities import GoogleSerperAPIWrapper

from langchain_google_genai import ( ChatGoogleGenerativeAI, HarmBlockThreshold, HarmCategory, )

llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, safety_settings = { HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,

})

search = GoogleSerperAPIWrapper() search_google = Tool( name="Search Google", func=search.run, description="useful for when you need to ask with search")

researcher = Agent( role='Pesquisador', goal='Encontrar as 10 maiores empresas brasileiras em recuperação judicial', backstory="""Você trabalha numa empresa e quer descobrir qual a possibilidade de calote dessas empresas""", verbose=True, allow_delegation=False, tools=[search_google], memory=True, max_iter=10, llm=llm ) financial_analyst = Agent( role='Analista Financeiro', goal='Faça um resumo dos resultados disponibilizados pelo pesquisador', backstory="""Você é um analista financeiro renomado.""", verbose=True, tools=[search_google], allow_delegation=False, memory=True, max_iter=10, llm=llm )

task1 = Task( description="""Encontre as 10 maiores empresas brasileiras em recuperação judicial""", expected_output="Análise completa em bullet point", agent=researcher )

task2 = Task( description="""Usando os dados fornecidos faça um resumo da situação de cada uma das 10 maiores empresas em recuperação judicial""", expected_output="Um parágrafo por empresa", agent=financial_analyst )

crew = Crew( agents=[researcher, financial_analyst], tasks=[task1, task2],
verbose=2, # You can set it to 1 or 2 to different logging levels )

result_recuperacao_judicial = crew.kickoff()

print("######################")

The error I got is:

`2024-06-13 12:33:18,309 - 140704711422208 - init.py-init:518 - WARNING: Overriding of current TracerProvider is not allowed [DEBUG]: == Working Agent: Pesquisador [INFO]: == Starting Task: Encontre as 10 maiores empresas brasileiras em recuperação judicial

Entering new CrewAgentExecutor chain...

AttributeError Traceback (most recent call last) Cell In[4], line 78 71 crew = Crew( 72 agents=[researcher, financial_analyst], 73 tasks=[task1, task2],
74 verbose=2, # You can set it to 1 or 2 to different logging levels 75 ) 77 # Get your crew to work! ---> 78 result_recuperacao_judicial = crew.kickoff() 80 print("######################")

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/crewai/crew.py:264, in Crew.kickoff(self, inputs) 261 metrics = [] 263 if self.process == Process.sequential: --> 264 result = self._run_sequential_process() 265 elif self.process == Process.hierarchical: 266 result, manager_metrics = self._run_hierarchical_process() # type: ignore # Unpacking a string is disallowed

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/crewai/crew.py:305, in Crew._run_sequential_process(self) 300 if self.output_log_file: 301 self._file_handler.log( 302 agent=role, task=task.description, status="started" 303 ) --> 305 output = task.execute(context=task_output) 306 if not task.async_execution: 307 task_output = output

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/crewai/task.py:183, in Task.execute(self, agent, context, tools) 181 self.thread.start() 182 else: --> 183 result = self._execute( 184 task=self, 185 agent=agent, 186 context=context, 187 tools=tools, 188 ) 189 return result

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/crewai/task.py:192, in Task._execute(self, agent, task, context, tools) 191 def _execute(self, agent, task, context, tools): --> 192 result = agent.execute_task( 193 task=task, 194 context=context, 195 tools=tools, 196 ) 198 exported_output = self._export_output(result) 200 self.output = TaskOutput( 201 description=self.description, 202 exported_output=exported_output, 203 raw_output=result, 204 )

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/crewai/agent.py:236, in Agent.execute_task(self, task, context, tools) 233 self.agent_executor.tools_description = render_text_description(parsed_tools) 234 self.agent_executor.tools_names = self.__tools_names(parsed_tools) --> 236 result = self.agent_executor.invoke( 237 { 238 "input": task_prompt, 239 "tool_names": self.agent_executor.tools_names, 240 "tools": self.agent_executor.tools_description, 241 } 242 )["output"] 244 if self.max_rpm: 245 self._rpm_controller.stop_rpm_counter()

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain/chains/base.py:163, in Chain.invoke(self, input, config, **kwargs) 161 except BaseException as e: 162 run_manager.on_chain_error(e) --> 163 raise e 164 run_manager.on_chain_end(outputs) 166 if include_run_info:

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain/chains/base.py:153, in Chain.invoke(self, input, config, **kwargs) 150 try: 151 self._validate_inputs(inputs) 152 outputs = ( --> 153 self._call(inputs, run_manager=run_manager) 154 if new_arg_supported 155 else self._call(inputs) 156 ) 158 final_outputs: Dict[str, Any] = self.prep_outputs( 159 inputs, outputs, return_only_outputs 160 ) 161 except BaseException as e:

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/crewai/agents/executor.py:128, in CrewAgentExecutor._call(self, inputs, run_manager) 126 while self._should_continue(self.iterations, time_elapsed): 127 if not self.request_within_rpm_limit or self.request_within_rpm_limit(): --> 128 next_step_output = self._take_next_step( 129 name_to_tool_map, 130 color_mapping, 131 inputs, 132 intermediate_steps, 133 run_manager=run_manager, 134 ) 136 if self.step_callback: 137 self.step_callback(next_step_output)

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain/agents/agent.py:1138, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1129 def _take_next_step( 1130 self, 1131 name_to_tool_map: Dict[str, BaseTool], (...) 1135 run_manager: Optional[CallbackManagerForChainRun] = None, 1136 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]: 1137 return self._consume_next_step( -> 1138 [ 1139 a 1140 for a in self._iter_next_step( 1141 name_to_tool_map, 1142 color_mapping, 1143 inputs, 1144 intermediate_steps, 1145 run_manager, 1146 ) 1147 ] 1148 )

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain/agents/agent.py:1138, in (.0) 1129 def _take_next_step( 1130 self, 1131 name_to_tool_map: Dict[str, BaseTool], (...) 1135 run_manager: Optional[CallbackManagerForChainRun] = None, 1136 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]: 1137 return self._consume_next_step( -> 1138 [ 1139 a 1140 for a in self._iter_next_step( 1141 name_to_tool_map, 1142 color_mapping, 1143 inputs, 1144 intermediate_steps, 1145 run_manager, 1146 ) 1147 ] 1148 )

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/crewai/agents/executor.py:192, in CrewAgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 189 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps) 191 # Call the LLM to see what to do. --> 192 output = self.agent.plan( # type: ignore # Incompatible types in assignment (expression has type "AgentAction | AgentFinish | list[AgentAction]", variable has type "AgentAction") 193 intermediate_steps, 194 callbacks=run_manager.get_child() if run_manager else None, 195 **inputs, 196 ) 198 except OutputParserException as e: 199 if isinstance(self.handle_parsing_errors, bool):

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain/agents/agent.py:397, in RunnableAgent.plan(self, intermediate_steps, callbacks, **kwargs) 389 final_output: Any = None 390 if self.stream_runnable: 391 # Use streaming to make sure that the underlying LLM is invoked in a 392 # streaming (...) 395 # Because the response from the plan is not a generator, we need to 396 # accumulate the output into final output and return that. --> 397 for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}): 398 if final_output is None: 399 final_output = chunk

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/runnables/base.py:2873, in RunnableSequence.stream(self, input, config, kwargs) 2867 def stream( 2868 self, 2869 input: Input, 2870 config: Optional[RunnableConfig] = None, 2871 kwargs: Optional[Any], 2872 ) -> Iterator[Output]: -> 2873 yield from self.transform(iter([input]), config, **kwargs)

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/runnables/base.py:2860, in RunnableSequence.transform(self, input, config, kwargs) 2854 def transform( 2855 self, 2856 input: Iterator[Input], 2857 config: Optional[RunnableConfig] = None, 2858 kwargs: Optional[Any], 2859 ) -> Iterator[Output]: -> 2860 yield from self._transform_stream_with_config( 2861 input, 2862 self._transform, 2863 patch_config(config, run_name=(config or {}).get("run_name") or self.name), 2864 **kwargs, 2865 )

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/runnables/base.py:1865, in Runnable._transform_stream_with_config(self, input, transformer, config, run_type, **kwargs) 1863 try: 1864 while True: -> 1865 chunk: Output = context.run(next, iterator) # type: ignore 1866 yield chunk 1867 if final_output_supported:

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/runnables/base.py:2822, in RunnableSequence._transform(self, input, run_manager, config, **kwargs) 2819 else: 2820 final_pipeline = step.transform(final_pipeline, config) -> 2822 for output in final_pipeline: 2823 yield output

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/runnables/base.py:1161, in Runnable.transform(self, input, config, **kwargs) 1158 final: Input 1159 got_first_val = False -> 1161 for ichunk in input: 1162 # The default implementation of transform is to buffer input and 1163 # then call stream. 1164 # It'll attempt to gather all input into a single chunk using 1165 # the + operator. 1166 # If the input is not addable, then we'll assume that we can 1167 # only operate on the last chunk, 1168 # and we'll iterate until we get to the last chunk. 1169 if not got_first_val: 1170 final = ichunk

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/runnables/base.py:4769, in RunnableBindingBase.transform(self, input, config, kwargs) 4763 def transform( 4764 self, 4765 input: Iterator[Input], 4766 config: Optional[RunnableConfig] = None, 4767 kwargs: Any, 4768 ) -> Iterator[Output]: -> 4769 yield from self.bound.transform( 4770 input, 4771 self._merge_configs(config), 4772 {self.kwargs, **kwargs}, 4773 )

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/runnables/base.py:1179, in Runnable.transform(self, input, config, kwargs) 1176 final = ichunk 1178 if got_first_val: -> 1179 yield from self.stream(final, config, kwargs)

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:265, in BaseChatModel.stream(self, input, config, stop, **kwargs) 258 except BaseException as e: 259 run_manager.on_llm_error( 260 e, 261 response=LLMResult( 262 generations=[[generation]] if generation else [] 263 ), 264 ) --> 265 raise e 266 else: 267 run_manager.on_llm_end(LLMResult(generations=[[generation]]))

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:245, in BaseChatModel.stream(self, input, config, stop, kwargs) 243 generation: Optional[ChatGenerationChunk] = None 244 try: --> 245 for chunk in self._stream(messages, stop=stop, kwargs): 246 if chunk.message.id is None: 247 chunk.message.id = f"run-{run_manager.run_id}"

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_google_genai/chat_models.py:833, in ChatGoogleGenerativeAI._stream(self, messages, stop, run_manager, tools, functions, safety_settings, tool_config, generation_config, kwargs) 826 response: GenerateContentResponse = _chat_with_retry( 827 request=request, 828 generation_method=self.client.stream_generate_content, 829 kwargs, 830 metadata=self.default_metadata, 831 ) 832 for chunk in response: --> 833 _chat_result = _response_to_result(chunk, stream=True) 834 gen = cast(ChatGenerationChunk, _chat_result.generations[0]) 836 if run_manager:

File /opt/anaconda3/envs/crewai/lib/python3.11/site-packages/langchain_google_genai/chat_models.py:551, in _response_to_result(response, stream) 549 generation_info = {} 550 if candidate.finish_reason: --> 551 generation_info["finish_reason"] = candidate.finish_reason.name 552 generation_info["safety_ratings"] = [ 553 proto.Message.to_dict(safety_rating, use_integers_for_enums=False) 554 for safety_rating in candidate.safety_ratings 555 ] 556 message = _parse_response_candidate(candidate, streaming=stream)

AttributeError: 'int' object has no attribute 'name'`

I'm using these libs in my conda env:

| packages | in | environment | at | /opt/anaconda3/envs/crewai:

| Name | Version | Build | Channel

crewAIInc / crewAI

[Vertex AI - Gemini]: AttributeError: 'int' object has no attribute 'name' #771

Entering new CrewAgentExecutor chain...

I'm using these libs in my conda env:

| packages | in | environment | at | /opt/anaconda3/envs/crewai:

| Name | Version | Build | Channel