{"name": self.__class__.__name__},        130     inputs,        131 )        132 try:        133     outputs = (    --> 134         self._call(inputs, run_manager=run_manager)        135         if new_arg_supported        136         else self._call(inputs)        137     )        138 except (KeyboardInterrupt, Exception) as e:        139     run_manager.on_chain_error(e)    File ~/workplace/langchain/langchain/agents/agent.py:947, in AgentExecutor._call(self, inputs, run_manager)        945 # We now enter the agent loop (until it returns something).        946 while self._should_continue(iterations, time_elapsed):    --> 947     next_step_output = self._take_next_step(        948         name_to_tool_map,        949         color_mapping,        950         inputs,        951         intermediate_steps,        952         run_manager=run_manager,        953     )        954     if isinstance(next_step_output, AgentFinish):        955         return self._return(        956             next_step_output, intermediate_steps, run_manager=run_manager        957         )    File ~/workplace/langchain/langchain/agents/agent.py:773, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)        771     raise_error = False        772 if raise_error:    --> 773     raise e        774 text = str(e)        775 if isinstance(self.handle_parsing_errors, bool):    File ~/workplace/langchain/langchain/agents/agent.py:762, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)        756 """Take a single step in the thought-action-observation loop.        757         758 Override this to take control of how the agent makes and acts on choices.        759 """        760 try:        761     # Call the LLM to see what to do.    --> 762     output = self.agent.plan(        763         intermediate_steps,        764         callbacks=run_manager.get_child() if
Occasionally the LLM cannot determine what step to take because its outputs are not correctly formatted to be handled by the output parser. In this case, by default the agent errors. But you can easily control this functionality with handleparsingerrors! Let's explore how.
Occasionally the LLM cannot determine what step to take because its outputs are not correctly formatted to be handled by the output parser. In this case, by default the agent errors. But you can easily control this functionality with handleparsingerrors! Let's explore how. ->: {"name": self.__class__.__name__},        130     inputs,        131 )        132 try:        133     outputs = (    --> 134         self._call(inputs, run_manager=run_manager)        135         if new_arg_supported        136         else self._call(inputs)        137     )        138 except (KeyboardInterrupt, Exception) as e:        139     run_manager.on_chain_error(e)    File ~/workplace/langchain/langchain/agents/agent.py:947, in AgentExecutor._call(self, inputs, run_manager)        945 # We now enter the agent loop (until it returns something).        946 while self._should_continue(iterations, time_elapsed):    --> 947     next_step_output = self._take_next_step(        948         name_to_tool_map,        949         color_mapping,        950         inputs,        951         intermediate_steps,        952         run_manager=run_manager,        953     )        954     if isinstance(next_step_output, AgentFinish):        955         return self._return(        956             next_step_output, intermediate_steps, run_manager=run_manager        957         )    File ~/workplace/langchain/langchain/agents/agent.py:773, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)        771     raise_error = False        772 if raise_error:    --> 773     raise e        774 text = str(e)        775 if isinstance(self.handle_parsing_errors, bool):    File ~/workplace/langchain/langchain/agents/agent.py:762, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)        756 """Take a single step in the thought-action-observation loop.        757         758 Override this to take control of how the agent makes and acts on choices.        759 """        760 try:        761     # Call the LLM to see what to do.    --> 762     output = self.agent.plan(        763         intermediate_steps,        764         callbacks=run_manager.get_child() if