= agent.run("What's the main title on langchain.com?")print(answer)    The main title of langchain.com is "LANG CHAIN ü¶úÔ∏èüîó Official Home Page"agent.run("What's the main title on google.com?")    ---------------------------------------------------------------------------    ValidationError                           Traceback (most recent call last)    Cell In[7], line 1    ----> 1 agent.run("What's the main title on google.com?")    File ~/code/lc/lckg/langchain/chains/base.py:213, in Chain.run(self, *args, **kwargs)        211     if len(args) != 1:        212         raise ValueError("`run` supports only one positional argument.")    --> 213     return self(args[0])[self.output_keys[0]]        215 if kwargs and not args:        216     return self(kwargs)[self.output_keys[0]]    File ~/code/lc/lckg/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)        114 except (KeyboardInterrupt, Exception) as e:        115     self.callback_manager.on_chain_error(e, verbose=self.verbose)    --> 116     raise e        117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)        118 return self.prep_outputs(inputs, outputs, return_only_outputs)    File ~/code/lc/lckg/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)        107 self.callback_manager.on_chain_start(        108     {"name": self.__class__.__name__},        109     inputs,        110     verbose=self.verbose,        111 )        112 try:    --> 113     outputs = self._call(inputs)        114 except (KeyboardInterrupt, Exception) as e:        115     self.callback_manager.on_chain_error(e, verbose=self.verbose)    File ~/code/lc/lckg/langchain/agents/agent.py:792, in AgentExecutor._call(self, inputs)        790 # We now enter the agent loop (until it returns something).        791 while self._should_continue(iterations, time_elapsed):    --> 792     next_step_output = self._take_next_step(        793         name_to_tool_map,
By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic.
By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic. ->: = agent.run("What's the main title on langchain.com?")print(answer)    The main title of langchain.com is "LANG CHAIN ü¶úÔ∏èüîó Official Home Page"agent.run("What's the main title on google.com?")    ---------------------------------------------------------------------------    ValidationError                           Traceback (most recent call last)    Cell In[7], line 1    ----> 1 agent.run("What's the main title on google.com?")    File ~/code/lc/lckg/langchain/chains/base.py:213, in Chain.run(self, *args, **kwargs)        211     if len(args) != 1:        212         raise ValueError("`run` supports only one positional argument.")    --> 213     return self(args[0])[self.output_keys[0]]        215 if kwargs and not args:        216     return self(kwargs)[self.output_keys[0]]    File ~/code/lc/lckg/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)        114 except (KeyboardInterrupt, Exception) as e:        115     self.callback_manager.on_chain_error(e, verbose=self.verbose)    --> 116     raise e        117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)        118 return self.prep_outputs(inputs, outputs, return_only_outputs)    File ~/code/lc/lckg/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)        107 self.callback_manager.on_chain_start(        108     {"name": self.__class__.__name__},        109     inputs,        110     verbose=self.verbose,        111 )        112 try:    --> 113     outputs = self._call(inputs)        114 except (KeyboardInterrupt, Exception) as e:        115     self.callback_manager.on_chain_error(e, verbose=self.verbose)    File ~/code/lc/lckg/langchain/agents/agent.py:792, in AgentExecutor._call(self, inputs)        790 # We now enter the agent loop (until it returns something).        791 while self._should_continue(iterations, time_elapsed):    --> 792     next_step_output = self._take_next_step(        793         name_to_tool_map,