DOC: how_to/migrate_agent/#memory => example does not work with other models

URL

https://python.langchain.com/v0.2/docs/how_to/migrate_agent/#memory

Checklist

[X] I added a very descriptive title to this issue.
[X] I included a link to the documentation page I am referring to (if applicable).

Issue with current documentation:

If I swap model = ChatOpenAI(model="gpt-4o") for: ChatAnthropicVertex(model_name="claude-3-haiku@20240307", location="us-east5", project="my_gcp_project"), then the memory example throws the following error:

{
    "name": "ValueError",
    "message": "Message dict must contain 'role' and 'content' keys, got {'text': '\
\
The magic_function applied to the input of 3 returns the output of 5.', 'type': 'text', 'index': 0}",
    "stack": "---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File /usr/local/lib/python3.11/site-packages/langchain_core/messages/utils.py:271, in _convert_to_message(message)
    270         msg_type = msg_kwargs.pop(\"type\")
--> 271     msg_content = msg_kwargs.pop(\"content\")
    272 except KeyError:

KeyError: 'content'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
Cell In[79], line 55
     49 print(
     50     agent_with_chat_history.invoke(
     51         {\"input\": \"Hi, I'm polly! What's the output of magic_function of 3?\"}, config
     52     )[\"output\"]
     53 )
     54 print(\"---\")
---> 55 print(agent_with_chat_history.invoke({\"input\": \"Remember my name?\"}, config)[\"output\"])
     56 print(\"---\")
     57 print(
     58     agent_with_chat_history.invoke({\"input\": \"what was that output again?\"}, config)[
     59         \"output\"
     60     ]
     61 )

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:4580, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   4574 def invoke(
   4575     self,
   4576     input: Input,
   4577     config: Optional[RunnableConfig] = None,
   4578     **kwargs: Optional[Any],
   4579 ) -> Output:
-> 4580     return self.bound.invoke(
   4581         input,
   4582         self._merge_configs(config),
   4583         **{**self.kwargs, **kwargs},
   4584     )

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:4580, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   4574 def invoke(
   4575     self,
   4576     input: Input,
   4577     config: Optional[RunnableConfig] = None,
   4578     **kwargs: Optional[Any],
   4579 ) -> Output:
-> 4580     return self.bound.invoke(
   4581         input,
   4582         self._merge_configs(config),
   4583         **{**self.kwargs, **kwargs},
   4584     )

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:2499, in RunnableSequence.invoke(self, input, config, **kwargs)
   2497             input = step.invoke(input, config, **kwargs)
   2498         else:
-> 2499             input = step.invoke(input, config)
   2500 # finish the root run
   2501 except BaseException as e:

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/branch.py:212, in RunnableBranch.invoke(self, input, config, **kwargs)
    210             break
    211     else:
--> 212         output = self.default.invoke(
    213             input,
    214             config=patch_config(
    215                 config, callbacks=run_manager.get_child(tag=\"branch:default\")
    216             ),
    217             **kwargs,
    218         )
    219 except BaseException as e:
    220     run_manager.on_chain_error(e)

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:4580, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   4574 def invoke(
   4575     self,
   4576     input: Input,
   4577     config: Optional[RunnableConfig] = None,
   4578     **kwargs: Optional[Any],
   4579 ) -> Output:
-> 4580     return self.bound.invoke(
   4581         input,
   4582         self._merge_configs(config),
   4583         **{**self.kwargs, **kwargs},
   4584     )

File /usr/local/lib/python3.11/site-packages/langchain/chains/base.py:166, in Chain.invoke(self, input, config, **kwargs)
    164 except BaseException as e:
    165     run_manager.on_chain_error(e)
--> 166     raise e
    167 run_manager.on_chain_end(outputs)
    169 if include_run_info:

File /usr/local/lib/python3.11/site-packages/langchain/chains/base.py:156, in Chain.invoke(self, input, config, **kwargs)
    153 try:
    154     self._validate_inputs(inputs)
    155     outputs = (
--> 156         self._call(inputs, run_manager=run_manager)
    157         if new_arg_supported
    158         else self._call(inputs)
    159     )
    161     final_outputs: Dict[str, Any] = self.prep_outputs(
    162         inputs, outputs, return_only_outputs
    163     )
    164 except BaseException as e:

File /usr/local/lib/python3.11/site-packages/langchain/agents/agent.py:1636, in AgentExecutor._call(self, inputs, run_manager)
   1634 # We now enter the agent loop (until it returns something).
   1635 while self._should_continue(iterations, time_elapsed):
-> 1636     next_step_output = self._take_next_step(
   1637         name_to_tool_map,
   1638         color_mapping,
   1639         inputs,
   1640         intermediate_steps,
   1641         run_manager=run_manager,
   1642     )
   1643     if isinstance(next_step_output, AgentFinish):
   1644         return self._return(
   1645             next_step_output, intermediate_steps, run_manager=run_manager
   1646         )

File /usr/local/lib/python3.11/site-packages/langchain/agents/agent.py:1342, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
   1333 def _take_next_step(
   1334     self,
   1335     name_to_tool_map: Dict[str, BaseTool],
   (...)
   1339     run_manager: Optional[CallbackManagerForChainRun] = None,
   1340 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
   1341     return self._consume_next_step(
-> 1342         [
   1343             a
   1344             for a in self._iter_next_step(
   1345                 name_to_tool_map,
   1346                 color_mapping,
   1347                 inputs,
   1348                 intermediate_steps,
   1349                 run_manager,
   1350             )
   1351         ]
   1352     )

File /usr/local/lib/python3.11/site-packages/langchain/agents/agent.py:1342, in <listcomp>(.0)
   1333 def _take_next_step(
   1334     self,
   1335     name_to_tool_map: Dict[str, BaseTool],
   (...)
   1339     run_manager: Optional[CallbackManagerForChainRun] = None,
   1340 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
   1341     return self._consume_next_step(
-> 1342         [
   1343             a
   1344             for a in self._iter_next_step(
   1345                 name_to_tool_map,
   1346                 color_mapping,
   1347                 inputs,
   1348                 intermediate_steps,
   1349                 run_manager,
   1350             )
   1351         ]
   1352     )

File /usr/local/lib/python3.11/site-packages/langchain/agents/agent.py:1370, in AgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
   1367     intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
   1369     # Call the LLM to see what to do.
-> 1370     output = self.agent.plan(
   1371         intermediate_steps,
   1372         callbacks=run_manager.get_child() if run_manager else None,
   1373         **inputs,
   1374     )
   1375 except OutputParserException as e:
   1376     if isinstance(self.handle_parsing_errors, bool):

File /usr/local/lib/python3.11/site-packages/langchain/agents/agent.py:580, in RunnableMultiActionAgent.plan(self, intermediate_steps, callbacks, **kwargs)
    572 final_output: Any = None
    573 if self.stream_runnable:
    574     # Use streaming to make sure that the underlying LLM is invoked in a
    575     # streaming
   (...)
    578     # Because the response from the plan is not a generator, we need to
    579     # accumulate the output into final output and return that.
--> 580     for chunk in self.runnable.stream(inputs, config={\"callbacks\": callbacks}):
    581         if final_output is None:
    582             final_output = chunk

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:2877, in RunnableSequence.stream(self, input, config, **kwargs)
   2871 def stream(
   2872     self,
   2873     input: Input,
   2874     config: Optional[RunnableConfig] = None,
   2875     **kwargs: Optional[Any],
   2876 ) -> Iterator[Output]:
-> 2877     yield from self.transform(iter([input]), config, **kwargs)

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:2864, in RunnableSequence.transform(self, input, config, **kwargs)
   2858 def transform(
   2859     self,
   2860     input: Iterator[Input],
   2861     config: Optional[RunnableConfig] = None,
   2862     **kwargs: Optional[Any],
   2863 ) -> Iterator[Output]:
-> 2864     yield from self._transform_stream_with_config(
   2865         input,
   2866         self._transform,
   2867         patch_config(config, run_name=(config or {}).get(\"run_name\") or self.name),
   2868         **kwargs,
   2869     )

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:1862, in Runnable._transform_stream_with_config(self, input, transformer, config, run_type, **kwargs)
   1860 try:
   1861     while True:
-> 1862         chunk: Output = context.run(next, iterator)  # type: ignore
   1863         yield chunk
   1864         if final_output_supported:

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:2826, in RunnableSequence._transform(self, input, run_manager, config, **kwargs)
   2823     else:
   2824         final_pipeline = step.transform(final_pipeline, config)
-> 2826 for output in final_pipeline:
   2827     yield output

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:1157, in Runnable.transform(self, input, config, **kwargs)
   1154 final: Input
   1155 got_first_val = False
-> 1157 for ichunk in input:
   1158     # The default implementation of transform is to buffer input and
   1159     # then call stream.
   1160     # It'll attempt to gather all input into a single chunk using
   1161     # the `+` operator.
   1162     # If the input is not addable, then we'll assume that we can
   1163     # only operate on the last chunk,
   1164     # and we'll iterate until we get to the last chunk.
   1165     if not got_first_val:
   1166         final = ichunk

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:4787, in RunnableBindingBase.transform(self, input, config, **kwargs)
   4781 def transform(
   4782     self,
   4783     input: Iterator[Input],
   4784     config: Optional[RunnableConfig] = None,
   4785     **kwargs: Any,
   4786 ) -> Iterator[Output]:
-> 4787     yield from self.bound.transform(
   4788         input,
   4789         self._merge_configs(config),
   4790         **{**self.kwargs, **kwargs},
   4791     )

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:1157, in Runnable.transform(self, input, config, **kwargs)
   1154 final: Input
   1155 got_first_val = False
-> 1157 for ichunk in input:
   1158     # The default implementation of transform is to buffer input and
   1159     # then call stream.
   1160     # It'll attempt to gather all input into a single chunk using
   1161     # the `+` operator.
   1162     # If the input is not addable, then we'll assume that we can
   1163     # only operate on the last chunk,
   1164     # and we'll iterate until we get to the last chunk.
   1165     if not got_first_val:
   1166         final = ichunk

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:1175, in Runnable.transform(self, input, config, **kwargs)
   1172             final = ichunk
   1174 if got_first_val:
-> 1175     yield from self.stream(final, config, **kwargs)

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:812, in Runnable.stream(self, input, config, **kwargs)
    802 def stream(
    803     self,
    804     input: Input,
    805     config: Optional[RunnableConfig] = None,
    806     **kwargs: Optional[Any],
    807 ) -> Iterator[Output]:
    808     \"\"\"
    809     Default implementation of stream, which calls invoke.
    810     Subclasses should override this method if they support streaming output.
    811     \"\"\"
--> 812     yield self.invoke(input, config, **kwargs)

File /usr/local/lib/python3.11/site-packages/langchain_core/prompts/base.py:179, in BasePromptTemplate.invoke(self, input, config)
    177 if self.tags:
    178     config[\"tags\"] = config[\"tags\"] + self.tags
--> 179 return self._call_with_config(
    180     self._format_prompt_with_error_handling,
    181     input,
    182     config,
    183     run_type=\"prompt\",
    184 )

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py:1593, in Runnable._call_with_config(self, func, input, config, run_type, **kwargs)
   1589     context = copy_context()
   1590     context.run(_set_config_context, child_config)
   1591     output = cast(
   1592         Output,
-> 1593         context.run(
   1594             call_func_with_variable_args,  # type: ignore[arg-type]
   1595             func,  # type: ignore[arg-type]
   1596             input,  # type: ignore[arg-type]
   1597             config,
   1598             run_manager,
   1599             **kwargs,
   1600         ),
   1601     )
   1602 except BaseException as e:
   1603     run_manager.on_chain_error(e)

File /usr/local/lib/python3.11/site-packages/langchain_core/runnables/config.py:380, in call_func_with_variable_args(func, input, config, run_manager, **kwargs)
    378 if run_manager is not None and accepts_run_manager(func):
    379     kwargs[\"run_manager\"] = run_manager
--> 380 return func(input, **kwargs)

File /usr/local/lib/python3.11/site-packages/langchain_core/prompts/base.py:154, in BasePromptTemplate._format_prompt_with_error_handling(self, inner_input)
    152 def _format_prompt_with_error_handling(self, inner_input: Dict) -> PromptValue:
    153     _inner_input = self._validate_input(inner_input)
--> 154     return self.format_prompt(**_inner_input)

File /usr/local/lib/python3.11/site-packages/langchain_core/prompts/chat.py:765, in BaseChatPromptTemplate.format_prompt(self, **kwargs)
    756 def format_prompt(self, **kwargs: Any) -> PromptValue:
    757     \"\"\"Format prompt. Should return a PromptValue.
    758 
    759     Args:
   (...)
    763         PromptValue.
    764     \"\"\"
--> 765     messages = self.format_messages(**kwargs)
    766     return ChatPromptValue(messages=messages)

File /usr/local/lib/python3.11/site-packages/langchain_core/prompts/chat.py:1142, in ChatPromptTemplate.format_messages(self, **kwargs)
   1138     result.extend([message_template])
   1139 elif isinstance(
   1140     message_template, (BaseMessagePromptTemplate, BaseChatPromptTemplate)
   1141 ):
-> 1142     message = message_template.format_messages(**kwargs)
   1143     result.extend(message)
   1144 else:

File /usr/local/lib/python3.11/site-packages/langchain_core/prompts/chat.py:235, in MessagesPlaceholder.format_messages(self, **kwargs)
    230 if not isinstance(value, list):
    231     raise ValueError(
    232         f\"variable {self.variable_name} should be a list of base messages, \"
    233         f\"got {value}\"
    234     )
--> 235 value = convert_to_messages(value)
    236 if self.n_messages:
    237     value = value[-self.n_messages :]

File /usr/local/lib/python3.11/site-packages/langchain_core/messages/utils.py:296, in convert_to_messages(messages)
    285 def convert_to_messages(
    286     messages: Sequence[MessageLikeRepresentation],
    287 ) -> List[BaseMessage]:
    288     \"\"\"Convert a sequence of messages to a list of messages.
    289 
    290     Args:
   (...)
    294         List of messages (BaseMessages).
    295     \"\"\"
--> 296     return [_convert_to_message(m) for m in messages]

File /usr/local/lib/python3.11/site-packages/langchain_core/messages/utils.py:296, in <listcomp>(.0)
    285 def convert_to_messages(
    286     messages: Sequence[MessageLikeRepresentation],
    287 ) -> List[BaseMessage]:
    288     \"\"\"Convert a sequence of messages to a list of messages.
    289 
    290     Args:
   (...)
    294         List of messages (BaseMessages).
    295     \"\"\"
--> 296     return [_convert_to_message(m) for m in messages]

File /usr/local/lib/python3.11/site-packages/langchain_core/messages/utils.py:273, in _convert_to_message(message)
    271         msg_content = msg_kwargs.pop(\"content\")
    272     except KeyError:
--> 273         raise ValueError(
    274             f\"Message dict must contain 'role' and 'content' keys, got {message}\"
    275         )
    276     _message = _create_message_from_message_type(
    277         msg_type, msg_content, **msg_kwargs
    278     )
    279 else:

ValueError: Message dict must contain 'role' and 'content' keys, got {'text': '\
\
The magic_function applied to the input of 3 returns the output of 5.', 'type': 'text', 'index': 0}"
}

...and it is unclear why.

My installed langchain packages:

langchain                               0.2.7
langchain-community                     0.2.7
langchain-core                          0.2.12
langchain-google-vertexai               1.0.6
langchain-groq                          0.1.6
langchain-openai                        0.1.14
langchain-text-splitters                0.2.2
langchainhub                            0.1.20

Note: the example code works fine, if model = ChatOpenAI(model="gpt-4o") is used instead of the Claude-3 model.

Idea or request for content:

It would be very helpful to show how one must change the memory example code, depending on the LLM used

langchain-ai / langchain

DOC: how_to/migrate_agent/#memory => example does not work with other models #24003

URL

Checklist

Issue with current documentation:

Idea or request for content:

gpt-4o

Claude-Haiku