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INVALID_TOOL_RESULTS

You are passing too many, too few, or mismatched ToolMessages to a model.

When using a model to call tools, the AIMessage the model responds with will contain a tool_calls array. To continue the flow, the next messages you pass back to the model must be exactly one ToolMessage for each item in that array containing the result of that tool call. Each ToolMessage must have a tool_call_id field that matches one of the tool_calls on the AIMessage.

For example, given the following response from a model:

from typing import List

from langchain_core.messages import BaseMessage, HumanMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o-mini")


@tool
def foo_tool() -> str:
    """
    A dummy tool that returns 'action complete!'
    """
    return "action complete!"


model_with_tools = model.bind_tools([foo_tool])

chat_history: List[BaseMessage] = [
    HumanMessage(content='Call tool "foo" twice with no arguments')
]

response_message = model_with_tools.invoke(chat_history)

print(response_message.tool_calls)
[{'name': 'foo_tool', 'args': {}, 'id': 'call_dq9O0eGHrryBwDRCnk0deHK4', 'type': 'tool_call'}, {'name': 'foo_tool', 'args': {}, 'id': 'call_mjLuNyXNHoUIXHiBtXhaWdxN', 'type': 'tool_call'}]

Calling the model with only one tool response would result in an error:

from langchain_core.messages import AIMessage, ToolMessage

tool_call = response_message.tool_calls[0]
tool_response = foo_tool.invoke(tool_call)

chat_history.append(
    AIMessage(
        content=response_message.content,
        additional_kwargs=response_message.additional_kwargs,
    )
)
chat_history.append(
    ToolMessage(content=str(tool_response), tool_call_id=tool_call.get("id"))
)

final_response = model_with_tools.invoke(chat_history)
print(final_response)
API Reference:AIMessage | ToolMessage
---------------------------------------------------------------------------
``````output
BadRequestError                           Traceback (most recent call last)
``````output
Cell In[3], line 9
      6 chat_history.append(AIMessage(content=response_message.content, additional_kwargs=response_message.additional_kwargs))
      7 chat_history.append(ToolMessage(content=str(tool_response), tool_call_id=tool_call.get('id')))
----> 9 final_response = model_with_tools.invoke(chat_history)
     10 print(final_response)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   5348 def invoke(
   5349     self,
   5350     input: Input,
   5351     config: Optional[RunnableConfig] = None,
   5352     **kwargs: Optional[Any],
   5353 ) -> Output:
-> 5354     return self.bound.invoke(
   5355         input,
   5356         self._merge_configs(config),
   5357         **{**self.kwargs, **kwargs},
   5358     )
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    275 def invoke(
    276     self,
    277     input: LanguageModelInput,
   (...)
    281     **kwargs: Any,
    282 ) -> BaseMessage:
    283     config = ensure_config(config)
    284     return cast(
    285         ChatGeneration,
--> 286         self.generate_prompt(
    287             [self._convert_input(input)],
    288             stop=stop,
    289             callbacks=config.get("callbacks"),
    290             tags=config.get("tags"),
    291             metadata=config.get("metadata"),
    292             run_name=config.get("run_name"),
    293             run_id=config.pop("run_id", None),
    294             **kwargs,
    295         ).generations[0][0],
    296     ).message
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    778 def generate_prompt(
    779     self,
    780     prompts: list[PromptValue],
   (...)
    783     **kwargs: Any,
    784 ) -> LLMResult:
    785     prompt_messages = [p.to_messages() for p in prompts]
--> 786     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    641         if run_managers:
    642             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 643         raise e
    644 flattened_outputs = [
    645     LLMResult(generations=[res.generations], llm_output=res.llm_output)  # type: ignore[list-item]
    646     for res in results
    647 ]
    648 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    630 for i, m in enumerate(messages):
    631     try:
    632         results.append(
--> 633             self._generate_with_cache(
    634                 m,
    635                 stop=stop,
    636                 run_manager=run_managers[i] if run_managers else None,
    637                 **kwargs,
    638             )
    639         )
    640     except BaseException as e:
    641         if run_managers:
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    849 else:
    850     if inspect.signature(self._generate).parameters.get("run_manager"):
--> 851         result = self._generate(
    852             messages, stop=stop, run_manager=run_manager, **kwargs
    853         )
    854     else:
    855         result = self._generate(messages, stop=stop, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686, in BaseChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
    684     generation_info = {"headers": dict(raw_response.headers)}
    685 else:
--> 686     response = self.client.create(**payload)
    687 return self._create_chat_result(response, generation_info)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
    272             msg = f"Missing required argument: {quote(missing[0])}"
    273     raise TypeError(msg)
--> 274 return func(*args, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
    704 @required_args(["messages", "model"], ["messages", "model", "stream"])
    705 def create(
    706     self,
   (...)
    739     timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    740 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
    741     validate_response_format(response_format)
--> 742     return self._post(
    743         "/chat/completions",
    744         body=maybe_transform(
    745             {
    746                 "messages": messages,
    747                 "model": model,
    748                 "frequency_penalty": frequency_penalty,
    749                 "function_call": function_call,
    750                 "functions": functions,
    751                 "logit_bias": logit_bias,
    752                 "logprobs": logprobs,
    753                 "max_completion_tokens": max_completion_tokens,
    754                 "max_tokens": max_tokens,
    755                 "metadata": metadata,
    756                 "n": n,
    757                 "parallel_tool_calls": parallel_tool_calls,
    758                 "presence_penalty": presence_penalty,
    759                 "response_format": response_format,
    760                 "seed": seed,
    761                 "service_tier": service_tier,
    762                 "stop": stop,
    763                 "store": store,
    764                 "stream": stream,
    765                 "stream_options": stream_options,
    766                 "temperature": temperature,
    767                 "tool_choice": tool_choice,
    768                 "tools": tools,
    769                 "top_logprobs": top_logprobs,
    770                 "top_p": top_p,
    771                 "user": user,
    772             },
    773             completion_create_params.CompletionCreateParams,
    774         ),
    775         options=make_request_options(
    776             extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
    777         ),
    778         cast_to=ChatCompletion,
    779         stream=stream or False,
    780         stream_cls=Stream[ChatCompletionChunk],
    781     )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
   1256 def post(
   1257     self,
   1258     path: str,
   (...)
   1265     stream_cls: type[_StreamT] | None = None,
   1266 ) -> ResponseT | _StreamT:
   1267     opts = FinalRequestOptions.construct(
   1268         method="post", url=path, json_data=body, files=to_httpx_files(files), **options
   1269     )
-> 1270     return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
    944 else:
    945     retries_taken = 0
--> 947 return self._request(
    948     cast_to=cast_to,
    949     options=options,
    950     stream=stream,
    951     stream_cls=stream_cls,
    952     retries_taken=retries_taken,
    953 )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051, in SyncAPIClient._request(self, cast_to, options, retries_taken, stream, stream_cls)
   1048         err.response.read()
   1050     log.debug("Re-raising status error")
-> 1051     raise self._make_status_error_from_response(err.response) from None
   1053 return self._process_response(
   1054     cast_to=cast_to,
   1055     options=options,
   (...)
   1059     retries_taken=retries_taken,
   1060 )
``````output
BadRequestError: Error code: 400 - {'error': {'message': "An assistant message with 'tool_calls' must be followed by tool messages responding to each 'tool_call_id'. The following tool_call_ids did not have response messages: call_mjLuNyXNHoUIXHiBtXhaWdxN", 'type': 'invalid_request_error', 'param': 'messages', 'code': None}}

If we add a second response, the call will succeed as expected because we now have one tool response per tool call:

tool_response_2 = foo_tool.invoke(response_message.tool_calls[1])

chat_history.append(tool_response_2)

model_with_tools.invoke(chat_history)
AIMessage(content='Both calls to the tool "foo" have been completed successfully. The output for each call is "action complete!".', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 24, 'prompt_tokens': 137, 'total_tokens': 161, 'completion_tokens_details': {'audio_tokens': None, 'reasoning_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_e2bde53e6e', 'finish_reason': 'stop', 'logprobs': None}, id='run-b5ac3c54-4e26-4da4-853a-d0ab1cba90e0-0', usage_metadata={'input_tokens': 137, 'output_tokens': 24, 'total_tokens': 161, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 0}})

But if we add a duplicate, extra tool response, the call will fail again:

duplicate_tool_response_2 = foo_tool.invoke(response_message.tool_calls[1])

chat_history.append(duplicate_tool_response_2)

await model_with_tools.invoke(chat_history)
---------------------------------------------------------------------------
``````output
BadRequestError                           Traceback (most recent call last)
``````output
Cell In[7], line 5
      1 duplicate_tool_response_2 = foo_tool.invoke(response_message.tool_calls[1])
      3 chat_history.append(duplicate_tool_response_2)
----> 5 await model_with_tools.invoke(chat_history)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   5348 def invoke(
   5349     self,
   5350     input: Input,
   5351     config: Optional[RunnableConfig] = None,
   5352     **kwargs: Optional[Any],
   5353 ) -> Output:
-> 5354     return self.bound.invoke(
   5355         input,
   5356         self._merge_configs(config),
   5357         **{**self.kwargs, **kwargs},
   5358     )
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    275 def invoke(
    276     self,
    277     input: LanguageModelInput,
   (...)
    281     **kwargs: Any,
    282 ) -> BaseMessage:
    283     config = ensure_config(config)
    284     return cast(
    285         ChatGeneration,
--> 286         self.generate_prompt(
    287             [self._convert_input(input)],
    288             stop=stop,
    289             callbacks=config.get("callbacks"),
    290             tags=config.get("tags"),
    291             metadata=config.get("metadata"),
    292             run_name=config.get("run_name"),
    293             run_id=config.pop("run_id", None),
    294             **kwargs,
    295         ).generations[0][0],
    296     ).message
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    778 def generate_prompt(
    779     self,
    780     prompts: list[PromptValue],
   (...)
    783     **kwargs: Any,
    784 ) -> LLMResult:
    785     prompt_messages = [p.to_messages() for p in prompts]
--> 786     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    641         if run_managers:
    642             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 643         raise e
    644 flattened_outputs = [
    645     LLMResult(generations=[res.generations], llm_output=res.llm_output)  # type: ignore[list-item]
    646     for res in results
    647 ]
    648 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    630 for i, m in enumerate(messages):
    631     try:
    632         results.append(
--> 633             self._generate_with_cache(
    634                 m,
    635                 stop=stop,
    636                 run_manager=run_managers[i] if run_managers else None,
    637                 **kwargs,
    638             )
    639         )
    640     except BaseException as e:
    641         if run_managers:
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    849 else:
    850     if inspect.signature(self._generate).parameters.get("run_manager"):
--> 851         result = self._generate(
    852             messages, stop=stop, run_manager=run_manager, **kwargs
    853         )
    854     else:
    855         result = self._generate(messages, stop=stop, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686, in BaseChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
    684     generation_info = {"headers": dict(raw_response.headers)}
    685 else:
--> 686     response = self.client.create(**payload)
    687 return self._create_chat_result(response, generation_info)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
    272             msg = f"Missing required argument: {quote(missing[0])}"
    273     raise TypeError(msg)
--> 274 return func(*args, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
    704 @required_args(["messages", "model"], ["messages", "model", "stream"])
    705 def create(
    706     self,
   (...)
    739     timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    740 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
    741     validate_response_format(response_format)
--> 742     return self._post(
    743         "/chat/completions",
    744         body=maybe_transform(
    745             {
    746                 "messages": messages,
    747                 "model": model,
    748                 "frequency_penalty": frequency_penalty,
    749                 "function_call": function_call,
    750                 "functions": functions,
    751                 "logit_bias": logit_bias,
    752                 "logprobs": logprobs,
    753                 "max_completion_tokens": max_completion_tokens,
    754                 "max_tokens": max_tokens,
    755                 "metadata": metadata,
    756                 "n": n,
    757                 "parallel_tool_calls": parallel_tool_calls,
    758                 "presence_penalty": presence_penalty,
    759                 "response_format": response_format,
    760                 "seed": seed,
    761                 "service_tier": service_tier,
    762                 "stop": stop,
    763                 "store": store,
    764                 "stream": stream,
    765                 "stream_options": stream_options,
    766                 "temperature": temperature,
    767                 "tool_choice": tool_choice,
    768                 "tools": tools,
    769                 "top_logprobs": top_logprobs,
    770                 "top_p": top_p,
    771                 "user": user,
    772             },
    773             completion_create_params.CompletionCreateParams,
    774         ),
    775         options=make_request_options(
    776             extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
    777         ),
    778         cast_to=ChatCompletion,
    779         stream=stream or False,
    780         stream_cls=Stream[ChatCompletionChunk],
    781     )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
   1256 def post(
   1257     self,
   1258     path: str,
   (...)
   1265     stream_cls: type[_StreamT] | None = None,
   1266 ) -> ResponseT | _StreamT:
   1267     opts = FinalRequestOptions.construct(
   1268         method="post", url=path, json_data=body, files=to_httpx_files(files), **options
   1269     )
-> 1270     return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
    944 else:
    945     retries_taken = 0
--> 947 return self._request(
    948     cast_to=cast_to,
    949     options=options,
    950     stream=stream,
    951     stream_cls=stream_cls,
    952     retries_taken=retries_taken,
    953 )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051, in SyncAPIClient._request(self, cast_to, options, retries_taken, stream, stream_cls)
   1048         err.response.read()
   1050     log.debug("Re-raising status error")
-> 1051     raise self._make_status_error_from_response(err.response) from None
   1053 return self._process_response(
   1054     cast_to=cast_to,
   1055     options=options,
   (...)
   1059     retries_taken=retries_taken,
   1060 )
``````output
BadRequestError: Error code: 400 - {'error': {'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error', 'param': 'messages.[4].role', 'code': None}}

You should additionally not pass ToolMessages back to to a model if they are not preceded by an AIMessage with tool calls. For example, this will fail:

model_with_tools.invoke(
    [ToolMessage(content="action completed!", tool_call_id="dummy")]
)
---------------------------------------------------------------------------
``````output
BadRequestError                           Traceback (most recent call last)
``````output
Cell In[8], line 1
----> 1 model_with_tools.invoke([ToolMessage(content="action completed!", tool_call_id="dummy")])
``````output
File ~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   5348 def invoke(
   5349     self,
   5350     input: Input,
   5351     config: Optional[RunnableConfig] = None,
   5352     **kwargs: Optional[Any],
   5353 ) -> Output:
-> 5354     return self.bound.invoke(
   5355         input,
   5356         self._merge_configs(config),
   5357         **{**self.kwargs, **kwargs},
   5358     )
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    275 def invoke(
    276     self,
    277     input: LanguageModelInput,
   (...)
    281     **kwargs: Any,
    282 ) -> BaseMessage:
    283     config = ensure_config(config)
    284     return cast(
    285         ChatGeneration,
--> 286         self.generate_prompt(
    287             [self._convert_input(input)],
    288             stop=stop,
    289             callbacks=config.get("callbacks"),
    290             tags=config.get("tags"),
    291             metadata=config.get("metadata"),
    292             run_name=config.get("run_name"),
    293             run_id=config.pop("run_id", None),
    294             **kwargs,
    295         ).generations[0][0],
    296     ).message
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    778 def generate_prompt(
    779     self,
    780     prompts: list[PromptValue],
   (...)
    783     **kwargs: Any,
    784 ) -> LLMResult:
    785     prompt_messages = [p.to_messages() for p in prompts]
--> 786     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    641         if run_managers:
    642             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 643         raise e
    644 flattened_outputs = [
    645     LLMResult(generations=[res.generations], llm_output=res.llm_output)  # type: ignore[list-item]
    646     for res in results
    647 ]
    648 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    630 for i, m in enumerate(messages):
    631     try:
    632         results.append(
--> 633             self._generate_with_cache(
    634                 m,
    635                 stop=stop,
    636                 run_manager=run_managers[i] if run_managers else None,
    637                 **kwargs,
    638             )
    639         )
    640     except BaseException as e:
    641         if run_managers:
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    849 else:
    850     if inspect.signature(self._generate).parameters.get("run_manager"):
--> 851         result = self._generate(
    852             messages, stop=stop, run_manager=run_manager, **kwargs
    853         )
    854     else:
    855         result = self._generate(messages, stop=stop, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686, in BaseChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
    684     generation_info = {"headers": dict(raw_response.headers)}
    685 else:
--> 686     response = self.client.create(**payload)
    687 return self._create_chat_result(response, generation_info)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
    272             msg = f"Missing required argument: {quote(missing[0])}"
    273     raise TypeError(msg)
--> 274 return func(*args, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
    704 @required_args(["messages", "model"], ["messages", "model", "stream"])
    705 def create(
    706     self,
   (...)
    739     timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    740 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
    741     validate_response_format(response_format)
--> 742     return self._post(
    743         "/chat/completions",
    744         body=maybe_transform(
    745             {
    746                 "messages": messages,
    747                 "model": model,
    748                 "frequency_penalty": frequency_penalty,
    749                 "function_call": function_call,
    750                 "functions": functions,
    751                 "logit_bias": logit_bias,
    752                 "logprobs": logprobs,
    753                 "max_completion_tokens": max_completion_tokens,
    754                 "max_tokens": max_tokens,
    755                 "metadata": metadata,
    756                 "n": n,
    757                 "parallel_tool_calls": parallel_tool_calls,
    758                 "presence_penalty": presence_penalty,
    759                 "response_format": response_format,
    760                 "seed": seed,
    761                 "service_tier": service_tier,
    762                 "stop": stop,
    763                 "store": store,
    764                 "stream": stream,
    765                 "stream_options": stream_options,
    766                 "temperature": temperature,
    767                 "tool_choice": tool_choice,
    768                 "tools": tools,
    769                 "top_logprobs": top_logprobs,
    770                 "top_p": top_p,
    771                 "user": user,
    772             },
    773             completion_create_params.CompletionCreateParams,
    774         ),
    775         options=make_request_options(
    776             extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
    777         ),
    778         cast_to=ChatCompletion,
    779         stream=stream or False,
    780         stream_cls=Stream[ChatCompletionChunk],
    781     )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
   1256 def post(
   1257     self,
   1258     path: str,
   (...)
   1265     stream_cls: type[_StreamT] | None = None,
   1266 ) -> ResponseT | _StreamT:
   1267     opts = FinalRequestOptions.construct(
   1268         method="post", url=path, json_data=body, files=to_httpx_files(files), **options
   1269     )
-> 1270     return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
    944 else:
    945     retries_taken = 0
--> 947 return self._request(
    948     cast_to=cast_to,
    949     options=options,
    950     stream=stream,
    951     stream_cls=stream_cls,
    952     retries_taken=retries_taken,
    953 )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051, in SyncAPIClient._request(self, cast_to, options, retries_taken, stream, stream_cls)
   1048         err.response.read()
   1050     log.debug("Re-raising status error")
-> 1051     raise self._make_status_error_from_response(err.response) from None
   1053 return self._process_response(
   1054     cast_to=cast_to,
   1055     options=options,
   (...)
   1059     retries_taken=retries_taken,
   1060 )
``````output
BadRequestError: Error code: 400 - {'error': {'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error', 'param': 'messages.[0].role', 'code': None}}

See this guide for more details on tool calling.

Troubleshooting​

The following may help resolve this error:

  • If you are using a custom executor rather than a prebuilt one like LangGraph's ToolNode or the legacy LangChain AgentExecutor, verify that you are invoking and returning the result for one tool per tool call.
  • If you are using few-shot tool call examples with messages that you manually create, and you want to simulate a failure, you still need to pass back a ToolMessage whose content indicates that failure.

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