new_messages() include ToolReturnPart in the first user prompt of ModelRequest when output_type is specified #3067
Description
Initial Checks
- I confirm that I'm using the latest version of Pydantic AI
- I confirm that I searched for my issue in https://github.com/pydantic/pydantic-ai/issues before opening this issue
Description
If an agent is set up with a specific output_type, the output function is called in the last message from the LLM (e.g. ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='<id>')]). This message is leaked into the starting ModelRequest of the second run's new_messages().
Notice the 2nd and 3rd run new_messages() below starts with:
[
ModelRequest(
parts=[
ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='<id>'), # <- shouldn't be here!
UserPromptPart(content='Roll me a dice again.')
]
),
...
]When chained in a chat history, this caused two ModelRequest(parts=[ToolReturnPart()]) in a row, which when validated on OpenAI's API will cause an error on the 3rd run with the error message:
pydantic_ai.exceptions.ModelHTTPError: status_code: 400, model_name: gpt-5-mini, body: {'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error', 'param': 'messages.[6].role', 'code': None}
i.e.
# 1st message
[...,
ModelResponse(
parts=[
ToolCallPart()
],
),
ModelRequest(
parts=[
ToolReturnPart()
]
),
] +
# 2nd message
[
ModelRequest(
parts=[
ToolReturnPart(), # <-- this return part does not have a call part before
UserPromptPart()
]
),
...]The bug won't appear when there is no output_type specified as the output tool is not called nor returned.
Example Code
from pydantic_ai import Agent
from pydantic_ai.messages import ModelMessagesTypeAdapter
from pydantic import BaseModel
import random
class Result(BaseModel):
message: str
result: int
# change this model between gpt-5-mini and gemini-2.5-flash, bug will still be same
agent = Agent("gpt-5-mini", system_prompt="Be a helpful assistant.", output_type=Result)
@agent.tool_plain
def roll_dice() -> str:
"""Roll a six-sided die and return the result."""
return str(random.randint(1, 6))
result1 = agent.run_sync("Roll me a dice.")
print(result1.output)
print(result1.new_messages())
# simulate database store and load
msghistory = ModelMessagesTypeAdapter.validate_json(result1.new_messages_json())
result2 = agent.run_sync("Roll me a dice again.", message_history=msghistory)
print(result2.output)
print(result2.new_messages())
# simulate database store and load again for different run
msghistory2 = msghistory + ModelMessagesTypeAdapter.validate_json(
result2.new_messages_json()
)
result3 = agent.run_sync("I bet on 4.", message_history=msghistory2)
print(result3.output)
print(result3.new_messages())Using gemini-2.5-flash:
message='The dice rolled a 5.' result=5
[ModelRequest(parts=[SystemPromptPart(content='Be a helpful assistant.', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 50, 545483, tzinfo=datetime.timezone.utc)), UserPromptPart(content='Roll me a dice.', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 50, 545483, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ThinkingPart(content='', signature='CsQCAdHtim/EE7wTU7SQTmrfEJhMbVncoOAcH9O8xcIqwnT8AKrNQOmkshFgSmeWxFiRwlYR568PvmosxoOYvJr2G7hORzPCD95sD0WQMu55d9lwEVbMb2KT3pfAxaIYkjvxb/1pXqZD88lowEmiveUN30GTLhMf561TsSAVmjrgbSZaufXZQrlVDYui8T0CpuNanXlrR6u+x7eemsFePfiH8ye5NB9TpNNfHM1AaXXeOGvcu6Lyx6kOJ1g//Fl92YWEXsTyJ4fCEDWvoGsShciaV8CaYq9W75ecNtzauFuKx4Mw1vTDq4KUlJRsQCLdc8Gfd1JwvOh+n6zYpmg+r/RkE1cp7oEGZjtDcrqnWpAOFD2QEscGYPOPpFclfrCXNfkFv3BDbA4aoo56cUAZg8AiLsqY+mFKVl/V8+IeEw3Mi3o6TLeG', provider_name='google-gla'), ToolCallPart(tool_name='roll_dice', args={}, tool_call_id='pyd_ai_b8fa0062a0974c0cbc54e093bcb315c0')], usage=RequestUsage(input_tokens=94, output_tokens=79, details={'thoughts_tokens': 69, 'text_prompt_tokens': 94}), model_name='gemini-2.5-flash', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 52, 42445, tzinfo=datetime.timezone.utc), provider_name='google-gla', provider_details={'finish_reason': 'STOP'}, provider_response_id='AFbeaOiLA6ic1e8PjpKE2Ag', finish_reason='stop'), ModelRequest(parts=[ToolReturnPart(tool_name='roll_dice', content='5', tool_call_id='pyd_ai_b8fa0062a0974c0cbc54e093bcb315c0', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 52, 43443, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ThinkingPart(content='', signature='CtMCAdHtim84Ii6OxfOI2a3tn9MVW+Vx3QkfmT/87yPL+uyYsxVKe5cpQ2URM3hpAbGaURucB8swj779Ow+eTCoxNxy6ZQI8HQj6UfEF9DycR3XiSUC/82zCRm8jP5qAmkHqjqt7mV0II34TI9PblLE/tR7jg9EbC7aWpClmyk1IVdEIi9zd4KfpNet4tGEzfXWlakggU/8chumcNY5jBrGysTLFOPGgJ0sV1lLMxIMOi67oOlG/3uoSjDvAGqPSm0VvPm94DrA9/KHbyGqN+8jwvfEZoVC8BULVn3AVNA/PI9BQRJgnxkAVBArSTsk3VRsXrM4ocAMAAUOD5c/6oWs1YB4yXr5vuVmGrEtptQV1odFSGMbxGPE5GZrQMhzOqvNERtQKmTtond2Kj54r8IvWZ9yyl8vYyZUPxGHEMTfLBcWsAnH7xiO+3GFezBDMXBeG8tsK', provider_name='google-gla'), ToolCallPart(tool_name='final_result', args={'result': 5, 'message': 'The dice rolled a 5.'}, tool_call_id='pyd_ai_7cf69f84d59e4938ada1c9bb278094ab')], usage=RequestUsage(input_tokens=121, output_tokens=111, details={'thoughts_tokens': 86, 'text_prompt_tokens': 121}), model_name='gemini-2.5-flash', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 53, 403471, tzinfo=datetime.timezone.utc), provider_name='google-gla', provider_details={'finish_reason': 'STOP'}, provider_response_id='AVbeaNyBGfi6vr0PweP92Ag', finish_reason='stop'), ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='pyd_ai_7cf69f84d59e4938ada1c9bb278094ab', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 53, 403471, tzinfo=datetime.timezone.utc))])]
message='The dice rolled a 4.' result=4
[ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='pyd_ai_7cf69f84d59e4938ada1c9bb278094ab', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 53, 403471, tzinfo=TzInfo(UTC))), UserPromptPart(content='Roll me a dice again.', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 53, 418642, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ToolCallPart(tool_name='roll_dice', args={}, tool_call_id='pyd_ai_c8ea5ecbe7e64052b1b93371e9636f51')], usage=RequestUsage(input_tokens=172, output_tokens=10, details={'text_prompt_tokens': 172}), model_name='gemini-2.5-flash', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 54, 179652, tzinfo=datetime.timezone.utc), provider_name='google-gla', provider_details={'finish_reason': 'STOP'}, provider_response_id='AlbeaP-wC-6_vr0Pgun82Ao', finish_reason='stop'), ModelRequest(parts=[ToolReturnPart(tool_name='roll_dice', content='4', tool_call_id='pyd_ai_c8ea5ecbe7e64052b1b93371e9636f51', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 54, 180650, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ThinkingPart(content='', signature='CtsCAdHtim/yLLKElifI6VDDr+tM9InlU9E7UN1t8wCv+BZ8r8Wk6JQtspLNSgru32Z3prtEJ0kGSFAGDi9xczYqKZNYFK3RGKf0Oy8Evbg8K/0X0l6dK4B24cKC75oO/acXS1Ewj5g8dVWOsnsMwKS6a9ozXk5bbwvISCTTfOcy6gSKlmvwnNLGGAQh6O5pGg0TPqvd6EN+pSNsXC7LTT/v86L+F7NRxsDL0mzO3xE+qN+oevUlrDoJRaiY+0FCN6i0NgmH8+V37bTeMvJt0nelS0GHFEvx7SBalVNrpPP4f1Lvcr0u29cqwLIGiaLJg7eH6GZ547WHoYu7JUUd8I9jbqNgIBZcb0bJITQcU2g4fj5MoqSWrGzuN0iTXn6SVJtOb/yRn95uuBQuT2ogzhm8L09Jz84aNKIQUpD8zyrWEwYaKBS8ZlKZj3JU4OL9HqXpBfr0sFu4+eCMJP4=', provider_name='google-gla'), ToolCallPart(tool_name='final_result', args={'result': 4, 'message': 'The dice rolled a 4.'}, tool_call_id='pyd_ai_b6c0268e27084433806801c9e8f8b117')], usage=RequestUsage(input_tokens=199, output_tokens=112, details={'thoughts_tokens': 87, 'text_prompt_tokens': 199}), model_name='gemini-2.5-flash', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 55, 523640, tzinfo=datetime.timezone.utc), provider_name='google-gla', provider_details={'finish_reason': 'STOP'}, provider_response_id='A1beaLbyH7fZ1e8PhpaAwAg', finish_reason='stop'), ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='pyd_ai_b6c0268e27084433806801c9e8f8b117', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 55, 523640, tzinfo=datetime.timezone.utc))])]
message='The dice rolled a 3. You bet on 4, so you lost.' result=3
[ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='pyd_ai_b6c0268e27084433806801c9e8f8b117', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 55, 523640, tzinfo=TzInfo(UTC))), UserPromptPart(content='I bet on 4.', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 55, 524640, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ToolCallPart(tool_name='roll_dice', args={}, tool_call_id='pyd_ai_1bc3237478a94c278f05c1463c00d0fd')], usage=RequestUsage(input_tokens=268, output_tokens=10, details={'text_prompt_tokens': 268}), model_name='gemini-2.5-flash', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 56, 43228, tzinfo=datetime.timezone.utc), provider_name='google-gla', provider_details={'finish_reason': 'STOP'}, provider_response_id='BFbeaP6tA5C91e8P1rDisAo', finish_reason='stop'), ModelRequest(parts=[ToolReturnPart(tool_name='roll_dice', content='3', tool_call_id='pyd_ai_1bc3237478a94c278f05c1463c00d0fd', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 56, 44228, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ThinkingPart(content='', signature='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', provider_name='google-gla'), ToolCallPart(tool_name='final_result', args={'result': 3, 'message': 'The dice rolled a 3. You bet on 4, so you lost.'}, tool_call_id='pyd_ai_03c53af0c48d4791a207bda332ec7038')], usage=RequestUsage(input_tokens=295, output_tokens=152, details={'thoughts_tokens': 117, 'text_prompt_tokens': 295}), model_name='gemini-2.5-flash', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 57, 397528, tzinfo=datetime.timezone.utc), provider_name='google-gla', provider_details={'finish_reason': 'STOP'}, provider_response_id='BVbeaOHXGO3avr0Pip_44Ag', finish_reason='stop'), ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='pyd_ai_03c53af0c48d4791a207bda332ec7038', timestamp=datetime.datetime(2025, 10, 2, 10, 37, 57, 397528, tzinfo=datetime.timezone.utc))])]
3 runs are completed, but the bug exist.
Using gpt-5-mini:
message='You rolled a 4.' result=4
[ModelRequest(parts=[SystemPromptPart(content='Be a helpful assistant.', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 1, 580280, tzinfo=datetime.timezone.utc)), UserPromptPart(content='Roll me a dice.', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 1, 580280, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ToolCallPart(tool_name='roll_dice', args='{}', tool_call_id='call_8Wsfly9UVyEiSzjMPnoBeqyi')], usage=RequestUsage(input_tokens=162, output_tokens=84, details={'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 64, 'rejected_prediction_tokens': 0}), model_name='gpt-5-mini-2025-08-07', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 2, tzinfo=TzInfo(UTC)), provider_name='openai', provider_details={'finish_reason': 'tool_calls'}, provider_response_id='chatcmpl-CMAuMAnbp2IbCvvPeGHPCgDcqXsrX', finish_reason='tool_call'), ModelRequest(parts=[ToolReturnPart(tool_name='roll_dice', content='4', tool_call_id='call_8Wsfly9UVyEiSzjMPnoBeqyi', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 5, 219298, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ToolCallPart(tool_name='final_result', args='{"message":"You rolled a 4.","result":4}', tool_call_id='call_1zujp4MXfrhDCOMa6N0fMsJK')], usage=RequestUsage(input_tokens=189, output_tokens=223, details={'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 192, 'rejected_prediction_tokens': 0}), model_name='gpt-5-mini-2025-08-07', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 5, tzinfo=TzInfo(UTC)), provider_name='openai', provider_details={'finish_reason': 'tool_calls'}, provider_response_id='chatcmpl-CMAuPTQ8WL7zVIplbXHiEmXD5Guzc', finish_reason='tool_call'), ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='call_1zujp4MXfrhDCOMa6N0fMsJK', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 9, 467906, tzinfo=datetime.timezone.utc))])]
message='You rolled a 5.' result=5
[ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='call_1zujp4MXfrhDCOMa6N0fMsJK', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 9, 467906, tzinfo=TzInfo(UTC))), UserPromptPart(content='Roll me a dice again.', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 9, 488044, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ToolCallPart(tool_name='roll_dice', args='{}', tool_call_id='call_ARPOV1Saff7YrOXgp8CqnHob')], usage=RequestUsage(input_tokens=239, output_tokens=84, details={'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 64, 'rejected_prediction_tokens': 0}), model_name='gpt-5-mini-2025-08-07', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 9, tzinfo=TzInfo(UTC)), provider_name='openai', provider_details={'finish_reason': 'tool_calls'}, provider_response_id='chatcmpl-CMAuT54Xg3JCWeYz74P5OMjs5P9IA', finish_reason='tool_call'), ModelRequest(parts=[ToolReturnPart(tool_name='roll_dice', content='5', tool_call_id='call_ARPOV1Saff7YrOXgp8CqnHob', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 12, 659422, tzinfo=datetime.timezone.utc))]), ModelResponse(parts=[ToolCallPart(tool_name='final_result', args='{"message":"You rolled a 5.","result":5}', tool_call_id='call_2hAovRZPEG7LrBZsPODlWtUo')], usage=RequestUsage(input_tokens=266, output_tokens=25, details={'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}), model_name='gpt-5-mini-2025-08-07', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 13, tzinfo=TzInfo(UTC)), provider_name='openai', provider_details={'finish_reason': 'tool_calls'}, provider_response_id='chatcmpl-CMAuX39JiCfNBXTHxPQsD4hl1K5Qu', finish_reason='tool_call'), ModelRequest(parts=[ToolReturnPart(tool_name='final_result', content='Final result processed.', tool_call_id='call_2hAovRZPEG7LrBZsPODlWtUo', timestamp=datetime.datetime(2025, 10, 2, 10, 33, 14, 469651, tzinfo=datetime.timezone.utc))])]
Traceback (most recent call last):
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_ai\models\openai.py", line 482, in _completions_create
return await self.client.chat.completions.create(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\openai\resources\chat\completions\completions.py", line 2585, in create
return await self._post(
^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\openai\_base_client.py", line 1794, in post
return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\openai\_base_client.py", line 1594, in request
raise self._make_status_error_from_response(err.response) from None
openai.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.[6].role', 'code': None}}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "c:\Users\User\Desktop\Platform-Integration\API\test2.py", line 35, in <module>
result3 = agent.run_sync("I bet on 4.", message_history=msghistory2)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_ai\agent\abstract.py", line 326, in run_sync
return get_event_loop().run_until_complete(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\scoop\apps\python\current\Lib\asyncio\base_events.py", line 691, in run_until_complete
return future.result()
^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_ai\agent\abstract.py", line 227, in run
async for node in agent_run:
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_ai\run.py", line 149, in __anext__
next_node = await self._graph_run.__anext__()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_graph\graph.py", line 758, in __anext__
return await self.next(self._next_node)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_graph\graph.py", line 731, in next
self._next_node = await node.run(ctx)
^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_ai\_agent_graph.py", line 400, in run
return await self._make_request(ctx)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_ai\_agent_graph.py", line 442, in _make_request
model_response = await ctx.deps.model.request(message_history, model_settings, model_request_parameters)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_ai\models\openai.py", line 400, in request
response = await self._completions_create(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\Desktop\Platform-Integration\API\.venv\Lib\site-packages\pydantic_ai\models\openai.py", line 512, in _completions_create
raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.body) from e
pydantic_ai.exceptions.ModelHTTPError: status_code: 400, model_name: gpt-5-mini, body: {'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error', 'param': 'messages.[6].role', 'code': None}
Python, Pydantic AI & LLM client version
Python 3.12.9, Pydantic 2.11.9, Pydantic AI 1.0.13