Non-streaming Chat Completions run fails when provider usage has null token counts (streaming handles it since #1179)
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Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 84/100
Research direction
Start in openai_chatcompletions.py at the non-streaming get_response usage construction, then compare it with the null-count handling in chatcmpl_stream_handler.py. Review the corresponding Usage construction in any_llm_model.py and run the self-contained httpx.MockTransport reproduction; done means the non-streaming run completes and reports zero for missing token counts.
Written by the indexing model from the issue text.
Description
Describe the bug
With OpenAIChatCompletionsModel, a non-streaming run fails when the provider's usage has a null token count. This happens even though the model returned a valid answer. The streaming path already accepts the same payload: since #1179, chatcmpl_stream_handler.py maps None to 0 (usage.prompt_tokens or 0, …). The non-streaming get_response in openai_chatcompletions.py still passes response.usage.prompt_tokens / completion_tokens / total_tokens straight into Usage(...), so pydantic rejects None.
any_llm_model.py builds Usage the same way.
Debug information
- Agents SDK version:
main@ 8e435004 (0.22.3) - Python version: 3.13
- Operating system: macOS
- Model and model provider:
OpenAIChatCompletionsModelagainst an OpenAI-compatible server (mocked below) - Does the issue reproduce with the latest Agents SDK release? Yes
- Does the issue occur consistently or intermittently? Consistently, whenever a token count is
null
pydantic_core._pydantic_core.ValidationError: 1 validation error for Usage
output_tokens
Input should be a valid integer [type=int_type, input_value=None, input_type=NoneType]
Repro steps
Self-contained; the HTTP call is mocked with httpx.MockTransport.
import asyncio
import httpx
from openai import AsyncOpenAI
from agents import Agent, Runner, OpenAIChatCompletionsModel, set_tracing_disabled
set_tracing_disabled(True)
def handler(request: httpx.Request) -> httpx.Response:
return httpx.Response(200, json={
"id": "r1", "object": "chat.completion", "created": 1, "model": "m",
"choices": [{"index": 0, "finish_reason": "stop",
"message": {"role": "assistant", "content": "hi"}}],
# Some OpenAI-compatible providers send null token counts
"usage": {"prompt_tokens": 10, "completion_tokens": None, "total_tokens": 10},
})
client = AsyncOpenAI(api_key="x", base_url="http://test/v1",
http_client=httpx.AsyncClient(transport=httpx.MockTransport(handler)))
agent = Agent(name="a", model=OpenAIChatCompletionsModel(model="m", openai_client=client))
print(asyncio.run(Runner.run(agent, "hi")).final_output)
The same payload streamed through Runner.run_streamed completes and reports input_tokens=10.
Expected behavior
The run should complete the same way as the streaming path: treat the missing counts as 0 (... or 0) in openai_chatcompletions.get_response and any_llm_model.
I used an AI assistant (Claude Code) to help find and reproduce this; I ran the repro myself on main.
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