[mosafariuk]

💡 Motivation and Context

PostHogTracingProcessor never reports cached input tokens or reasoning tokens for real OpenAI Agents runs, on either model path:

• Generation spans (OpenAIChatCompletionsModel): the Agents SDK builds span_data.usage with the counts nested under input_tokens_details.cached_tokens and output_tokens_details.reasoning_tokens (see agents.usage.model_usage_to_span_usage and openai_chatcompletions.py). The processor reads flat usage["cache_read_input_tokens"] and usage["reasoning_tokens"], which the SDK never sets.
• Response spans (OpenAIResponsesModel, the default): the processor reads only input_tokens and output_tokens from response.usage, and ignores its input_tokens_details and output_tokens_details.

So $ai_cache_read_input_tokens and $ai_reasoning_tokens are always missing. Cached input is then costed at the full input rate, and reasoning usage is invisible.

This PR reads the nested fields on both paths, and keeps the flat keys for custom models that report them that way. The values are mapped the same way as the Python OpenAI wrapper's extract_openai_usage_from_response: cached_tokens → $ai_cache_read_input_tokens and reasoning_tokens → $ai_reasoning_tokens, each only when greater than zero. It's the Python counterpart of the cache-read part of PostHog/posthog-js#5056. Cache writes are left out, because the Python OpenAI wrapper doesn't report them either.

💚 How did you test it?

• End to end with the real Agents SDK (openai-agents 0.18.3): a script runs Runner.run with OpenAIChatCompletionsModel and with OpenAIResponsesModel, against an httpx.MockTransport returning usage with 100 input, 60 cached, 50 output and 40 reasoning tokens, and the PostHog processor registered. On main, both runs capture $ai_cache_read_input_tokens: null and $ai_reasoning_tokens: null. With this change, both capture 60 and 40.
• Regression tests in posthog/test/ai/openai_agents/test_processor.py:
  • test_generation_span_reads_nested_cache_and_reasoning_tokens builds the span usage with the SDK's own model_usage_to_span_usage(Usage(...)).
  • test_response_span_reads_cache_and_reasoning_tokens uses a real openai.types.responses.Response.
  • Both fail on main with KeyError: '$ai_cache_read_input_tokens' and pass with this change. The existing flat-key test (test_generation_span_with_reasoning_tokens) still passes.
• Full suite: 5133 passed, 16 skipped. ruff format --check, ruff check, mypy | mypy-baseline filter and make public_api_check are clean.

📝 Checklist

• I reviewed the submitted code.
• I added tests to verify the changes.
• I updated the docs if needed.
• No breaking change or entry added to the changelog.

If releasing new changes

• Ran sampo add to generate a changeset file

🤖 Agent context

Autonomy: Human-driven (agent-assisted)

• An AI coding agent helped me find this by comparing recent @posthog/ai fixes with the Python SDK and reading the Agents SDK's span-usage serialization. It drafted the fix and tests. I reviewed and ran all of it.
• The existing tests passed because they used a flat usage dict, which isn't the shape the SDK produces. The new tests build usage with the SDK's own types and serializer.

[dustinbyrne]

Nested token extraction is verified on both Agents model paths. One cache-accounting issue remains for OpenAI-compatible Claude requests: the new cache count increases estimated input cost without an inclusive-reporting flag. See the inline reproduction.

AI-assisted review.

[dustinbyrne]

and thank you for the pull request @mosafariuk! 🙌

[mosafariuk]

@dustinbyrne the inclusive-reporting fix is in 4de76ae, together with your regression test. Ready for another look when you have a moment.

[dustinbyrne]

The inclusive cache-accounting correction is verified, including the compatible-Claude case. One remaining interaction affects Gemini through OpenRouter: reasoning is already included in the completion total, but forwarding it separately causes the backend to count it again. See the inline base/head reproduction.

AI-assisted review.

[marandaneto]

The code looks good; no qualifying findings. Verdict: correct.