What does this PR do?

The bundled observability/langfuse plugin's _serialize_assistant_message reads only the top-level reasoning attribute, so providers that emit chain-of-thought via the reasoning_content convention (LM Studio, Moonshot, Qwen3 thinking, DeepSeek) silently lose their reasoning in Langfuse observations even though Hermes captures it correctly. Every LLM call N shows reasoning: None for these providers.

Root cause: NormalizedResponse (agent/transports/types.py) stores the reasoning text in provider_data["reasoning_content"] and exposes it via the reasoning_content property (line 115). The serializer never reads that property — only the top-level reasoning field. Fall back to reasoning_content when reasoning is empty/missing so the property is read on the production code path the issue reporter hits.

Mirrors the existing reasoning resolution shape used downstream — top-level reasoning (Anthropic / Codex) wins when set, reasoning_content (OpenAI-compatible reasoning_content convention) fills in for the providers that use it.

Audited siblings: the other two output-construction sites in the same hook (post_llm_call string path at line 828, post_api_request summary path at line 833) do not receive an assistant message object and hardcode reasoning: None. No widening needed in this PR.

Related Issue

Fixes #29482

Type of Change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 🔒 Security fix
• 📝 Documentation update
• ✅ Tests (adding or improving test coverage)
• ♻️ Refactor (no behavior change)
• 🎯 New skill (bundled or hub)

Changes Made

• plugins/observability/langfuse/__init__.py — _serialize_assistant_message falls back to getattr(message, "reasoning_content", None) when the top-level reasoning is empty. Comment explains the two reasoning conventions.
• tests/plugins/test_langfuse_plugin.py — new TestSerializeAssistantMessageReasoning class with 5 cases: top-level precedence, fallback when reasoning is None, fallback when reasoning == "", end-to-end via real NormalizedResponse(provider_data={"reasoning_content": ...}), and both-missing returns None.

How to Test

1. uv run --with pytest --with pytest-xdist --with pytest-asyncio --with pytest-timeout python3 -m pytest tests/plugins/test_langfuse_plugin.py::TestSerializeAssistantMessageReasoning -v
2. Full file regression: uv run --with pytest --with pytest-xdist --with pytest-asyncio --with pytest-timeout python3 -m pytest tests/plugins/test_langfuse_plugin.py -v → 42 passed.
3. Regression guard: reverting only the production change makes the 3 fallback tests fail with AssertionError: assert '' == 'qwen3 thinking' (and similar); restoring it makes them pass — confirming the tests actually exercise the new behavior.

Checklist

Code

• I've read the Contributing Guide
• My commit messages follow Conventional Commits (fix(plugins):)
• I searched for existing PRs to make sure this isn't a duplicate
• My PR contains only changes related to this fix (no unrelated commits)
• I've run focused tests for the touched code and all pass (42/42 in the langfuse file)
• I've added tests for my changes
• I've tested on my platform: macOS 15.x

Documentation & Housekeeping

• I've updated relevant documentation — N/A (internal serializer)
• I've updated cli-config.yaml.example if I added/changed config keys — N/A
• I've updated CONTRIBUTING.md or AGENTS.md if I changed architecture or workflows — N/A
• I've considered cross-platform impact (Windows, macOS) — N/A (pure-Python serializer, no platform paths)
• I've updated tool descriptions/schemas if I changed tool behavior — N/A

Screenshots / Logs

Before (current main):

# Langfuse "LLM call N" observation, LM Studio + Qwen3 thinking
output:
  content: "final answer …"
  reasoning: None     # ← lost
  tool_calls: [...]

After this PR:

output:
  content: "final answer …"
  reasoning: "the model's chain of thought …"   # ← surfaced
  tool_calls: [...]