feat(llm): forward configured reasoning_effort to native LLM calls

Add an optional reasoning_effort field to LLMConfig (e.g. low/medium/high/max)
and forward it to litellm.acompletion in both chat() and chat_stream() when
set. Unset by default so non-OpenAI providers are unaffected. Callers can
still override per-call via kwargs.

opc/core/config.py

@@ -273,6 +273,7 @@ class LLMConfig(BaseModel):
     fallback: dict[str, Any] = Field(default_factory=dict)
     temperature: float = 0.3
     max_tokens: int = 32768
+    reasoning_effort: str | None = None
     # Total input context window (tokens) for the active model. Set this when
     # the model is not mapped in litellm (e.g. proxy/self-hosted models like
     # doubao/minimax/glm), so the context-usage ring and compaction thresholds

opc/llm/provider.py

@@ -569,6 +569,8 @@ class LLMProvider:
             "max_tokens": max_tok,
             **kwargs,
         }
+        if self.config.reasoning_effort and "reasoning_effort" not in call_kwargs:
+            call_kwargs["reasoning_effort"] = self.config.reasoning_effort
         if self._api_base:
             call_kwargs["api_base"] = self._api_base
         if self._api_key:
@@ -715,6 +717,8 @@ class LLMProvider:
             "stream": True,
             **kwargs,
         }
+        if self.config.reasoning_effort and "reasoning_effort" not in call_kwargs:
+            call_kwargs["reasoning_effort"] = self.config.reasoning_effort
         if self._api_base:
             call_kwargs["api_base"] = self._api_base
         if self._api_key: