fix(actor): top-level config keys take precedence over options block duplicates

In both ToolCallingLLMCaller._resolve_llm and SimpleLLMAgent._resolve_llm,
the options block was merged after top-level keys were applied, causing
llm_kwargs.update(...) to overwrite top-level temperature/max_tokens/
max_retries with any duplicate values from the options block.

Fix: apply build_llm_kwargs_from_options first, then re-apply the
top-level extracted values so they always win.

ISSUES CLOSED: #11243 #11223

src/cleveragents/reactive/stream_router.py

@@ -220,16 +220,19 @@ class SimpleLLMAgent:
         max_tokens = self.config.get("max_tokens")
         max_retries = self.config.get("max_retries")
         llm_kwargs: dict[str, Any] = {}
+        # Options are applied first so top-level keys take precedence over
+        # any duplicates in the options block.
+        options = dict(self.config.get("options") or {})
+        from cleveragents.actor.config import build_llm_kwargs_from_options
+
+        llm_kwargs.update(build_llm_kwargs_from_options(options, logger=logger_sr))
+
         if temperature is not None:
             llm_kwargs["temperature"] = temperature
         if max_tokens is not None:
             llm_kwargs["max_tokens"] = max_tokens
         if max_retries is not None:
             llm_kwargs["max_retries"] = max_retries
-        options = dict(self.config.get("options") or {})
-        from cleveragents.actor.config import build_llm_kwargs_from_options
-
-        llm_kwargs.update(build_llm_kwargs_from_options(options, logger=logger_sr))
         registry = get_provider_registry()
         self._llm = registry.create_llm(
             provider_type=provider, model_id=model, **llm_kwargs

src/cleveragents/reactive/tool_caller.py

@@ -131,6 +131,18 @@ class ToolCallingLLMCaller:
         max_retries = self._actor_config.get("max_retries")
 
         llm_kwargs: dict[str, Any] = {}
+
+        # M7 (#11243): merge options block so custom LLM backend kwargs
+        # (e.g. openai_api_base, openai_api_key) are forwarded to the
+        # LLM constructor — mirrors the fix applied to SimpleLLMAgent in
+        # stream_router.py (PR #11225 / commit b3851693).
+        # Options are applied first so top-level keys take precedence over
+        # any duplicates in the options block.
+        options = dict(self._actor_config.get("options") or {})
+        from cleveragents.actor.config import build_llm_kwargs_from_options
+
+        llm_kwargs.update(build_llm_kwargs_from_options(options, logger=logger))
+
         if temperature is not None:
             llm_kwargs["temperature"] = temperature
         if max_tokens is not None:
@@ -138,15 +150,6 @@ class ToolCallingLLMCaller:
         if max_retries is not None:
             llm_kwargs["max_retries"] = max_retries
 
-        # M7 (#11243): merge options block so custom LLM backend kwargs
-        # (e.g. openai_api_base, openai_api_key) are forwarded to the
-        # LLM constructor — mirrors the fix applied to SimpleLLMAgent in
-        # stream_router.py (PR #11225 / commit b3851693).
-        options = dict(self._actor_config.get("options") or {})
-        from cleveragents.actor.config import build_llm_kwargs_from_options
-
-        llm_kwargs.update(build_llm_kwargs_from_options(options, logger=logger))
-
         registry = get_provider_registry()
         # Annotated as Any because create_llm() returns BaseLanguageModel but the
         # real runtime object is BaseChatModel which has bind_tools().