fix: runners prepend config messages; managed layer passes plain str to run()

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: jsonbailey

packages/ai-providers/server-ai-langchain/src/ldai_langchain/langchain_model_runner.py

@@ -24,8 +24,9 @@ class LangChainModelRunner(Runner):
     :meth:`run`.
     """
 
-    def __init__(self, llm: BaseChatModel):
+    def __init__(self, llm: BaseChatModel, config_messages: Optional[List[LDMessage]] = None):
         self._llm = llm
+        self._config_messages: List[LDMessage] = list(config_messages or [])
 
     def get_llm(self) -> BaseChatModel:
         """
@@ -43,14 +44,17 @@ async def run(
         """
         Run the LangChain model with the given input.
 
+        Prepends any config messages (system prompt, instructions, etc.) stored
+        at construction time before the user message.
+
         :param input: A string prompt
         :param output_type: Optional JSON schema dict requesting structured output.
             When provided, ``parsed`` on the returned :class:`RunnerResult` is
             populated with the parsed JSON document.
         :return: :class:`RunnerResult` containing ``content``, ``metrics``,
             ``raw`` and (when ``output_type`` is set) ``parsed``.
         """
-        messages = [LDMessage(role='user', content=input)]
+        messages = self._config_messages + [LDMessage(role='user', content=input)]
 
         if output_type is not None:
             return await self._run_structured(messages, output_type)

packages/ai-providers/server-ai-langchain/src/ldai_langchain/langchain_runner_factory.py

@@ -69,4 +69,5 @@ def create_model(self, config: AIConfigKind) -> LangChainModelRunner:
         :return: LangChainModelRunner ready to invoke the model
         """
         llm = create_langchain_model(config)
-        return LangChainModelRunner(llm)
+        config_messages = list(getattr(config, 'messages', None) or [])
+        return LangChainModelRunner(llm, config_messages)

packages/ai-providers/server-ai-openai/src/ldai_openai/openai_model_runner.py

@@ -28,10 +28,12 @@ def __init__(
         client: AsyncOpenAI,
         model_name: str,
         parameters: Dict[str, Any],
+        config_messages: Optional[List[LDMessage]] = None,
     ):
         self._client = client
         self._model_name = model_name
         self._parameters = parameters
+        self._config_messages: List[LDMessage] = list(config_messages or [])
 
     async def run(
         self,
@@ -41,14 +43,17 @@ async def run(
         """
         Run the OpenAI model with the given input.
 
+        Prepends any config messages (system prompt, instructions, etc.) stored
+        at construction time before the user message.
+
         :param input: A string prompt
         :param output_type: Optional JSON schema dict requesting structured output.
             When provided, ``parsed`` on the returned :class:`RunnerResult` is
             populated with the parsed JSON document.
         :return: :class:`RunnerResult` containing ``content``, ``metrics``,
             ``raw`` and (when ``output_type`` is set) ``parsed``.
         """
-        messages = [LDMessage(role='user', content=input)]
+        messages = self._config_messages + [LDMessage(role='user', content=input)]
 
         if output_type is not None:
             return await self._run_structured(messages, output_type)

packages/ai-providers/server-ai-openai/src/ldai_openai/openai_runner_factory.py

@@ -100,7 +100,8 @@ def create_model(self, config: AIConfigKind) -> OpenAIModelRunner:
         tool_defs = parameters.pop('tools', None) or []
         if tool_defs:
             parameters['tools'] = normalize_tool_types(tool_defs)
-        return OpenAIModelRunner(self._client, model_name, parameters)
+        config_messages = list(getattr(config, 'messages', None) or [])
+        return OpenAIModelRunner(self._client, model_name, parameters, config_messages)
 
     def get_client(self) -> AsyncOpenAI:
         """

packages/sdk/server-ai/src/ldai/managed_model.py

@@ -43,12 +43,9 @@ async def run(self, prompt: str) -> ManagedResult:
         user_message = LDMessage(role='user', content=prompt)
         self._messages.append(user_message)
 
-        config_messages = self._ai_config.messages or []
-        all_messages = config_messages + self._messages
-
         result: RunnerResult = await tracker.track_metrics_of_async(
             lambda r: r.metrics,
-            lambda: self._model_runner.run(all_messages),
+            lambda: self._model_runner.run(prompt),
         )
 
         assistant_message = LDMessage(role='assistant', content=result.content)