fix(structured): harden structured output for local servers and thinking models

- Local servers (LM Studio, vLLM) reject the object-form tool_choice langchain
  sends for function calling. The generic openai_compatible provider now binds
  the schema as a tool without forcing tool_choice.
- A structured call can return no parsed result (a thinking model answering in
  plain text); fall back to free text with a clear reason instead of an opaque
  render error.

tradingagents/agents/utils/structured.py

@@ -63,6 +63,11 @@ def invoke_structured_or_freetext(
     if structured_llm is not None:
         try:
             result = structured_llm.invoke(prompt)
+            if result is None:
+                # A thinking model can answer in plain text instead of calling
+                # the tool, leaving the parser with nothing to return. Treat it
+                # as a structured miss and fall back, with a clear reason.
+                raise ValueError("structured output returned no parsed result")
             return render(result)
         except Exception as exc:
             logger.warning(

tradingagents/llm_clients/openai_client.py

@@ -51,6 +51,23 @@ class NormalizedChatOpenAI(ChatOpenAI):
         return super().with_structured_output(schema, method=method, **kwargs)
 
 
+class LocalCompatibleChatOpenAI(NormalizedChatOpenAI):
+    """OpenAI-compatible client for arbitrary local servers (LM Studio, vLLM,
+    llama.cpp via the generic ``openai_compatible`` provider).
+
+    Their tool-calling support varies, and many reject the object-form
+    ``tool_choice`` langchain sends for function-calling structured output. Bind
+    the schema as a tool but don't force tool_choice, so structured output works
+    across local servers regardless of the model ID's capabilities (#1057).
+    """
+
+    def with_structured_output(self, schema, *, method=None, **kwargs):
+        resolved = method or get_capabilities(self.model_name).preferred_structured_method
+        if resolved == "function_calling":
+            kwargs.setdefault("tool_choice", None)
+        return super().with_structured_output(schema, method=method, **kwargs)
+
+
 def _input_to_messages(input_: Any) -> list:
     """Normalise a langchain LLM input to a list of message objects.
 
@@ -210,7 +227,9 @@ OPENAI_COMPATIBLE_PROVIDERS: dict[str, ProviderSpec] = {
     "ollama":     ProviderSpec(base_url="http://localhost:11434/v1", base_url_env="OLLAMA_BASE_URL",
                                key_optional=True, placeholder_key="ollama"),
     # Generic endpoint: user supplies base_url; key optional (keyless local).
-    "openai_compatible": ProviderSpec(require_base_url=True, key_optional=True),
+    "openai_compatible": ProviderSpec(
+        require_base_url=True, key_optional=True, chat_class=LocalCompatibleChatOpenAI
+    ),
 }