feat: DeepSeek V4 thinking-mode round-trip via DeepSeekChatOpenAI subclass

Resolves #599: thinking-mode models require reasoning_content to be
echoed back across turns; multi-turn agent runs failed with HTTP 400.

The fix isolates DeepSeek's quirks (reasoning_content round-trip and
the deepseek-reasoner no-tool_choice limitation) into a subclass so
the general OpenAI-compatible client stays untouched. Adds DeepSeek
V4 Pro/Flash to the catalog. 9 new tests; rationale documented in
the class docstrings.

Design adapted from #600; #611 closed in favour of this approach.

tests/test_deepseek_reasoning.py

@@ -0,0 +1,169 @@
+"""Tests for DeepSeekChatOpenAI thinking-mode behaviour.
+
+Two pieces verified:
+
+1. ``reasoning_content`` is captured on receive into the AIMessage's
+   ``additional_kwargs`` and re-attached on send so DeepSeek's API
+   sees the same value across turns.
+2. ``with_structured_output`` raises NotImplementedError for
+   ``deepseek-reasoner`` so the agent factories' free-text fallback
+   handles the request instead of failing at runtime.
+"""
+
+import os
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.prompt_values import ChatPromptValue
+
+from tradingagents.llm_clients.openai_client import (
+    DeepSeekChatOpenAI,
+    NormalizedChatOpenAI,
+    _input_to_messages,
+)
+
+
+# ---------------------------------------------------------------------------
+# _input_to_messages — the helper that handles list / ChatPromptValue / other
+# (Gemini bot review note: non-list inputs must also work)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestInputToMessages:
+    def test_list_input_returned_as_is(self):
+        msgs = [HumanMessage(content="hi")]
+        assert _input_to_messages(msgs) is msgs
+
+    def test_chat_prompt_value_unwrapped(self):
+        msgs = [HumanMessage(content="hi")]
+        prompt_value = ChatPromptValue(messages=msgs)
+        assert _input_to_messages(prompt_value) == msgs
+
+    def test_string_input_yields_empty_list(self):
+        # A bare string isn't a message-bearing input; the caller's normal
+        # langchain conversion happens upstream of _get_request_payload.
+        assert _input_to_messages("hello") == []
+
+
+# ---------------------------------------------------------------------------
+# Reasoning content propagation across turns
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestDeepSeekReasoningContent:
+    def _client(self):
+        os.environ.setdefault("DEEPSEEK_API_KEY", "placeholder")
+        return DeepSeekChatOpenAI(
+            model="deepseek-v4-flash",
+            api_key="placeholder",
+            base_url="https://api.deepseek.com",
+        )
+
+    def test_capture_on_receive(self):
+        """When the response carries reasoning_content, it lands on the
+        AIMessage's additional_kwargs so the next turn can echo it back."""
+        client = self._client()
+        result = client._create_chat_result(
+            {
+                "model": "deepseek-v4-flash",
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {
+                            "role": "assistant",
+                            "content": "Plan: buy NVDA.",
+                            "reasoning_content": "Step 1: trend is up. Step 2: ...",
+                        },
+                        "finish_reason": "stop",
+                    }
+                ],
+                "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+            }
+        )
+        ai = result.generations[0].message
+        assert ai.additional_kwargs["reasoning_content"] == "Step 1: trend is up. Step 2: ..."
+
+    def test_propagate_on_send(self):
+        """When an outgoing AIMessage carries reasoning_content, the request
+        payload echoes it on the corresponding message dict."""
+        client = self._client()
+        prior = AIMessage(
+            content="Plan",
+            additional_kwargs={"reasoning_content": "weighed bull case"},
+        )
+        new_user = HumanMessage(content="Refine.")
+        payload = client._get_request_payload([prior, new_user])
+        # Find the assistant message in the payload
+        assistant_dicts = [m for m in payload["messages"] if m.get("role") == "assistant"]
+        assert assistant_dicts, "assistant message missing from outgoing payload"
+        assert assistant_dicts[0]["reasoning_content"] == "weighed bull case"
+
+    def test_propagate_through_chat_prompt_value(self):
+        """Gemini bot review note: non-list inputs (ChatPromptValue) must
+        also propagate reasoning_content."""
+        client = self._client()
+        prior = AIMessage(
+            content="Plan",
+            additional_kwargs={"reasoning_content": "weighed bull case"},
+        )
+        prompt_value = ChatPromptValue(messages=[prior, HumanMessage(content="Refine.")])
+        payload = client._get_request_payload(prompt_value)
+        assistant_dicts = [m for m in payload["messages"] if m.get("role") == "assistant"]
+        assert assistant_dicts[0]["reasoning_content"] == "weighed bull case"
+
+
+# ---------------------------------------------------------------------------
+# deepseek-reasoner: structured output unavailable, falls through to free-text
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestDeepSeekReasonerStructuredOutput:
+    def test_with_structured_output_raises_for_reasoner(self):
+        client = DeepSeekChatOpenAI(
+            model="deepseek-reasoner",
+            api_key="placeholder",
+            base_url="https://api.deepseek.com",
+        )
+        from pydantic import BaseModel
+
+        class _Sample(BaseModel):
+            answer: str
+
+        with pytest.raises(NotImplementedError):
+            client.with_structured_output(_Sample)
+
+    def test_with_structured_output_works_for_v4(self):
+        """V4 models (non-reasoner) accept tool_choice; structured output works."""
+        client = DeepSeekChatOpenAI(
+            model="deepseek-v4-flash",
+            api_key="placeholder",
+            base_url="https://api.deepseek.com",
+        )
+        from pydantic import BaseModel
+
+        class _Sample(BaseModel):
+            answer: str
+
+        # Should return a Runnable, not raise. (The actual API call would
+        # require a real key; we only assert binding succeeds.)
+        wrapped = client.with_structured_output(_Sample)
+        assert wrapped is not None
+
+
+# ---------------------------------------------------------------------------
+# Base class isolation: NormalizedChatOpenAI does NOT have DeepSeek behaviour
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestBaseClassIsolation:
+    def test_normalized_does_not_propagate_reasoning_content(self):
+        """The general-purpose NormalizedChatOpenAI must not carry
+        DeepSeek-specific behaviour. Only the subclass does."""
+        assert not hasattr(NormalizedChatOpenAI, "_get_request_payload") or (
+            NormalizedChatOpenAI._get_request_payload
+            is NormalizedChatOpenAI.__bases__[0]._get_request_payload
+        )

tradingagents/llm_clients/model_catalog.py

@@ -65,10 +65,12 @@ MODEL_OPTIONS: ProviderModeOptions = {
     },
     "deepseek": {
         "quick": [
+            ("DeepSeek V4 Flash - Latest V4 fast model", "deepseek-v4-flash"),
             ("DeepSeek V3.2", "deepseek-chat"),
             ("Custom model ID", "custom"),
         ],
         "deep": [
+            ("DeepSeek V4 Pro - Latest V4 flagship model", "deepseek-v4-pro"),
             ("DeepSeek V3.2 (thinking)", "deepseek-reasoner"),
             ("DeepSeek V3.2", "deepseek-chat"),
             ("Custom model ID", "custom"),

tradingagents/llm_clients/openai_client.py

@@ -1,6 +1,7 @@
 import os
 from typing import Any, Optional
 
+from langchain_core.messages import AIMessage
 from langchain_openai import ChatOpenAI
 
 from .base_client import BaseLLMClient, normalize_content
@@ -11,29 +12,97 @@ class NormalizedChatOpenAI(ChatOpenAI):
     """ChatOpenAI with normalized content output.
 
     The Responses API returns content as a list of typed blocks
-    (reasoning, text, etc.). This normalizes to string for consistent
+    (reasoning, text, etc.). ``invoke`` normalizes to string for
-    downstream handling.
+    consistent downstream handling. ``with_structured_output`` defaults
+    to function-calling so the Responses-API parse path is avoided
+    (langchain-openai's parse path emits noisy
+    PydanticSerializationUnexpectedValue warnings per call without
+    affecting correctness).
+
+    Provider-specific quirks (e.g. DeepSeek's thinking mode) live in
+    purpose-built subclasses below so this base class stays small.
     """
 
     def invoke(self, input, config=None, **kwargs):
         return normalize_content(super().invoke(input, config, **kwargs))
 
     def with_structured_output(self, schema, *, method=None, **kwargs):
-        """Wrap with structured output, defaulting to function_calling for OpenAI.
-
-        langchain-openai's Responses-API-parse path (the default for json_schema
-        when use_responses_api=True) calls response.model_dump(...) on the OpenAI
-        SDK's union-typed parsed response, which makes Pydantic emit ~20
-        PydanticSerializationUnexpectedValue warnings per call. The function-calling
-        path returns a plain tool-call shape that does not trigger that
-        serialization, so it is the cleaner choice for our combination of
-        use_responses_api=True + with_structured_output. Both paths use OpenAI's
-        strict mode and produce the same typed Pydantic instance.
-        """
         if method is None:
             method = "function_calling"
         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.
+
+    Accepts a list of messages, a ``ChatPromptValue`` (from a
+    ChatPromptTemplate), or anything else (treated as no messages).
+    Used by providers that need to walk the outgoing message history;
+    in particular DeepSeek thinking-mode propagation must work for
+    both bare-list invocations and ChatPromptTemplate-driven ones, so
+    treating only ``list`` here would silently skip half the call sites.
+    """
+    if isinstance(input_, list):
+        return input_
+    if hasattr(input_, "to_messages"):
+        return input_.to_messages()
+    return []
+
+
+class DeepSeekChatOpenAI(NormalizedChatOpenAI):
+    """DeepSeek-specific overrides on top of the OpenAI-compatible client.
+
+    Two quirks that don't apply to other OpenAI-compatible providers:
+
+    1. **Thinking-mode round-trip.** When DeepSeek's thinking models return
+       a response with ``reasoning_content``, that field must be echoed
+       back as part of the assistant message on the next turn or the API
+       fails with HTTP 400. ``_create_chat_result`` captures the field on
+       receive and ``_get_request_payload`` re-attaches it on send.
+
+    2. **deepseek-reasoner has no tool_choice.** Structured output via
+       function-calling is unavailable, so we raise NotImplementedError
+       and let the agent factories fall back to free-text generation
+       (see ``tradingagents/agents/utils/structured.py``).
+    """
+
+    def _get_request_payload(self, input_, *, stop=None, **kwargs):
+        payload = super()._get_request_payload(input_, stop=stop, **kwargs)
+        outgoing = payload.get("messages", [])
+        for message_dict, message in zip(outgoing, _input_to_messages(input_)):
+            if not isinstance(message, AIMessage):
+                continue
+            reasoning = message.additional_kwargs.get("reasoning_content")
+            if reasoning is not None:
+                message_dict["reasoning_content"] = reasoning
+        return payload
+
+    def _create_chat_result(self, response, generation_info=None):
+        chat_result = super()._create_chat_result(response, generation_info)
+        response_dict = (
+            response
+            if isinstance(response, dict)
+            else response.model_dump(
+                exclude={"choices": {"__all__": {"message": {"parsed"}}}}
+            )
+        )
+        for generation, choice in zip(
+            chat_result.generations, response_dict.get("choices", [])
+        ):
+            reasoning = choice.get("message", {}).get("reasoning_content")
+            if reasoning is not None:
+                generation.message.additional_kwargs["reasoning_content"] = reasoning
+        return chat_result
+
+    def with_structured_output(self, schema, *, method=None, **kwargs):
+        if self.model_name == "deepseek-reasoner":
+            raise NotImplementedError(
+                "deepseek-reasoner does not support tool_choice; structured "
+                "output is unavailable. Agent factories fall back to "
+                "free-text generation automatically."
+            )
+        return super().with_structured_output(schema, method=method, **kwargs)
+
 # Kwargs forwarded from user config to ChatOpenAI
 _PASSTHROUGH_KWARGS = (
     "timeout", "max_retries", "reasoning_effort",
@@ -75,10 +144,12 @@ class OpenAIClient(BaseLLMClient):
         self.warn_if_unknown_model()
         llm_kwargs = {"model": self.model}
 
-        # Provider-specific base URL and auth
+        # Provider-specific base URL and auth. An explicit base_url on the
+        # client (e.g. a corporate proxy) takes precedence over the
+        # provider default so users can route through their own gateway.
         if self.provider in _PROVIDER_CONFIG:
-            base_url, api_key_env = _PROVIDER_CONFIG[self.provider]
+            default_base, api_key_env = _PROVIDER_CONFIG[self.provider]
-            llm_kwargs["base_url"] = base_url
+            llm_kwargs["base_url"] = self.base_url or default_base
             if api_key_env:
                 api_key = os.environ.get(api_key_env)
                 if api_key:
@@ -98,7 +169,10 @@ class OpenAIClient(BaseLLMClient):
         if self.provider == "openai":
             llm_kwargs["use_responses_api"] = True
 
-        return NormalizedChatOpenAI(**llm_kwargs)
+        # DeepSeek's thinking-mode quirks live in their own subclass so the
+        # base NormalizedChatOpenAI stays free of provider-specific branches.
+        chat_cls = DeepSeekChatOpenAI if self.provider == "deepseek" else NormalizedChatOpenAI
+        return chat_cls(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for the provider."""