fix(llm): MiniMax integration polish vs official docs

M2.x tool_choice is enum-only (none/auto), so route through the
no-tool_choice dispatch. MinimaxChatOpenAI injects reasoning_split
so <think> blocks stay out of content. Catalog rounded out to the
full official M2.x lineup plus forward-compat regex.

tradingagents/llm_clients/capabilities.py

@@ -61,6 +61,21 @@ _DEEPSEEK_CHAT = ModelCapabilities(
     preferred_structured_method="function_calling",
 )
 
+# MiniMax M2.x reasoning models accept the tools array, but their
+# tool_choice parameter is restricted to the enum {"none", "auto"}
+# (platform.minimax.io/docs/api-reference/text-post). Langchain's
+# function_calling path sends tool_choice as a function-spec dict, which
+# MiniMax 400s — same shape as the DeepSeek bug. supports_tool_choice=False
+# makes the dispatch in NormalizedChatOpenAI suppress the kwarg; the schema
+# still ships as a tool. json_mode response_format is only for
+# MiniMax-Text-01, not M2.x.
+_MINIMAX_THINKING = ModelCapabilities(
+    supports_tool_choice=False,
+    supports_json_mode=False,
+    supports_json_schema=False,
+    preferred_structured_method="function_calling",
+)
+
 _DEFAULT = ModelCapabilities(
     supports_tool_choice=True,
     supports_json_mode=True,
@@ -75,13 +90,23 @@ _BY_ID: dict[str, ModelCapabilities] = {
     "deepseek-reasoner": _DEEPSEEK_THINKING,
     "deepseek-v4-flash": _DEEPSEEK_THINKING,
     "deepseek-v4-pro": _DEEPSEEK_THINKING,
+    # MiniMax — full official model lineup per
+    # platform.minimax.io/docs/api-reference/text-openai-api
+    "MiniMax-M2.7": _MINIMAX_THINKING,
+    "MiniMax-M2.7-highspeed": _MINIMAX_THINKING,
+    "MiniMax-M2.5": _MINIMAX_THINKING,
+    "MiniMax-M2.5-highspeed": _MINIMAX_THINKING,
+    "MiniMax-M2.1": _MINIMAX_THINKING,
+    "MiniMax-M2.1-highspeed": _MINIMAX_THINKING,
+    "MiniMax-M2": _MINIMAX_THINKING,
 }
 
-# Forward-compat patterns. A new ``deepseek-v5-*`` or ``deepseek-reasoner-*``
-# variant inherits the thinking-mode quirks automatically.
+# Forward-compat patterns. New ``deepseek-v5-*`` / ``deepseek-reasoner-*``
+# or ``MiniMax-M3*`` variants inherit the thinking-mode quirks automatically.
 _BY_PATTERN: list[tuple[re.Pattern[str], ModelCapabilities]] = [
     (re.compile(r"^deepseek-v\d"), _DEEPSEEK_THINKING),
     (re.compile(r"^deepseek-reasoner"), _DEEPSEEK_THINKING),
+    (re.compile(r"^MiniMax-M\d"), _MINIMAX_THINKING),
 ]
 
 

tradingagents/llm_clients/model_catalog.py

@@ -8,17 +8,22 @@ ModelOption = Tuple[str, str]
 ProviderModeOptions = Dict[str, Dict[str, List[ModelOption]]]
 
 
-# Shared model list for MiniMax's global and CN endpoints (same model IDs).
+# Shared model list for MiniMax's global and CN endpoints (same IDs).
+# Full official lineup per platform.minimax.io/docs/api-reference/text-openai-api.
+# All M2.x models share a 204,800-token context window.
 _MINIMAX_MODELS: Dict[str, List[ModelOption]] = {
     "quick": [
-        ("MiniMax M2.7 Highspeed — Fast, 204K ctx", "MiniMax-M2.7-highspeed"),
-        ("MiniMax M2.5 Highspeed — Previous-gen fast", "MiniMax-M2.5-highspeed"),
+        ("MiniMax-M2.7-highspeed - Faster M2.7, 204K ctx, ~100 TPS", "MiniMax-M2.7-highspeed"),
+        ("MiniMax-M2.5-highspeed - Previous-gen highspeed, 204K ctx", "MiniMax-M2.5-highspeed"),
+        ("MiniMax-M2.1-highspeed - M2.1 highspeed, 204K ctx", "MiniMax-M2.1-highspeed"),
         ("Custom model ID", "custom"),
     ],
     "deep": [
-        ("MiniMax M2.7 — Flagship, 204K ctx", "MiniMax-M2.7"),
-        ("MiniMax M2.5 — Previous-gen flagship", "MiniMax-M2.5"),
-        ("MiniMax M2.7 Highspeed — Faster M2.7, 204K ctx", "MiniMax-M2.7-highspeed"),
+        ("MiniMax-M2.7 - Flagship, SOTA on coding/agent benchmarks, 204K ctx", "MiniMax-M2.7"),
+        ("MiniMax-M2.7-highspeed - Same quality as M2.7, ~100 TPS", "MiniMax-M2.7-highspeed"),
+        ("MiniMax-M2.5 - Previous-gen flagship, 204K ctx", "MiniMax-M2.5"),
+        ("MiniMax-M2.1 - Earlier M2 line, 204K ctx", "MiniMax-M2.1"),
+        ("MiniMax-M2 - Base M2, 204K ctx", "MiniMax-M2"),
         ("Custom model ID", "custom"),
     ],
 }

tradingagents/llm_clients/openai_client.py

@@ -107,6 +107,28 @@ class DeepSeekChatOpenAI(NormalizedChatOpenAI):
                 generation.message.additional_kwargs["reasoning_content"] = reasoning
         return chat_result
 
+
+class MinimaxChatOpenAI(NormalizedChatOpenAI):
+    """MiniMax-specific overrides on top of the OpenAI-compatible client.
+
+    M2.x reasoning models embed ``<think>...</think>`` blocks directly in
+    ``message.content`` by default, which would pollute saved reports.
+    Per platform.minimax.io/docs/api-reference/text-openai-api, setting
+    ``reasoning_split=True`` in the request body redirects the thinking
+    block into ``reasoning_details`` so ``content`` stays clean.
+
+    Tool-choice handling for M2.x — those models accept only the string
+    enum ``{"none", "auto"}`` and reject langchain's function-spec dict —
+    is handled by the capability dispatch in
+    ``NormalizedChatOpenAI.with_structured_output``, not here.
+    """
+
+    def _get_request_payload(self, input_, *, stop=None, **kwargs):
+        payload = super()._get_request_payload(input_, stop=stop, **kwargs)
+        payload.setdefault("reasoning_split", True)
+        return payload
+
+
 # Kwargs forwarded from user config to ChatOpenAI
 _PASSTHROUGH_KWARGS = (
     "timeout", "max_retries", "reasoning_effort",
@@ -183,9 +205,14 @@ class OpenAIClient(BaseLLMClient):
         if self.provider == "openai":
             llm_kwargs["use_responses_api"] = True
 
-        # 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
+        # Provider-specific quirks live in their own subclasses so the
+        # base NormalizedChatOpenAI stays free of provider branches.
+        if self.provider == "deepseek":
+            chat_cls = DeepSeekChatOpenAI
+        elif self.provider in ("minimax", "minimax-cn"):
+            chat_cls = MinimaxChatOpenAI
+        else:
+            chat_cls = NormalizedChatOpenAI
         return chat_cls(**llm_kwargs)
 
     def validate_model(self) -> bool: