feat: add multi-provider LLM support with thinking configurations

Models added:
- OpenAI: GPT-5.2, GPT-5.1, GPT-5, GPT-5 Mini, GPT-5 Nano, GPT-4.1
- Anthropic: Claude Opus 4.5/4.1, Claude Sonnet 4.5/4, Claude Haiku 4.5
- Google: Gemini 3 Pro/Flash, Gemini 2.5 Flash/Flash Lite
- xAI: Grok 4, Grok 4.1 Fast (Reasoning/Non-Reasoning)

Configs updated:
- Add unified thinking_level for Gemini (maps to thinking_level for Gemini 3,
  thinking_budget for Gemini 2.5; handles Pro's lack of "minimal" support)
- Add OpenAI reasoning_effort configuration
- Add NormalizedChatGoogleGenerativeAI for consistent response handling

Fixes:
- Fix Bull/Bear researcher display truncation
- Replace ChromaDB with BM25 for memory retrieval

tradingagents/graph/trading_graph.py

@@ -69,16 +69,20 @@ class TradingAgentsGraph:
             exist_ok=True,
         )
 
-        # Initialize LLMs
+        # Initialize LLMs with provider-specific thinking configuration
+        llm_kwargs = self._get_provider_kwargs()
+
         deep_client = create_llm_client(
             provider=self.config["llm_provider"],
             model=self.config["deep_think_llm"],
             base_url=self.config.get("backend_url"),
+            **llm_kwargs,
         )
         quick_client = create_llm_client(
             provider=self.config["llm_provider"],
             model=self.config["quick_think_llm"],
             base_url=self.config.get("backend_url"),
+            **llm_kwargs,
         )
         self.deep_thinking_llm = deep_client.get_llm()
         self.quick_thinking_llm = quick_client.get_llm()
@@ -119,6 +123,23 @@ class TradingAgentsGraph:
         # Set up the graph
         self.graph = self.graph_setup.setup_graph(selected_analysts)
 
+    def _get_provider_kwargs(self) -> Dict[str, Any]:
+        """Get provider-specific kwargs for LLM client creation."""
+        kwargs = {}
+        provider = self.config.get("llm_provider", "").lower()
+
+        if provider == "google":
+            thinking_level = self.config.get("google_thinking_level")
+            if thinking_level:
+                kwargs["thinking_level"] = thinking_level
+
+        elif provider == "openai":
+            reasoning_effort = self.config.get("openai_reasoning_effort")
+            if reasoning_effort:
+                kwargs["reasoning_effort"] = reasoning_effort
+
+        return kwargs
+
     def _create_tool_nodes(self) -> Dict[str, ToolNode]:
         """Create tool nodes for different data sources using abstract methods."""
         return {