feat: add Anthropic effort level support for Claude models

Add effort parameter (high/medium/low) for Claude 4.5+ and 4.6 models,
consistent with OpenAI reasoning_effort and Google thinking_level.
Also add content normalization for Anthropic responses.

tradingagents/default_config.py

@@ -15,6 +15,7 @@ DEFAULT_CONFIG = {
     # Provider-specific thinking configuration
     "google_thinking_level": None,      # "high", "minimal", etc.
     "openai_reasoning_effort": None,    # "medium", "high", "low"
+    "anthropic_effort": None,           # "high", "medium", "low"
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,

tradingagents/graph/trading_graph.py

@@ -148,6 +148,11 @@ class TradingAgentsGraph:
             if reasoning_effort:
                 kwargs["reasoning_effort"] = reasoning_effort
 
+        elif provider == "anthropic":
+            effort = self.config.get("anthropic_effort")
+            if effort:
+                kwargs["effort"] = effort
+
         return kwargs
 
     def _create_tool_nodes(self) -> Dict[str, ToolNode]:

tradingagents/llm_clients/anthropic_client.py

@@ -2,9 +2,26 @@ from typing import Any, Optional
 
 from langchain_anthropic import ChatAnthropic
 
-from .base_client import BaseLLMClient
+from .base_client import BaseLLMClient, normalize_content
 from .validators import validate_model
 
+_PASSTHROUGH_KWARGS = (
+    "timeout", "max_retries", "api_key", "max_tokens",
+    "callbacks", "http_client", "http_async_client", "effort",
+)
+
+
+class NormalizedChatAnthropic(ChatAnthropic):
+    """ChatAnthropic with normalized content output.
+
+    Claude models with extended thinking or tool use return content as a
+    list of typed blocks. This normalizes to string for consistent
+    downstream handling.
+    """
+
+    def invoke(self, input, config=None, **kwargs):
+        return normalize_content(super().invoke(input, config, **kwargs))
+
 
 class AnthropicClient(BaseLLMClient):
     """Client for Anthropic Claude models."""
@@ -16,11 +33,11 @@ class AnthropicClient(BaseLLMClient):
         """Return configured ChatAnthropic instance."""
         llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks", "http_client", "http_async_client"):
+        for key in _PASSTHROUGH_KWARGS:
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
-        return ChatAnthropic(**llm_kwargs)
+        return NormalizedChatAnthropic(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for Anthropic."""