feat: add AI reasoning summary generator with fallback

- Implement ReasoningSummarizer class for generating 2-3 sentence AI summaries
- Add fallback to statistical summary when AI generation fails
- Format reasoning logs for summary prompt with truncation
- Handle empty reasoning logs with default message
- Add comprehensive unit tests with async mocking

agent/reasoning_summarizer.py

@@ -0,0 +1,110 @@
+"""AI reasoning summary generation."""
+
+import logging
+from typing import List, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+
+class ReasoningSummarizer:
+    """Generate summaries of AI trading session reasoning."""
+
+    def __init__(self, model: Any):
+        """Initialize summarizer.
+
+        Args:
+            model: LangChain chat model for generating summaries
+        """
+        self.model = model
+
+    async def generate_summary(self, reasoning_log: List[Dict]) -> str:
+        """Generate AI summary of trading session reasoning.
+
+        Args:
+            reasoning_log: List of message dicts with role and content
+
+        Returns:
+            Summary string (2-3 sentences)
+        """
+        if not reasoning_log:
+            return "No trading activity recorded."
+
+        try:
+            # Build condensed version of reasoning log
+            log_text = self._format_reasoning_for_summary(reasoning_log)
+
+            summary_prompt = f"""You are reviewing your own trading decisions for the day.
+Summarize your trading strategy and key decisions in 2-3 sentences.
+
+Focus on:
+- What you analyzed
+- Why you made the trades you did
+- Your overall strategy for the day
+
+Trading session log:
+{log_text}
+
+Provide a concise summary:"""
+
+            response = await self.model.ainvoke([
+                {"role": "user", "content": summary_prompt}
+            ])
+
+            # Extract content from response
+            if hasattr(response, 'content'):
+                return response.content
+            else:
+                return str(response)
+
+        except Exception as e:
+            logger.error(f"Failed to generate AI reasoning summary: {e}")
+            return self._generate_fallback_summary(reasoning_log)
+
+    def _format_reasoning_for_summary(self, reasoning_log: List[Dict]) -> str:
+        """Format reasoning log into concise text for summary prompt.
+
+        Args:
+            reasoning_log: List of message dicts
+
+        Returns:
+            Formatted text representation
+        """
+        formatted_parts = []
+
+        for msg in reasoning_log:
+            role = msg.get("role", "")
+            content = msg.get("content", "")
+
+            if role == "assistant":
+                # AI's thoughts
+                formatted_parts.append(f"AI: {content[:200]}")
+            elif role == "tool":
+                # Tool results
+                tool_name = msg.get("name", "tool")
+                formatted_parts.append(f"{tool_name}: {content[:100]}")
+
+        return "\n".join(formatted_parts)
+
+    def _generate_fallback_summary(self, reasoning_log: List[Dict]) -> str:
+        """Generate simple statistical summary without AI.
+
+        Args:
+            reasoning_log: List of message dicts
+
+        Returns:
+            Fallback summary string
+        """
+        trade_count = sum(
+            1 for msg in reasoning_log
+            if msg.get("role") == "tool" and msg.get("name") == "trade"
+        )
+
+        search_count = sum(
+            1 for msg in reasoning_log
+            if msg.get("role") == "tool" and msg.get("name") == "search"
+        )
+
+        return (
+            f"Executed {trade_count} trades using {search_count} market searches. "
+            f"Full reasoning log available."
+        )