fix: extract tool messages before checking FINISH_SIGNAL

**Critical Bug:**
When agent returns FINISH_SIGNAL, the code breaks immediately (line 640)
BEFORE extracting tool messages (lines 642-650). This caused tool messages
to never be captured when agent completes in single step.

**Timeline:**
1. Agent calls buy tools (MSFT, AMZN, NVDA)
2. Agent returns response with <FINISH_SIGNAL>
3. Code detects signal → break (line 640)
4. Lines 642-650 NEVER EXECUTE
5. Tool messages not captured → summarizer sees 0 tools

**Evidence from logs:**
- Console: 'Bought NVDA 10 shares'
- API: 3 trades executed (MSFT 5, AMZN 15, NVDA 10)
- Debug: 'Tool messages: 0' ❌

**Fix:**
Move tool extraction BEFORE stop signal check.
Agent can call tools AND return FINISH_SIGNAL in same response,
so we must process tools first.

**Impact:**
Now tool messages will be captured even when agent finishes in
single step. Summarizer will see actual trades executed.

This is the true root cause of empty tool messages in conversation_history.

agent/base_agent/base_agent.py

@@ -419,7 +419,7 @@ class BaseAgent:
         }
 
         if tool_name:
-            message["tool_name"] = tool_name
+            message["name"] = tool_name  # Use "name" not "tool_name" for consistency with summarizer
         if tool_input:
             message["tool_input"] = tool_input
 
@@ -633,21 +633,28 @@ Summary:"""
                 # Capture assistant response
                 self._capture_message("assistant", agent_response)
 
-                # Check stop signal
-                if STOP_SIGNAL in agent_response:
-                    print("✅ Received stop signal, trading session ended")
-                    print(agent_response)
-                    break
-
-                # Extract tool messages and count trade actions
+                # Extract tool messages BEFORE checking stop signal
+                # (agent may call tools AND return FINISH_SIGNAL in same response)
                 tool_msgs = extract_tool_messages(response)
+                print(f"[DEBUG] Extracted {len(tool_msgs)} tool messages from response")
                 for tool_msg in tool_msgs:
                     tool_name = getattr(tool_msg, 'name', None) or tool_msg.get('name') if isinstance(tool_msg, dict) else None
+                    tool_content = getattr(tool_msg, 'content', '') or tool_msg.get('content', '') if isinstance(tool_msg, dict) else str(tool_msg)
+
+                    # Capture tool message to conversation history
+                    self._capture_message("tool", tool_content, tool_name=tool_name)
+
                     if tool_name in ['buy', 'sell']:
                         action_count += 1
 
                 tool_response = '\n'.join([msg.content for msg in tool_msgs])
 
+                # Check stop signal AFTER processing tools
+                if STOP_SIGNAL in agent_response:
+                    print("✅ Received stop signal, trading session ended")
+                    print(agent_response)
+                    break
+
                 # Prepare new messages
                 new_messages = [
                     {"role": "assistant", "content": agent_response},
@@ -665,6 +672,15 @@ Summary:"""
         session_duration = time.time() - session_start
 
         # 7. Generate reasoning summary
+        # Debug: Log conversation history size
+        print(f"\n[DEBUG] Generating summary from {len(self.conversation_history)} messages")
+        assistant_msgs = [m for m in self.conversation_history if m.get('role') == 'assistant']
+        tool_msgs = [m for m in self.conversation_history if m.get('role') == 'tool']
+        print(f"[DEBUG] Assistant messages: {len(assistant_msgs)}, Tool messages: {len(tool_msgs)}")
+        if assistant_msgs:
+            first_assistant = assistant_msgs[0]
+            print(f"[DEBUG] First assistant message preview: {first_assistant.get('content', '')[:200]}...")
+
         summarizer = ReasoningSummarizer(model=self.model)
         summary = await summarizer.generate_summary(self.conversation_history)
 

agent/context_injector.py

@@ -52,10 +52,6 @@ class ContextInjector:
         """
         # Inject context parameters for trade tools
         if request.name in ["buy", "sell"]:
-            # Debug: Log self attributes BEFORE injection
-            print(f"[ContextInjector.__call__] ENTRY: id={id(self)}, self.signature={self.signature}, self.today_date={self.today_date}, self.job_id={self.job_id}, self.session_id={self.session_id}, self.trading_day_id={self.trading_day_id}")
-            print(f"[ContextInjector.__call__] Args BEFORE injection: {request.args}")
-
             # ALWAYS inject/override context parameters (don't trust AI-provided values)
             request.args["signature"] = self.signature
             request.args["today_date"] = self.today_date
@@ -66,8 +62,5 @@ class ContextInjector:
             if self.trading_day_id:
                 request.args["trading_day_id"] = self.trading_day_id
 
-            # Debug logging
-            print(f"[ContextInjector] Tool: {request.name}, Args after injection: {request.args}")
-
         # Call the actual tool handler
         return await handler(request)

agent/reasoning_summarizer.py

@@ -36,15 +36,17 @@ class ReasoningSummarizer:
             summary_prompt = f"""You are reviewing your own trading decisions for the day.
 Summarize your trading strategy and key decisions in 2-3 sentences.
 
+IMPORTANT: Explicitly state what trades you executed (e.g., "sold 2 GOOGL shares" or "bought 10 NVDA shares"). If you made no trades, state that clearly.
+
 Focus on:
-- What you analyzed
-- Why you made the trades you did
+- What specific trades you executed (buy/sell, symbols, quantities)
+- Why you made those trades
 - Your overall strategy for the day
 
 Trading session log:
 {log_text}
 
-Provide a concise summary:"""
+Provide a concise summary that includes the actual trades executed:"""
 
             response = await self.model.ainvoke([
                 {"role": "user", "content": summary_prompt}
@@ -67,21 +69,39 @@ Provide a concise summary:"""
             reasoning_log: List of message dicts
 
         Returns:
-            Formatted text representation
+            Formatted text representation with emphasis on trades
         """
+        # Debug: Log what we're formatting
+        print(f"[DEBUG ReasoningSummarizer] Formatting {len(reasoning_log)} messages")
+        assistant_count = sum(1 for m in reasoning_log if m.get('role') == 'assistant')
+        tool_count = sum(1 for m in reasoning_log if m.get('role') == 'tool')
+        print(f"[DEBUG ReasoningSummarizer] Breakdown: {assistant_count} assistant, {tool_count} tool")
+
         formatted_parts = []
+        trades_executed = []
 
         for msg in reasoning_log:
             role = msg.get("role", "")
             content = msg.get("content", "")
+            tool_name = msg.get("name", "")
 
             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]}")
+                # Highlight trade tool calls
+                if tool_name in ["buy", "sell"]:
+                    trades_executed.append(f"{tool_name.upper()}: {content[:150]}")
+                    formatted_parts.append(f"TRADE - {tool_name.upper()}: {content[:150]}")
+                else:
+                    # Other tool results (search, price, etc.)
+                    formatted_parts.append(f"{tool_name}: {content[:100]}")
+
+        # Add summary of trades at the top
+        if trades_executed:
+            trade_summary = f"TRADES EXECUTED ({len(trades_executed)}):\n" + "\n".join(trades_executed)
+            formatted_parts.insert(0, trade_summary)
+            formatted_parts.insert(1, "\n--- FULL LOG ---")
 
         return "\n".join(formatted_parts)