Fix all review issues

agent_os/backend/services/langgraph_engine.py

@@ -329,15 +329,14 @@ class LangGraphEngine:
         # Save portfolio reports (Holding Reviews, Risk Metrics, PM Decision, Execution Result)
         if final_state:
             try:
-                # 1. Holding Reviews
+                # 1. Holding Reviews — save the raw string via ReportStore
                 holding_reviews_str = final_state.get("holding_reviews")
                 if holding_reviews_str:
                     try:
                         reviews = json.loads(holding_reviews_str) if isinstance(holding_reviews_str, str) else holding_reviews_str
-                        # ReportStore doesn't have batch save for reviews, but we can save the string or iterate.
+                        store.save_holding_reviews(date, portfolio_id, reviews)
-                        # For now, let's at least save the PM decision and Execution Result which are most critical.
+                    except Exception as exc:
-                        pass
+                        logger.warning("Failed to save holding_reviews run=%s: %s", run_id, exc)
-                    except Exception: pass
 
                 # 2. Risk Metrics
                 risk_metrics_str = final_state.get("risk_metrics")
@@ -345,7 +344,8 @@ class LangGraphEngine:
                     try:
                         metrics = json.loads(risk_metrics_str) if isinstance(risk_metrics_str, str) else risk_metrics_str
                         store.save_risk_metrics(date, portfolio_id, metrics)
-                    except Exception: pass
+                    except Exception as exc:
+                        logger.warning("Failed to save risk_metrics run=%s: %s", run_id, exc)
 
                 # 3. PM Decision
                 pm_decision_str = final_state.get("pm_decision")
@@ -353,7 +353,8 @@ class LangGraphEngine:
                     try:
                         decision = json.loads(pm_decision_str) if isinstance(pm_decision_str, str) else pm_decision_str
                         store.save_pm_decision(date, portfolio_id, decision)
-                    except Exception: pass
+                    except Exception as exc:
+                        logger.warning("Failed to save pm_decision run=%s: %s", run_id, exc)
 
                 # 4. Execution Result
                 execution_result_str = final_state.get("execution_result")
@@ -361,7 +362,8 @@ class LangGraphEngine:
                     try:
                         execution = json.loads(execution_result_str) if isinstance(execution_result_str, str) else execution_result_str
                         store.save_execution_result(date, portfolio_id, execution)
-                    except Exception: pass
+                    except Exception as exc:
+                        logger.warning("Failed to save execution_result run=%s: %s", run_id, exc)
 
                 yield self._system_log(f"Portfolio stage reports (decision & execution) saved for {portfolio_id} on {date}")
             except Exception as exc:
@@ -371,7 +373,8 @@ class LangGraphEngine:
         logger.info("Completed PORTFOLIO run=%s", run_id)
 
     async def run_trade_execution(
-        self, run_id: str, date: str, portfolio_id: str, decision: dict, prices: dict
+        self, run_id: str, date: str, portfolio_id: str, decision: dict, prices: dict,
+        store: ReportStore | None = None,
     ) -> AsyncGenerator[Dict[str, Any], None]:
         """Manually execute a pre-computed PM decision (for resumability)."""
         logger.info("Starting TRADE_EXECUTION run=%s portfolio=%s date=%s", run_id, portfolio_id, date)
@@ -380,6 +383,12 @@ class LangGraphEngine:
         from tradingagents.portfolio.trade_executor import TradeExecutor
         from tradingagents.portfolio.repository import PortfolioRepository
 
+        if not prices:
+            logger.warning("TRADE_EXECUTION run=%s: no prices available — execution may produce incomplete results", run_id)
+            yield self._system_log(f"Warning: no prices found for {portfolio_id} on {date} — trade execution may be incomplete.")
+
+        _store = store or ReportStore()
+
         try:
             repo = PortfolioRepository(config=self.config)
             executor = TradeExecutor(repo=repo, config=self.config)
@@ -387,8 +396,8 @@ class LangGraphEngine:
             # Execute decisions
             result = executor.execute_decisions(portfolio_id, decision, prices, date=date)
 
-            # Save results
+            # Save results using the shared store instance
-            ReportStore().save_execution_result(date, portfolio_id, result)
+            _store.save_execution_result(date, portfolio_id, result)
 
             yield self._system_log(f"Trade execution completed for {portfolio_id}. {result.get('summary', {})}")
             logger.info("Completed TRADE_EXECUTION run=%s", run_id)
@@ -456,7 +465,8 @@ class LangGraphEngine:
                 scan_data = store.load_scan(date) or {}
                 prices = scan_data.get("prices") or {}
                 async for evt in self.run_trade_execution(
-                    f"{run_id}_resume_trades", date, portfolio_id, saved_decision, prices
+                    f"{run_id}_resume_trades", date, portfolio_id, saved_decision, prices,
+                    store=store,
                 ):
                     yield evt
             else:
@@ -537,8 +547,8 @@ class LangGraphEngine:
         """Extract ticker symbols from a ReportStore scan summary dict.
 
         Handles two shapes from the macro synthesis LLM output:
-        * List of dicts: ``[{"ticker": "AAPL", ...}, ...]``
+        * List of dicts: ``[{'ticker': 'AAPL', ...}, ...]``
-        * List of strings: ``["AAPL", "TSLA", ...]``
+        * List of strings: ``['AAPL', 'TSLA', ...]``
 
         Also checks both ``stocks_to_investigate`` and ``watchlist`` keys.
         Returns an uppercase, deduplicated list in original order.
@@ -661,7 +671,7 @@ class LangGraphEngine:
         Handles several structures observed across LangChain / LangGraph versions:
         - flat list of message objects  ``[SystemMessage, HumanMessage, ...]``
         - list-of-lists (batched)       ``[[SystemMessage, HumanMessage, ...]]``
-        - list of plain dicts            ``[{"role": "system", "content": "..."}]``
+        - list of plain dicts            ``[{'role': 'system', 'content': '...'}]``
         - tuple wrapper                  ``([SystemMessage, ...],)``
         """
         if not messages:
@@ -690,7 +700,7 @@ class LangGraphEngine:
 
     def _extract_model(self, event: Dict[str, Any]) -> str:
         """Best-effort extraction of the model name from a LangGraph event."""
-        data = event.get("data") or {}
+        data = event.get("data") or {};
 
         # 1. invocation_params (standard LangChain)
         inv = data.get("invocation_params") or {}
@@ -784,7 +794,7 @@ class LangGraphEngine:
                 )
 
                 return {
-                    "id": event.get("run_id", f"thought_{time.time_ns()}"),
+                    "id": event.get("run_id", f"thought_{time.time_ns()}").strip(),
                     "node_id": node_name,
                     "parent_node_id": "start",
                     "type": "thought",
@@ -819,7 +829,7 @@ class LangGraphEngine:
                 logger.info("Tool start tool=%s node=%s run=%s", name, node_name, run_id)
 
                 return {
-                    "id": event.get("run_id", f"tool_{time.time_ns()}"),
+                    "id": event.get("run_id", f"tool_{time.time_ns()}").strip(),
                     "node_id": f"tool_{name}",
                     "parent_node_id": node_name,
                     "type": "tool",