refactor(agents): optimize token usage in AdvisoryCouncil and PortfolioBuilderAgent

backend/agents/council/advisory_council.py

@@ -343,20 +343,20 @@ Bull Score: {consensus['bull_score']:.0f}% • Bear Score: {consensus['bear_scor
             if not data:
                 return {
                     "specialist": spec["name"],
-                    "analysis": f"No {spec['data_key']} data available for analysis.",
+                    "analysis": f"No {spec['data_key']} data available.",
                     "data_used": spec["data_key"]
                 }
 
-            prompt = f"""You are a {spec['name']} focusing on {spec['focus']}.
+            # Format data specifically for this specialist to save tokens
+            formatted_data = self._format_data_for_specialist(spec["name"], data)
 
-Data: {str(data)[:800]}
+            prompt = f"""Role: {spec['name']} ({spec['focus']}).
+Data: {formatted_data}
 
-Provide:
+Output:
 1. 3 key findings
-2. Bullish/bearish factors
-3. Confidence (0-100)
-
-Be concise."""
+2. Bull/Bear factors
+3. Confidence (0-100)"""
 
             response = await self.model.ainvoke([HumanMessage(content=prompt)])
 
@@ -384,17 +384,17 @@ Be concise."""
             f"**{a['specialist']}**: {a['analysis']}" for a in analyses
         ])
 
-        prompt = f"""Bull Researcher: Build bullish case from analyses.
+        prompt = f"""Role: Bull Researcher.
+Source: Specialist analyses below.
 
 {analyses_text[:1500]}
 
-Provide:
+Task: Build bullish case.
+Output:
 1. Thesis (2 sentences)
-2. 3-4 key supporting points
-3. Expected returns
-4. Confidence (0-100)
-
-Be concise and persuasive."""
+2. 3 key points
+3. Exp. Return
+4. Confidence (0-100)"""
 
         response = await self.model.ainvoke([HumanMessage(content=prompt)])
 
@@ -416,17 +416,17 @@ Be concise and persuasive."""
             f"**{a['specialist']}**: {a['analysis']}" for a in analyses
         ])
 
-        prompt = f"""Bear Researcher: Build bearish case from analyses.
+        prompt = f"""Role: Bear Researcher.
+Source: Specialist analyses below.
 
 {analyses_text[:1500]}
 
-Provide:
+Task: Build bearish case.
+Output:
 1. Thesis (2 sentences)
-2. 3-4 key warning signs
-3. Downside risks
-4. Confidence (0-100)
-
-Be concise and critical."""
+2. 3 key risks
+3. Downside
+4. Confidence (0-100)"""
 
         response = await self.model.ainvoke([HumanMessage(content=prompt)])
 
@@ -608,18 +608,16 @@ Provide:
         latest_opponent = opponent_history[-1] if opponent_history else "No argument yet"
 
         # Build prompt
-        prompt = f"""You are the {role} Researcher in round {round_num} of a debate.
-
-Your position: {own_history[0] if own_history else ''}
-
-{opponent_role}'s latest argument:
-{latest_opponent}
-
-Respond with a counter-argument. Be {'very aggressive' if contentiousness > 7 else 'moderately contentious' if contentiousness > 4 else 'measured'}.
-
-Include your confidence level (0-100) in your response.
-Format: "Confidence: X%"
-"""
+        # Build prompt
+        prompt = f"""Role: {role} (Round {round_num}).
+My Stance: {own_history[0] if own_history else ''}
+Opponent ({opponent_role}): {latest_opponent}
+
+Task: Counter-argue. Tone: {'Aggressive' if contentiousness > 7 else 'Measured'}.
+Output:
+1. Counter-points
+2. Evidence
+3. Confidence (0-100)"""
         return prompt
 
     def _extract_confidence(self, text: str) -> float:
@@ -636,3 +634,65 @@ Format: "Confidence: X%"
                 return float(match.group(1))
 
         return 50.0
+
+    def _format_data_for_specialist(self, specialist_name: str, data: Any) -> str:
+        """Format data specifically for each specialist to minimize token usage.
+        
+        Instead of dumping raw JSON, extracts only relevant metrics.
+        """
+        if not data:
+            return "No data"
+
+        try:
+            if specialist_name == "Fundamental Analyst":
+                # Extract key ratios and metrics
+                if isinstance(data, dict):
+                    # Handle FMP profile data
+                    if "symbol" in data:
+                        return (f"Symbol: {data.get('symbol')}, Price: {data.get('price')}, "
+                               f"Mkt Cap: {data.get('mktCap')}, P/E: {data.get('mktCap')}, "
+                               f"Beta: {data.get('beta')}, Ind: {data.get('industry')}")
+                    # Handle general dict
+                    return str({k: v for k, v in data.items() if k in 
+                               ['pe_ratio', 'eps', 'revenue_growth', 'profit_margin', 'debt_to_equity']})
+                return str(data)[:300]
+
+            elif specialist_name == "Technical Analyst":
+                # Extract indicators
+                if isinstance(data, dict):
+                    return str({k: v for k, v in data.items() if k in 
+                               ['rsi', 'macd', 'bollinger', 'sma_50', 'sma_200', 'trend']})
+                return str(data)[:300]
+
+            elif specialist_name == "Sentiment Analyst":
+                # Extract sentiment scores and top headlines
+                if isinstance(data, dict):
+                    summary = f"Score: {data.get('sentiment_score', 'N/A')}"
+                    if 'news' in data and isinstance(data['news'], list):
+                        headlines = [n.get('title', '') for n in data['news'][:3]]
+                        summary += f", Headlines: {'; '.join(headlines)}"
+                    return summary
+                return str(data)[:300]
+
+            elif specialist_name == "Macro Analyst":
+                # Extract key economic indicators
+                if isinstance(data, dict):
+                    return str({k: v for k, v in data.items() if k in 
+                               ['gdp_growth', 'inflation_rate', 'interest_rate', 'unemployment']})
+                return str(data)[:300]
+
+            elif specialist_name == "Risk Analyst":
+                # Extract risk metrics
+                if isinstance(data, dict):
+                    # Handle nested risk metrics
+                    metrics = data.get('risk_metrics', data)
+                    return str({k: v for k, v in metrics.items() if k in 
+                               ['sharpe_ratio', 'volatility', 'var_95', 'max_drawdown']})
+                return str(data)[:300]
+
+            else:
+                return str(data)[:300]
+
+        except Exception as e:
+            logger.warning(f"Error formatting data for {specialist_name}: {e}")
+            return str(data)[:300]

backend/agents/react_agent.py

@@ -303,17 +303,42 @@ class PortfolioBuilderAgent:
             """Execute tools and truncate outputs to reduce token usage."""
             result = await base_tool_node.ainvoke(state)
 
-            # Truncate tool message content to prevent excessive token usage
-            max_tool_output_length = 300  # chars, ~75 tokens
+            # Smart truncation based on tool type
             if "messages" in result:
                 truncated_messages = []
                 for msg in result["messages"]:
                     if isinstance(msg, ToolMessage):
                         content = str(msg.content)
-                        if len(content) > max_tool_output_length:
-                            truncated_content = content[:max_tool_output_length] + f"... (truncated {len(content) - max_tool_output_length} chars)"
+                        tool_name = msg.name
+                        
+                        # Smart summarization based on tool name
+                        if tool_name == "fetch_market_data":
+                            # Extract just price and change
+                            try:
+                                import json
+                                data = json.loads(content) if isinstance(content, str) else content
+                                if isinstance(data, dict):
+                                    summary = {k: {"price": v.get("regularMarketPrice"), "change": v.get("regularMarketChangePercent")} 
+                                              for k, v in data.items() if isinstance(v, dict)}
+                                    msg.content = str(summary)
+                            except:
+                                msg.content = content[:200] + "..."
+                                
+                        elif tool_name == "get_news_sentiment":
+                            # Extract just headlines and score
+                            try:
+                                import json
+                                data = json.loads(content) if isinstance(content, str) else content
+                                if isinstance(data, dict):
+                                    headlines = [n.get('title', '')[:50] for n in data.get('news', [])[:2]]
+                                    msg.content = f"Score: {data.get('sentiment_score')}, News: {headlines}"
+                            except:
+                                msg.content = content[:200] + "..."
+                        
+                        elif len(content) > 300:
+                            truncated_content = content[:300] + f"... (truncated {len(content) - 300} chars)"
                             msg.content = truncated_content
-                            logger.debug(f"Truncated tool output from {len(content)} to {len(truncated_content)} chars")
+                            
                     truncated_messages.append(msg)
                 result["messages"] = truncated_messages
 
@@ -368,7 +393,15 @@ class PortfolioBuilderAgent:
 
         return f"""Build portfolio. Goals: {goals}. Risk: {risk}/10. Constraints: {constraints}.
 
-Use tools efficiently. Output format:
+Tools: fetch_market_data, get_fundamentals, get_historical_prices, calculate_technicals, optimise_allocation, assess_risk, get_news_sentiment.
+
+Process:
+1. Get data for candidates
+2. Filter by fundamentals/technicals
+3. Optimise weights
+4. Output portfolio
+
+Output format:
 - TICKER: X% - brief reason
 Expected return: X%, Risk: Y%"""