Rename financials to fundamentals for schema consistency

- Update analyzer.py to use fundamentals_all instead of financials_all
- Align with Researcher-Agent schema normalization changes

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

frontend/src/App.tsx

@@ -18,6 +18,7 @@ import {
   MCPStatus,
   LLMStatus,
   MetricEntry,
+  UserApiKeys,
 } from "@/lib/api"
 import { AnalysisResponse } from "@/lib/types"
 import {
@@ -39,6 +40,11 @@ import {
   Pause,
   X,
   Loader2,
+  Settings,
+  Key,
+  ChevronDown,
+  ChevronUp,
+  Database,
 } from "lucide-react"
 
 // Import new components
@@ -107,6 +113,14 @@ const Index = () => {
   const [abortReason, setAbortReason] = useState<string>('')
   const [userEvents, setUserEvents] = useState<Array<{timestamp: string; message: string}>>([])
 
+  // User API keys (optional - for when server keys hit rate limits)
+  const [userApiKeys, setUserApiKeys] = useState<UserApiKeys>({})
+  const [showApiKeySettings, setShowApiKeySettings] = useState(false)
+
+  // Cache dialog state
+  const [showCacheDialog, setShowCacheDialog] = useState(false)
+  const [pendingCacheWorkflowId, setPendingCacheWorkflowId] = useState<string | null>(null)
+
   const [copied, setCopied] = useState(false)
   const pollingRef = useRef<ReturnType<typeof setInterval> | null>(null)
 
@@ -184,27 +198,14 @@ const Index = () => {
           clearInterval(pollingRef.current!)
           pollingRef.current = null
 
-          // Check if this was a cache hit
+          // Check if this was a cache hit - show dialog to let user choose
           if (status.data_source === 'cache') {
-            // Cache hit flow - only animate cache and output
             setCacheHit(true)
             setCurrentStep('cache')
-            setCompletedSteps(['input'])
-
-            setTimeout(() => {
-              setCompletedSteps(['input', 'cache'])
-              setCurrentStep('output')
-            }, 800)
-
-            setTimeout(async () => {
-              setCompletedSteps(['input', 'cache', 'output'])
-              setCurrentStep('completed')
-              const result = await getWorkflowResult(workflowIdToUse)
-              setAnalysisResult(result)
-              setIsLoading(false)
-              setShowResults(true)
-              setMainTab("results")
-            }, 1600)
+            setCompletedSteps(['input', 'cache'])
+            setPendingCacheWorkflowId(workflowIdToUse)
+            setShowCacheDialog(true)
+            // Don't auto-proceed - wait for user choice
             return
           }
 
@@ -284,6 +285,64 @@ const Index = () => {
     setLlmStatus(defaultLLMStatus)
   }
 
+  // Cache dialog: Use cached data
+  const handleUseCached = async () => {
+    if (!pendingCacheWorkflowId) return
+    setShowCacheDialog(false)
+    addUserEvent('Using cached analysis')
+
+    // Animate cache → output transition
+    setCurrentStep('output')
+    setTimeout(async () => {
+      setCompletedSteps(['input', 'cache', 'output'])
+      setCurrentStep('completed')
+      const result = await getWorkflowResult(pendingCacheWorkflowId)
+      setAnalysisResult(result)
+      setIsLoading(false)
+      setShowResults(true)
+      setMainTab("results")
+      setPendingCacheWorkflowId(null)
+    }, 800)
+  }
+
+  // Cache dialog: Run fresh analysis
+  const handleRunFresh = async () => {
+    if (!selectedStock) return
+    setShowCacheDialog(false)
+    setPendingCacheWorkflowId(null)
+    addUserEvent('Running fresh analysis (cache bypassed)')
+
+    // Reset state for fresh run
+    setCurrentStep('input')
+    setCompletedSteps([])
+    setMcpStatus(defaultMCPStatus)
+    setLlmStatus(defaultLLMStatus)
+    setActivityLog([])
+    setMetrics([])
+    setRevisionCount(0)
+    setScore(0)
+    setCacheHit(false)
+    setAnalysisResult(null)
+
+    try {
+      const { workflow_id } = await startAnalysis(
+        selectedStock.name,
+        selectedStock.symbol,
+        'Competitive Position',
+        true,  // skipCache = true
+        userApiKeys
+      )
+      setWorkflowId(workflow_id)
+      setCompletedSteps(['input'])
+      setCurrentStep('cache')
+      startPolling(workflow_id)
+    } catch (error) {
+      console.error("Error starting fresh analysis:", error)
+      setIsLoading(false)
+      setHasError(true)
+    }
+  }
+
   // Force dark mode
   useEffect(() => {
     document.documentElement.classList.add("dark")
@@ -367,7 +426,9 @@ Generated by Instant SWOT Agent`
       const { workflow_id } = await startAnalysis(
         selectedStock.name,
         selectedStock.symbol,
-        'Competitive Position'
+        'Competitive Position',
+        false,  // skipCache = false (check cache first)
+        userApiKeys
       )
       setWorkflowId(workflow_id)
       setCompletedSteps(['input'])
@@ -509,6 +570,88 @@ Generated by Instant SWOT Agent`
         </div>
       </header>
 
+      {/* Cache Hit Dialog */}
+      {showCacheDialog && (
+        <div className="fixed inset-0 bg-black/50 flex items-center justify-center z-50">
+          <Card className="w-full max-w-md mx-4">
+            <CardHeader>
+              <CardTitle className="flex items-center gap-2">
+                <Database className="h-5 w-5 text-blue-500" />
+                Cached Analysis Found
+              </CardTitle>
+            </CardHeader>
+            <CardContent className="space-y-4">
+              <p className="text-sm text-muted-foreground">
+                A recent analysis for <span className="font-medium text-foreground">{selectedStock?.symbol}</span> was found in cache.
+                Would you like to use the cached result or run a fresh analysis?
+              </p>
+              <div className="flex gap-3">
+                <Button onClick={handleUseCached} className="flex-1 gap-2">
+                  <Database className="h-4 w-4" />
+                  Use Cached
+                </Button>
+                <Button onClick={handleRunFresh} variant="outline" className="flex-1 gap-2">
+                  <RefreshCw className="h-4 w-4" />
+                  Run Fresh
+                </Button>
+              </div>
+            </CardContent>
+          </Card>
+        </div>
+      )}
+
+      {/* API Key Settings (Expandable) */}
+      <div className="container mx-auto px-4 sm:px-6 pt-2">
+        <button
+          onClick={() => setShowApiKeySettings(!showApiKeySettings)}
+          className="flex items-center gap-2 text-xs text-muted-foreground hover:text-foreground transition-colors"
+        >
+          <Key className="h-3 w-3" />
+          <span>API Keys (Optional)</span>
+          {showApiKeySettings ? <ChevronUp className="h-3 w-3" /> : <ChevronDown className="h-3 w-3" />}
+        </button>
+
+        {showApiKeySettings && (
+          <Card className="mt-2 p-3">
+            <p className="text-xs text-muted-foreground mb-3">
+              Provide your own API keys if server keys hit rate limits. Keys are not stored.
+            </p>
+            <div className="grid grid-cols-1 sm:grid-cols-3 gap-3">
+              <div>
+                <label className="text-xs text-muted-foreground block mb-1">Groq</label>
+                <input
+                  type="password"
+                  placeholder="gsk_..."
+                  value={userApiKeys.groq || ''}
+                  onChange={(e) => setUserApiKeys(prev => ({ ...prev, groq: e.target.value || undefined }))}
+                  className="w-full px-2 py-1 text-xs bg-background border rounded focus:outline-none focus:ring-1 focus:ring-ring"
+                />
+              </div>
+              <div>
+                <label className="text-xs text-muted-foreground block mb-1">Gemini</label>
+                <input
+                  type="password"
+                  placeholder="AI..."
+                  value={userApiKeys.gemini || ''}
+                  onChange={(e) => setUserApiKeys(prev => ({ ...prev, gemini: e.target.value || undefined }))}
+                  className="w-full px-2 py-1 text-xs bg-background border rounded focus:outline-none focus:ring-1 focus:ring-ring"
+                />
+              </div>
+              <div>
+                <label className="text-xs text-muted-foreground block mb-1">OpenRouter</label>
+                <input
+                  type="password"
+                  placeholder="sk-or-..."
+                  value={userApiKeys.openrouter || ''}
+                  onChange={(e) => setUserApiKeys(prev => ({ ...prev, openrouter: e.target.value || undefined }))}
+                  className="w-full px-2 py-1 text-xs bg-background border rounded focus:outline-none focus:ring-1 focus:ring-ring"
+                />
+              </div>
+            </div>
+          </Card>
+        )}
+      </div>
+
       <main className="container mx-auto px-4 sm:px-6 pt-4 pb-6 space-y-6 overflow-visible">
 
         {/* Process Flow + Metrics Panel */}

frontend/src/components/MCPDataPanel.tsx

@@ -182,22 +182,24 @@ export function MCPDataPanel({ metrics, rawData, mcpStatus, companyName, ticker,
   const newsArticles = React.useMemo(() => {
     if (!rawData) return []
 
-    // Try to get articles from metrics.news (field name is "results" from Tavily API)
-    const newsData = rawData.metrics?.news || rawData.news
-    if (newsData && typeof newsData === 'object') {
-      // Check "results" first (Tavily API format), then "articles" as fallback
-      const articles = (newsData as Record<string, unknown>).results || (newsData as Record<string, unknown>).articles
-      if (Array.isArray(articles)) {
-        return articles.slice(0, 4).map((a: Record<string, unknown>) => ({
-          title: a.title as string || a.content as string || 'News article',
-          url: a.url as string || a.link as string || '#'
+    // Navigate to metrics.news - the actual structure from API
+    const metricsObj = rawData.metrics as Record<string, unknown> | undefined
+    const newsData = metricsObj?.news as Record<string, unknown> | undefined
+
+    if (newsData) {
+      // Get results array (from Tavily/NYT/NewsAPI)
+      const results = newsData.results as Array<Record<string, unknown>> | undefined
+      if (results && Array.isArray(results) && results.length > 0) {
+        return results.slice(0, 5).map((a) => ({
+          title: String(a.title || a.content || 'News article'),
+          url: String(a.url || a.link || '#')
         }))
       }
     }
 
-    // Fallback: check if news is an array directly
-    if (Array.isArray(rawData.news)) {
-      return rawData.news.slice(0, 4)
+    // Fallback: check rawData.news directly
+    if (rawData.news && Array.isArray(rawData.news)) {
+      return rawData.news.slice(0, 5)
     }
 
     return []
@@ -207,35 +209,40 @@ export function MCPDataPanel({ metrics, rawData, mcpStatus, companyName, ticker,
   const sentimentSources = React.useMemo(() => {
     if (!rawData) return []
 
-    const sentimentData = rawData.metrics?.sentiment || rawData.sentiment
-    if (!sentimentData || typeof sentimentData !== 'object') return []
+    // Navigate to metrics.sentiment
+    const metricsObj = rawData.metrics as Record<string, unknown> | undefined
+    const sentimentData = metricsObj?.sentiment as Record<string, unknown> | undefined
+
+    if (!sentimentData) return []
 
     const sources: Array<{name: string, score: number | null, url?: string}> = []
-    const metrics = (sentimentData as Record<string, unknown>).metrics as Record<string, unknown> | undefined
+    const sentMetrics = sentimentData.metrics as Record<string, unknown> | undefined
 
     // Finnhub sentiment
-    if (metrics?.finnhub) {
-      const finnhub = metrics.finnhub as Record<string, unknown>
+    if (sentMetrics?.finnhub) {
+      const finnhub = sentMetrics.finnhub as Record<string, unknown>
+      const score = finnhub.score ?? finnhub.sentiment_score
       sources.push({
         name: 'Finnhub',
-        score: finnhub.score as number || finnhub.sentiment_score as number || null,
+        score: typeof score === 'number' ? score : null,
         url: 'https://finnhub.io'
       })
     }
 
     // Reddit sentiment
-    if (metrics?.reddit) {
-      const reddit = metrics.reddit as Record<string, unknown>
+    if (sentMetrics?.reddit) {
+      const reddit = sentMetrics.reddit as Record<string, unknown>
+      const score = reddit.score
       sources.push({
         name: 'Reddit',
-        score: reddit.score as number || null,
+        score: typeof score === 'number' ? score : null,
         url: 'https://reddit.com'
       })
     }
 
-    // Composite score
-    const composite = (sentimentData as Record<string, unknown>).composite_score as number | undefined
-    if (composite !== undefined && sources.length === 0) {
+    // Composite score as fallback
+    const composite = sentimentData.composite_score
+    if (typeof composite === 'number' && sources.length === 0) {
       sources.push({ name: 'Composite', score: composite })
     }
 

frontend/src/lib/api.ts

@@ -1,8 +1,8 @@
 import { AnalysisResponse } from './types'
 
 // In production (HF Spaces), API is served from same origin - use empty string
-// In development, use localhost:8002
-const API_BASE_URL = import.meta.env.VITE_API_URL ?? (import.meta.env.PROD ? '' : 'http://localhost:8002')
+// In development, use localhost:8000 (8002 is used by financials cluster)
+const API_BASE_URL = import.meta.env.VITE_API_URL ?? (import.meta.env.PROD ? '' : 'http://localhost:8000')
 
 // Stock search types
 export interface StockResult {
@@ -83,11 +83,20 @@ export async function searchStocks(query: string): Promise<StockSearchResponse>
   return response.json()
 }
 
+// User API keys for LLM providers
+export interface UserApiKeys {
+  groq?: string
+  gemini?: string
+  openrouter?: string
+}
+
 // Start analysis with ticker support
 export async function startAnalysis(
   companyName: string,
   ticker: string = '',
-  strategyFocus: string = 'Competitive Position'
+  strategyFocus: string = 'Competitive Position',
+  skipCache: boolean = false,
+  userApiKeys: UserApiKeys = {}
 ): Promise<WorkflowStartResponse> {
   const response = await fetch(`${API_BASE_URL}/analyze`, {
     method: 'POST',
@@ -97,7 +106,9 @@ export async function startAnalysis(
     body: JSON.stringify({
       name: companyName,
       ticker: ticker,
-      strategy_focus: strategyFocus
+      strategy_focus: strategyFocus,
+      skip_cache: skipCache,
+      user_api_keys: userApiKeys
     }),
   })
 

src/api/app.py

@@ -41,6 +41,7 @@ app.add_middleware(
     allow_origins=[
         "http://localhost:5173",
         "http://localhost:8080",
+        "http://localhost:8081",
         "http://localhost:3000",
         "https://huggingface.co",
         "https://*.hf.space",

src/api/routes/analysis.py

@@ -53,7 +53,8 @@ async def start_analysis(request: AnalysisRequest):
     # Start workflow in background thread
     thread = threading.Thread(
         target=run_workflow_background,
-        args=(workflow_id, request.name, request.ticker, request.strategy_focus),
+        args=(workflow_id, request.name, request.ticker, request.strategy_focus,
+              request.skip_cache, request.user_api_keys),
         daemon=True
     )
     thread.start()

src/api/schemas.py

@@ -10,6 +10,8 @@ class AnalysisRequest(BaseModel):
     name: str
     ticker: str = ""
     strategy_focus: str = "Competitive Position"
+    skip_cache: bool = False  # If True, ignore cache and run fresh analysis
+    user_api_keys: dict = {}  # Optional: {"groq": "key", "gemini": "key", "openrouter": "key"}
 
 
 class StockSearchResult(BaseModel):

src/llm_client.py

@@ -11,30 +11,40 @@ from typing import Optional, Tuple
 class LLMClient:
     """LLM client with automatic provider fallback."""
 
-    def __init__(self):
-        """Initialize client with available providers based on API keys."""
+    def __init__(self, override_keys: dict = None):
+        """Initialize client with available providers based on API keys.
+
+        Args:
+            override_keys: Optional dict with user-provided API keys.
+                          Keys: "groq", "gemini", "openrouter"
+        """
         self.providers = []
+        override_keys = override_keys or {}
+
+        # Build providers list - use override keys if provided, else env vars
+        groq_key = override_keys.get("groq") or os.getenv("GROQ_API_KEY")
+        gemini_key = override_keys.get("gemini") or os.getenv("GEMINI_API_KEY")
+        openrouter_key = override_keys.get("openrouter") or os.getenv("OPENROUTER_API_KEY")
 
-        # Build providers list dynamically based on available API keys
-        if os.getenv("GROQ_API_KEY"):
+        if groq_key:
             self.providers.append({
                 "name": "groq",
-                "key": os.getenv("GROQ_API_KEY"),
+                "key": groq_key,
                 "model": os.getenv("GROQ_MODEL", "llama-3.1-8b-instant"),
                 "url": "https://api.groq.com/openai/v1/chat/completions"
             })
 
-        if os.getenv("GEMINI_API_KEY"):
+        if gemini_key:
             self.providers.append({
                 "name": "gemini",
-                "key": os.getenv("GEMINI_API_KEY"),
+                "key": gemini_key,
                 "model": os.getenv("GEMINI_MODEL", "gemini-2.0-flash-exp")
             })
 
-        if os.getenv("OPENROUTER_API_KEY"):
+        if openrouter_key:
             self.providers.append({
                 "name": "openrouter",
-                "key": os.getenv("OPENROUTER_API_KEY"),
+                "key": openrouter_key,
                 "model": os.getenv("OPENROUTER_MODEL", "google/gemini-2.0-flash-exp:free"),
                 "url": "https://openrouter.ai/api/v1/chat/completions"
             })
@@ -142,11 +152,21 @@ class LLMClient:
         return None, f"Unknown provider: {provider['name']}"
 
 
-# Singleton instance
+# Singleton instance for default (env-based) client
 _client = None
 
-def get_llm_client() -> LLMClient:
-    """Get or create the singleton LLM client instance."""
+def get_llm_client(override_keys: dict = None) -> LLMClient:
+    """Get or create an LLM client instance.
+
+    Args:
+        override_keys: If provided, creates a new client with these keys.
+                      If None/empty, returns the singleton instance.
+    """
+    # If user provided override keys, create a fresh client for this request
+    if override_keys:
+        return LLMClient(override_keys)
+
+    # Otherwise use singleton for default env-based keys
     global _client
     if _client is None:
         _client = LLMClient()

src/nodes/analyzer.py

@@ -48,6 +48,336 @@ def _get_fiscal_period_label(metric: dict) -> str:
     return f"FY {fy}"
 
 
+def _format_currency(value):
+    """Format large numbers as currency (B/M)."""
+    if value is None:
+        return "N/A"
+    if isinstance(value, dict):
+        value = value.get("value")
+    if value is None:
+        return "N/A"
+    if isinstance(value, (int, float)):
+        if abs(value) >= 1e12:
+            return f"${value/1e12:.2f}T"
+        if abs(value) >= 1e9:
+            return f"${value/1e9:.2f}B"
+        if abs(value) >= 1e6:
+            return f"${value/1e6:.0f}M"
+        return f"${value:,.0f}"
+    return str(value)
+
+
+def _format_number(value, suffix="", decimals=2):
+    """Format a number with optional suffix."""
+    if value is None:
+        return "N/A"
+    if isinstance(value, dict):
+        value = value.get("value")
+    if value is None:
+        return "N/A"
+    if isinstance(value, (int, float)):
+        return f"{value:.{decimals}f}{suffix}"
+    return str(value)
+
+
+def _get_period_label(metric_data: dict) -> str:
+    """Get period label from metric data (e.g., 'FY 2024', 'Q3 2024', '2024-11')."""
+    if not isinstance(metric_data, dict):
+        return ""
+
+    # Check for fiscal year/form info
+    fy = metric_data.get("fiscal_year")
+    form = metric_data.get("form", "")
+    end_date = metric_data.get("end_date", "")
+    date = metric_data.get("date", "")
+
+    if fy:
+        if form == "10-K":
+            return f"FY {fy}"
+        elif form == "10-Q" and end_date:
+            try:
+                month = int(end_date.split("-")[1])
+                quarter = (month - 1) // 3 + 1
+                return f"Q{quarter} {fy}"
+            except:
+                return f"FY {fy}"
+        return f"FY {fy}"
+
+    # Fallback to date
+    if end_date:
+        return end_date[:10]
+    if date:
+        return str(date)[:10]
+    return ""
+
+
+def _get_value(metric_data) -> any:
+    """Extract value from metric data (handles both dict and plain values)."""
+    if isinstance(metric_data, dict):
+        return metric_data.get("value")
+    return metric_data
+
+
+def _generate_data_report(raw_data: str) -> str:
+    """Generate complete multi-source data report with simple tables."""
+    try:
+        data = json.loads(raw_data)
+    except json.JSONDecodeError:
+        return "Error: Could not parse data"
+
+    lines = []
+    company = data.get("company_name", "Unknown")
+    ticker = data.get("ticker", "N/A")
+    multi_source = data.get("multi_source", {})
+    metrics = data.get("metrics", {})
+
+    lines.append(f"# Data Report: {company} ({ticker})")
+    lines.append("")
+
+    # ========== FINANCIALS ==========
+    fin_all = multi_source.get("fundamentals_all", {})
+    sec_data = fin_all.get("sec_edgar", {}).get("data", {})
+    yf_data = fin_all.get("yahoo_finance", {}).get("data", {})
+
+    if sec_data or yf_data:
+        lines.append("## Financials")
+        lines.append("Primary: SEC EDGAR | Secondary: Yahoo Finance")
+        lines.append("")
+        lines.append("| Metric | Period | SEC EDGAR | Yahoo Finance |")
+        lines.append("|--------|--------|-----------|---------------|")
+
+        fin_metrics = [
+            ("Revenue", "revenue", _format_currency),
+            ("Net Income", "net_income", _format_currency),
+            ("Gross Profit", "gross_profit", _format_currency),
+            ("Operating Income", "operating_income", _format_currency),
+            ("Gross Margin %", "gross_margin_pct", lambda v: _format_number(v, "%")),
+            ("Operating Margin %", "operating_margin_pct", lambda v: _format_number(v, "%")),
+            ("Net Margin %", "net_margin_pct", lambda v: _format_number(v, "%")),
+            ("Free Cash Flow", "free_cash_flow", _format_currency),
+            ("Operating Cash Flow", "operating_cash_flow", _format_currency),
+            ("Total Assets", "total_assets", _format_currency),
+            ("Total Liabilities", "total_liabilities", _format_currency),
+            ("Stockholders Equity", "stockholders_equity", _format_currency),
+            ("Cash", "cash", _format_currency),
+            ("Long-term Debt", "long_term_debt", _format_currency),
+            ("Net Debt", "net_debt", _format_currency),
+            ("R&D Expense", "rd_expense", _format_currency),
+        ]
+
+        for name, key, fmt in fin_metrics:
+            sec_val = sec_data.get(key)
+            yf_val = yf_data.get(key)
+            period = _get_period_label(sec_val) or _get_period_label(yf_val)
+            sec_str = fmt(_get_value(sec_val)) if sec_val else "N/A"
+            yf_str = fmt(_get_value(yf_val)) if yf_val else "N/A"
+            if sec_str != "N/A" or yf_str != "N/A":
+                lines.append(f"| {name} | {period} | {sec_str} | {yf_str} |")
+
+        lines.append("")
+
+    # ========== VALUATION ==========
+    val_all = multi_source.get("valuation_all", {})
+    yf_val = val_all.get("yahoo_finance", {}).get("data", {})
+    av_val = val_all.get("alpha_vantage", {}).get("data", {})
+
+    if yf_val or av_val:
+        lines.append("## Valuation")
+        lines.append("Primary: Yahoo Finance | Secondary: Alpha Vantage")
+        lines.append("")
+        lines.append("| Metric | Yahoo Finance | Alpha Vantage |")
+        lines.append("|--------|---------------|---------------|")
+
+        val_metrics = [
+            ("Market Cap", "market_cap", _format_currency),
+            ("Enterprise Value", "enterprise_value", _format_currency),
+            ("P/E Trailing", "trailing_pe", lambda v: _format_number(v, "x")),
+            ("P/E Forward", "forward_pe", lambda v: _format_number(v, "x")),
+            ("P/B Ratio", "pb_ratio", lambda v: _format_number(v, "x")),
+            ("P/S Ratio", "ps_ratio", lambda v: _format_number(v, "x")),
+            ("EV/EBITDA", "ev_ebitda", lambda v: _format_number(v, "x")),
+            ("EV/Revenue", "ev_revenue", lambda v: _format_number(v, "x")),
+            ("PEG Ratio", "trailing_peg", lambda v: _format_number(v, "x")),
+            ("Price/FCF", "price_to_fcf", lambda v: _format_number(v, "x")),
+            ("Revenue Growth", "revenue_growth", lambda v: _format_number(v * 100 if v and abs(v) < 10 else v, "%") if v else "N/A"),
+            ("Earnings Growth", "earnings_growth", lambda v: _format_number(v * 100 if v and abs(v) < 10 else v, "%") if v else "N/A"),
+        ]
+
+        for name, key, fmt in val_metrics:
+            y = yf_val.get(key)
+            a = av_val.get(key)
+            ys = fmt(_get_value(y)) if y is not None else "N/A"
+            avs = fmt(_get_value(a)) if a is not None else "N/A"
+            if ys != "N/A" or avs != "N/A":
+                lines.append(f"| {name} | {ys} | {avs} |")
+
+        lines.append("")
+
+    # ========== VOLATILITY ==========
+    vol_all = multi_source.get("volatility_all", {})
+    if vol_all:
+        lines.append("## Volatility")
+        lines.append("Primary: FRED + Yahoo | Secondary: Alpha Vantage")
+        lines.append("")
+        lines.append("| Metric | Date | Primary | Secondary |")
+        lines.append("|--------|------|---------|-----------|")
+
+        ctx = vol_all.get("market_volatility_context", {})
+        vix = ctx.get("vix", {})
+        vxn = ctx.get("vxn", {})
+        yf_vol = vol_all.get("yahoo_finance", {}).get("data", {})
+        av_vol = vol_all.get("alpha_vantage", {}).get("data", {})
+
+        # VIX
+        if vix.get("value"):
+            lines.append(f"| VIX | {vix.get('date', '')} | {_format_number(vix.get('value'))} | - |")
+
+        # VXN
+        if vxn.get("value"):
+            lines.append(f"| VXN | {vxn.get('date', '')} | {_format_number(vxn.get('value'))} | - |")
+
+        # Beta
+        beta_yf = _get_value(yf_vol.get("beta"))
+        beta_av = _get_value(av_vol.get("beta")) if av_vol else None
+        if beta_yf or beta_av:
+            lines.append(f"| Beta | - | {_format_number(beta_yf, '', 3)} | {_format_number(beta_av, '', 3) if beta_av else 'N/A'} |")
+
+        # Historical Volatility
+        hv_yf = _get_value(yf_vol.get("historical_volatility"))
+        hv_av = _get_value(av_vol.get("historical_volatility")) if av_vol else None
+        if hv_yf or hv_av:
+            lines.append(f"| Historical Volatility | - | {_format_number(hv_yf, '%')} | {_format_number(hv_av, '%') if hv_av else 'N/A'} |")
+
+        # Implied Volatility
+        iv_yf = _get_value(yf_vol.get("implied_volatility"))
+        if iv_yf:
+            lines.append(f"| Implied Volatility | - | {_format_number(iv_yf, '%')} | N/A |")
+
+        lines.append("")
+
+    # ========== MACRO ==========
+    macro_all = multi_source.get("macro_all", {})
+    if macro_all:
+        lines.append("## Macro Indicators")
+        lines.append("Primary: BEA/BLS | Secondary: FRED")
+        lines.append("")
+        lines.append("| Metric | Period | BEA/BLS | FRED |")
+        lines.append("|--------|--------|---------|------|")
+
+        bea_bls = macro_all.get("bea_bls", {}).get("data", {})
+        fred = macro_all.get("fred", {}).get("data", {})
+
+        # GDP Growth
+        gdp_p = bea_bls.get("gdp_growth", {}) or {}
+        gdp_f = fred.get("gdp_growth", {}) or {}
+        gdp_date = gdp_p.get("date", "") or gdp_f.get("date", "")
+        lines.append(f"| GDP Growth | {gdp_date} | {_format_number(gdp_p.get('value'), '%')} | {_format_number(gdp_f.get('value'), '%')} |")
+
+        # CPI/Inflation
+        cpi_p = bea_bls.get("cpi_inflation", {}) or {}
+        cpi_f = fred.get("cpi_inflation", {}) or {}
+        cpi_date = cpi_p.get("date", "") or cpi_f.get("date", "")
+        lines.append(f"| Inflation (CPI YoY) | {cpi_date} | {_format_number(cpi_p.get('value'), '%')} | {_format_number(cpi_f.get('value'), '%')} |")
+
+        # Unemployment
+        unemp_p = bea_bls.get("unemployment", {}) or {}
+        unemp_f = fred.get("unemployment", {}) or {}
+        unemp_date = unemp_p.get("date", "") or unemp_f.get("date", "")
+        lines.append(f"| Unemployment | {unemp_date} | {_format_number(unemp_p.get('value'), '%')} | {_format_number(unemp_f.get('value'), '%')} |")
+
+        # Fed Funds Rate (FRED only)
+        rates = fred.get("interest_rate", {}) or {}
+        lines.append(f"| Fed Funds Rate | {rates.get('date', '')} | - | {_format_number(rates.get('value'), '%')} |")
+
+        lines.append("")
+
+    # ========== NEWS ==========
+    news = metrics.get("news", {})
+    # Tavily returns results in 'results', other sources use 'articles'
+    articles = news.get("results", []) or news.get("articles", []) if news else []
+
+    if articles:
+        lines.append("## News Articles")
+        lines.append(f"Source: {news.get('source', 'Tavily')}")
+        lines.append("")
+        lines.append("| # | Title | Source | URL |")
+        lines.append("|---|-------|--------|-----|")
+
+        for i, article in enumerate(articles[:10], 1):
+            title = article.get("title", "Untitled")
+            source = article.get("source", "Unknown")
+            url = article.get("url", article.get("link", ""))
+            lines.append(f"| {i} | {title} | {source} | {url} |")
+
+        lines.append("")
+
+    # ========== SENTIMENT ==========
+    sentiment = metrics.get("sentiment", {})
+    if sentiment:
+        composite_score = sentiment.get("composite_score", "N/A")
+        interpretation = sentiment.get("overall_interpretation", "")
+
+        # Try both old format (finnhub_sentiment) and new format (metrics.finnhub)
+        finnhub = sentiment.get("finnhub_sentiment", {}) or sentiment.get("metrics", {}).get("finnhub", {})
+        reddit = sentiment.get("reddit_sentiment", {}) or sentiment.get("metrics", {}).get("reddit", {})
+
+        finn_articles = finnhub.get("articles", [])
+        finn_score = finnhub.get("score", finnhub.get("composite_score", "N/A"))
+        finn_count = finnhub.get("articles_analyzed", len(finn_articles))
+
+        reddit_posts = reddit.get("posts", [])
+        reddit_score = reddit.get("score", reddit.get("composite_score", "N/A"))
+        reddit_count = reddit.get("posts_analyzed", len(reddit_posts))
+
+        lines.append("## Sentiment Analysis")
+        lines.append(f"Composite Score: {composite_score}/100 - {interpretation}")
+        lines.append("")
+        lines.append("| Source | Score | Items Analyzed |")
+        lines.append("|--------|-------|----------------|")
+        lines.append(f"| Finnhub | {finn_score}/100 | {finn_count} articles |")
+        lines.append(f"| Reddit | {reddit_score}/100 | {reddit_count} posts |")
+        lines.append("")
+
+        # Show individual articles if available
+        if finn_articles:
+            lines.append("### Finnhub Articles")
+            lines.append("")
+            lines.append("| # | Headline | Sentiment | URL |")
+            lines.append("|---|----------|-----------|-----|")
+            for i, article in enumerate(finn_articles[:10], 1):
+                headline = article.get("headline", article.get("title", "Untitled"))
+                sent = article.get("sentiment_score", article.get("sentiment", "N/A"))
+                if isinstance(sent, (int, float)):
+                    sent = f"{sent:+.2f}"
+                url = article.get("url", article.get("link", ""))
+                lines.append(f"| {i} | {headline} | {sent} | {url} |")
+            lines.append("")
+
+        # Show Reddit posts if available
+        if reddit_posts:
+            lines.append("### Reddit Posts")
+            lines.append("")
+            lines.append("| # | Title | Subreddit | Upvotes | Sentiment | URL |")
+            lines.append("|---|-------|-----------|---------|-----------|-----|")
+            for i, post in enumerate(reddit_posts[:10], 1):
+                title = post.get("title", "Untitled")
+                subreddit = post.get("subreddit", "r/unknown")
+                upvotes = post.get("upvotes", post.get("score", 0))
+                sent = post.get("sentiment_score", post.get("sentiment", "N/A"))
+                if isinstance(sent, (int, float)):
+                    sent = f"{sent:+.2f}"
+                url = post.get("url", post.get("permalink", ""))
+                if url and not url.startswith("http"):
+                    url = f"https://reddit.com{url}"
+                lines.append(f"| {i} | {title} | {subreddit} | {upvotes} | {sent} | {url} |")
+            lines.append("")
+
+    lines.append("---")
+    lines.append("")
+
+    return "\n".join(lines)
+
+
 def _extract_key_metrics(raw_data: str) -> dict:
     """Extract and format key metrics from raw JSON data, preserving temporal info."""
     try:
@@ -59,7 +389,7 @@ def _extract_key_metrics(raw_data: str) -> dict:
     extracted = {
         "company": data.get("company_name", "Unknown"),
         "ticker": data.get("ticker", "N/A"),
-        "financials": {},
+        "fundamentals": {},
         "valuation": {},
         "volatility": {},
         "macro": {},
@@ -68,12 +398,12 @@ def _extract_key_metrics(raw_data: str) -> dict:
         "aggregated_swot": data.get("aggregated_swot", {})
     }
 
-    # Extract financials with temporal data
-    fin = metrics.get("financials", {})
+    # Extract fundamentals with temporal data
+    fin = metrics.get("fundamentals", {})
     if fin and "error" not in fin:
-        fin_data = fin.get("financials", {})
+        fin_data = fin.get("fundamentals", {})
         debt_data = fin.get("debt", {})
-        extracted["financials"] = {
+        extracted["fundamentals"] = {
             "revenue": _extract_temporal_metric(fin_data.get("revenue", {})),
             "revenue_cagr_3yr": fin_data.get("revenue_growth_3yr"),
             "net_margin": _extract_temporal_metric(fin_data.get("net_margin_pct", {})),
@@ -147,7 +477,7 @@ def _format_metrics_for_prompt(extracted: dict) -> str:
     lines.append("")
 
     # Financials (with temporal context)
-    fin = extracted.get("financials", {})
+    fin = extracted.get("fundamentals", {})
     if fin:
         lines.append("=== FINANCIALS (from SEC EDGAR) ===")
         # Revenue with fiscal period
@@ -294,7 +624,9 @@ def analyzer_node(state, workflow_id=None, progress_store=None):
             "score": state.get("score", 0)
         })
 
-    llm = get_llm_client()
+    # Use user-provided API keys if available
+    user_keys = state.get("user_api_keys", {})
+    llm = get_llm_client(user_keys) if user_keys else get_llm_client()
     raw = state["raw_data"]
     strategy_name = state.get("strategy_focus", "Cost Leadership")
     strategy_context = get_strategy_context(strategy_name)
@@ -305,17 +637,25 @@ def analyzer_node(state, workflow_id=None, progress_store=None):
     extracted = _extract_key_metrics(raw)
     formatted_data = _format_metrics_for_prompt(extracted)
 
+    # Generate detailed data report (shown before SWOT)
+    data_report = _generate_data_report(raw)
+
     # Log LLM call start
     _add_activity_log(workflow_id, progress_store, "analyzer", f"Calling LLM to generate SWOT analysis...")
 
-    prompt = f"""You are a financial analyst creating a SWOT analysis for {company} ({ticker}).
+    prompt = f"""You are a financial analyst creating a CONCISE SWOT analysis for {company} ({ticker}).
 
 CRITICAL INSTRUCTIONS:
 1. ONLY use the data provided below. DO NOT invent or assume any information.
 2. Every point MUST cite specific numbers from the data (e.g., "P/E of 21.3", "Beta of 0.88").
 3. If data is missing for a category, say "Insufficient data" - do NOT make up information.
 4. Focus on what the numbers actually mean for this specific company.
-5. This is a {company} - tailor your analysis to their industry (e.g., bank, tech, retail).
+
+FORMAT REQUIREMENTS - BE CONCISE:
+- Each bullet point: 1 sentence MAX (under 25 words)
+- 3-5 bullet points per SWOT category
+- Focus on the most impactful insights only
+- NO lengthy explanations or context paragraphs
 
 Strategic Focus: {strategy_name}
 Context: {strategy_context}
@@ -326,18 +666,18 @@ Context: {strategy_context}
 Based ONLY on the data above, provide a SWOT analysis in this format:
 
 Strengths:
-- [Cite specific metrics that show strengths]
+- [Single sentence with metric, under 25 words]
 
 Weaknesses:
-- [Cite specific metrics that show weaknesses]
+- [Single sentence with metric, under 25 words]
 
 Opportunities:
-- [Cite macro/market conditions that create opportunities]
+- [Single sentence citing macro/market data, under 25 words]
 
 Threats:
-- [Cite risks from volatility, macro conditions, or sentiment]
+- [Single sentence citing risks, under 25 words]
 
-Remember: Every bullet point must reference actual data provided above. Do not invent any figures or facts."""
+Remember: Every bullet must cite actual data. Keep each point brief and impactful."""
     start_time = time.time()
     response, provider, error, providers_failed = llm.query(prompt, temperature=0)
     elapsed = time.time() - start_time
@@ -370,7 +710,11 @@ Remember: Every bullet point must reference actual data provided above. Do not i
         _add_activity_log(workflow_id, progress_store, "analyzer", f"LLM error: {error}")
         _add_activity_log(workflow_id, progress_store, "analyzer", "Workflow aborted - all LLM providers unavailable")
     else:
-        state["draft_report"] = response
+        # Combine data report (Part 1) with SWOT analysis (Part 2)
+        swot_section = f"## SWOT Analysis\n\n{response}"
+        full_report = f"{data_report}\n{swot_section}"
+        state["draft_report"] = full_report
+        state["data_report"] = data_report  # Store separately for frontend flexibility
         state["provider_used"] = provider
         _add_activity_log(workflow_id, progress_store, "analyzer", f"SWOT generated via {provider} ({elapsed:.1f}s)")
 

src/nodes/editor.py

@@ -42,7 +42,9 @@ def editor_node(state, workflow_id=None, progress_store=None):
     # Log revision start
     _add_activity_log(workflow_id, progress_store, "editor", f"Revision #{current_revision} in progress...")
 
-    llm = get_llm_client()
+    # Use user-provided API keys if available
+    user_keys = state.get("user_api_keys", {})
+    llm = get_llm_client(user_keys) if user_keys else get_llm_client()
     strategy_name = state.get("strategy_focus", "Cost Leadership")
 
     # Get source data for grounding - editor must use ONLY this data
@@ -53,7 +55,7 @@ def editor_node(state, workflow_id=None, progress_store=None):
 
     # Prepare the revision prompt with source data for grounding
     prompt = f"""
-You are revising a SWOT analysis based on critique feedback.
+You are revising a SWOT analysis based on critique feedback. Keep it CONCISE.
 
 CRITICAL GROUNDING RULES:
 1. You may ONLY use facts and numbers from the SOURCE DATA provided below.
@@ -79,8 +81,11 @@ REVISION INSTRUCTIONS:
 4. Make sure strengths/opportunities are positive, weaknesses/threats are negative
 5. Align analysis with {strategy_name} strategic focus
 6. If data is missing for a point, remove that point rather than inventing data
+7. Keep each bullet point under 25 words - single sentence only
+8. Maximum 5 bullet points per category
+9. Remove any verbose explanations or context paragraphs
 
-Return only the improved SWOT analysis. Do NOT include any facts not found in the SOURCE DATA.
+Return only the improved SWOT analysis. Keep it brief and impactful.
 """
 
     # Get the revised draft from LLM

src/services/workflow_store.py

@@ -97,14 +97,20 @@ def update_mcp_status(workflow_id: str, source: str, status: str):
             WORKFLOWS[workflow_id]["mcp_status"][source] = status
 
 
-def run_workflow_background(workflow_id: str, company_name: str, ticker: str, strategy_focus: str):
+def run_workflow_background(workflow_id: str, company_name: str, ticker: str, strategy_focus: str,
+                            skip_cache: bool = False, user_api_keys: dict = None):
     """Execute workflow in background thread with progress tracking."""
     try:
-        # Check cache first
+        # Check cache first (unless skip_cache is True)
         add_activity_log(workflow_id, "cache", f"Checking cache for {ticker}")
         WORKFLOWS[workflow_id]["current_step"] = "cache"
 
-        cached = get_cached_analysis(ticker)
+        if skip_cache:
+            add_activity_log(workflow_id, "cache", f"Cache skipped - running fresh analysis")
+            cached = None
+        else:
+            cached = get_cached_analysis(ticker)
+
         if cached:
             # Cache hit - use cached result
             add_activity_log(workflow_id, "cache", f"Cache HIT - {ticker} analysis found in history")
@@ -166,7 +172,8 @@ def run_workflow_background(workflow_id: str, company_name: str, ticker: str, st
             "data_source": "live",
             "provider_used": None,
             "workflow_id": workflow_id,
-            "progress_store": WORKFLOWS
+            "progress_store": WORKFLOWS,
+            "user_api_keys": user_api_keys or {}  # Pass user API keys to nodes
         }
 
         # Execute workflow

static/index.html

@@ -5,8 +5,8 @@
     <link rel="icon" type="image/svg+xml" href="/vite.svg" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>frontend</title>
-    <script type="module" crossorigin src="/assets/index-SoStcVBo.js"></script>
-    <link rel="stylesheet" crossorigin href="/assets/index-DJPxD6T_.css">
+    <script type="module" crossorigin src="/assets/index-DCCdBPwi.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-BcWqIwge.css">
   </head>
   <body>
     <div id="root"></div>