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README.md

@@ -1,64 +1,28 @@
----
-app_port: 7860
-colorFrom: blue
-colorTo: gray
-emoji: 📈
-license: mit
-pinned: false
-sdk: docker
-short_description: Multi-agent AI Market Analyst
-title: AI Market Analyst v2
----
-
-# 📈 AI Market Analyst --- Upgraded Architecture
-
-This application has been upgraded to a decoupled architecture with a **FastAPI backend** and a **Vue.js frontend**. The multi-agent swarm now communicates via Server-Sent Events (SSE) for real-time analysis streaming.
-
-## 🏗️ New Architecture
-
-### 📂 `backend/`
-- **FastAPI**: Exposes an SSE endpoint for real-time agent streams.
-- **Agents**: Same powerful multi-agent team (Market, Sentiment, Strategy, Risk).
-- **Tools**: Technical analysis, DuckDuckGo news scraping, and FinBERT sentiment analysis.
-
-### 📂 `frontend/`
-- **Vue.js 3 + Vite**: A modern, glassy UI built for performance and intuition.
-- **Glassmorphism Design**: High-end visuals with backdrop blurs and neon accents.
-- **Real-time Streaming**: Watch agents "think" and communicate as it happens.
-
----
-
-## 🚀 Getting Started
-
-### 1. Run the Backend
-```bash
-cd src/market-analyst/backend
-# Install dependencies (if not already in root env)
-pip install -r requirements.txt
-# Launch FastAPI
-uvicorn main:app --reload --port 8000
-```
-Backend API will be at `http://localhost:8000`
-
-### 2. Run the Frontend
-```bash
-cd src/market-analyst/frontend
-# Install dependencies
-npm install
-# Launch Vite
-npm run dev
-```
-Frontend UI will be at `http://localhost:5173`
-
----
-
-## ✨ Features
-- **SSE Streaming**: Agents stream results character-by-character for a more interactive experience.
-- **Glassy Dashboard**: A premium, dark-mode adaptive UI.
-- **Multi-Agent Coordination**: MarketAnalyst (Technicals), SentimentAnalyst (News), StrategyAdvisor (Logic), RiskManager (Validation).
-- **FinBERT Integration**: Real-time financial sentiment analysis on full article text.
-
----
-
-## ⚠️ Financial Disclaimer
-**Educational use only.** This tool is not a financial advisor. Do not use for real-money trading without consulting a certified professional.
+---
+title: Agora
+emoji: 🏛️
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_port: 7860
+pinned: false
+license: mit
+short_description: Real-time streaming multi-agent market intelligence
+---
+
+# Agora
+
+Agora — named after the ancient Greek marketplace — is a real-time multi-agent market intelligence platform. A swarm of four specialist agents analyses a stock simultaneously and streams their findings character-by-character to a modern Vue.js dashboard.
+
+## What it does
+
+- **Market Analyst** — technical indicators: SMA, RSI, MACD, Bollinger Bands
+- **Sentiment Analyst** — FinBERT sentiment scoring on live news articles
+- **Strategy Advisor** — synthesises technicals and sentiment into actionable strategy
+- **Risk Manager** — validates the strategy against volatility and risk thresholds
+
+## Stack
+
+Vue.js 3 + Vite · FastAPI · Server-Sent Events (SSE) · OpenAI Agents SDK · FinBERT · DuckDuckGo
+
+> **Disclaimer:** For informational purposes only. Not financial advice.

pyproject.toml

@@ -23,7 +23,7 @@ dependencies = [
     # =======================
     "langchain>=1.0.7",
     "langchain-community>=0.4.1",
-    "langgraph>=1.0.3",
+    "langgraph>=1.0.10rc1",
     "langgraph-checkpoint-sqlite>=3.0.0",
     "langsmith>=0.8.0",
     "langchain-text-splitters>=1.1.2",
@@ -74,8 +74,8 @@ dependencies = [
     # =======================
     "psutil>=7.0.0",
     "python-dotenv>=1.2.2",
-    "requests>=2.32.3",
-    "aiohttp>=3.8.5",
+    "requests>=2.33.0",
+    "aiohttp>=3.14.0",
     "httpx>=0.28.1",
     "speedtest-cli>=2.1.3",
     "logfire",
@@ -98,7 +98,7 @@ dependencies = [
     # VISUAL / UI / PDF
     # =======================
     "plotly>=6.5.0",
-    "streamlit>=1.51.0",
+    "streamlit>=1.54.0",
     "reportlab>=4.4.5",
     "fastapi",
     "Pillow",
@@ -110,7 +110,7 @@ dependencies = [
     # =======================
     # AUDIO / VIDEO
     # =======================
-    "yt_dlp>=2025.11.12",
+    "yt_dlp>=2026.2.21",
     "openai-whisper==20250625",
     "numba==0.63.1",
     "llvmlite==0.46.0",
@@ -138,6 +138,7 @@ dependencies = [
     # =======================
     # OBSERVABILITY
     # =======================
+    "langfuse>=3.0.0",
     "openinference-instrumentation-autogen>=0.1.0",
     "openinference-instrumentation-openai>=0.1.15",
     "opentelemetry-sdk>=1.20.0",
@@ -157,9 +158,10 @@ dependencies = [
 
 [dependency-groups]
 dev = [
-    "pytest>=8.3.3",
+    "pytest>=9.0.3",
     "ipykernel>=7.1.0",
     "pytest-asyncio",
+    "pytest-cov>=7.1.0",
 ]
 
 # ============================================================
@@ -175,20 +177,16 @@ build-backend = "setuptools.build_meta"
 # PACKAGING & DISCOVERY
 # ============================================================
 # Tells setuptools where to find the source code.
-# This makes 'common' and 'src' importable when installed (pip install -e .).
 [tool.setuptools.packages.find]
-where = ["."]  # Look in the project root
-include = ["common*", "src*"]  # Treat 'common' and 'src' folders as packages
+where = ["."]
+include = ["src*"]
 
 
 # ============================================================
 # PYTEST SETTINGS
 # ============================================================
-# Configures the test runner to automatically find code.
 [tool.pytest.ini_options]
-# Adds 'src' and 'common' to the python path during tests.
-# This allows tests to import modules (e.g., 'import travel_agent')
-# just like the apps do locally, preventing ModuleNotFoundError.
-pythonpath = ["src", "common"]
+pythonpath = ["src"]
 testpaths = ["tests"]  # Only look for tests in the 'tests' directory
 addopts = "-q"  # Run in quiet mode (less verbose output)
+asyncio_mode = "auto"  # Auto-detect async tests (requires pytest-asyncio)

run.py

@@ -29,152 +29,42 @@ load_dotenv(override=True)
 
 # App registry - maps app names to their paths and entry points
 APP_REGISTRY: Dict[str, Dict[str, str]] = {
-    "healthcare": {
-        "path": "src/healthcare-assistant",
+    "nexus": {
+        "path": "src/nexus",
         "entry": "app.py",
-        "description": "Healthcare Assistant - Medical information with RAG and web search"
+        "description": "Nexus - AI Research Assistant - Multi-specialist orchestrator for finance, news, and web research"
     },
-    "deep-research_v1": {
-        "path": "src/deep-research_v1",
+    "athena": {
+        "path": "src/athena",
         "entry": "app.py",
-        "description": "Deep Research AI - Comprehensive research assistant"
+        "description": "Athena - Deep Research Reporter - Plans, searches, and synthesises comprehensive research reports"
     },
-    "deep-research_v2": {
-        "path": "src/deep-research_v2",
+    "remedy": {
+        "path": "src/remedy",
         "entry": "app.py",
-        "description": "Deep Research AI - Comprehensive research assistant"
+        "description": "Remedy - Healthcare RAG Advisor - Medical information retrieval using RAG and web search"
     },
-    "stock-analyst": {
-        "path": "src/stock-analyst",
+    "midas": {
+        "path": "src/midas",
         "entry": "app.py",
-        "description": "Stock Analyst - Financial analysis and stock recommendations"
+        "description": "Midas - Stock Investment Analyst - Multi-agent investment team for technical and sentiment analysis"
     },
-    "travel-agent": {
-        "path": "src/travel-agent",
+    "odyssey": {
+        "path": "src/odyssey",
         "entry": "app.py",
-        "description": "Travel Agent - Trip planning and travel recommendations"
+        "description": "Odyssey - Travel Planner - AI-powered trip planning with flight, hotel, and itinerary recommendations"
     },
-    "trip-planner": {
-        "path": "src/trip-planner",
+    "waypoint": {
+        "path": "src/waypoint",
         "entry": "main.py",
         "type": "fastapi",
-        "description": "Trip Planner - Detailed trip itinerary planning"
+        "description": "Waypoint - Trip Planner API - Phidata-powered trip itinerary planning REST API"
     },
-    "chatbot_v1": {
-        "path": "src/chatbot_v1",
-        "entry": "app.py",
-        "description": "General Chatbot - Multi-purpose conversational AI"
-    },
-    "chatbot_v2": {
-        "path": "src/chatbot_v2",
-        "entry": "app.py",
-        "description": "Layered Chatbot (ReAct) - Advanced Architecture"
-    },
-    "accessibility_v2": {
-        "path": "src/accessibility_v2",
-        "entry": "app.py",
-        "description": "Accessibility Auditor V2 - Layered Architecture"
-    },
-    "accessibility_v1": {
-        "path": "src/accessibility_v1",
-        "entry": "app.py",
-        "description": "Accessibility Tools - Assistive technology applications"
-    },
-    "literature-review": {
-        "path": "src/literature-review",
-        "entry": "app.py",
-        "description": "Literature Review Assistant - Multi-agent literature review tool"
-    },
-    "market-analyst": {
-        "path": "src/market-analyst",
+    "agora": {
+        "path": "src/agora",
         "entry": "backend/main.py",
         "type": "fastapi",
-        "description": "Market Analyst - Decoupled Multi-agent market analysis (Vue.js + FastAPI)"
-    },
-    "image": {
-        "path": "src/image-generator",
-        "entry": "app.py",
-        "description": "Image Generator - Multi-agent image generation tool"
-    },
-    "interview-assistant": {
-        "path": "src/interview-assistant",
-        "entry": "app.py",
-        "description": "Interview Assistant - Multi-agent interview tool"
-    },
-    "finadvisor-lg": {
-        "path": "src/finadvisor",
-        "entry": "app-lg.py",
-        "description": "Financial Advisor - Multi-agent financial advisor tool using LangGraph"
-    },
-    "finadvisor-oai": {
-        "path": "src/finadvisor",
-        "entry": "app-oai.py",
-        "description": "Financial Advisor - Multi-agent financial advisor tool using OpenAI"
-    },
-    "finadvisor-phi": {
-        "path": "src/finadvisor",
-        "entry": "app-phi.py",
-        "description": "Financial Advisor - Multi-agent financial advisor tool using Phidata"
-    }
-    ,
-    "finadvisor-ag": {
-        "path": "src/finadvisor",
-        "entry": "app-ag.py",
-        "description": "Financial Advisor - Multi-agent financial advisor tool using Autogen"
-    },
-    "mcp-trader": {
-        "path": "src/mcp-trader",
-        "entry": "server.py",
-        "type": "script",
-        "description": "Strategies MCP Server - FastMCP server for trading strategies"
-    },
-    "mcp-web": {
-        "path": "src/mcp-web",
-        "entry": "server.py",
-        "type": "script",
-        "description": "Web MCP Server - Search, Extract, Wikipedia, Arxiv"
-    },
-    "mcp-azure-sre": {
-        "path": "src/mcp-azure-sre",
-        "entry": "server.py",
-        "type": "script",
-        "description": "Azure SRE MCP Server - Manage Azure Resources & Monitoring"
-    },
-    "mcp-rag-secure": {
-        "path": "src/mcp-rag-secure",
-        "entry": "server.py",
-        "type": "script",
-        "description": "Secure RAG MCP Server - Multi-tenant knowledge base"
-    },
-    "mcp-trading-research": {
-        "path": "src/mcp-trading-research",
-        "entry": "server.py",
-        "type": "script",
-        "description": "Trading Research MCP Server - News, Insider, Analysts"
-    },
-    "mcp-github": {
-        "path": "src/mcp-github",
-        "entry": "server.py",
-        "type": "script",
-        "description": "GitHub MCP Server - Issues, PRs, Alerts"
-    },
-    "mcp-seo": {
-        "path": "src/mcp-seo",
-        "entry": "server.py",
-        "type": "script",
-        "description": "SEO & ADA MCP Server - Website Audits"
-    },
-    "github-portal": {
-        "path": "src/github-portal",
-        "entry": "app.py",
-        "type": "streamlit",
-        "description": "GitHub Portal - Repository health dashboard for issues, security, and pipelines"
-    },
-    "mcp-hub": {
-        "path": "src/mcp-hub",
-        "entry": "package.json",
-        "type": "npm",
-        "description": "MCP HUB - Discovery and monitoring portal (Vue.js)"
+        "description": "Agora - AI Market Analyst - Real-time multi-agent market analysis with streaming (Vue.js + FastAPI)"
     },
     "test": {
         "path": ".",
@@ -182,12 +72,6 @@ APP_REGISTRY: Dict[str, Dict[str, str]] = {
         "type": "test",
         "description": "Run Project Tests - Executes pytest suite"
     },
-    "salesdata": {
-        "path": "src/salesdata",
-        "entry": "app.py",
-        "type": "salesdata",
-        "description": "Sales Data Agent - Agentic RAG over sales CSV (FastAPI + Streamlit)"
-    }
 }
 
 
@@ -272,8 +156,8 @@ def launch_app(app_name: str, port: Optional[int] = None):
     env["PYTHONPATH"] = str(project_root) + os.pathsep + env.get("PYTHONPATH", "")
     
     # Decoupled App Logic: Build frontend if needed
-    if app_name == "market-analyst":
-        frontend_dir = project_root / "src/market-analyst/frontend"
+    if app_name == "agora":
+        frontend_dir = project_root / "src/agora/frontend"
         dist_dir = frontend_dir / "dist"
         if not dist_dir.exists():
             print("\n🛠️ Frontend build missing. Building now...")
@@ -334,70 +218,6 @@ def launch_app(app_name: str, port: Optional[int] = None):
         # Change to app directory and run
         os.chdir(app_dir)
         
-        # Special case for mcp-hub: launch backend API first
-        if app_name == "mcp-hub":
-            print("🚀 Starting MCP Hub Backend API on port 8001...")
-            api_cmd = [python_exe, "api.py"]
-            subprocess.Popen(api_cmd, env=env, shell=is_windows)
-
-        # Special case for salesdata: launch FastAPI backend + Streamlit frontend
-        if app_name == "salesdata":
-            api_port = 8080
-            ui_port = port if port else 8501
-
-            # Aggressively kill any stale processes on both ports
-            import platform
-            if platform.system() != "Windows":
-                for p in [api_port, ui_port]:
-                    # lsof is more reliable than fuser for finding PIDs by port
-                    result = subprocess.run(
-                        ["lsof", "-ti", f":{p}"],
-                        capture_output=True, text=True
-                    )
-                    pids = result.stdout.strip().split()
-                    for pid in pids:
-                        subprocess.run(["kill", "-9", pid],
-                                       stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL)
-                time.sleep(1)  # let OS release ports
-                print(f"🧹 Cleaned up ports {api_port} and {ui_port}")
-
-            print(f"🚀 Starting Sales Agent API on port {api_port}...")
-            api_cmd = [
-                python_exe, "-m", "uvicorn", "api:app",
-                "--host", "0.0.0.0", "--port", str(api_port)
-            ]
-            subprocess.Popen(api_cmd, env=env, shell=is_windows, cwd=app_dir)
-
-            # Poll /health until agent is ready (instead of a fixed sleep)
-            import urllib.request, urllib.error, json as _json
-            health_url = f"http://localhost:{api_port}/health"
-            print(f"⏳ Waiting for API + agent to be ready", end="", flush=True)
-            timeout = 90  # seconds
-            ready = False
-            for _ in range(timeout):
-                time.sleep(1)
-                print(".", end="", flush=True)
-                try:
-                    with urllib.request.urlopen(health_url, timeout=2) as r:
-                        data = _json.loads(r.read())
-                        if data.get("agent_ready"):
-                            ready = True
-                            break
-                except Exception:
-                    pass
-            print()
-            if not ready:
-                print("⚠️  API did not become ready within 90s — launching UI anyway")
-
-            print(f"🌐 Starting Streamlit UI on port {ui_port}...")
-            ui_cmd = [
-                python_exe, "-m", "streamlit", "run", "app.py",
-                "--server.port", str(ui_port)
-            ]
-            subprocess.run(ui_cmd, env=env, shell=is_windows)
-            return
-
-
 
 
         subprocess.run(cmd, env=env, shell=is_windows)

src/agora/Dockerfile

@@ -0,0 +1,63 @@
+# Stage 1: Build Frontend
+FROM node:20-slim AS frontend-builder
+WORKDIR /app/frontend
+COPY src/market-analyst/frontend/package*.json ./
+RUN npm install
+COPY src/market-analyst/frontend/ ./
+RUN npm run build
+
+# Stage 2: Backend & Runtime
+FROM python:3.12-slim
+WORKDIR /app
+
+ENV PYTHONUNBUFFERED=1 \
+    DEBIAN_FRONTEND=noninteractive \
+    PYTHONPATH=/app:$PYTHONPATH
+
+# System deps
+RUN apt-get update && apt-get install -y \
+    git build-essential curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+
+# Copy project metadata
+COPY pyproject.toml .
+COPY uv.lock .
+
+# Copy application code
+COPY common/ ./common/
+COPY src/market-analyst/backend/ ./src/market-analyst/backend/
+
+# Copy built frontend from Stage 1 to the location FastAPI expects
+COPY --from=frontend-builder /app/frontend/dist ./src/market-analyst/frontend/dist
+
+# Install dependencies
+RUN uv sync --frozen --no-dev && \
+    uv pip install -e . --system
+
+# Create a non-root user for HF Spaces security compliance
+RUN useradd -m -u 1000 user
+
+# Set env vars for cache (must be writable by user)
+ENV HF_HOME=/app/hf_cache \
+    TRANSFORMERS_CACHE=/app/hf_cache \
+    NUMBA_CACHE_DIR=/app/numba_cache
+
+# Create cache directories and ownership
+RUN mkdir -p /app/hf_cache /app/numba_cache && \
+    chown -R user:user /app
+
+# Switch to non-root user
+USER user
+
+# Preload FinBERT model (as user, to ensure cache writes to correct location)
+RUN python -c "from transformers import pipeline; pipeline('sentiment-analysis', model='ProsusAI/finbert', device=-1)"
+
+# HF Spaces default port is 7860
+EXPOSE 7860
+
+# Run uvicorn on port 7860
+CMD ["uvicorn", "src.market-analyst.backend.main:app", "--host", "0.0.0.0", "--port", "7860"]

src/agora/README.md

@@ -0,0 +1,28 @@
+---
+title: Agora
+emoji: 🏛️
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_port: 7860
+pinned: false
+license: mit
+short_description: Real-time streaming multi-agent market intelligence
+---
+
+# Agora
+
+Agora — named after the ancient Greek marketplace — is a real-time multi-agent market intelligence platform. A swarm of four specialist agents analyses a stock simultaneously and streams their findings character-by-character to a modern Vue.js dashboard.
+
+## What it does
+
+- **Market Analyst** — technical indicators: SMA, RSI, MACD, Bollinger Bands
+- **Sentiment Analyst** — FinBERT sentiment scoring on live news articles
+- **Strategy Advisor** — synthesises technicals and sentiment into actionable strategy
+- **Risk Manager** — validates the strategy against volatility and risk thresholds
+
+## Stack
+
+Vue.js 3 + Vite · FastAPI · Server-Sent Events (SSE) · OpenAI Agents SDK · FinBERT · DuckDuckGo
+
+> **Disclaimer:** For informational purposes only. Not financial advice.

src/agora/backend/aagents/fundamental_analyst.py

@@ -0,0 +1,62 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_core.tools import FunctionTool
+from tools.market_data import get_fundamental_data, get_market_indices, get_analyst_consensus
+
+def get_fundamental_analyst(model_client):
+    
+    fundamental_tool = FunctionTool(get_fundamental_data, description="Fetch fundamental financial metrics like P/E, PEG, Debt/Equity, and Profit Margin.")
+    market_tool = FunctionTool(get_market_indices, description="Get Market Context (SPY/VIX).")
+    consensus_tool = FunctionTool(get_analyst_consensus, description="Get Analyst Ratings & Targets.")
+
+    return AssistantAgent(
+        name="FundamentalAnalyst",
+        model_client=model_client,
+        tools=[market_tool, fundamental_tool, consensus_tool],
+        system_message="""
+        You are a Fundamental Analyst specializing in company valuation and financial health.
+        
+        MANDATORY WORKFLOW:
+        
+        STEP 1: CALL get_market_indices
+        STEP 2: CALL get_fundamental_data
+        STEP 3: CALL get_analyst_consensus
+        
+        DO NOT proceed without calling ALL THREE tools.
+        
+        CRITICAL: Provide your full analysis in ROUND 1 ONLY. In subsequent rounds, simply say "Fundamentals stable."
+        
+        STEP 4: Evaluate Valuation & Growth (USE 'valuation_score' & 'quality_score')
+        - If UNDERVALUED: Bullish Factor.
+        - If PREMIUM: Bearish/Neutral Factor (unless High Growth).
+        - If HIGH_QUALITY: Bullish Factor.
+        
+        STEP 5: BINARY EVENT RISK (CRITICAL)
+        - Check "next_earnings_date" from tool.
+        - If Earnings is within 14 days: "CRITICAL BINARY EVENT". Recommend avoiding short-duration short-volatility strategies (like Iron Condors).
+        - If Earnings is 14-30 days: "ELEVATED EVENT RISK".
+        
+        STEP 6: Assess Financial Health
+        - Debt/Equity Ratio: <0.5 (Safe), >1.0 (Risky).
+        - Profit Margin: >20% (Excellent), <10% (Weak).
+        
+        STEP 7: Market & Sector Context
+        - Market: If VIX > 25, PENALIZE High Debt/High P/E.
+        - Sector Standards: Tech (Higher P/E ok), Utilities (High Debt ok).
+        
+        STEP 8: Analyst Consensus Check
+        - Ratings: "buy" or "strong buy" = Positive. "sell" = Negative.
+        - Price Target: If Target < Current Price = Downside Risk (Bearish).
+        - Upside Potential: >20% is Strong Bullish factor.
+        
+        STEP 9: Assign Fundamental Strength Rating
+        - "Strong": Great Valuation + Safe Debt + Analyst Buy Support.
+        - "Stable": Fair metrics + Neutral Analysts.
+        - "Weak": Overvalued OR High Debt OR Analyst Sell Ratings.
+        
+        STEP 10: Output Structured Summary
+        - BE CONCISE: Use maximum 5 bullet points.
+        - NO conversational filler.
+        - Include: Strength Rating, Binary Event (Earnings), Valuation vs Peers, Health Summary, and Consensus.
+        - TERMINATION: End your message with [[DATA_COLLECTION_COMPLETE]] to move to the strategy phase.
+        """
+    )

src/agora/backend/aagents/market_analyst.py

@@ -0,0 +1,49 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_core.tools import FunctionTool
+from tools.market_data import get_current_price, get_technical_indicators, get_market_indices
+
+def get_technical_analyst(model_client):
+    
+    # Wrap tools
+    price_tool = FunctionTool(get_current_price, description="Get current price of a stock.")
+    tech_tool = FunctionTool(get_technical_indicators, description="Calculate SMA (20/50/200), EMA (20), RSI (14), and MACD technical indicators.")
+    market_tool = FunctionTool(get_market_indices, description="Get Market Context (SPY/VIX).")
+
+    return AssistantAgent(
+        name="TechnicalAnalyst",
+        model_client=model_client,
+        tools=[market_tool, price_tool, tech_tool],
+        system_message="""
+        You are a Master Technical Analyst specializing in chart patterns and momentum.
+        
+        MANDATORY WORKFLOW:
+        
+        STEP 1: CALL get_market_indices (Market Context)
+        STEP 2: CALL get_current_price (Stock Price)
+        STEP 3: CALL get_technical_indicators (Indicators)
+        
+        DO NOT proceed without calling ALL tools first.
+        
+        STEP 4: Analyze Market Context (Top-Down)
+        - If SPY is BEARISH, bias is SHORT/HEDGE.
+        - If VIX is HIGH (>30), bias is CAUTION.
+        
+        STEP 5: Analyze Trend & Structure (USE 'trend_signal' from tool)
+        - If 'trend_signal' is STRONG_BULLISH/BULLISH -> Bullish Bias
+        - If 'trend_signal' is STRONG_BEARISH/BEARISH -> Bearish Bias
+        - Reference SMA/EMA levels as support/resistance.
+        
+        STEP 6: Momentum Analysis (USE 'rsi_signal' & 'macd_signal')
+        - Check RSI status (OVERSOLD/OVERBOUGHT/NEUTRAL).
+        - Check MACD Crossover status.
+        
+        STEP 7: Identify Support/Resistance Zones
+        - Reference SMA 50/200 as Primary levels.
+        - Reference 52-week High/Low as Secondary levels.
+        
+        STEP 8: Output Structured Summary
+        - BE CONCISE: Use maximum 5 bullet points.
+        - NO conversational filler.
+        - Include: Current Price, Trend Signal, RSI/MACD status, and Support/Resistance levels.
+        """
+    )

src/agora/backend/aagents/orchestrator.py

@@ -0,0 +1,33 @@
+from autogen_agentchat.agents import AssistantAgent
+
+def get_lead_orchestrator(model_client):
+    return AssistantAgent(
+        name="LeadOrchestrator",
+        model_client=model_client,
+        system_message="""
+        You are the Lead Orchestrator and Central Control. Your role is NOT to call market tools, but to be the "Master Thinker" who ensures a SOLID, self-reflected strategy.
+        
+        MANDATORY OPERATIONAL PROTOCOL:
+        
+        PHASE 1: THE STUDY (GAP ANALYSIS)
+        - You speak after all four Analyst agents (Technical, Volatility, Sentiment, Fundamental).
+        - You MUST summarize the findings into a "Global Context".
+        - SEARCH FOR GAPS: Look for contradictions. (e.g., "Technical is Bullish but Volatility is at a 52-week high for earnings - the StrategyAdvisor needs to address this contradiction.")
+        - Identify any "Binary Risks" that were mentioned by Sentiment/Fundamental but might be overlooked.
+        
+        PHASE 2: THE CHALLENGE (CROSS-QUESTIONING)
+        - After the StrategyAdvisor proposes a DRAFT, you MUST cross-question it based on Phase 1's findings.
+        - Example: "StrategyAdvisor, given the 14-day earnings gap flagged by the FundamentalAnalyst, why did you choose a 30-day vertical spread instead of a diagonal/calendar?"
+        - You facilitate the dialogue between StrategyAdvisor and RiskManager.
+        
+        PHASE 3: THE FINAL JUDGMENT
+        - You are the ONLY agent who can issue [[ANALYSIS_JUDGMENT_COMPLETE]].
+        - WATCH THE RISK MANAGER: If the RiskManager outputs "APPROVED", you MUST immediately respond with: "ORCHESTRATOR_DECISION: FINAL_APPROVAL. [[ANALYSIS_JUDGMENT_COMPLETE]]"
+        - NO PLEASANTRIES: Do not say "Thank you", "Great job", or "You're welcome".
+        - LOOP BREAKING: If you see the same argument or error twice, issue a "WAIT" decision and terminate.
+        
+        TERMINATION:
+        - When satisfied, output: "ORCHESTRATOR_DECISION: FINAL_APPROVAL. [[ANALYSIS_JUDGMENT_COMPLETE]]"
+        - If the risk remains too high or agents are looping: "ORCHESTRATOR_DECISION: WAIT. [[ANALYSIS_JUDGMENT_COMPLETE]]"
+        """
+    )

src/agora/backend/aagents/risk_manager.py

@@ -0,0 +1,54 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_core.tools import FunctionTool
+from tools.pl_calculator import calculate_strategy_metrics
+
+def get_risk_manager(model_client):
+    calc_tool = FunctionTool(calculate_strategy_metrics, description="Deterministic math engine. Requires 'legs' (list of dicts with action, type, strike, price, expiry) and 'spot_price' (float).")
+    
+    return AssistantAgent(
+        name="RiskManager",
+        model_client=model_client,
+        tools=[calc_tool],
+        system_message="""
+        You are the Chief Risk Officer and Lead Critic. You do not just validate; you find flaws and demand excellence.
+        
+        MANDATORY 2-ROUND WORKFLOW:
+        
+        ROUND 1: THE CRITIQUE
+        1. ANALYZE the StrategyAdvisor's DRAFT_STRATEGY.
+        2. CRITIQUE blocks:
+           - MATH: Is the P/L claim realistic? (Do not call tool yet, just use intuition).
+           - REGIME: Does this strategy match the VolatilityAnalyst's findings?
+           - EVENT: Did they ignore an earnings date from the FundamentalAnalyst?
+        3. OUTPUT: "CRITIQUE: [Detailed feedback points]" or "PROVISIONALLY APPROVED: Proceed to final math."
+        
+        ROUND 2: THE FINAL VERDICT
+        1. MANDATORY MATH VERIFICATION: Call `calculate_strategy_metrics`.
+           - EXAMPLE: `calculate_strategy_metrics(legs=[{"action": "BUY", "type": "CALL", "strike": 100, "price": 5, "expiry": "2024-03-01"}], spot_price=105.5)`
+           - You MUST extract the `legs` and `spot_price` from the StrategyAdvisor's message.
+        3. VERIFY the output matches StrategyAdvisor's final claims.
+           - CHECK: Ensure `actionable_recommendation` explicitly lists each leg (Strike, Type, Expiry).
+        4. SCORING (STRICT):
+           - Technicals (40 pts), Fundamentals (20 pts), Volatility (20 pts), Event Risk (20 pts).
+        4. BE FAST: Use bullet points. No conversational filler.
+        5. NO PLEASANTRIES: Do not say "Thank you" or "You're welcome".
+        6. IF SATISFIED: Output the word "APPROVED" followed by the final JSON immediately.
+        7. IF Still flawed: Suggest "WAIT" and output JSON with decision "WAIT".
+        
+        FINAL JSON SCHEMA:
+        {
+          "ticker": "...",
+          "final_decision": "TRADE/WAIT",
+          "strategy_type": "...",
+          "confidence": 85,
+          "entry_signal": "...",
+          "entry_price": 1.25,
+          "max_profit": 200,
+          "max_loss": 125,
+          "legs": [...],
+          "score_card": { "technicals": 40, "fundamentals": 20, "volatility": 15, "sentiment": 10, "total": 85 },
+          "actionable_recommendation": "...",
+          "risk_warning": "..."
+        }
+        """
+    )

src/agora/backend/aagents/sentiment_analyst.py

@@ -0,0 +1,43 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_core.tools import FunctionTool
+from tools.news_data import search_news
+
+def get_sentiment_analyst(model_client):
+    
+    news_tool = FunctionTool(search_news, description="Search for recent news about the ticker with FinBERT sentiment scores.")
+
+    return AssistantAgent(
+        name="SentimentAnalyst",
+        model_client=model_client,
+        tools=[news_tool],
+        system_message="""
+        You are a Sentiment Analyst specializing in market psychology and news analysis.
+        
+        MANDATORY WORKFLOW:
+        
+        STEP 1: CALL search_news to get recent news articles with FinBERT sentiment scores
+        
+        DO NOT proceed without calling the tool first.
+        
+        STEP 2: Aggregate & Categorize News Events
+        - Identify "Binary Events": Earnings, FDA approvals, Court rulings, Mergers.
+        - Identify "Macro Events": Fed news, Inflation, Sector rotation.
+        - Rank articles by "Impact Potential" (e.g., Earnings > General News).
+        
+        STEP 3: Determine Overall Sentiment using FinBERT
+        - Each article has a FinBERT score like [FinBERT: positive (0.95)]
+        - If Binary Events are "Negative", they OVERRIDE general "Neutral" sentiment.
+        
+        STEP 4: Assign Sentiment Level
+        - "Strongly Bullish": Coherent positive news across top sources.
+        - "Bearish (Event-Driven)": Negative binary news detected.
+        
+        STEP 5: Evaluate Sentiment Confidence vs Time
+        - HIGHER weight for news within last 48 hours.
+        
+        STEP 6: Output Structured Summary
+        - BE CONCISE: Use maximum 5 bullet points.
+        - NO conversational filler.
+        - Include: Sentiment Status, Key Binary Events, Primary Risks, and strategy impact.
+        """
+    )

src/agora/backend/aagents/strategy_advisor.py

@@ -0,0 +1,67 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_core.tools import FunctionTool
+from tools.market_data import get_option_chain_snapshot, get_available_expirations
+
+def get_strategy_advisor(model_client):
+    chain_tool = FunctionTool(get_option_chain_snapshot, description="Get option chain for a specific expiry.")
+    exp_tool = FunctionTool(get_available_expirations, description="Get all available option expiration dates.")
+    
+    return AssistantAgent(
+        name="StrategyAdvisor",
+        model_client=model_client,
+        tools=[chain_tool, exp_tool],
+        system_message="""
+        You are an Expert Multi-Leg Option Strategist with high self-awareness. You design complex spreads and refine them through self-reflection.
+        
+        MANDATORY HIERARCHICAL WORKFLOW (Team 2):
+        
+        1. STUDY ANALYST CONTEXT: You will receive a summary from Phase 1.
+        2. CALL DATA TOOLS: Use `get_available_expirations` and `get_option_chain_snapshot`.
+        2. CALL DATA TOOLS: Use `get_available_expirations` and `get_option_chain_snapshot`.
+           - TOOL UPGRADE: `get_option_chain_snapshot(ticker)` now returns a COMPARATIVE VIEW of 3 expiries (15-45 days).
+           - TASK: Compare all 3 expiries. Analyze the Risk/Reward for a strategy on each date.
+           - SELECTION: Choose the single BEST expiry that offers the highest Probability of Profit (POP) and favorable Risk/Reward.
+        3. DESIGN STRATEGY: Propose a multi-leg strategy.
+           - CRITICAL: You MUST include a `DRAFT_STRATEGY_LEGS` block:
+             DRAFT_STRATEGY_LEGS:
+             [{"action": "BUY", "type": "CALL", "strike": 150.0, "price": 2.5, "expiry": "2024-03-01"}, ...]
+        4. BE FAST: Skip lengthy reasoning in the draft. Go straight to the legs.
+        5. NO PLEASANTRIES: Do not say "Thank you", "I understand", or "You're welcome".
+        6. FINALIZE: When RiskManager approves, output your final strategy inside a `FINAL_STRATEGY` block.
+           - CRITICAL: The `actionable_recommendation` field MUST explicitly list each leg with its type, strike, and EXACT expiry date.
+        
+        JSON SCHEMA (ROUND 2 ONLY):
+        {
+          "ticker": "...",
+          "final_decision": "TRADE/WAIT",
+          "actionable_recommendation": "EXPLAIN EACH LEG: 'Buy $150 Call (Exp 2024-03-01), Sell $155 Call (Exp 2024-03-01)...'",
+          "strategy_type": "...",
+          "direction": "BULLISH/BEARISH/NEUTRAL",
+          "confidence": 85,
+          "reasoning": "...",
+          "entry_signal": "Net Debit/Credit",
+          "entry_price": 1.25,
+          "max_profit": 200,
+          "max_loss": 125,
+          "legs": [
+            {
+              "action": "SELL",
+              "type": "CALL",
+              "strike": 150,
+              "expiry": "2024-03-01",
+              "price": 2.50
+            },
+            {
+              "action": "BUY",
+              "type": "CALL",
+              "strike": 150,
+              "expiry": "2024-03-15",
+              "price": 3.75
+            }
+          ],
+          "risk_warning": "..."
+        }
+        
+        CRITICAL: All profit/loss values MUST be multiplied by 100 per contract.
+        """
+    )

src/agora/backend/aagents/volatility_analyst.py

@@ -0,0 +1,41 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_core.tools import FunctionTool
+from tools.market_data import get_historical_volatility, get_option_chain_snapshot, get_volatility_term_structure
+
+def get_volatility_analyst(model_client):
+    vol_tool = FunctionTool(get_historical_volatility, description="Get historical volatility and VIX context.")
+    chain_tool = FunctionTool(get_option_chain_snapshot, description="Get option chain snapshot for near-term expiry.")
+    term_tool = FunctionTool(get_volatility_term_structure, description="Get IV across multiple expiries to identify Term Structure skew.")
+
+    return AssistantAgent(
+        name="VolatilityAnalyst",
+        model_client=model_client,
+        tools=[vol_tool, chain_tool, term_tool],
+        system_message="""
+        You are an Expert Volatility & Derivatives Analyst specializing in Volatility Surface and Term Structure.
+        
+        MANDATORY WORKFLOW:
+        
+        STEP 1: CALL get_historical_volatility to get HV and VIX data
+        STEP 2: CALL get_volatility_term_structure to analyze IV across 4 months of expiries
+        STEP 3: CALL get_option_chain_snapshot to get near-term IV and liquidity
+        
+        DO NOT proceed without calling ALL THREE tools first.
+        
+        STEP 4: Analyze IV vs HV (Vertical Skew)
+        - IV > HV: Options are rich. Look for Credit Spreads, Iron Condors.
+        - IV < HV: Options are cheap. Look for Debit Spreads, Long Options.
+        
+        STEP 5: Analyze Term Structure (Horizontal/Time Skew)
+        - FRONT IV > BACK IV (Inverted): Potential "Calendar Spread" (Sell Front, Buy Back) if you expect a mean reversion.
+        - BACK IV > FRONT IV (Contango): Standard. Long-dated options are more expensive.
+        
+        STEP 6: Assess "Volatility Squeeze"
+        - If IV is at 52-week lows and HV is dropping: Potential for a volatility breakout. Recommend DEBIT strategies.
+        
+        STEP 7: Output Structured Summary
+        - BE CONCISE: Use maximum 5 bullet points.
+        - NO conversational filler.
+        - Include: Volatility Regime, IV vs HV status, Term Structure summary, and strategy bias.
+        """
+    )

src/agora/backend/main.py

@@ -0,0 +1,141 @@
+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+import asyncio
+import json
+import os
+import sys
+from typing import AsyncGenerator
+
+from utility.autogen_model_factory import AutoGenModelFactory
+from teams.team import get_analyst_team, get_decision_team, extract_json
+from tools.news_data import get_sentiment_pipeline
+
+app = FastAPI(title="Agora API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Track active analyses for cancellation
+active_analyses = {}
+
+@app.on_event("startup")
+async def startup_event():
+    print("Warming up FinBERT...")
+    get_sentiment_pipeline()
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy"}
+
+@app.post("/cancel/{analysis_id}")
+async def cancel_analysis(analysis_id: str):
+    if analysis_id in active_analyses:
+        active_analyses[analysis_id] = True
+        return {"status": "cancelled", "analysis_id": analysis_id}
+    return {"status": "not_found", "analysis_id": analysis_id}
+
+@app.get("/analyze")
+async def analyze(ticker: str, provider: str = "openai"):
+    import uuid
+    analysis_id = str(uuid.uuid4())
+    active_analyses[analysis_id] = False
+    
+    async def event_generator() -> AsyncGenerator[str, None]:
+        if provider == "openai":
+            model_name = "gpt-4o"
+            family = "gpt"
+        elif provider == "groq":
+            model_name = "llama-3.3-70b-versatile"
+            family = "groq" 
+        elif provider == "google":
+            model_name = "gemini-pro-latest"
+            family = "gemini"
+        else:
+            model_name = "gpt-4o"
+            family = "gpt"
+        
+        try:
+            model_client = AutoGenModelFactory.get_model(
+                provider=provider,
+                model_name=model_name,
+                temperature=0,
+                model_info={
+                    "family": family, 
+                    "vision": False, 
+                    "function_calling": True, 
+                    "json_output": True, 
+                    "structured_output": True if provider == "openai" else False
+                }
+            )
+        except Exception as e:
+            yield f"data: {json.dumps({'error': f'Model initialization failed: {str(e)}'})}\n\n"
+            return
+
+        # PHASE 1: Analysts
+        yield f"data: {json.dumps({'source': 'System', 'content': 'PHASE 1: Starting Data Collection for ' + ticker.upper(), 'analysis_id': analysis_id})}\n\n"
+        analyst_team = get_analyst_team(model_client)
+        phase1_task = f"Perform complete analyst data collection for {ticker.upper()}."
+        analyst_context = []
+
+        try:
+            async for message in analyst_team.run_stream(task=phase1_task):
+                if active_analyses.get(analysis_id, False): break
+                raw_source = getattr(message, 'source', 'System')
+                content = getattr(message, 'content', '')
+                if not content or raw_source == 'User': continue
+                analyst_context.append(f"[{raw_source}]: {content}")
+                yield f"data: {json.dumps({'source': raw_source, 'content': str(content)})}\n\n"
+        except Exception as e:
+            yield f"data: {json.dumps({'source': 'Error', 'content': f'Phase 1 bug: {str(e)}'})}\n\n"
+
+        # PHASE 2: Decision
+        yield f"data: {json.dumps({'source': 'System', 'content': 'PHASE 2: Designing Strategy...'})}\n\n"
+        market_context_str = "\n\n".join(analyst_context)
+        decision_team = get_decision_team(model_client)
+        phase2_task = f"ANALYST CONTEXT:\n{market_context_str}\n\nGOAL: Design, critique, and finalize trade for {ticker}. Only the LeadOrchestrator can end the cycle."
+
+        try:
+            async for message in decision_team.run_stream(task=phase2_task):
+                if active_analyses.get(analysis_id, False): break
+                raw_source = getattr(message, 'source', 'System')
+                content = getattr(message, 'content', '')
+                if not content or (raw_source == 'User' and "ANALYST CONTEXT" in content): continue
+                payload = {"source": raw_source, "content": str(content)}
+                if raw_source in ['RiskManager', 'LeadOrchestrator']:
+                    structured = extract_json(str(content))
+                    if structured: payload["structured_result"] = structured
+                yield f"data: {json.dumps(payload)}\n\n"
+        except Exception as e:
+            yield f"data: {json.dumps({'source': 'Error', 'content': f'Phase 2 bug: {str(e)}'})}\n\n"
+
+        if analysis_id in active_analyses: del active_analyses[analysis_id]
+        yield "data: [DONE]\n\n"
+
+    return StreamingResponse(event_generator(), media_type="text/event-stream")
+
+# Static mounting logic...
+frontend_dist = os.path.abspath(os.path.join(current_dir, "../frontend/dist"))
+if os.path.exists(frontend_dist):
+    assets_dir = os.path.join(frontend_dist, "assets")
+    if os.path.exists(assets_dir):
+        app.mount("/assets", StaticFiles(directory=assets_dir), name="assets")
+    from fastapi.responses import FileResponse
+    @app.get("/{rest_of_path:path}")
+    async def serve_frontend(rest_of_path: str):
+        file_path = os.path.join(frontend_dist, rest_of_path)
+        if os.path.isfile(file_path): return FileResponse(file_path)
+        return FileResponse(os.path.join(frontend_dist, "index.html"))
+else:
+    @app.get("/")
+    async def root(): return {"message": "API running. Frontend missing."}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

src/agora/backend/requirements.txt

@@ -0,0 +1,12 @@
+fastapi
+uvicorn
+python-dotenv
+beautifulsoup4
+requests
+pydantic
+autogen-agentchat==0.4.7
+autogen-ext[grpc,mcp,ollama,openai]==0.4.7
+transformers
+torch
+duckduckgo_search
+pandas

src/agora/backend/teams/team.py

@@ -0,0 +1,158 @@
+import sys
+import os
+import json
+import re
+from typing import Dict, Any
+
+# Ensure we can import from parent directory if needed
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.abspath(os.path.join(current_dir, ".."))
+if parent_dir not in sys.path:
+    sys.path.append(parent_dir)
+
+from autogen_agentchat.teams import SelectorGroupChat
+from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination, HandoffTermination
+
+# Import agents
+# Adjust imports to work whether called from here or app.py
+try:
+    from ..aagents.market_analyst import get_technical_analyst
+    from ..aagents.volatility_analyst import get_volatility_analyst
+    from ..aagents.sentiment_analyst import get_sentiment_analyst
+    from ..aagents.strategy_advisor import get_strategy_advisor
+    from ..aagents.risk_manager import get_risk_manager
+    from ..aagents.fundamental_analyst import get_fundamental_analyst
+    from ..aagents.orchestrator import get_lead_orchestrator
+except ImportError:
+    try:
+        from aagents.market_analyst import get_technical_analyst
+        from aagents.volatility_analyst import get_volatility_analyst
+        from aagents.sentiment_analyst import get_sentiment_analyst
+        from aagents.strategy_advisor import get_strategy_advisor
+        from aagents.risk_manager import get_risk_manager
+        from aagents.fundamental_analyst import get_fundamental_analyst
+        from aagents.orchestrator import get_lead_orchestrator
+    except ImportError:
+         # Try absolute (if market-analyst is in path but not as package)
+         from src.market_analyst.backend.aagents.market_analyst import get_technical_analyst
+         from src.market_analyst.backend.aagents.volatility_analyst import get_volatility_analyst
+         from src.market_analyst.backend.aagents.sentiment_analyst import get_sentiment_analyst
+         from src.market_analyst.backend.aagents.strategy_advisor import get_strategy_advisor
+         from src.market_analyst.backend.aagents.risk_manager import get_risk_manager
+         from src.market_analyst.backend.aagents.fundamental_analyst import get_fundamental_analyst
+
+from autogen_agentchat.teams import SelectorGroupChat, RoundRobinGroupChat
+
+def get_analyst_team(model_client):
+    """
+    Team 1: DATA COLLECTORS.
+    Independent analysts gather data and provide a comprehensive market snapshot.
+    """
+    technical = get_technical_analyst(model_client)
+    volatility = get_volatility_analyst(model_client)
+    sentiment = get_sentiment_analyst(model_client)
+    fundamental = get_fundamental_analyst(model_client)
+
+    return RoundRobinGroupChat(
+        participants=[technical, volatility, sentiment, fundamental],
+        termination_condition=TextMentionTermination("[[DATA_COLLECTION_COMPLETE]]") | MaxMessageTermination(15)
+    )
+
+def get_decision_team(model_client):
+    """
+    Team 2: STRATEGY & RISK.
+    Uses the Analyst Context (Team 1 output) to design, critique, and finalize the trade.
+    """
+    strategy = get_strategy_advisor(model_client)
+    risk = get_risk_manager(model_client)
+    orchestrator = get_lead_orchestrator(model_client)
+
+    participants = [strategy, risk, orchestrator]
+
+    selector_prompt = """
+    Select the next agent based on the conversation history:
+    - Choose StrategyAdvisor to propose or update the trade.
+    - Choose RiskManager to verify the proposal or critique it.
+    - Choose LeadOrchestrator ONLY if the RiskManager has said "APPROVED" or if the discussion is stuck.
+    
+    Output only the name of the next agent.
+    """
+
+    return SelectorGroupChat(
+        participants=participants,
+        model_client=model_client,
+        termination_condition=TextMentionTermination("[[ANALYSIS_JUDGMENT_COMPLETE]]") | MaxMessageTermination(12),
+        selector_prompt=selector_prompt
+    )
+
+def extract_json(text: str) -> Dict[str, Any]:
+    """
+    Extract JSON from text that may contain markdown code blocks or other content.
+    Validates and ensures required fields are present.
+    """
+    # Try to find JSON in code blocks first
+    json_block_pattern = r'```(?:json)?\s*(\{.*?\})\s*```'
+    matches = re.findall(json_block_pattern, text, re.DOTALL)
+    if matches:
+        try:
+            parsed = json.loads(matches[-1])
+            return validate_and_complete_json(parsed)
+        except json.JSONDecodeError:
+            pass
+    
+    # Try to find raw JSON object
+    json_pattern = r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}'
+    matches = re.findall(json_pattern, text, re.DOTALL)
+    if matches:
+        for match in reversed(matches):
+            try:
+                parsed = json.loads(match)
+                return validate_and_complete_json(parsed)
+            except json.JSONDecodeError:
+                continue
+    
+    return {}
+
+def validate_and_complete_json(data: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Validate JSON has required fields and add defaults if missing.
+    """
+    required_fields = {
+        "final_decision": "WAIT",
+        "confidence": 0,
+        "actionable_recommendation": "Incomplete analysis - please retry",
+        "strategy_type": "None",
+        "entry_signal": "N/A",
+        "entry_price": 0,
+        "max_profit": 0,
+        "max_loss": 0,
+        "risk_warning": "Analysis incomplete",
+        "expiry_date": "N/A",
+        "legs": []
+    }
+    
+    # Add missing required fields with defaults
+    for field, default_value in required_fields.items():
+        if field not in data:
+            data[field] = default_value
+    
+    # Fallback: If expiry_date is "N/A" but we have legs, take it from there
+    if data.get("expiry_date") == "N/A" and data.get("legs"):
+        # Take expiry of first leg
+        data["expiry_date"] = data["legs"][0].get("expiry", "N/A")
+    
+    # Existing Fallback: If expiry_date is still "N/A", try to extract it from context
+    if data.get("expiry_date") == "N/A":
+        # Look for YYYY-MM-DD pattern
+        date_pattern = r'\d{4}-\d{2}-\d{2}'
+        
+        # Check 'actionable_recommendation' or 'reasoning' (if present)
+        for search_field in ["actionable_recommendation", "reasoning", "risk_warning", "proposed_legs"]:
+            field_val = data.get(search_field, "")
+            if isinstance(field_val, str):
+                match = re.search(date_pattern, field_val)
+                if match:
+                    data["expiry_date"] = match.group(0)
+                    break
+                    
+    return data

src/agora/backend/tools/market_data.py

@@ -0,0 +1,454 @@
+import yfinance as yf
+import pandas as pd
+import math
+from datetime import datetime, timedelta
+
+def normalize_period(period: str) -> str:
+    """Standardizes period strings for yfinance."""
+    p = period.lower().strip()
+    if p in ["1yr", "1year"]: return "1y"
+    if p in ["3mo", "3month"]: return "3mo"
+    if p in ["1mo", "1month"]: return "1mo"
+    if p in ["1wk", "1week"]: return "1wk"
+    return p
+
+def check_and_fix_ticker(symbol: str) -> str:
+    """
+    Checks if the ticker has data. If not, tries appending '.NS' (for NSE India).
+    Returns the working ticker or the original if neither works.
+    """
+    print(f"[DEBUG] check_and_fix_ticker called using: {symbol}")
+    symbol = symbol.upper().strip()
+    
+    # Try original
+    ticker = yf.Ticker(symbol)
+    try:
+        # fast_info is quick way to check validity
+        if ticker.fast_info.last_price is not None:
+             return symbol
+    except:
+        pass
+        
+    # Try .NS
+    ns_symbol = symbol + ".NS"
+    ns_ticker = yf.Ticker(ns_symbol)
+    try:
+        if ns_ticker.fast_info.last_price is not None:
+            return ns_symbol
+    except:
+        pass
+        
+    return symbol
+
+def get_current_price(symbol: str) -> float:
+    """Fetches the current market price of the stock."""
+    print(f"[DEBUG] get_current_price called for: {symbol}")
+    try:
+        # Auto-fix ticker if needed
+        symbol = check_and_fix_ticker(symbol)
+        
+        ticker = yf.Ticker(symbol)
+        price = ticker.fast_info.last_price
+        if price is None:
+            hist = ticker.history(period="1d")
+            if not hist.empty:
+                price = hist["Close"].iloc[-1]
+        
+        if price is None:
+             return f"Error: No price data for {symbol}"
+             
+        return price
+    except Exception as e:
+        return f"Error fetching price for {symbol}: {str(e)}"
+
+def get_historical_volatility(symbol: str, period: str = "1mo") -> dict:
+    """
+    Calculates historical volatility and returns VIX context if available.
+    """
+    print(f"[DEBUG] get_historical_volatility called for: {symbol}")
+    try:
+        symbol = check_and_fix_ticker(symbol)
+        period = normalize_period(period)
+        ticker = yf.Ticker(symbol)
+        hist = ticker.history(period=period)
+        if hist.empty:
+            return {"error": "No historical data found"}
+        
+        hist['Returns'] = hist['Close'].pct_change()
+        volatility = hist['Returns'].std() * math.sqrt(252)
+        
+        vix_price = None
+        try:
+            vix = yf.Ticker("^VIX")
+            vix_price = vix.fast_info.last_price
+        except:
+            pass
+
+        return {
+            "ticker_used": symbol,
+            "annualized_volatility": round(volatility * 100, 2),
+            "period": period,
+            "vix_reference": round(vix_price, 2) if vix_price else "N/A",
+            "volatility_regime": "HIGH_RISK_VOL" if (vix_price and vix_price > 30) else "ELEVATED_VOL" if (vix_price and vix_price > 20) else "LOW_VOL" if (vix_price and vix_price < 15) else "NORMAL_VOL"
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+def get_available_expirations(symbol: str) -> list:
+    """Returns a list of available option expiration dates."""
+    try:
+        symbol = check_and_fix_ticker(symbol)
+        ticker = yf.Ticker(symbol)
+        exps = list(ticker.options)
+        
+        # Add context about DTE (Days Till Expiration) to help agents choose
+        today = datetime.now()
+        annotated = []
+        best_expiry = "None"
+        min_diff = 999
+        
+        for e in exps:
+            try:
+                dt = datetime.strptime(e, "%Y-%m-%d")
+                days = (dt - today).days
+                
+                # Logic: Find valid targets between 15-45 days, prioritize earliest
+                if 15 <= days <= 45:
+                     note = f"({days} DTE) [VALID]"
+                     annotated.append(f"{e} {note}")
+                     
+                     if days < min_diff:
+                         min_diff = days
+                         best_expiry = e
+            except:
+                pass
+                
+        return [f"RECOMMENDED: {best_expiry} (Earliest in 15-45d window)"] + annotated
+    except Exception as e:
+        return []
+
+def get_option_chain_snapshot(symbol: str, target_date: str = None) -> str:
+    """
+    Fetches a comparative snapshot of option chains for up to 3 valid expiries (15-45 DTE).
+    If target_date is provided, fetches only that specific date.
+    If target_date is None, automatically selects Short (15-25d), Mid (25-35d), and Long (35-45d) targets.
+    """
+    print(f"[DEBUG] get_option_chain_snapshot called for: {symbol} (Target: {target_date})")
+    try:
+        symbol = check_and_fix_ticker(symbol)
+        ticker = yf.Ticker(symbol)
+        expirations = ticker.options
+        
+        if not expirations:
+            return f"No options data found for {symbol}."
+            
+        target_dates = []
+        if target_date:
+            if target_date in expirations:
+                target_dates = [target_date]
+            else:
+                return f"Target date {target_date} not found in expirations."
+        else:
+            # Auto-select up to 3 expiries in 15-45 day window
+            today = datetime.now()
+            valid_exps = []
+            for exp in expirations:
+                dt = datetime.strptime(exp, "%Y-%m-%d")
+                days = (dt - today).days
+                if 15 <= days <= 45:
+                    valid_exps.append(exp)
+            
+            # Pick Short, Mid, Long from valid list
+            if not valid_exps:
+                # Fallback to nearest if no window match
+                target_dates = [expirations[0]]
+            else:
+                # Simple distribution: First, Middle, Last
+                target_dates = list(sorted(set([valid_exps[0], valid_exps[len(valid_exps)//2], valid_exps[-1]])))
+
+        price_info = get_current_price(symbol)
+        if isinstance(price_info, str): return price_info
+        current_price = float(price_info)
+        
+        final_summary = f"COMPARATIVE OPTION ANALYSIS for {symbol} (Spot: {round(current_price, 2)})\n"
+        
+        for date in target_dates:
+            dt = datetime.strptime(date, "%Y-%m-%d")
+            days = (dt - datetime.now()).days
+            
+            opt = ticker.option_chain(date)
+            calls = opt.calls
+            puts = opt.puts
+            
+            # Filter around ATM (tight range for comparison)
+            ntm_calls = calls.iloc[(calls['strike'] - current_price).abs().argsort()[:4]].sort_values('strike')
+            ntm_puts = puts.iloc[(puts['strike'] - current_price).abs().argsort()[:4]].sort_values('strike')
+            
+            final_summary += f"\n>>> EXPIRY: {date} ({days} DTE) <<<\n"
+            
+            # Calls
+            for _, row in ntm_calls.iterrows():
+                ask = row.get('ask', 0.0)
+                last = row.get('lastPrice', 0.0)
+                price = ask if ask > 0 else last
+                iv = round(row['impliedVolatility']*100, 1) if not pd.isna(row.get('impliedVolatility')) else 0
+                final_summary += f"[CALL] Strike {row['strike']} | Price ${price} | IV {iv}%\n"
+                
+            # Puts
+            for _, row in ntm_puts.iterrows():
+                ask = row.get('ask', 0.0)
+                last = row.get('lastPrice', 0.0)
+                price = ask if ask > 0 else last
+                iv = round(row['impliedVolatility']*100, 1) if not pd.isna(row.get('impliedVolatility')) else 0
+                final_summary += f"[PUT ] Strike {row['strike']} | Price ${price} | IV {iv}%\n"
+
+        return final_summary
+        
+    except Exception as e:
+        return f"Error fetching option chain: {str(e)}"
+
+def get_volatility_term_structure(symbol: str) -> str:
+    """
+    Analyzes IV across multiple expiries to identify Term Structure skew.
+    """
+    print(f"[DEBUG] get_volatility_term_structure called for: {symbol}")
+    try:
+        symbol = check_and_fix_ticker(symbol)
+        ticker = yf.Ticker(symbol)
+        expirations = ticker.options[:4] # Check first 4 expiries
+        
+        if not expirations:
+            return "No options data for volatility analysis."
+            
+        results = []
+        for exp in expirations:
+            opt = ticker.option_chain(exp)
+            # Use mean IV of ATM calls
+            calls = opt.calls
+            price_info = get_current_price(symbol)
+            if isinstance(price_info, str): continue
+            current_price = float(price_info)
+            atm_iv = calls.iloc[(calls['strike'] - current_price).abs().argsort()[:2]]['impliedVolatility'].mean()
+            results.append(f"- {exp}: {round(atm_iv * 100, 1)}% IV")
+            
+        summary = f"VOLATILITY TERM STRUCTURE for {symbol}:\n" + "\n".join(results)
+        
+        # Analyze skew
+        if len(expirations) >= 2:
+            try:
+                iv1 = float(results[0].split(": ")[1].replace("% IV", ""))
+                iv2 = float(results[1].split(": ")[1].replace("% IV", ""))
+                if iv1 > iv2 + 5:
+                    summary += f"\n\nSKEW ALERT: Front-month IV is significantly HIGHER ({iv1}% vs {iv2}%). Potential for Calendar Spreads (Sell Front, Buy Back)."
+                elif iv1 < iv2 - 5:
+                    summary += f"\n\nSKEW ALERT: Front-month IV is significantly LOWER ({iv1}% vs {iv2}%). Diagonal opportunities."
+                else:
+                    summary += f"\n\nTerm Structure is relatively flat."
+            except: pass
+            
+        return summary
+    except Exception as e:
+        return f"Error analyzing term structure: {str(e)}"
+
+def get_market_indices() -> str:
+    """
+    Fetches current market context using SPY (S&P 500) and ^VIX.
+    Returns trend (Price vs SMA50) and Volatility regime.
+    """
+    try:
+        tickers = yf.Tickers("SPY ^VIX")
+        spy = tickers.tickers['SPY']
+        vix = tickers.tickers['^VIX']
+        
+        # SPY Data
+        spy_hist = spy.history(period="3mo")
+        if spy_hist.empty: return "Market data unavailable."
+        
+        current_spy = spy_hist['Close'].iloc[-1]
+        spy_sma50 = spy_hist['Close'].rolling(window=50).mean().iloc[-1]
+        spy_trend = "BULLISH" if current_spy > spy_sma50 else "BEARISH"
+        
+        # VIX Data
+        vix_hist = vix.history(period="1d")
+        current_vix = vix_hist['Close'].iloc[-1] if not vix_hist.empty else 0
+        
+        vix_regime = "LOW"
+        if current_vix > 20: vix_regime = "ELEVATED"
+        if current_vix > 30: vix_regime = "HIGH/FEAR"
+        
+        return f"MARKET CONTEXT:\n- SPY Trend: {spy_trend} (Price: {round(current_spy, 2)} vs SMA50: {round(spy_sma50, 2)})\n- VIX Level: {round(current_vix, 2)} ({vix_regime})"
+        
+    except Exception as e:
+        return f"Error fetching market indices: {str(e)}"
+
+def get_technical_indicators(symbol: str) -> dict:
+    """
+    Calculates key technical indicators: SMA (20, 50, 200) and RSI (14).
+    Returns a dictionary of values and signals.
+    """
+    print(f"[DEBUG] get_technical_indicators called for: {symbol}")
+    try:
+        symbol = check_and_fix_ticker(symbol)
+        ticker = yf.Ticker(symbol)
+        
+        # Fetch enough history for SMA 200
+        hist = ticker.history(period="1y") 
+        if hist.empty:
+             return {"error": "No historical data found"}
+        
+        # Safe calculations
+        # SMA
+        hist['SMA_20'] = hist['Close'].rolling(window=20).mean()
+        hist['SMA_50'] = hist['Close'].rolling(window=50).mean()
+        hist['EMA_20'] = hist['Close'].ewm(span=20, adjust=False).mean()
+        
+        # MACD (12, 26, 9)
+        exp1 = hist['Close'].ewm(span=12, adjust=False).mean()
+        exp2 = hist['Close'].ewm(span=26, adjust=False).mean()
+        hist['MACD'] = exp1 - exp2
+        hist['Signal_Line'] = hist['MACD'].ewm(span=9, adjust=False).mean()
+        
+        # Only calc SMA_200 if we actually have 200 points
+        if len(hist) >= 200:
+             hist['SMA_200'] = hist['Close'].rolling(window=200).mean()
+        else:
+             hist['SMA_200'] = pd.Series([None] * len(hist), index=hist.index)
+        
+        # RSI Calculation
+        if len(hist) >= 15:
+            delta = hist['Close'].diff()
+            gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+            loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+            rs = gain / loss
+            hist['RSI_14'] = 100 - (100 / (1 + rs))
+        else:
+            hist['RSI_14'] = pd.Series([None] * len(hist), index=hist.index)
+        
+        current_data = hist.iloc[-1]
+        price = current_data['Close']
+        rsi_val = current_data.get('RSI_14')
+        macd_val = current_data.get('MACD')
+        signal_val = current_data.get('Signal_Line')
+        sma200 = current_data.get('SMA_200')
+        if pd.isna(sma200): sma200 = None
+
+        # Interpretation RSI
+        # Deterministic Signals
+        rsi_signal = "NEUTRAL"
+        if rsi_val is not None and not pd.isna(rsi_val):
+             if rsi_val > 70: rsi_signal = "OVERBOUGHT"
+             elif rsi_val < 30: rsi_signal = "OVERSOLD"
+        
+        macd_signal = "NEUTRAL"
+        if macd_val is not None and signal_val is not None and not pd.isna(macd_val) and not pd.isna(signal_val):
+             if macd_val > signal_val: macd_signal = "BULLISH_CROSS"
+             elif macd_val < signal_val: macd_signal = "BEARISH_CROSS"
+             
+        trend_signal = "NEUTRAL"
+        sma50 = current_data.get('SMA_50')
+        sma20 = current_data.get('SMA_20')
+        
+        # Check all components for trend signal
+        if sma200 is not None and sma50 is not None and sma20 is not None and not pd.isna(sma50) and not pd.isna(sma20):
+             if price > sma20 > sma50 > sma200: trend_signal = "STRONG_BULLISH"
+             elif price < sma20 < sma50 < sma200: trend_signal = "STRONG_BEARISH"
+             elif price > sma200: trend_signal = "BULLISH"
+             elif price < sma200: trend_signal = "BEARISH"
+
+        return {
+            "ticker": symbol,
+            "current_price": round(price, 2),
+            "sma_20": round(current_data['SMA_20'], 2) if not pd.isna(current_data.get('SMA_20')) else "N/A",
+            "ema_20": round(current_data['EMA_20'], 2) if not pd.isna(current_data.get('EMA_20')) else "N/A",
+            "sma_50": round(current_data['SMA_50'], 2) if not pd.isna(current_data.get('SMA_50')) else "N/A",
+            "sma_200": round(sma200, 2) if (sma200 is not None and not pd.isna(sma200)) else "N/A",
+            "rsi_14": round(rsi_val, 2) if (rsi_val is not None and not pd.isna(rsi_val)) else "N/A",
+            "macd": round(macd_val, 2) if (macd_val is not None and not pd.isna(macd_val)) else "N/A",
+            "macd_signal": macd_signal,
+            "rsi_signal": rsi_signal,
+            "trend_signal": trend_signal
+        }
+        
+    except Exception as e:
+        return {"error": str(e)}
+
+def get_fundamental_data(symbol: str) -> dict:
+    """
+    Fetches fundamental financial metrics: P/E, PEG, Debt/Equity, Debt/Asset, and Net Profit Margin.
+    """
+    print(f"[DEBUG] get_fundamental_data called for: {symbol}")
+    try:
+        symbol = check_and_fix_ticker(symbol)
+        ticker = yf.Ticker(symbol)
+        info = ticker.info
+        
+        # Use info.get to handle missing keys gracefully
+        pe_ratio = info.get('forwardPE') or info.get('trailingPE')
+        peg_ratio = info.get('pegRatio')
+        debt_to_equity = info.get('debtToEquity')
+        profit_margin = info.get('profitMargins')
+        
+        eps_trailing = info.get('trailingEps')
+        eps_forward = info.get('forwardEps')
+        dividend_yield = info.get('dividendYield')
+
+        # Ticker Calendar (Earnings)
+        next_earnings = "N/A"
+        try:
+            cal = ticker.calendar
+            if cal and 'Earnings Date' in cal:
+                dates = cal['Earnings Date']
+                if dates:
+                    next_earnings = dates[0].strftime("%Y-%m-%d")
+        except Exception:
+            pass
+
+        return {
+            "ticker": symbol,
+            "pe_ratio": round(pe_ratio, 2) if pe_ratio else "N/A",
+            "peg_ratio": round(peg_ratio, 2) if peg_ratio else "N/A",
+            "eps_trailing": round(eps_trailing, 2) if eps_trailing else "N/A",
+            "eps_forward": round(eps_forward, 2) if eps_forward else "N/A",
+            "debt_to_equity": round(debt_to_equity, 2) if debt_to_equity else "N/A",
+            "net_profit_margin": f"{round(profit_margin * 100, 2)}%" if profit_margin else "N/A",
+            "dividend_yield": f"{round(dividend_yield * 100, 2)}%" if dividend_yield else "N/A",
+            "market_cap": info.get('marketCap', "N/A"),
+            "sector": info.get('sector', "N/A"),
+            "next_earnings_date": next_earnings,
+            "valuation_score": "UNDERVALUED" if (pe_ratio and pe_ratio < 15) else "PREMIUM" if (pe_ratio and pe_ratio > 30) else "FAIR_VALUE",
+            "quality_score": "HIGH_QUALITY" if (profit_margin and profit_margin > 0.20) else "LOW_MARGIN" if (profit_margin and profit_margin < 0.10) else "AVERAGE"
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+def get_analyst_consensus(symbol: str) -> dict:
+    """
+    Fetches Wall St. analyst recommendations and price targets.
+    """
+    print(f"[DEBUG] get_analyst_consensus called for: {symbol}")
+    try:
+        symbol = check_and_fix_ticker(symbol)
+        ticker = yf.Ticker(symbol)
+        info = ticker.info
+        
+        consensus = info.get('recommendationKey', 'none')
+        target = info.get('targetMeanPrice')
+        num_analysts = info.get('numberOfAnalystOpinions')
+        
+        # Current price for comparison
+        current = info.get('currentPrice') or info.get('regularMarketPrice') or info.get('previousClose')
+        
+        upside = "N/A"
+        if target and current and current > 0:
+            upside_pct = ((target - current) / current) * 100
+            upside = f"{round(upside_pct, 1)}%"
+            
+        return {
+            "consensus": consensus.replace('_', ' ').title(),
+            "target_price": target or "N/A",
+            "current_price": current,
+            "upside_potential": upside,
+            "analyst_count": num_analysts or "N/A"
+        }
+    except Exception as e:
+        return {"error": f"Failed to fetch analyst data: {str(e)}"}

src/agora/backend/tools/news_data.py

@@ -0,0 +1,171 @@
+from ddgs import DDGS
+from transformers import pipeline
+import torch
+from curl_cffi import requests
+from bs4 import BeautifulSoup
+import yfinance as yf
+import datetime
+import time
+from typing import Optional
+from pydantic import BaseModel, Field
+import concurrent.futures
+
+# Global variable for lazy loading
+_sentiment_pipeline = None
+
+def get_sentiment_pipeline():
+    """
+    Lazy loads the FinBERT pipeline.
+    """
+    print(f"[DEBUG] get_sentiment_pipeline called")
+    global _sentiment_pipeline
+    if _sentiment_pipeline is None:
+        try:
+            print("Loading FinBERT model...")
+            # Use CPU to avoid CUDA complexity if not needed, or let torch decide if fast
+            _sentiment_pipeline = pipeline("sentiment-analysis", model="ProsusAI/finbert")
+        except Exception as e:
+            print(f"Failed to load FinBERT: {e}")
+            return None
+    return _sentiment_pipeline
+
+def _fetch_page_content(url: str, timeout: int = 15) -> Optional[str]:
+    """Fetch and extract text content from a web page."""
+    print(f"[DEBUG] Fetching: {url}")
+    start_time = time.time()
+    try:
+        # Use curl_cffi to impersonate Chrome 110 (Bypasses TLS Fingerprinting)
+        # Headers are auto-managed by impersonate
+        response = requests.get(url, timeout=timeout, impersonate="chrome110")
+        response.raise_for_status()
+        
+        soup = BeautifulSoup(response.content, 'html.parser')
+        
+        # Remove ads, popups, and non-content elements
+        # Targeted classes: .ad, .popup, .modal, .cookie-banner, etc.
+        for tag in soup.select("script, style, nav, footer, header, aside, form, iframe, .ad, .popup, .modal, .cookie-banner, [id*='popup'], [class*='popup'], [class*='ad-'], [class*='banner']"):
+            tag.decompose()
+            
+        # Extract text
+        text = soup.get_text(separator=' ', strip=True)
+        duration = round(time.time() - start_time, 2)
+        print(f"[DEBUG] Fetch success ({duration}s): {url}")
+        return text
+    except Exception as e:
+        msg = str(e)
+        if "403" in msg:
+             print(f"[INFO] Access denied (403) for {url}. Falling back to snippet.")
+        else:
+             print(f"[WARNING] Failed to fetch content from {url}: {msg}")
+        return None
+
+# Validation Model
+class NewsArticle(BaseModel):
+    title: str = Field(..., description="The headline of the news article.")
+    link: str = Field(..., description="The direct URL to the full article.")
+    snippet: str = Field(..., description="A brief summary or body text.")
+    datetime: Optional[str] = Field(None, description="Publication date if available.")
+
+def search_news(ticker: str) -> str:
+    """
+    Searches for the latest news regarding the stock ticker using DuckDuckGo.
+    Analyzes each headline with FinBERT for sentiment.
+    Returns a summary string of the top 5 results with sentiment scores.
+    """
+    print(f"[DEBUG] search_news called with: {ticker}")
+    try:
+        if not ticker:
+            return "No ticker provided for news search."
+            
+        ticker = ticker.upper().strip()
+        articles_pool = []
+        
+        # 1. Fetch from Yahoo Finance API (Reliable)
+        try:
+             print("[DEBUG] Fetching YF API news...")
+             yf_ticker = yf.Ticker(ticker)
+             yf_raw = yf_ticker.news
+             if yf_raw:
+                 for item in yf_raw:
+                     ts = item.get('providerPublishTime')
+                     date_str = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d') if ts else 'Unknown'
+                     articles_pool.append(NewsArticle(
+                         title=item.get('title', 'No Title'),
+                         link=item.get('link', ''),
+                         snippet=f"Source: {item.get('publisher')} - {date_str}",
+                         datetime=date_str
+                     ))
+        except Exception as e:
+             print(f"[WARNING] YF API failed: {e}")
+             
+        # 2. Add DuckDuckGo Targeted Search (Secondary)
+        # Targeted sites: CNBC, Bloomberg, Investing.com, MarketWatch
+        query = f"{ticker} stock news (site:cnbc.com OR site:bloomberg.com OR site:investing.com OR site:marketwatch.com)"
+        
+        with DDGS() as ddgs:
+            raw_results = list(ddgs.news(query, max_results=10))
+            for raw_item in raw_results:
+                try:
+                    articles_pool.append(NewsArticle(
+                        title=raw_item.get('title', 'No Title'),
+                        link=raw_item.get('url', ''),
+                        snippet=raw_item.get('body', ''),
+                        datetime=raw_item.get('date', 'Unknown')
+                    ))
+                except: continue
+
+        if not articles_pool:
+             return f"No recent news found for {ticker}."
+             
+        # Deduplicate by Title
+        seen_titles = set()
+        unique_articles = []
+        for a in articles_pool:
+            if a.title not in seen_titles:
+                seen_titles.add(a.title)
+                unique_articles.append(a)
+        
+        # Analyze Top 5
+        top_articles = unique_articles[:5]
+        sentiment_pipe = get_sentiment_pipeline()
+        
+        results = []
+        
+        # Helper to process
+        def process_article(article):
+            # FinBERT Analysis
+            sentiment_tag = ""
+            if sentiment_pipe:
+                try:
+                    # Prefer full text fetch, fallback to Snippet
+                    content = article.snippet if article.snippet else article.title
+                    analysis_type = "Snippet" if article.snippet else "Headline"
+                    
+                    
+                    # Try fetch full text with improved headers
+                    if article.link:
+                        full_text = _fetch_page_content(article.link)
+                        if full_text and len(full_text) > 100:
+                             content = full_text
+                             analysis_type = "Full Text"
+                        else:
+                             # Fallback log
+                             print(f"[DEBUG] Content too short/failed for {article.title[:30]}... using Snippet.")
+                    
+                    # Truncate for BERT
+                    score = sentiment_pipe(content[:2000])[0]
+                    label = score['label']
+                    conf = round(score['score'], 2)
+                    sentiment_tag = f" [FinBERT ({analysis_type}): {label} ({conf})]"
+                except Exception as e:
+                    sentiment_tag = f" [FinBERT: Error]"
+            
+            return f"- [{article.datetime}] {article.title}{sentiment_tag}"
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
+            results = list(executor.map(process_article, top_articles))
+            
+        return f"Recent News for {ticker} (Sources: YF, CNBC, Bloomberg, Investing):\n" + "\n".join(results)
+        
+    except Exception as e:
+        return f"Error fetching news for {ticker}: {str(e)}"

src/agora/backend/tools/pl_calculator.py

@@ -0,0 +1,98 @@
+import math
+from typing import List, Dict, Any
+
+def calculate_strategy_metrics(legs: List[Dict[str, Any]], spot_price: float) -> Dict[str, Any]:
+    """
+    Calculates Max Profit, Max Loss, and Breakeven for a given set of option legs.
+    Leg format: {'action': 'BUY'/'SELL', 'type': 'CALL'/'PUT', 'strike': float, 'price': float, 'expiry': str}
+    """
+    if not legs:
+        return {"max_profit": 0, "max_loss": 0, "breakeven": 0, "net_cost": 0}
+
+    # 1. Calculate Net Debit/Credit
+    net_premium = 0
+    for leg in legs:
+        multiplier = 1 if leg['action'].upper() == 'BUY' else -1
+        net_premium += leg['price'] * multiplier
+    
+    # Positive net_premium = Debit (Paying)
+    # Negative net_premium = Credit (Receiving)
+    is_debit = net_premium > 0
+    net_cost = abs(net_premium) * 100 # Multiplied by contract size
+    
+    # 2. Identify Strategy Type and Calculate Risk
+    leg_count = len(legs)
+    expiries = set(leg['expiry'] for leg in legs)
+    is_multi_expiry = len(expiries) > 1
+    
+    # Sort legs by strike for easier analysis
+    sorted_legs = sorted(legs, key=lambda x: x['strike'])
+    
+    max_profit = 0
+    max_loss = 0
+    
+    if is_multi_expiry:
+        # Complex calculation for Calendars/Diagonals
+        # For simplicity in this version, we provide an ESTIMATE based on premium paid
+        # Usually Max Loss = Net Debit Paid
+        if is_debit:
+            max_loss = net_cost
+            # Max profit is capped by the back-month value at front-month expiration
+            # This is hard to calculate without a model, so we flag it as an estimate
+            max_profit = "Estimated (Limited)" 
+        else:
+            # Net Credit Calendar (Rare/Risky)
+            max_loss = "Unlimited"
+            max_profit = net_cost
+            
+    elif leg_count == 1:
+        # Long/Short Call/Put
+        if legs[0]['action'].upper() == 'BUY':
+            max_loss = net_cost
+            max_profit = "Unlimited"
+        else:
+            max_profit = net_cost
+            max_loss = "Unlimited"
+            
+    elif leg_count == 2:
+        # Spreads (Vertical)
+        s1, s2 = sorted_legs[0]['strike'], sorted_legs[1]['strike']
+        spread_width = (s2 - s1) * 100
+        
+        if is_debit:
+            max_loss = net_cost
+            max_profit = spread_width - net_cost
+        else:
+            max_profit = net_cost
+            max_loss = spread_width - net_cost
+            
+    elif leg_count == 4:
+        # Iron Condor / Iron Butterfly
+        # Max Profit = Net Credit
+        # Max Loss = Width of widest wing - Net Credit
+        if not is_debit:
+            put_spread_width = (sorted_legs[1]['strike'] - sorted_legs[0]['strike']) * 100
+            call_spread_width = (sorted_legs[3]['strike'] - sorted_legs[2]['strike']) * 100
+            widest_wing = max(put_spread_width, call_spread_width)
+            max_profit = net_cost
+            max_loss = widest_wing - net_cost
+        else:
+            # Reverse Iron Condor (Debit)
+            max_loss = net_cost
+            max_profit = max(sorted_legs[1]['strike'] - sorted_legs[0]['strike'], sorted_legs[3]['strike'] - sorted_legs[2]['strike']) * 100 - net_cost
+
+    elif leg_count == 3:
+        # Butterfly / Christmas Tree
+        # S1 (Buy 1), S2 (Sell 2), S3 (Buy 1)
+        if is_debit:
+            wing_width = (sorted_legs[1]['strike'] - sorted_legs[0]['strike']) * 100
+            max_loss = net_cost
+            max_profit = wing_width - net_cost
+
+    return {
+        "max_profit": max_profit,
+        "max_loss": max_loss,
+        "net_premium": round(net_premium, 2),
+        "is_debit": is_debit,
+        "leg_details": [f"{l['action']} {l['type']} {l['strike']} @ {l['price']} (Exp: {l['expiry']})" for l in legs]
+    }

src/agora/backend/utility/autogen_model_factory.py

@@ -0,0 +1,112 @@
+import os
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+
+class AutoGenModelFactory:
+    """
+    Factory for creating AutoGen compatible model instances.
+    """
+
+    @staticmethod
+    def get_model(provider: str = "azure", # azure, openai, google, groq, ollama
+                  model_name: str = "gpt-4o", 
+                  temperature: float = 0,
+                  model_info: dict = None
+                  ):
+        """
+        Returns an AutoGen OpenAIChatCompletionClient instance.
+        """
+        
+        # Lazy import to avoid dependency issues if autogen is not installed
+        try:
+            from autogen_ext.models.openai import OpenAIChatCompletionClient
+        except ImportError as e:
+            raise ImportError("AutoGen libraries (autogen-agentchat, autogen-ext[openai]) are not installed.") from e
+
+        # ----------------------------------------------------------------------
+        # AZURE
+        # ----------------------------------------------------------------------
+        if provider.lower() == "azure":
+            token_provider = get_bearer_token_provider(
+                DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+            )
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                azure_endpoint=os.environ["AZURE_OPENAI_API_URI"],
+                api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                azure_ad_token_provider=token_provider,
+                temperature=temperature,
+            )
+
+        # ----------------------------------------------------------------------
+        # OPENAI
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "openai":
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                api_key=os.environ["OPENAI_API_KEY"],
+                temperature=temperature,
+            )
+        
+        # ----------------------------------------------------------------------
+        # GOOGLE (GEMINI) via OpenAI Compat
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "google" or provider.lower() == "gemini":
+            if model_info is None:
+                model_info = {
+                    "family": "gemini",
+                    "vision": False,
+                    "function_calling": True,
+                    "json_output": True,
+                    "structured_output": False
+                }
+            
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+                api_key=os.environ["GOOGLE_API_KEY"],
+                model_info=model_info,
+                temperature=temperature,
+                max_tokens=2048,
+                structured_output=False, # Disable for Gemini compatibility
+                extra_headers={"x-goog-api-key": os.environ["GOOGLE_API_KEY"]}
+            )
+
+        # ----------------------------------------------------------------------
+        # GROQ
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "groq":
+            if model_info is None:
+                model_info = {
+                    "family": "llama", # Use llama family for Groq
+                    "vision": False,
+                    "function_calling": True,
+                    "json_output": True,
+                    "structured_output": False
+                }
+            
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                base_url="https://api.groq.com/openai/v1",
+                api_key=os.environ["GROQ_API_KEY"],
+                model_info=model_info,
+                temperature=temperature,
+                structured_output=False, # Disable for Groq compatibility
+                max_tokens=2048
+            )
+        
+        # ----------------------------------------------------------------------
+        # OLLAMA
+        # ----------------------------------------------------------------------
+        elif provider.lower() == "ollama":
+            # Ensure model_info defaults to empty dict if None
+            info = model_info if model_info is not None else {}
+            return OpenAIChatCompletionClient(
+                model=model_name,
+                base_url="http://localhost:11434/v1",
+                api_key="ollama", # dummy key
+                model_info=info,
+                temperature=temperature,
+            )
+        
+        else:
+            raise ValueError(f"Unsupported AutoGen provider: {provider}")

src/agora/frontend/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

src/agora/frontend/.vscode/extensions.json

@@ -0,0 +1,3 @@
+{
+  "recommendations": ["Vue.volar"]
+}

src/agora/frontend/README.md

@@ -0,0 +1,5 @@
+# Vue 3 + Vite
+
+This template should help get you started developing with Vue 3 in Vite. The template uses Vue 3 `<script setup>` SFCs, check out the [script setup docs](https://v3.vuejs.org/api/sfc-script-setup.html#sfc-script-setup) to learn more.
+
+Learn more about IDE Support for Vue in the [Vue Docs Scaling up Guide](https://vuejs.org/guide/scaling-up/tooling.html#ide-support).

src/agora/frontend/index.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Agora - Multi-Agent Trading Intelligence</title>
+  </head>
+  <body>
+    <div id="app"></div>
+    <script type="module" src="/src/main.js"></script>
+  </body>
+</html>

src/agora/frontend/package-lock.json

@@ -0,0 +1,1627 @@
+{
+  "name": "frontend",
+  "version": "0.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "frontend",
+      "version": "0.0.0",
+      "dependencies": {
+        "axios": "^1.16.0",
+        "lucide-vue-next": "^0.563.0",
+        "vue": "^3.5.24"
+      },
+      "devDependencies": {
+        "@vitejs/plugin-vue": "^6.0.1",
+        "vite": "^7.3.2"
+      }
+    },
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+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
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+}

src/agora/frontend/package.json

@@ -0,0 +1,20 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "axios": "^1.16.0",
+    "lucide-vue-next": "^0.563.0",
+    "vue": "^3.5.24"
+  },
+  "devDependencies": {
+    "@vitejs/plugin-vue": "^6.0.1",
+    "vite": "^7.3.2"
+  }
+}

src/agora/frontend/src/App.vue

@@ -0,0 +1,1888 @@
+<script setup>
+import { ref, reactive, onMounted, nextTick, computed, watch } from 'vue'
+import { 
+  Rocket, 
+  Search, 
+  Activity, 
+  MessageSquare, 
+  LayoutDashboard,
+  BrainCircuit,
+  ShieldCheck,
+  Landmark,
+  AlertTriangle,
+  ExternalLink,
+  ChevronRight,
+  Loader2,
+  TrendingUp,
+  XCircle,
+  Clock,
+  Calendar,
+  Heart,
+  Zap,
+  LineChart,
+  Crown,
+  Sparkles
+} from 'lucide-vue-next'
+
+const ticker = ref('NVDA')
+const provider = ref('openai')
+const isAnalyzing = ref(false)
+const activeAgent = ref(null)
+const logs = ref([])
+const results = ref([])
+const error = ref(null)
+const logContainer = ref(null)
+
+const workflowAgents = [
+  { id: 'TechnicalAnalyst', name: 'Technical', role: 'Analyzes Market Trend (SPY), SMAs, EMAs, RSI, and MACD for directional bias.' },
+  { id: 'VolatilityAnalyst', name: 'Volatility', role: 'Analyzes VIX Fear Gauge, IV vs HV, and Option Liquidity.' },
+  { id: 'SentimentAnalyst', name: 'Sentiment', role: 'Evaluates market sentiment from news and monitors earnings risks.' },
+  { id: 'FundamentalAnalyst', name: 'Fundamental', role: 'Evaluates Valuation (P/E, PEG), EPS Growth, and Financial Health vs Market Risk.' },
+  { id: 'StrategyAdvisor', name: 'Strategy', role: 'Formulates multi-leg option strategies with risk/reward calculation.' },
+  { id: 'RiskManager', name: 'Risk', role: 'Critiques strategy in 2-round debate and performs math verification.' },
+  { id: 'LeadOrchestrator', name: 'Orchestrator', role: 'Central Control. Performs gap analysis, cross-questions agents, and issues final approval.' }
+]
+
+const providers = [
+  { id: 'openai', name: 'OpenAI GPT-4o' },
+  { id: 'google', name: 'Google Gemini Pro' },
+  { id: 'groq', name: 'Groq Llama 3.3' }
+]
+
+const agentIcons = {
+  'TeamManager': Crown,
+  'TechnicalAnalyst': LineChart,
+  'VolatilityAnalyst': Zap,
+  'SentimentAnalyst': MessageSquare,
+  'FundamentalAnalyst': Landmark,
+  'StrategyAdvisor': BrainCircuit,
+  'RiskManager': ShieldCheck,
+  'LeadOrchestrator': Crown,
+  'System': LayoutDashboard,
+  'User': Search,
+  // Backward compatibility
+  'MarketAnalyst': Activity 
+}
+
+const agentColors = {
+  'TeamManager': '#facc15', // Yellow/Gold
+  'TechnicalAnalyst': '#3b82f6', // Blue
+  'VolatilityAnalyst': '#ec4899', // Pink/Magenta
+  'SentimentAnalyst': '#10b981', // Emerald
+  'FundamentalAnalyst': '#f59e0b', // Amber
+  'StrategyAdvisor': '#8b5cf6', // Purple
+  'RiskManager': '#ef4444', // Red
+  'LeadOrchestrator': '#facc15', // Gold
+  'System': '#94a3b8', // Slate
+  'User': '#60a5fa',  // Light Blue
+  'MarketAnalyst': '#3b82f6'
+}
+
+// Cookie Helpers
+const setCookie = (name, value, days = 30) => {
+  const date = new Date();
+  date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
+  const expires = "expires=" + date.toUTCString();
+  document.cookie = name + "=" + value + ";" + expires + ";path=/;SameSite=Lax";
+}
+
+const getCookie = (name) => {
+  const nameEQ = name + "=";
+  const ca = document.cookie.split(';');
+  for(let i=0;i < ca.length;i++) {
+    let c = ca[i];
+    while (c.charAt(0)==' ') c = c.substring(1,c.length);
+    if (c.indexOf(nameEQ) == 0) return c.substring(nameEQ.length,c.length);
+  }
+  return null;
+}
+
+const getAgentIcon = (source) => {
+  return agentIcons[source] || LayoutDashboard
+}
+
+const getAgentColor = (source) => {
+  return agentColors[source] || '#94a3b8'
+}
+
+const scrollToBottom = async () => {
+  await nextTick()
+  if (logContainer.value) {
+    logContainer.value.scrollTop = logContainer.value.scrollHeight
+  }
+}
+
+const startAnalysis = () => {
+  if (!ticker.value || isAnalyzing.value) return
+  
+  isAnalyzing.value = true
+  logs.value = []
+  results.value = []
+  error.value = null
+  
+  const tickers = ticker.value.split(',').map(t => t.trim().toUpperCase()).filter(t => t)
+  
+  if (tickers.length === 0) {
+    error.value = "Please enter at least one ticker."
+    isAnalyzing.value = false
+    return
+  }
+
+  // Save last ticker
+  setCookie('last_ticker', tickers[0])
+
+  analyzeTicker(tickers[0])
+}
+
+const pendingResult = ref(null)
+const currentEventSource = ref(null)
+const currentAnalysisId = ref(null)
+
+const cancelAnalysis = async () => {
+  if (currentAnalysisId.value) {
+    try {
+      // Send cancel request to backend
+      await fetch(`/cancel/${currentAnalysisId.value}`, { method: 'POST' })
+    } catch (e) {
+      console.error('Failed to cancel analysis:', e)
+    }
+  }
+  
+  if (currentEventSource.value) {
+    currentEventSource.value.close()
+    currentEventSource.value = null
+  }
+  
+  isAnalyzing.value = false
+  activeAgent.value = null
+  currentAnalysisId.value = null
+  
+  logs.value.push({
+    id: Math.random().toString(36).substr(2, 9),
+    source: 'System',
+    content: 'Analysis cancelled by user.',
+    timestamp: new Date().toLocaleTimeString([], { hour: '2-digit', minute: '2-digit', second: '2-digit' })
+  })
+  scrollToBottom()
+}
+
+const analyzeTicker = (symbol) => {
+  // Use relative path for same-origin serving (FastAPI mounting frontend)
+  const url = `/analyze?ticker=${symbol}&provider=${provider.value}`
+  
+  const eventSource = new EventSource(url)
+  currentEventSource.value = eventSource
+  pendingResult.value = null
+
+  eventSource.onmessage = (event) => {
+      if (event.data === '[DONE]') {
+        // Delay clearing active agent so user can see the final Orchestrator state
+        setTimeout(() => {
+            activeAgent.value = null
+        }, 3000)
+
+        if (pendingResult.value) {
+          results.value.push(pendingResult.value)
+          saveToHistory(pendingResult.value) // Save to history
+          pendingResult.value = null
+        }
+        eventSource.close()
+        isAnalyzing.value = false
+        return
+      }
+
+    try {
+      const data = JSON.parse(event.data)
+      if (data.error) {
+        error.value = data.error
+        eventSource.close()
+        isAnalyzing.value = false
+        return
+      }
+
+      if (data.content || data.source) {
+        // Capture analysis_id from first message
+        if (data.analysis_id && !currentAnalysisId.value) {
+          currentAnalysisId.value = data.analysis_id
+        }
+        
+        if (data.source && data.source !== 'System') {
+          activeAgent.value = data.source
+        }
+        
+        logs.value.push({
+          id: Math.random().toString(36).substr(2, 9),
+          source: data.source,
+          content: data.content,
+          timestamp: new Date().toLocaleTimeString([], { hour: '2-digit', minute: '2-digit', second: '2-digit' })
+        })
+        scrollToBottom()
+      }
+
+      // Use backend-provided structured result if available (highly recommended for Groq/Llama)
+      if (data.source === 'RiskManager') {
+        if (data.structured_result) {
+            console.log("Using backend-provided structured result");
+            pendingResult.value = {
+                ticker: symbol,
+                model: providers.find(p => p.id === provider.value)?.name || provider.value,
+                ...data.structured_result
+            }
+        } else {
+            // Fallback to local regex if structured_result is missing
+            const jsonMatch = data.content.match(/```json\s*([\s\S]*?)\s*```/) || data.content.match(/\{[\s\S]*"final_decision"[\s\S]*\}/)
+            if (jsonMatch) {
+                try {
+                    const rawJson = jsonMatch[1] || jsonMatch[0]
+                    const parsed = JSON.parse(rawJson)
+                    pendingResult.value = {
+                        ticker: symbol,
+                        model: providers.find(p => p.id === provider.value)?.name || provider.value,
+                        ...parsed
+                    }
+                } catch (e) {
+                    console.log("Failed to parse result JSON", e)
+                }
+            }
+        }
+      }
+    } catch (e) {
+      console.error("Error parsing message", e)
+    }
+  }
+
+  eventSource.onerror = (err) => {
+    console.error("SSE Error:", err)
+    error.value = "Connection to server lost. Make sure the backend is running at http://localhost:8000"
+    eventSource.close()
+    currentEventSource.value = null
+    isAnalyzing.value = false
+  }
+}
+
+const formatContent = (content) => {
+    if (!content) return ''
+    let html = content
+        // Headers
+        .replace(/^### (.*$)/gm, '<h5 class="content-h3">$1</h5>')
+        .replace(/^## (.*$)/gm, '<h4 class="content-h2">$1</h4>')
+        .replace(/^# (.*$)/gm, '<h3 class="content-h1">$1</h3>')
+        // Bold
+        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
+        // Italic
+        .replace(/\*(.*?)\*/g, '<em>$1</em>')
+        // Inline Code
+        .replace(/`(.*?)`/g, '<code class="inline-code">$1</code>')
+        // Blockquotes
+        .replace(/^> (.*$)/gm, '<blockquote>$1</blockquote>')
+        // Bullet Lists
+        .replace(/^\s*[-*+]\s+(.*)$/gm, '<ul><li>$1</li></ul>')
+        .replace(/<\/ul>\n<ul>/g, '\n') // Combine consecutive lists
+        // Numbered Lists
+        .replace(/^\s*\d+\.\s+(.*)$/gm, '<ol><li>$1</li></ol>')
+        .replace(/<\/ol>\n<ol>/g, '\n') // Combine consecutive lists
+        // New lines to br (only after block elements or within paragraphs)
+        .replace(/\n/g, '<br>')
+    
+    return html
+}
+
+const getDecisionColor = (decision) => {
+  if (!decision) return 'var(--text-secondary)'
+  const d = decision.toUpperCase()
+  if (d.includes('TRADE') || d.includes('BUY')) return 'var(--success)'
+  if (d.includes('WAIT') || d.includes('HOLD')) return 'var(--warning)'
+  return 'var(--danger)'
+}
+
+// History Management
+const history = ref([])
+const selectedReport = ref(null)
+
+const openReport = (item) => {
+    selectedReport.value = item
+}
+
+const closeReport = () => {
+    selectedReport.value = null
+}
+
+const loadHistory = () => {
+  try {
+    const saved = localStorage.getItem('analysis_history')
+    if (saved) {
+      history.value = JSON.parse(saved)
+    }
+  } catch (e) {
+    console.error("Failed to load history", e)
+  }
+}
+
+const saveToHistory = (result) => {
+  const item = {
+    id: Date.now(),
+    timestamp: new Date().toLocaleString(),
+    ...result
+  }
+  // Add to beginning
+  history.value.unshift(item)
+  // Keep max 20 items
+  if (history.value.length > 20) {
+    history.value = history.value.slice(0, 20)
+  }
+  
+  try {
+    localStorage.setItem('analysis_history', JSON.stringify(history.value))
+  } catch (e) {
+    console.error("Failed to save history", e)
+  }
+}
+
+const clearHistory = () => {
+    history.value = []
+    localStorage.removeItem('analysis_history')
+}
+
+// Persistence Watcher
+watch(provider, (newVal) => {
+  setCookie('selected_provider', newVal);
+})
+
+// Computed Progress
+
+onMounted(() => {
+  loadHistory() // Load history on startup
+  
+  const savedProvider = getCookie('selected_provider');
+  if (savedProvider && providers.some(p => p.id === savedProvider)) {
+    provider.value = savedProvider;
+  }
+
+  const savedTicker = getCookie('last_ticker');
+  if (savedTicker) {
+    ticker.value = savedTicker;
+  }
+})
+</script>
+
+<template>
+  <div class="app-wrapper">
+    <!-- Header Area -->
+    <header class="main-header glass">
+      <div class="logo">
+        <div class="logo-icon">
+          <TrendingUp class="icon-primary" :size="32" />
+        </div>
+        <div class="logo-text">
+          <h1>AGO<span>RA</span></h1>
+          <p>Multi-Agent Trading Intelligence</p>
+        </div>
+      </div>
+
+      <div class="controls">
+        <div class="input-group glass">
+          <Search :size="18" class="search-icon" />
+          <input 
+            v-model="ticker" 
+            placeholder="Enter Ticker (e.g. NVDA, AAPL)" 
+            @keyup.enter="startAnalysis"
+            :disabled="isAnalyzing"
+          />
+        </div>
+
+        <div class="select-group glass">
+          <BrainCircuit :size="18" class="brain-icon" />
+          <select v-model="provider" :disabled="isAnalyzing">
+            <option v-for="p in providers" :key="p.id" :value="p.id">{{ p.name }}</option>
+          </select>
+        </div>
+
+        <button 
+          class="btn btn-primary" 
+          @click="startAnalysis" 
+          :disabled="isAnalyzing || !ticker"
+        >
+          <Loader2 v-if="isAnalyzing" class="animate-spin" :size="20" />
+          <Rocket v-else :size="20" />
+          <span>{{ isAnalyzing ? 'Analyzing...' : 'Launch Analysis' }}</span>
+        </button>
+
+        <button 
+          v-if="isAnalyzing"
+          class="btn btn-danger" 
+          @click="cancelAnalysis"
+          title="Cancel Analysis"
+        >
+          <XCircle :size="20" />
+          <span>Cancel</span>
+        </button>
+      </div>
+    </header>
+
+    <main class="dashboard-content">
+      <div class="breadcrumb-container glass">
+        <div class="breadcrumb-label">
+          <Sparkles :size="12" /> 
+          Active Intelligence Pipeline 
+          <span class="legend-text">(Glowing icons indicate agents in action)</span>
+        </div>
+        <div class="workflow-breadcrumb">
+          <div 
+            v-for="(agent, index) in workflowAgents" 
+            :key="agent.id" 
+            :class="['breadcrumb-item', { active: activeAgent === agent.id }]"
+            :data-tooltip="agent.role"
+          >
+            <div class="item-icon-wrapper" :style="{ color: getAgentColor(agent.id) }">
+              <component :is="getAgentIcon(agent.id)" :size="14" />
+              <div v-if="activeAgent === agent.id" class="bulb" :style="{ backgroundColor: getAgentColor(agent.id) }"></div>
+            </div>
+            <span class="item-label">{{ agent.name }}</span>
+            <ChevronRight v-if="index < workflowAgents.length - 1" :size="14" class="separator" />
+          </div>
+        </div>
+      </div>
+
+      <!-- Progress Bar Removed -->
+
+      <!-- Error Alert -->
+      <div v-if="error" class="error-alert glass">
+        <XCircle :size="24" />
+        <p>{{ error }}</p>
+        <button @click="error = null"><ChevronRight :size="20" /></button>
+      </div>
+
+      <div class="grid-layout">
+        <!-- Live Analysis Stream -->
+        <section class="panel-logs glass">
+          <div class="panel-header">
+            <h3><Activity :size="20" /> Live Agent Stream</h3>
+            <div v-if="isAnalyzing" class="status-indicator">
+              <span class="pulse"></span> Active
+            </div>
+          </div>
+          
+          <div class="log-viewport" ref="logContainer">
+            <div v-if="logs.length === 0 && !isAnalyzing" class="empty-state">
+              <LayoutDashboard :size="48" />
+              <p>Ready to analyze. Enter a ticker to begin.</p>
+            </div>
+
+            <div v-for="log in logs" :key="log.id" class="log-entry" :style="{ borderLeftColor: getAgentColor(log.source) }">
+              <div class="log-meta">
+                <component :is="getAgentIcon(log.source)" :size="16" :style="{ color: getAgentColor(log.source) }" />
+                <span class="source" :style="{ color: getAgentColor(log.source) }">{{ log.source }}</span>
+                <span class="time">{{ log.timestamp }}</span>
+              </div>
+              <div class="log-content" v-html="formatContent(log.content)"></div>
+            </div>
+          </div>
+        </section>
+
+        <!-- Intelligence Hub (Summary & Cards) -->
+        <section class="panel-results">
+          <div class="summary-section glass">
+            <div class="panel-header">
+              <h3><ShieldCheck :size="20" /> Executive Summary</h3>
+            </div>
+            
+            <div class="results-list">
+                <div v-if="isAnalyzing" class="loading-state">
+                  <div class="sparkles-wrapper">
+                    <Sparkles :size="48" class="sparkle-icon" />
+                    <div class="sparkle-particles">
+                      <span class="particle"></span>
+                      <span class="particle"></span>
+                      <span class="particle"></span>
+                      <span class="particle"></span>
+                    </div>
+                  </div>
+                  <p>Agents analyzing market data...</p>
+                </div>
+                <div v-else-if="results.length === 0 && !pendingResult" class="empty-state mini">
+                    <p>Analysis cards will appear here</p>
+                </div>
+                
+                <TransitionGroup name="list" tag="div" class="transition-container">
+                    <!-- Finalized Results -->
+                    <div v-for="res in results" :key="res.ticker" class="result-card glass" :style="{ borderColor: getDecisionColor(res.final_decision) }">
+                        <div class="card-header">
+                            <div class="ticker-badge">{{ res.ticker }}</div>
+                            <div class="decision-badge" :style="{ backgroundColor: getDecisionColor(res.final_decision) }">
+                                {{ res.final_decision }}
+                            </div>
+                            <div v-if="res.fundamental_rating || res.rating" class="fundamental-badge" :class="res.fundamental_rating || res.rating">
+                                <Heart :size="10" /> {{ res.fundamental_rating || res.rating }}
+                            </div>
+                            <div class="confidence-badge">
+                                {{ res.confidence }}% Confidence
+                            </div>
+                        </div>
+                        
+                        <div class="card-body">
+                            <h4 class="strat-title">{{ res.strategy_type }}</h4>
+                            
+                            <!-- Strategy Legs Table -->
+                            <div v-if="res.legs && res.legs.length" class="legs-container glass-inset">
+                                <div class="legs-header">Strategy Components</div>
+                                <table class="legs-table">
+                                    <thead>
+                                        <tr>
+                                            <th>Leg</th>
+                                            <th>Type</th>
+                                            <th>Strike</th>
+                                            <th>Expiry</th>
+                                            <th>Price</th>
+                                        </tr>
+                                    </thead>
+                                    <tbody>
+                                        <tr v-for="(leg, idx) in res.legs" :key="idx">
+                                            <td>
+                                                <span class="action-badge" :class="leg.action.toLowerCase()">
+                                                    {{ leg.action }}
+                                                </span>
+                                            </td>
+                                            <td>{{ leg.type }}</td>
+                                            <td><span class="strike-pill">${{ leg.strike }}</span></td>
+                                            <td class="col-expiry">{{ leg.expiry }}</td>
+                                            <td class="col-price">${{ leg.price }}</td>
+                                        </tr>
+                                    </tbody>
+                                </table>
+                            </div>
+
+                            <div class="risk-reward-grid">
+                                <div class="rr-item">
+                                    <span class="rr-label">Max Profit</span>
+                                    <span class="rr-val profit">${{ res.max_profit }}</span>
+                                </div>
+                                <div class="rr-item">
+                                    <span class="rr-label">Max Loss</span>
+                                    <span class="rr-val loss">${{ res.max_loss }}</span>
+                                </div>
+                                <div class="rr-item">
+                                    <span class="rr-label">Entry</span>
+                                    <span class="rr-val">${{ res.entry_price || res.estimated_entry_price }}</span>
+                                </div>
+                            </div>
+
+                            <p class="risk-info">
+                                <AlertTriangle v-if="res.risk_warning" :size="14" />
+                                {{ res.risk_warning || 'No specific risk warnings identified.' }}
+                            </p>
+                        </div>
+                    </div>
+
+                    <!-- Pending/Finalizing State -->
+                    <div v-if="pendingResult && isAnalyzing" class="result-card glass finalizing pulse-border">
+                        <div class="card-header">
+                            <div class="ticker-badge">{{ pendingResult.ticker }}</div>
+                            <div class="status-badge">FINALIZING...</div>
+                        </div>
+                        <div class="card-body">
+                            <p>Agents are confirming the strategy. Hang tight...</p>
+                        </div>
+                    </div>
+                </TransitionGroup>
+            </div>
+          </div>
+        </section>
+      </div>
+
+      <!-- History Section -->
+      <section v-if="history.length > 0" class="history-section glass">
+        <div class="panel-header">
+          <h3><Clock :size="20" /> Analysis History</h3>
+          <button @click="clearHistory" class="btn-clear">Clear History</button>
+        </div>
+        
+        <div class="history-table-container glass-inset">
+            <table class="history-table">
+                <thead>
+                    <tr>
+                        <th>Date</th>
+                        <th>Ticker</th>
+                        <th class="mobile-hide">Model</th>
+                        <th>Decision</th>
+                        <th class="mobile-hide">Confidence</th>
+                        <th class="mobile-hide">Strategy</th>
+                        <th class="mobile-hide">Expiry</th>
+                        <th class="mobile-hide">Max Profit</th>
+                        <th class="mobile-hide" style="width: 50px"></th>
+                    </tr>
+                </thead>
+                <tbody>
+                    <tr v-for="item in history" :key="item.id" @click="openReport(item)">
+                        <td class="col-date">{{ item.timestamp }}</td>
+                        <td class="col-ticker"><span class="ticker-pill">{{ item.ticker }}</span></td>
+                        <td class="col-model mobile-hide"><span class="model-badge">{{ item.model || 'Unknown' }}</span></td>
+                        <td>
+                            <span class="decision-pill" :style="{ color: getDecisionColor(item.final_decision), borderColor: getDecisionColor(item.final_decision) }">
+                                {{ item.final_decision }}
+                            </span>
+                        </td>
+                        <td class="mobile-hide">
+                            <div class="confidence-bar-wrapper">
+                                <div class="confidence-bar" :style="{ width: item.confidence + '%', backgroundColor: getDecisionColor(item.final_decision) }"></div>
+                                <span>{{ item.confidence }}%</span>
+                            </div>
+                        </td>
+                        <td class="mobile-hide">{{ item.strategy_type }}</td>
+                        <td class="mobile-hide">{{ item.expiry_date || 'N/A' }}</td>
+                        <td class="col-profit mobile-hide" :class="{ 'has-profit': item.max_profit > 0 }">
+                            {{ item.max_profit ? '$' + item.max_profit : '-' }}
+                        </td>
+                        <td class="col-action mobile-hide"><ChevronRight :size="16" /></td>
+                    </tr>
+                </tbody>
+            </table>
+        </div>
+      </section>
+    </main>
+
+    <!-- Detailed Report Modal -->
+    <div v-if="selectedReport" class="modal-overlay" @click.self="closeReport">
+        <div class="modal-content glass" :style="{ borderTopColor: getDecisionColor(selectedReport.final_decision) }">
+            <button class="modal-close" @click="closeReport"><XCircle :size="24" /></button>
+            
+            <div class="modal-header">
+                <h2>{{ selectedReport.ticker }} Analysis Report</h2>
+                <span class="timestamp">{{ selectedReport.timestamp }}</span>
+            </div>
+
+            <div class="modal-badges">
+                <div class="decision-badge large" :style="{ backgroundColor: getDecisionColor(selectedReport.final_decision) }">
+                    {{ selectedReport.final_decision }}
+                </div>
+                <div class="confidence-badge large">
+                    {{ selectedReport.confidence }}% Confidence
+                </div>
+                <div class="model-badge large">
+                    <Zap :size="14" /> {{ selectedReport.model || 'AI Model' }}
+                </div>
+                <div v-if="selectedReport.rating" class="rating-badge">
+                   <Heart :size="16" /> {{ selectedReport.rating }}
+                </div>
+            </div>
+
+            <div class="report-section">
+                <h3><BrainCircuit :size="18" /> Recommendation</h3>
+                <p class="highlight-text">{{ selectedReport.actionable_recommendation }}</p>
+            </div>
+
+            <div class="metrics-grid">
+                 <div class="metric-box">
+                    <label>Strategy</label>
+                    <span>{{ selectedReport.strategy_type }}</span>
+                 </div>
+                 <div class="metric-box">
+                    <label>Expiry</label>
+                    <span>{{ selectedReport.expiry_date || 'N/A' }}</span>
+                 </div>
+                 <div class="metric-box">
+                    <label>Entry</label>
+                    <span>{{ selectedReport.entry_signal }} @ ${{ selectedReport.entry_price || 'N/A' }}</span>
+                 </div>
+                 <div class="metric-box">
+                    <label>Max Profit</label>
+                    <span class="profit">${{ selectedReport.max_profit || 0 }}</span>
+                 </div>
+                 <div class="metric-box">
+                    <label>Max Loss</label>
+                    <span class="loss">${{ selectedReport.max_loss || 0 }}</span>
+                 </div>
+            </div>
+
+            <div class="report-section risk-section">
+                <h3><ShieldCheck :size="18" /> Risk Analysis</h3>
+                <p>{{ selectedReport.risk_warning }}</p>
+            </div>
+        </div>
+    </div>
+
+    <!-- Footer Disclaimer -->
+    <footer class="main-footer glass">
+      <div class="footer-content">
+        <div class="disclaimer-info">
+          <AlertTriangle :size="16" />
+          <span><strong>Trading Disclaimer:</strong> Educational and research purposes only. AI-generated insights may be subject to latency or error. Consult a professional advisor before trading.</span>
+        </div>
+        <div class="copyright">
+          &copy; 2024 Agora AI
+        </div>
+      </div>
+    </footer>
+  </div>
+</template>
+
+<style scoped>
+.app-wrapper {
+  display: flex;
+  flex-direction: column;
+  min-height: 100vh;
+  padding: 1.5rem;
+  gap: 1.5rem;
+}
+
+.main-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 1rem 2rem;
+  z-index: 100;
+}
+
+.logo {
+  display: flex;
+  align-items: center;
+  gap: 1rem;
+}
+
+.logo-icon {
+  background: var(--accent-gradient);
+  padding: 0.5rem;
+  border-radius: 0.75rem;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  box-shadow: 0 4px 12px rgba(59, 130, 246, 0.4);
+}
+
+.icon-primary {
+  color: white;
+}
+
+.btn-danger {
+  background: linear-gradient(135deg, #ef4444 0%, #dc2626 100%);
+  border: 1px solid rgba(239, 68, 68, 0.3);
+}
+
+.btn-danger:hover:not(:disabled) {
+  background: linear-gradient(135deg, #dc2626 0%, #b91c1c 100%);
+  box-shadow: 0 8px 24px rgba(239, 68, 68, 0.4);
+  transform: translateY(-2px);
+}
+
+.logo-text h1 {
+  font-size: 1.5rem;
+  margin: 0;
+  line-height: 1;
+}
+
+.logo-text h1 span {
+  color: var(--accent-primary);
+  font-weight: 400;
+}
+
+.logo-text p {
+  font-size: 0.75rem;
+  color: var(--text-secondary);
+  font-weight: 500;
+  letter-spacing: 0.1em;
+  text-transform: uppercase;
+  margin-top: 0.25rem;
+}
+
+.controls {
+  display: flex;
+  gap: 1rem;
+  align-items: center;
+}
+
+.input-group, .select-group {
+  display: flex;
+  align-items: center;
+  padding: 0.5rem 1rem;
+  gap: 0.75rem;
+}
+
+.input-group input {
+  background: transparent;
+  border: none;
+  color: var(--text-primary);
+  outline: none;
+  font-size: 0.9rem;
+  width: 200px;
+}
+
+.select-group select {
+  background: transparent;
+  border: none;
+  color: var(--text-primary);
+  outline: none;
+  font-size: 0.9rem;
+  cursor: pointer;
+}
+
+.select-group select option {
+    background-color: var(--bg-color);
+}
+
+.dashboard-content {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+  flex: 1;
+}
+
+.grid-layout {
+  display: grid;
+  grid-template-columns: 400px 1fr;
+  gap: 1.5rem;
+  flex: 1;
+}
+
+.panel-logs {
+  grid-column: 2; /* Move to right column */
+  display: flex;
+  flex-direction: column;
+  overflow: hidden;
+  height: calc(100vh - 240px);
+}
+
+.panel-header {
+  padding: 1.25rem;
+  border-bottom: 1px solid var(--glass-border);
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+}
+
+.panel-header h3 {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  font-size: 1.1rem;
+  color: var(--text-primary);
+}
+
+.header-hint {
+  font-size: 11px;
+  color: var(--text-tertiary);
+  background: rgba(255, 255, 255, 0.05);
+  padding: 2px 8px;
+  border-radius: 4px;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+}
+
+.log-viewport {
+  flex: 1;
+  overflow-y: auto;
+  padding: 1.25rem;
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+}
+
+.log-entry {
+  padding: 1.25rem;
+  background: rgba(255, 255, 255, 0.02);
+  border-radius: 0.75rem;
+  border-left: 4px solid var(--accent-primary);
+  border-top: 1px solid rgba(255, 255, 255, 0.05);
+  border-right: 1px solid rgba(255, 255, 255, 0.05);
+  border-bottom: 1px solid rgba(255, 255, 255, 0.03);
+  box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+  transition: all 0.2s ease;
+  margin-bottom: 0.5rem;
+}
+
+.log-entry:hover {
+  background: rgba(255, 255, 255, 0.04);
+  transform: translateY(-2px);
+  box-shadow: 0 6px 16px rgba(0, 0, 0, 0.15);
+}
+
+.log-meta {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  margin-bottom: 0.75rem;
+  color: var(--text-secondary);
+  font-size: 0.75rem;
+  letter-spacing: 0.05em;
+}
+
+.log-meta .source {
+  font-weight: 800;
+  text-transform: uppercase;
+}
+
+.log-meta .time {
+  margin-left: auto;
+  opacity: 0.5;
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 0.7rem;
+}
+
+.log-content {
+  font-size: 1rem;
+  line-height: 1.6;
+  color: var(--text-primary);
+  font-weight: 400;
+  word-wrap: break-word;
+}
+
+.log-content :deep(strong) {
+  color: #fff;
+  font-weight: 700;
+}
+
+.log-content :deep(em) {
+  color: var(--text-secondary);
+  font-style: italic;
+}
+
+.log-content :deep(br) {
+  display: block;
+  content: "";
+  margin-top: 0.5rem;
+}
+
+.log-content :deep(ul), .log-content :deep(ol) {
+  margin: 0.75rem 0;
+  padding-left: 1.5rem;
+}
+
+.log-content :deep(li) {
+  margin-bottom: 0.4rem;
+}
+
+.log-content :deep(h3), .log-content :deep(h4), .log-content :deep(h5) {
+  margin: 1.25rem 0 0.75rem 0;
+  color: var(--text-primary);
+  font-weight: 700;
+  line-height: 1.3;
+}
+
+.log-content :deep(.inline-code) {
+  background: rgba(255, 255, 255, 0.1);
+  padding: 0.2rem 0.4rem;
+  border-radius: 0.25rem;
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 0.85em;
+  color: var(--accent-primary);
+}
+
+.log-content :deep(blockquote) {
+  border-left: 3px solid var(--accent-primary);
+  background: rgba(255, 255, 255, 0.03);
+  padding: 0.75rem 1rem;
+  margin: 1rem 0;
+  font-style: italic;
+  border-radius: 0 0.5rem 0.5rem 0;
+}
+
+/* Loading State */
+.loading-state {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  padding: 3rem 2rem;
+  gap: 1.5rem;
+}
+
+.sparkles-wrapper {
+  position: relative;
+  width: 80px;
+  height: 80px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.sparkle-icon {
+  color: var(--accent-primary);
+  animation: sparkleGlow 2s ease-in-out infinite;
+  filter: drop-shadow(0 0 12px rgba(59, 130, 246, 0.6));
+}
+
+@keyframes sparkleGlow {
+  0%, 100% { 
+    opacity: 1; 
+    transform: scale(1) rotate(0deg);
+  }
+  50% { 
+    opacity: 0.7; 
+    transform: scale(1.15) rotate(180deg);
+  }
+}
+
+.sparkle-particles {
+  position: absolute;
+  width: 100%;
+  height: 100%;
+  top: 0;
+  left: 0;
+}
+
+.particle {
+  position: absolute;
+  width: 8px;
+  height: 8px;
+  background: linear-gradient(135deg, #60a5fa, #3b82f6);
+  border-radius: 50%;
+  animation: particleFloat 3s ease-in-out infinite;
+  box-shadow: 0 0 8px rgba(59, 130, 246, 0.8);
+}
+
+.particle:nth-child(1) {
+  top: 10%;
+  left: 10%;
+  animation-delay: 0s;
+}
+
+.particle:nth-child(2) {
+  top: 10%;
+  right: 10%;
+  animation-delay: 0.75s;
+}
+
+.particle:nth-child(3) {
+  bottom: 10%;
+  left: 10%;
+  animation-delay: 1.5s;
+}
+
+.particle:nth-child(4) {
+  bottom: 10%;
+  right: 10%;
+  animation-delay: 2.25s;
+}
+
+@keyframes particleFloat {
+  0%, 100% {
+    transform: translateY(0) scale(1);
+    opacity: 0;
+  }
+  10% {
+    opacity: 1;
+  }
+  50% {
+    transform: translateY(-20px) scale(1.2);
+    opacity: 1;
+  }
+  90% {
+    opacity: 1;
+  }
+  100% {
+    transform: translateY(-40px) scale(0.8);
+    opacity: 0;
+  }
+}
+
+.loading-state p {
+  color: var(--text-secondary);
+  font-size: 0.95rem;
+  font-weight: 500;
+  animation: fadeInOut 2s ease-in-out infinite;
+}
+
+@keyframes fadeInOut {
+  0%, 100% { opacity: 0.6; }
+  50% { opacity: 1; }
+}
+
+.panel-results {
+  grid-column: 1; /* Move to left column */
+  grid-row: 1;    /* Ensure it stays on the first row */
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+.summary-section {
+  flex: 1;
+  display: flex;
+  flex-direction: column;
+  min-height: 400px;
+}
+
+.results-list {
+    padding: 1.25rem;
+    display: flex;
+    flex-direction: column;
+    gap: 1rem;
+}
+
+.result-card {
+    padding: 1.5rem;
+    border-left: 4px solid var(--accent-primary);
+}
+
+.card-header {
+    display: flex;
+    align-items: center;
+    gap: 0.75rem;
+    margin-bottom: 1rem;
+}
+
+.ticker-badge {
+    padding: 0.25rem 0.75rem;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 0.5rem;
+    font-weight: 800;
+    font-size: 1rem;
+}
+
+.decision-badge {
+    padding: 0.25rem 0.75rem;
+    border-radius: 0.5rem;
+    font-weight: 700;
+    font-size: 0.8rem;
+    color: white;
+}
+
+.confidence-badge {
+    margin-left: auto;
+    font-size: 0.8rem;
+    color: var(--text-secondary);
+}
+
+.card-body h4 {
+    font-size: 1.1rem;
+    margin-bottom: 0.75rem;
+    line-height: 1.4;
+}
+
+.entry-info {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    font-size: 0.85rem;
+    margin-bottom: 1rem;
+    color: var(--text-primary);
+}
+
+.risk-info {
+    font-size: 0.85rem;
+    color: var(--text-secondary);
+    display: flex;
+    gap: 0.5rem;
+    font-style: italic;
+}
+
+.info-card {
+  padding: 1.25rem;
+  border-left: 4px solid var(--warning);
+}
+
+.info-card h4 {
+  color: var(--warning);
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  margin-bottom: 0.5rem;
+}
+
+.info-card p {
+    font-size: 0.8rem;
+    color: var(--text-secondary);
+}
+
+.error-alert {
+  padding: 1rem 1.5rem;
+  border-left: 4px solid var(--danger);
+  display: flex;
+  align-items: center;
+  gap: 1rem;
+  color: var(--danger);
+}
+
+.error-alert p {
+  flex: 1;
+  font-weight: 500;
+}
+
+.empty-state {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  height: 100%;
+  color: var(--text-muted);
+  gap: 1rem;
+}
+
+.status-indicator {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  font-size: 0.8rem;
+  font-weight: 600;
+  color: var(--success);
+}
+
+.pulse {
+  width: 8px;
+  height: 8px;
+  background: var(--success);
+  border-radius: 50%;
+  box-shadow: 0 0 0 rgba(16, 185, 129, 0.4);
+  animation: pulse 2s infinite;
+}
+
+@keyframes pulse {
+  0% { transform: scale(0.95); box-shadow: 0 0 0 0 rgba(16, 185, 129, 0.7); }
+  70% { transform: scale(1); box-shadow: 0 0 0 10px rgba(16, 185, 129, 0); }
+  100% { transform: scale(0.95); box-shadow: 0 0 0 0 rgba(16, 185, 129, 0); }
+}
+
+.animate-spin {
+  animation: spin 1s linear infinite;
+}
+
+@keyframes spin {
+  from { transform: rotate(0deg); }
+  to { transform: rotate(360deg); }
+}
+
+@media (max-width: 1024px) {
+  .grid-layout {
+    grid-template-columns: 1fr;
+  }
+  .panel-logs {
+    height: 500px;
+  }
+}
+
+.fundamental-badge {
+    padding: 0.25rem 0.5rem;
+    border-radius: 0.5rem;
+    font-weight: 800;
+    font-size: 0.7rem;
+    text-transform: uppercase;
+    display: flex;
+    align-items: center;
+    gap: 0.3rem;
+    background: rgba(255, 255, 255, 0.05);
+    border: 1px solid var(--glass-border);
+}
+
+.fundamental-badge.Strong { color: var(--success); border-color: rgba(16, 185, 129, 0.3); }
+.fundamental-badge.Stable { color: var(--accent-primary); border-color: rgba(59, 130, 246, 0.3); }
+.fundamental-badge.Weak { color: var(--danger); border-color: rgba(239, 68, 68, 0.3); }
+
+.status-badge {
+    font-size: 0.7rem;
+    font-weight: 800;
+    color: var(--accent-primary);
+    letter-spacing: 0.1em;
+}
+
+.expiry-badge {
+    padding: 0.25rem 0.5rem;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 0.5rem;
+    font-weight: 700;
+    font-size: 0.75rem;
+    color: var(--text-primary);
+    display: flex;
+    align-items: center;
+    gap: 0.4rem;
+    border: 1px solid rgba(255, 255, 255, 0.2);
+}
+
+.pulse-border {
+    animation: pulse-border 2s infinite;
+}
+
+@keyframes pulse-border {
+  0% { border-color: var(--glass-border); }
+  50% { border-color: var(--accent-primary); }
+  100% { border-color: var(--glass-border); }
+}
+
+/* Transitions */
+.list-enter-active,
+.list-leave-active {
+  transition: all 0.5s ease;
+}
+.list-enter-from,
+.list-leave-to {
+  opacity: 0;
+  transform: translateX(30px);
+}
+
+.transition-container {
+    display: flex;
+    flex-direction: column;
+    gap: 1rem;
+}
+.main-footer {
+  margin-top: auto;
+  padding: 1rem 2rem;
+  z-index: 100;
+}
+
+.footer-content {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 2rem;
+}
+
+.disclaimer-info {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  font-size: 0.85rem;
+  color: var(--text-secondary);
+  line-height: 1.4;
+}
+
+.disclaimer-info strong {
+  color: var(--warning);
+}
+
+.copyright {
+  font-size: 0.8rem;
+  color: var(--text-muted);
+  white-space: nowrap;
+}
+
+/* Strategy Legs Table */
+.legs-container {
+    margin: 1.25rem 0;
+    padding: 1rem;
+    border-radius: 0.75rem;
+    background: rgba(255, 255, 255, 0.02);
+    border: 1px solid rgba(255, 255, 255, 0.05);
+}
+
+.legs-header {
+    font-size: 0.7rem;
+    font-weight: 800;
+    text-transform: uppercase;
+    color: var(--text-muted);
+    margin-bottom: 0.75rem;
+    letter-spacing: 0.05em;
+    display: flex;
+    justify-content: space-between;
+}
+
+.legs-table {
+    width: 100%;
+    border-collapse: collapse;
+    font-size: 0.85rem;
+}
+
+.legs-table th {
+    text-align: left;
+    padding: 0.5rem;
+    color: var(--text-secondary);
+    font-weight: 600;
+    border-bottom: 1px solid rgba(255, 255, 255, 0.05);
+}
+
+.legs-table td {
+    padding: 0.6rem 0.5rem;
+    border-bottom: 1px solid rgba(255, 255, 255, 0.05);
+}
+
+.action-badge {
+    padding: 0.15rem 0.4rem;
+    border-radius: 0.25rem;
+    font-size: 0.7rem;
+    font-weight: 800;
+}
+
+.action-badge.buy { background: rgba(16, 185, 129, 0.15); color: var(--success); }
+.action-badge.sell { background: rgba(239, 68, 68, 0.15); color: var(--danger); }
+
+.strike-pill {
+    font-weight: 700;
+    color: var(--text-primary);
+}
+
+.col-expiry {
+    font-size: 0.75rem;
+    color: var(--text-secondary);
+}
+
+.col-price {
+    font-weight: 600;
+    color: var(--accent-primary);
+}
+
+.risk-reward-grid {
+    display: grid;
+    grid-template-columns: repeat(3, 1fr);
+    gap: 0.75rem;
+    margin: 1rem 0;
+}
+
+.rr-item {
+    padding: 0.75rem;
+    background: rgba(255, 255, 255, 0.03);
+    border-radius: 0.5rem;
+    display: flex;
+    flex-direction: column;
+    gap: 0.25rem;
+    border: 1px solid rgba(255, 255, 255, 0.05);
+}
+
+.rr-label {
+    font-size: 0.65rem;
+    font-weight: 700;
+    color: var(--text-muted);
+    text-transform: uppercase;
+}
+
+.rr-val {
+    font-size: 1.1rem;
+    font-weight: 800;
+}
+
+.rr-val.profit { color: var(--success); }
+.rr-val.loss { color: var(--danger); }
+
+.strat-title {
+    margin-bottom: 0.5rem;
+    color: var(--accent-primary);
+    font-weight: 800;
+}
+
+.strat-reasoning {
+    font-size: 0.95rem;
+    line-height: 1.5;
+    color: var(--text-secondary);
+    margin-bottom: 1rem;
+}
+
+@media (max-width: 1024px) {
+  .footer-content {
+    flex-direction: column;
+    text-align: center;
+    gap: 1rem;
+  }
+}
+/* Breadcrumb Styling */
+.workflow-breadcrumb {
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  padding: 0.75rem 2rem;
+  gap: 1.5rem;
+  margin-bottom: 0.5rem;
+  position: relative;
+  z-index: 50; /* Ensure tooltips appear above content below */
+}
+
+.breadcrumb-label {
+    text-align: center;
+    font-size: 0.65rem;
+    font-weight: 800;
+    text-transform: uppercase;
+    color: var(--text-muted);
+    letter-spacing: 0.1em;
+    margin-top: 0.5rem;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 0.4rem;
+    opacity: 0.7;
+}
+
+.legend-text {
+    font-size: 0.6rem;
+    color: var(--accent-primary);
+    text-transform: none;
+    letter-spacing: normal;
+    margin-left: 0.5rem;
+    font-weight: 500;
+    font-style: italic;
+    opacity: 0.9;
+}
+
+.breadcrumb-item {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  opacity: 0.4;
+  transition: all 0.4s ease;
+  cursor: help;
+  position: relative;
+}
+
+/* Fancy Tooltip */
+.breadcrumb-item::after {
+    content: attr(data-tooltip);
+    position: absolute;
+    top: 140%; /* Move below */
+    left: 50%;
+    transform: translateX(-50%) translateY(-10px);
+    background: rgba(15, 23, 42, 0.95);
+    color: #f1f5f9;
+    padding: 0.6rem 1rem;
+    border-radius: 0.5rem;
+    font-size: 0.75rem;
+    line-height: 1.4;
+    width: max-content;
+    max-width: 220px;
+    text-align: center;
+    border: 1px solid rgba(255, 255, 255, 0.1);
+    box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.5);
+    opacity: 0;
+    visibility: hidden;
+    transition: all 0.2s cubic-bezier(0.16, 1, 0.3, 1);
+    z-index: 1000; /* Ensure high z-index regardless */
+    backdrop-filter: blur(4px);
+    font-weight: 500;
+    pointer-events: none;
+}
+
+/* Tooltip Arrow */
+.breadcrumb-item::before {
+    content: '';
+    position: absolute;
+    top: 140%; /* Move below */
+    left: 50%;
+    transform: translateX(-50%) translateY(-10px);
+    border: 6px solid transparent;
+    border-bottom-color: rgba(15, 23, 42, 0.95); /* Point Up */
+    margin-top: -11px; /* Overlap slightly */
+    opacity: 0;
+    visibility: hidden;
+    transition: all 0.2s cubic-bezier(0.16, 1, 0.3, 1);
+    z-index: 1000;
+    pointer-events: none;
+}
+
+.breadcrumb-item:hover::after,
+.breadcrumb-item:hover::before {
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-50%) translateY(0);
+}
+
+
+.breadcrumb-item:hover {
+  opacity: 0.8;
+}
+
+.breadcrumb-item.active {
+  opacity: 1;
+  transform: scale(1.05);
+}
+
+.item-icon-wrapper {
+  position: relative;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  width: 28px;
+  height: 28px;
+  background: rgba(255, 255, 255, 0.05);
+  border-radius: 50%;
+  border: 1px solid rgba(255, 255, 255, 0.1);
+}
+
+.active .item-icon-wrapper {
+  background: rgba(255, 255, 255, 0.15);
+  border-color: currentColor;
+  box-shadow: 0 0 20px currentColor, inset 0 0 10px currentColor; /* Brighter glow */
+}
+
+.item-label {
+  font-size: 0.8rem;
+  font-weight: 700;
+  letter-spacing: 0.05em;
+  text-transform: uppercase;
+  color: var(--text-primary);
+}
+
+.separator {
+  color: var(--text-muted);
+  margin-left: 0.5rem;
+}
+
+.bulb {
+  position: absolute;
+  top: -2px;
+  right: -2px;
+  width: 8px;
+  height: 8px;
+  border-radius: 50%;
+  box-shadow: 0 0 10px currentColor;
+  animation: bulb-pulse 1.5s infinite;
+}
+
+@keyframes bulb-pulse {
+  0% { transform: scale(1); opacity: 1; box-shadow: 0 0 5px currentColor; }
+  50% { transform: scale(1.4); opacity: 0.8; box-shadow: 0 0 15px currentColor; }
+  100% { transform: scale(1); opacity: 1; box-shadow: 0 0 5px currentColor; }
+}
+
+@media (max-width: 1024px) {
+  .grid-layout {
+    grid-template-columns: 1fr; /* Stack columns on tablet/mobile */
+  }
+
+  .panel-results {
+    grid-column: 1; /* Both take full width */
+    min-height: auto;
+  }
+  
+  .panel-logs {
+    grid-column: 1;
+    height: 500px;
+  }
+}
+
+  .btn-clear {
+      background: transparent;
+      border: 1px solid var(--text-secondary);
+      color: var(--text-secondary);
+      padding: 0.25rem 0.75rem;
+      border-radius: 0.5rem;
+      font-size: 0.75rem;
+      cursor: pointer;
+      transition: all 0.2s;
+  }
+
+  .btn-clear:hover {
+      background: rgba(255,255,255,0.1);
+      color: white;
+  }
+
+  .history-section {
+    width: 100%;
+    margin-top: 2rem;
+    display: flex;
+    flex-direction: column;
+  }
+
+  .history-table-container {
+      margin: 1.25rem;
+      overflow-x: auto;
+      border-radius: 0.5rem;
+  }
+  
+  .glass-inset {
+      background: rgba(0, 0, 0, 0.2);
+      border: 1px solid rgba(255,255,255,0.05);
+      box-shadow: inset 0 0 20px rgba(0,0,0,0.5);
+  }
+
+  .history-table {
+      width: 100%;
+      border-collapse: collapse;
+      font-size: 0.9rem;
+      color: var(--text-primary);
+  }
+
+  .history-table th {
+      text-align: left;
+      padding: 1rem;
+      color: var(--text-secondary);
+      font-weight: 600;
+      text-transform: uppercase;
+      font-size: 0.75rem;
+      letter-spacing: 0.05em;
+      border-bottom: 1px solid rgba(255,255,255,0.1);
+      white-space: nowrap;
+  }
+
+  .history-table td {
+      padding: 1rem;
+      border-bottom: 1px solid rgba(255,255,255,0.05);
+  }
+
+  .history-table tbody tr {
+      cursor: pointer;
+      transition: background 0.2s;
+  }
+
+  .history-table tbody tr:hover {
+      background: rgba(255,255,255,0.05);
+  }
+
+  .ticker-pill {
+      font-weight: 700;
+      background: rgba(255,255,255,0.1);
+      padding: 0.2rem 0.5rem;
+      border-radius: 0.25rem;
+  }
+  
+  .decision-pill {
+      font-weight: 700;
+      padding: 0.2rem 0.6rem;
+      border-radius: 1rem;
+      border: 1px solid transparent;
+      font-size: 0.8rem;
+  }
+
+  .col-date { font-family: 'JetBrains Mono', monospace; font-size: 0.8rem; color: var(--text-secondary); white-space: nowrap; }
+  .col-profit { font-weight: 700; color: var(--text-secondary); }
+  .col-profit.has-profit { color: var(--success); }
+  
+  .model-badge {
+      font-size: 0.7rem;
+      background: rgba(255,255,255,0.05);
+      padding: 0.15rem 0.4rem;
+      border-radius: 0.25rem;
+      color: var(--text-secondary);
+      white-space: nowrap;
+  }
+  
+  .confidence-bar-wrapper {
+      display: flex;
+      align-items: center;
+      gap: 0.5rem;
+  }
+  
+  .confidence-bar {
+      height: 4px;
+      border-radius: 2px;
+      width: 60px;
+  }
+  
+  .col-action { color: var(--text-muted); text-align: right; }
+  .history-table tbody tr:hover .col-action { color: white; }
+
+  /* Modal Styles */
+  .modal-overlay {
+    position: fixed;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: rgba(0, 0, 0, 0.7);
+    backdrop-filter: blur(4px);
+    z-index: 1000;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    padding: 1.5rem;
+  }
+
+  .modal-content {
+    background: #0f172a; /* Fallback */
+    background: linear-gradient(135deg, rgba(30, 41, 59, 0.95), rgba(15, 23, 42, 0.98));
+    border: 1px solid rgba(255, 255, 255, 0.1);
+    border-top-width: 4px;
+    border-radius: 1rem;
+    padding: 2rem;
+    width: 100%;
+    max-width: 600px;
+    position: relative;
+    max-height: 90vh;
+    overflow-y: auto;
+    box-shadow: 0 20px 50px rgba(0,0,0,0.5);
+    animation: modalPop 0.3s cubic-bezier(0.175, 0.885, 0.32, 1.275);
+  }
+
+  @keyframes modalPop {
+    0% { transform: scale(0.9); opacity: 0; }
+    100% { transform: scale(1); opacity: 1; }
+  }
+
+  .modal-close {
+    position: absolute;
+    top: 1rem;
+    right: 1rem;
+    background: none;
+    border: none;
+    color: var(--text-secondary);
+    cursor: pointer;
+    transition: color 0.2s;
+  }
+  
+  .modal-close:hover { color: white; }
+
+  .modal-header { margin-bottom: 1.5rem; }
+  .modal-header h2 { font-size: 1.5rem; margin: 0; }
+  .modal-header .timestamp { font-size: 0.8rem; color: var(--text-secondary); }
+
+  .modal-badges {
+      display: flex;
+      gap: 1rem;
+      margin-bottom: 2rem;
+      flex-wrap: wrap;
+  }
+
+  .decision-badge.large { font-size: 1rem; padding: 0.4rem 1rem; }
+  .confidence-badge.large { font-size: 1rem; padding: 0.4rem 1rem; background: rgba(255,255,255,0.1); border-radius: 0.5rem; font-weight: 600; }
+  .rating-badge { display: flex; align-items: center; gap: 0.5rem; background: rgba(236, 72, 153, 0.2); color: #f472b6; padding: 0.4rem 1rem; border-radius: 0.5rem; font-weight: 700; }
+  .model-badge.large { display: flex; align-items: center; gap: 0.5rem; background: rgba(59, 130, 246, 0.1); color: #60a5fa; padding: 0.4rem 1rem; border-radius: 0.5rem; font-weight: 600; font-size: 0.9rem; border: 1px solid rgba(59, 130, 246, 0.2); }
+
+  .report-section { margin-bottom: 1.5rem; }
+  .report-section h3 { display: flex; align-items: center; gap: 0.5rem; font-size: 1rem; color: var(--text-secondary); margin-bottom: 0.5rem; text-transform: uppercase; letter-spacing: 0.05em; }
+  .highlight-text { font-size: 1.1rem; line-height: 1.5; color: white; }
+
+  .metrics-grid {
+      display: grid;
+      grid-template-columns: repeat(2, 1fr);
+      gap: 1rem;
+      margin-bottom: 1.5rem;
+  }
+
+  .metric-box {
+      background: rgba(255,255,255,0.03);
+      padding: 0.75rem;
+      border-radius: 0.5rem;
+      border: 1px solid rgba(255,255,255,0.05);
+      display: flex;
+      flex-direction: column;
+      gap: 0.25rem;
+  }
+
+  .metric-box label { font-size: 0.75rem; color: var(--text-secondary); text-transform: uppercase; }
+  .metric-box span { font-size: 1rem; font-weight: 600; color: white; }
+  .metric-box .profit { color: var(--success); }
+  .metric-box .loss { color: var(--danger); }
+
+  .risk-section p { color: var(--danger); background: rgba(239, 68, 68, 0.1); padding: 1rem; border-radius: 0.5rem; border: 1px solid rgba(239, 68, 68, 0.2); }
+
+  @media (max-width: 768px) {
+  .container {
+    padding: 0.5rem;
+  }
+
+  header {
+    flex-direction: column;
+    align-items: stretch;
+    gap: 1.5rem;
+    padding: 1.5rem 1rem;
+    height: auto;
+  }
+  
+  .logo {
+    justify-content: center;
+    width: 100%;
+  }
+
+  .controls {
+    flex-direction: column;
+    width: 100%;
+    gap: 0.75rem;
+  }
+  
+  .input-group, 
+  .select-group,
+  .btn-primary,
+  .btn-danger {
+    width: 100%;
+    min-width: 0;
+  }
+
+  .workflow-breadcrumb {
+    flex-wrap: wrap; /* Allow wrapping if needed, or row */
+    overflow-x: visible; /* No scroll needed now */
+    padding-bottom: 0;
+    width: 100%;
+    justify-content: center; /* Center icons */
+  }
+  
+  .item-label {
+    display: none; /* Hide text on mobile as requested */
+  }
+  
+  .mobile-hide {
+    display: none;
+  }
+}
+</style>

src/agora/frontend/src/components/HelloWorld.vue

@@ -0,0 +1,43 @@
+<script setup>
+import { ref } from 'vue'
+
+defineProps({
+  msg: String,
+})
+
+const count = ref(0)
+</script>
+
+<template>
+  <h1>{{ msg }}</h1>
+
+  <div class="card">
+    <button type="button" @click="count++">count is {{ count }}</button>
+    <p>
+      Edit
+      <code>components/HelloWorld.vue</code> to test HMR
+    </p>
+  </div>
+
+  <p>
+    Check out
+    <a href="https://vuejs.org/guide/quick-start.html#local" target="_blank"
+      >create-vue</a
+    >, the official Vue + Vite starter
+  </p>
+  <p>
+    Learn more about IDE Support for Vue in the
+    <a
+      href="https://vuejs.org/guide/scaling-up/tooling.html#ide-support"
+      target="_blank"
+      >Vue Docs Scaling up Guide</a
+    >.
+  </p>
+  <p class="read-the-docs">Click on the Vite and Vue logos to learn more</p>
+</template>
+
+<style scoped>
+.read-the-docs {
+  color: #888;
+}
+</style>

src/agora/frontend/src/main.js

@@ -0,0 +1,5 @@
+import { createApp } from 'vue'
+import './style.css'
+import App from './App.vue'
+
+createApp(App).mount('#app')

src/agora/frontend/src/style.css

@@ -0,0 +1,118 @@
+@import url('https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;500;600;700;800&family=Inter:wght@300;400;500;600;700&display=swap');
+
+:root {
+  --font-heading: 'Outfit', sans-serif;
+  --font-body: 'Inter', sans-serif;
+  
+  --bg-color: #030712;
+  --surface-color: #0f172a;
+  --glass-bg: rgba(15, 23, 42, 0.6);
+  --glass-border: rgba(255, 255, 255, 0.08);
+  
+  --text-primary: #f8fafc;
+  --text-secondary: #94a3b8;
+  --text-muted: #64748b;
+  
+  --accent-primary: #3b82f6;
+  --accent-secondary: #8b5cf6;
+  --accent-gradient: linear-gradient(135deg, #3b82f6 0%, #8b5cf6 100%);
+  
+  --success: #10b981;
+  --warning: #f59e0b;
+  --danger: #ef4444;
+}
+
+* {
+  box-sizing: border-box;
+  margin: 0;
+  padding: 0;
+}
+
+body {
+  font-family: var(--font-body);
+  background-color: var(--bg-color);
+  color: var(--text-primary);
+  min-height: 100vh;
+  line-height: 1.5;
+  background-image: 
+    radial-gradient(circle at 0% 0%, rgba(59, 130, 246, 0.15) 0%, transparent 40%),
+    radial-gradient(circle at 100% 100%, rgba(139, 92, 246, 0.15) 0%, transparent 40%);
+  background-attachment: fixed;
+}
+
+#app {
+  width: 100%;
+}
+
+.glass {
+  background: var(--glass-bg);
+  backdrop-filter: blur(12px);
+  -webkit-backdrop-filter: blur(12px);
+  border: 1px solid var(--glass-border);
+  border-radius: 1.25rem;
+}
+
+.container {
+  max-width: 1400px;
+  margin: 0 auto;
+  padding: 2rem;
+}
+
+h1, h2, h3, h4 {
+  font-family: var(--font-heading);
+}
+
+button {
+  cursor: pointer;
+  font-family: inherit;
+  transition: all 0.2s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.btn {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  gap: 0.5rem;
+  padding: 0.75rem 1.5rem;
+  border-radius: 0.75rem;
+  font-weight: 600;
+  border: 1px solid transparent;
+}
+
+.btn-primary {
+  background: var(--accent-gradient);
+  color: white;
+  box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3);
+}
+
+.btn-primary:hover:not(:disabled) {
+  transform: translateY(-2px);
+  box-shadow: 0 6px 16px rgba(59, 130, 246, 0.4);
+}
+
+.btn-primary:active:not(:disabled) {
+  transform: translateY(0);
+}
+
+.btn-primary:disabled {
+  opacity: 0.6;
+  cursor: not-allowed;
+}
+
+/* Scrollbar Styling */
+::-webkit-scrollbar {
+  width: 8px;
+}
+
+::-webkit-scrollbar-track {
+  background: transparent;
+}
+
+::-webkit-scrollbar-thumb {
+  background: rgba(255, 255, 255, 0.1);
+  border-radius: 10px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+  background: rgba(255, 255, 255, 0.2);
+}

src/agora/frontend/vite.config.js

@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite'
+import vue from '@vitejs/plugin-vue'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [vue()],
+})

uv.lock

@@ -2,9 +2,6 @@ version = 1
 revision = 3
 requires-python = "==3.12.*"
 
-[options]
-prerelease-mode = "allow"
-
 [[package]]
 name = "agenticai"
 version = "0.1.0"
@@ -111,7 +108,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "aiohttp", specifier = ">=3.8.5" },
+    { name = "aiohttp", specifier = ">=3.14.0" },
     { name = "anthropic", specifier = ">=0.49.0" },
     { name = "arxiv", specifier = ">=2.3.1" },
     { name = "asyncio" },
@@ -151,7 +148,7 @@ requires-dist = [
     { name = "langchain-ollama", specifier = ">=1.0.0" },
     { name = "langchain-openai", specifier = ">=1.1.14" },
     { name = "langchain-text-splitters", specifier = ">=1.1.2" },
-    { name = "langgraph", specifier = ">=1.0.3" },
+    { name = "langgraph", specifier = ">=1.0.10rc1" },
     { name = "langgraph-checkpoint-sqlite", specifier = ">=3.0.0" },
     { name = "langsmith", specifier = ">=0.8.0" },
     { name = "llama-index", specifier = ">=0.14.15" },
@@ -188,25 +185,25 @@ requires-dist = [
     { name = "python-docx" },
     { name = "python-dotenv", specifier = ">=1.2.2" },
     { name = "reportlab", specifier = ">=4.4.5" },
-    { name = "requests", specifier = ">=2.32.3" },
+    { name = "requests", specifier = ">=2.33.0" },
     { name = "scikit-learn", specifier = ">=1.7.2" },
     { name = "sendgrid" },
     { name = "sentence-transformers", specifier = ">=5.1.2" },
     { name = "smithery", specifier = ">=0.4.4" },
     { name = "speedtest-cli", specifier = ">=2.1.3" },
     { name = "sqlalchemy", specifier = ">=2.0.46" },
-    { name = "streamlit", specifier = ">=1.51.0" },
+    { name = "streamlit", specifier = ">=1.54.0" },
     { name = "textblob", specifier = ">=0.17.1" },
     { name = "traceloop-sdk", specifier = ">=0.33.0" },
     { name = "wikipedia", specifier = ">=1.4.0" },
     { name = "yfinance", specifier = ">=0.2.66" },
-    { name = "yt-dlp", specifier = ">=2025.11.12" },
+    { name = "yt-dlp", specifier = ">=2026.2.21" },
 ]
 
 [package.metadata.requires-dev]
 dev = [
     { name = "ipykernel", specifier = ">=7.1.0" },
-    { name = "pytest", specifier = ">=8.3.3" },
+    { name = "pytest", specifier = ">=9.0.3" },
     { name = "pytest-asyncio" },
 ]
 
@@ -230,7 +227,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.13.3"
+version = "3.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -239,27 +236,29 @@ dependencies = [
     { name = "frozenlist" },
     { name = "multidict" },
     { name = "propcache" },
+    { name = "typing-extensions" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/50/42/32cf8e7704ceb4481406eb87161349abb46a57fee3f008ba9cb610968646/aiohttp-3.13.3.tar.gz", hash = "sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88", size = 7844556, upload-time = "2026-01-03T17:33:05.204Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/a0/be/4fc11f202955a69e0db803a12a062b8379c970c7c84f4882b6da17337cc1/aiohttp-3.13.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:b903a4dfee7d347e2d87697d0713be59e0b87925be030c9178c5faa58ea58d5c", size = 739732, upload-time = "2026-01-03T17:30:14.23Z" },
-    { url = "https://files.pythonhosted.org/packages/97/2c/621d5b851f94fa0bb7430d6089b3aa970a9d9b75196bc93bb624b0db237a/aiohttp-3.13.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a45530014d7a1e09f4a55f4f43097ba0fd155089372e105e4bff4ca76cb1b168", size = 494293, upload-time = "2026-01-03T17:30:15.96Z" },
-    { url = "https://files.pythonhosted.org/packages/5d/43/4be01406b78e1be8320bb8316dc9c42dbab553d281c40364e0f862d5661c/aiohttp-3.13.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:27234ef6d85c914f9efeb77ff616dbf4ad2380be0cda40b4db086ffc7ddd1b7d", size = 493533, upload-time = "2026-01-03T17:30:17.431Z" },
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