Upload folder using huggingface_hub

Dockerfile

@@ -21,7 +21,7 @@ COPY uv.lock .
 
 # Copy required folders
 COPY common/ ./common/
-COPY src/chatbot_v2/ ./src/chatbot_v2/
+COPY src/chatbot/ ./src/chatbot/
 
 # Install dependencies using uv, then export and install with pip to system
 RUN uv sync --frozen --no-dev && \
@@ -32,4 +32,4 @@ COPY run.py .
 
 EXPOSE 7860
 
-CMD ["python", "run.py", "chatbot_v2", "--port", "7860"]
+CMD ["python", "run.py", "chatbot", "--port", "7860"]

common/utility/autogen_model_factory.py

@@ -51,23 +51,47 @@ class AutoGenModelFactory:
         # 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, # Pass full model_info for capabilities
+                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
             )
         
         # ----------------------------------------------------------------------

pyproject.toml

@@ -61,6 +61,7 @@ dependencies = [
     "autogen-agentchat==0.4.7",
     "autogen-ext[grpc,mcp,ollama,openai]==0.4.7",
     "asyncio",
+    "phidata>=2.0.0",
 
     # =======================
     # MCP
@@ -136,6 +137,11 @@ dependencies = [
     "ddgs>=9.9.2",
     "duckduckgo_search",
     "azure-identity>=1.25.1",
+    "azure-mgmt-resource>=23.0.1",
+    "azure-mgmt-compute>=30.3.0",
+    "azure-mgmt-monitor>=6.0.2",
+    "azure-monitor-query>=1.2.0",
+    "PyGithub>=2.1.1",
 
     # =======================
     # OBSERVABILITY

run.py

@@ -18,7 +18,7 @@ import subprocess
 import argparse
 from pathlib import Path
 from typing import Dict, Optional
-from agents import Runner, SQLiteSession
+# from agents import Runner, SQLiteSession
 # from agents import set_trace_processors
 # from langsmith.wrappers import OpenAIAgentsTracingProcessor
 
@@ -56,6 +56,7 @@ APP_REGISTRY: Dict[str, Dict[str, str]] = {
     "trip-planner": {
         "path": "src/trip-planner",
         "entry": "main.py",
+        "type": "fastapi",
         "description": "Trip Planner - Detailed trip itinerary planning"
     },
     "chatbot_v1": {
@@ -85,8 +86,9 @@ APP_REGISTRY: Dict[str, Dict[str, str]] = {
     },
     "market-analyst": {
         "path": "src/market-analyst",
-        "entry": "app.py",
-        "description": "Market Analyst - Multi-agent market analysis tool"
+        "entry": "backend/main.py",
+        "type": "fastapi",
+        "description": "Market Analyst - Decoupled Multi-agent market analysis (Vue.js + FastAPI)"
     },
     "image": {
         "path": "src/image-generator",
@@ -98,10 +100,88 @@ APP_REGISTRY: Dict[str, Dict[str, str]] = {
         "entry": "app.py",
         "description": "Interview Assistant - Multi-agent interview tool"
     },
-    "finadvisor": {
+    "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",
-        "description": "Financial Advisor - Multi-agent financial advisor tool"
+        "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)"
+    },
+    "test": {
+        "path": ".",
+        "entry": "tests",
+        "type": "test",
+        "description": "Run Project Tests - Executes pytest suite"
     }
 }
 
@@ -177,15 +257,63 @@ def launch_app(app_name: str, port: Optional[int] = None):
     print(f"📂 Location: {config['path']}")
     print(f"🌐 Entry Point: {app_file}")
     
-    # Build streamlit command
-    cmd = ["streamlit", "run", app_file]
+    app_type = config.get("type", "streamlit")
     
-    # Add port if specified
-    if port:
-        cmd.extend(["--server.port", str(port)])
-        print(f"🔌 Port: {port}")
+    # Decoupled App Logic: Build frontend if needed
+    if app_name == "market-analyst":
+        frontend_dir = project_root / "src/market-analyst/frontend"
+        dist_dir = frontend_dir / "dist"
+        if not dist_dir.exists():
+            print("\n🛠️ Frontend build missing. Building now...")
+            subprocess.run(["npm", "run", "build"], cwd=frontend_dir, shell=True)
+            print("✅ Frontend built.\n")
+    
+    python_exe = sys.executable
+    
+    # Build command based on app type
+    if app_type == "fastapi":
+        # Extract module name from entry point (e.g. backend/main.py -> backend.main)
+        module_path = app_file.replace(".py", "").replace("/", ".").replace("\\", ".")
+        cmd = [python_exe, "-m", "uvicorn", f"{module_path}:app", "--host", "0.0.0.0"]
+        default_port = 8000
+    elif app_type == "script":
+        cmd = [python_exe, app_file]
+        default_port = None
+    elif app_type == "test":
+        cmd = [python_exe, "-m", "pytest", app_file, "-v"]
+        default_port = None
+    elif app_type == "npm":
+        cmd = ["npm", "run", "dev"]
+        default_port = 5173
     else:
-        print(f"🔌 Port: 8501 (default)")
+        cmd = [python_exe, "-m", "streamlit", "run", app_file]
+        default_port = 8501
+    
+    # Add port if specified
+    actual_port = port if port else default_port
+    
+    if app_type in ["streamlit", "fastapi", "npm"]:
+        if port:
+            if app_type == "fastapi":
+                cmd.extend(["--port", str(port)])
+            elif app_type == "npm":
+                cmd.extend(["--", "--port", str(port)])
+            else:
+                cmd.extend(["--server.port", str(port)])
+            print(f"🔌 Port: {port}")
+        else:
+            print(f"🔌 Port: {default_port} (default)")
+            
+        # Kill any process using the target port (Port is only relevant for web apps)
+        try:
+            import platform
+            if platform.system() != "Windows":
+                # Use fuser on Linux/Mac to kill processes on the port
+                kill_cmd = ["fuser", "-k", f"{actual_port}/tcp"]
+                subprocess.run(kill_cmd, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL)
+                print(f"🧹 Cleaned up port {actual_port}")
+        except Exception:
+            pass  # Silently continue if cleanup fails
     
     print("\n" + "=" * 70)
     print("\n🎯 Starting application...\n")
@@ -198,12 +326,25 @@ def launch_app(app_name: str, port: Optional[int] = None):
     try:
         # Change to app directory and run
         os.chdir(app_dir)
-        subprocess.run(cmd, env=env)
+        is_windows = sys.platform == "win32"
+        
+        # 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)
+            
+        subprocess.run(cmd, env=env, shell=is_windows)
     except KeyboardInterrupt:
         print("\n\n👋 Application stopped by user")
     except FileNotFoundError:
-        print("\n❌ Error: Streamlit not found. Please install it:")
-        print("   pip install streamlit")
+        binary = "command"
+        if app_type == "fastapi": binary = "uvicorn"
+        elif app_type == "streamlit": binary = "streamlit"
+        elif app_type == "test": binary = "pytest"
+        
+        print(f"\n❌ Error: {binary} not found in the current environment.")
+        print(f"   Please install it: pip install {binary}")
         sys.exit(1)
     except Exception as e:
         print(f"\n❌ Error launching app: {e}")

src/chatbot_v1/Dockerfile

@@ -0,0 +1,35 @@
+FROM python:3.12-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    DEBIAN_FRONTEND=noninteractive \
+    PYTHONPATH=/app:/app/common:$PYTHONPATH
+
+WORKDIR /app
+
+# System deps
+RUN apt-get update && apt-get install -y \
+    git build-essential curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+
+# Copy project metadata
+COPY pyproject.toml .
+COPY uv.lock .
+
+# Copy required folders
+COPY common/ ./common/
+COPY src/chatbot/ ./src/chatbot/
+
+# Install dependencies using uv, then export and install with pip to system
+RUN uv sync --frozen --no-dev && \
+    uv pip install -e . --system
+
+# Copy entry point
+COPY run.py .
+
+EXPOSE 7860
+
+CMD ["python", "run.py", "chatbot", "--port", "7860"]

src/chatbot_v1/README.md

@@ -0,0 +1,223 @@
+---
+title: AI Chatbot
+emoji: 🤖
+colorFrom: pink
+colorTo: yellow
+sdk: docker
+sdk_version: "0.0.1"
+app_file: app.py
+pinned: false
+license: mit
+tags:
+  - text-generation
+  - agentic-ai
+  - openai-sdk
+short_description: An Experimental Agentic Chatbot (uses OpenAI Agent SDK)
+---
+
+# AI Chatbot
+
+This is an experimental chatbot for chatting with AI. It is equipped with agents & tools to give you realtime data from the web. It uses **OpenAI - SDK** and **OpenAI - Agents**...
+
+## Features
+- Predefined prompts for quick analysis
+- Chat interface with AI responses
+- Enter key support and responsive design
+- Latest messages appear on top
+
+## Usage
+1. Type a message or select a predefined prompt
+2. Press **Enter** or click **Send**
+3. AI responses appear instantly in the chat interface
+
+## Supported APIs
+- OpenAI
+- Google
+- GROQ
+- SERPER
+- News API
+
+## Notes
+- Make sure your API keys are configured in the Space secrets
+- Built using Streamlit and deployed as a Docker Space
+
+## References
+
+https://openai.github.io/openai-agents-python/
+
+https://github.com/openai/openai-agents-python/tree/main/examples/mcp
+
+## Project Folder Structure
+
+```
+chatbot/
+├── app.py                    # Main Streamlit chatbot interface
+├── appagents/
+│   ├── __init__.py               # Package initialization
+│   ├── OrchestratorAgent.py      # Main orchestrator - coordinates all agents
+│   ├── FinancialAgent.py         # Financial data and analysis agent
+│   ├── NewsAgent.py              # News retrieval and summarization agent
+│   ├── SearchAgent.py            # General web search agent
+│   └── InputValidationAgent.py   # Input validation and sanitization agent
+├── core/
+│   ├── __init__.py               # Package initialization
+│   └── logger.py                 # Centralized logging configuration
+├── tools/
+│   ├── __init__.py               # Package initialization
+│   ├── google_tools.py           # Google search API wrapper
+│   ├── yahoo_tools.py            # Yahoo Finance API wrapper
+│   ├── news_tools.py             # News API wrapper
+│   └── time_tools.py             # Time-related utility functions
+├── prompts/
+│   ├── economic_news.txt         # Prompt for economic news analysis
+│   ├── market_sentiment.txt      # Prompt for market sentiment analysis
+│   ├── news_headlines.txt        # Prompt for news headline summarization
+│   ├── trade_recommendation.txt  # Prompt for trade recommendations
+│   └── upcoming_earnings.txt     # Prompt for upcoming earnings alerts
+├── Dockerfile                     # Docker configuration for container deployment
+├── pyproject.toml                 # Project metadata and dependencies (copied from root)
+├── uv.lock                        # Locked dependency versions (copied from root)
+├── README.md                      # Project documentation
+└── run.py                         # Script to run the application locally
+```
+
+## File Descriptions
+
+### UI Layer
+- **app.py** - Main Streamlit chatbot interface that provides:
+  - Chat message display with user and AI messages
+  - Text input for user queries
+  - Predefined prompt buttons for quick analysis
+  - Real-time AI responses
+  - Support for Enter key submission
+  - Responsive design with latest messages appearing first
+
+### Agents (`appagents/`)
+- **OrchestratorAgent.py** - Main orchestrator that:
+  - Coordinates communication between all specialized agents
+  - Routes user queries to appropriate agents
+  - Manages conversation flow and context
+  - Integrates tool responses
+
+- **FinancialAgent.py** - Financial data and analysis:
+  - Retrieves stock prices and financial metrics
+  - Performs financial analysis using Yahoo Finance API
+  - Provides market insights and recommendations
+  - Integrates with yahoo_tools for data fetching
+
+- **NewsAgent.py** - News retrieval and summarization:
+  - Fetches latest news articles
+  - Summarizes news content
+  - Integrates with News API for real-time updates
+  - Provides news-based market insights
+
+- **SearchAgent.py** - General web search:
+  - Performs web searches for general queries
+  - Integrates with Google Search / Serper API
+  - Returns relevant search results
+  - Supports multi-source data gathering
+
+- **InputValidationAgent.py** - Input validation:
+  - Sanitizes user input
+  - Validates query format and content
+  - Prevents malicious inputs
+  - Ensures appropriate content
+
+### Core Utilities (`core/`)
+- **logger.py** - Centralized logging configuration:
+  - Provides consistent logging across agents
+  - Handles different log levels
+  - Formats log messages for clarity
+
+### Tools (`tools/`)
+- **google_tools.py** - Google Search API wrapper:
+  - Executes web searches via Google Search / Serper API
+  - Parses and returns search results
+  - Handles API authentication
+
+- **yahoo_tools.py** - Yahoo Finance API integration:
+  - Retrieves stock price data
+  - Fetches financial metrics and indicators
+  - Provides historical price data
+  - Returns earnings information
+
+- **news_tools.py** - News API integration:
+  - Fetches latest news articles
+  - Filters news by category and keywords
+  - Returns news headlines and summaries
+  - Provides market-related news feeds
+
+- **time_tools.py** - Time utility functions:
+  - Provides current time information
+  - Formats timestamps
+  - Handles timezone conversions
+
+### Prompts (`prompts/`)
+Predefined prompts for specialized analysis:
+- **economic_news.txt** - Analyzes economic news and implications
+- **market_sentiment.txt** - Analyzes market sentiment trends
+- **news_headlines.txt** - Summarizes and explains news headlines
+- **trade_recommendation.txt** - Provides trading recommendations
+- **upcoming_earnings.txt** - Alerts about upcoming earnings reports
+
+### Configuration Files
+- **Dockerfile** - Container deployment:
+  - Builds Docker image with Python 3.12
+  - Installs dependencies using `uv`
+  - Sets up Streamlit server on port 8501
+  - Configures PYTHONPATH for module imports
+
+- **pyproject.toml** - Project metadata:
+  - Package name: "agents"
+  - Python version requirement: 3.12
+  - Lists all dependencies (OpenAI, LangChain, Streamlit, etc.)
+
+- **uv.lock** - Dependency lock file:
+  - Ensures reproducible builds
+  - Pins exact versions of all dependencies
+
+## Key Technologies
+
+| Component | Technology | Purpose |
+|-----------|-----------|---------|
+| LLM Framework | OpenAI Agents | Multi-agent orchestration |
+| Chat Interface | Streamlit | User interaction and display |
+| Web Search | Google Search / Serper API | Web search results |
+| Financial Data | Yahoo Finance API | Stock prices and metrics |
+| News Data | News API | Latest news articles |
+| Async Operations | AsyncIO | Parallel agent execution |
+| Dependencies | UV | Fast Python package management |
+| Containerization | Docker | Cloud deployment |
+
+## Predefined Prompts
+
+The chatbot includes quick-access buttons for common analysis:
+
+1. **Economic News** - Analyzes current economic trends and news
+2. **Market Sentiment** - Provides market sentiment analysis
+3. **News Headlines** - Summarizes latest news headlines
+4. **Trade Recommendation** - Suggests trading strategies
+5. **Upcoming Earnings** - Lists upcoming company earnings
+
+## Running Locally
+
+```bash
+# Install dependencies
+uv sync
+
+# Set environment variables defined in .env.name file
+export OPENAI_API_KEY="your-key"
+export SERPER_API_KEY="your-key"
+export NEWS_API_KEY="your-key"
+
+# Run the Streamlit app (from the root)
+python run.py chatbot
+```
+
+## Deployment
+
+The project is deployed on Hugging Face Spaces as a Docker container:
+- **Space**: https://huggingface.co/spaces/mishrabp/chatbot-app
+- **URL**: https://mishrabp-chatbot-app.hf.space
+- **Trigger**: Automatic deployment on push to `main` branch
+- **Configuration**: `.github/workflows/chatbot-app-hf.yml`

src/chatbot_v1/aagents/input_validation_agent.py

@@ -0,0 +1,58 @@
+
+import os
+import json
+from agents import Agent, OpenAIChatCompletionsModel, Runner, GuardrailFunctionOutput
+from pydantic import BaseModel
+from openai import AsyncOpenAI
+from core.model import get_model_client
+
+class ValidatedOutput(BaseModel):
+    is_valid: bool
+    reasoning: str
+
+input_validation_agent = Agent(
+    name="Guardrail Input Validation Agent",
+    instructions="""
+        You are a highly efficient and specialized **Agent** 🌐. Your sole function is to validate the user inputs.
+        
+        ## Core Directives & Priorities
+        1. You should flag if the user uses unparaliamentary language ONLY.
+        2. You MUST give reasoning for the same.
+        
+        ## Rules
+        - If it contains any of these, mark `"is_valid": false` and explain **why** in `"reasoning"`.
+        - Otherwise, mark `"is_valid": true` with reasoning like "The input follows respectful communication guidelines."
+
+
+        ## Output Format (MANDATORY)
+        * Return a JSON object with the following structure:
+        {
+            "is_valid": <boolean>,
+            "reasoning": <string>
+        }
+    """,
+    model=get_model_client(),
+    output_type=ValidatedOutput,
+)
+input_validation_agent.description = "A guardrail agent that validates user input for unparliamentary language."
+
+async def input_validation_guardrail(ctx, agent, input_data):
+    result = await Runner.run(input_validation_agent, input_data, context=ctx.context)
+    raw_output = result.final_output
+
+    # Handle different return shapes gracefully
+    if isinstance(raw_output, ValidatedOutput):
+        final_output = raw_output
+        print("Parsed ValidatedOutput:", final_output)
+    else:
+        final_output = ValidatedOutput(
+            is_valid=False,
+            reasoning=f"Unexpected output type: {type(raw_output)}"
+        )
+
+    return GuardrailFunctionOutput(
+        output_info=final_output,
+        tripwire_triggered=not final_output.is_valid,
+    )
+
+__all__ = ["input_validation_agent", "input_validation_guardrail", "ValidatedOutput"]

src/chatbot_v1/aagents/orchestrator_agent.py

@@ -0,0 +1,96 @@
+
+import os
+import asyncio
+from common.aagents.search_agent import search_agent
+from common.aagents.news_agent import news_agent
+from common.aagents.yf_agent import yf_agent
+from aagents.input_validation_agent import input_validation_guardrail
+from agents import Agent, OpenAIChatCompletionsModel, Runner, function_tool
+from openai import AsyncOpenAI
+from core.model import get_model_client
+
+# ----------------------------------------------------------
+# PARALLEL EXECUTION TOOL
+# ----------------------------------------------------------
+@function_tool
+async def prompt_broadcaster(query: str, include_finance: bool = True, include_news: bool = True, include_search: bool = True) -> str:
+    """
+    Broadcasts the search query to selected specialized agents in parallel and aggregates their responses.
+    
+    Args:
+        query: The user's question or topic to research.
+        include_finance: Whether to include the Yahoo Finance agent (default: True).
+        include_news: Whether to include the News agent (default: True).
+        include_search: Whether to include the Web Search agent (default: True).
+
+    Returns:
+        Combined reports from the selected agents.
+    """
+    
+    
+    # A better approach for variable tasks is to map them.
+    active_agents = []
+    if include_finance: active_agents.append(("YahooFinanceAgent", Runner.run(yf_agent, query)))
+    if include_news: active_agents.append(("NewsAgent", Runner.run(news_agent, query)))
+    if include_search: active_agents.append(("WebSearchAgent", Runner.run(search_agent, query)))
+
+    if not active_agents:
+        return "No agents were selected for this query."
+
+    # Run in parallel
+    agent_names = [name for name, _ in active_agents]
+    coroutines = [coro for _, coro in active_agents]
+    
+    results = await asyncio.gather(*coroutines, return_exceptions=True)
+    
+    outputs = []
+    for name, res in zip(agent_names, results):
+        if isinstance(res, Exception):
+            outputs.append(f"❌ {name} Error: {str(res)}")
+        else:
+            outputs.append(f"✅ {name} Report:\n{res.final_output}")
+            
+    combined_response = "\n--- START OF AGENT REPORTS ---\n\n" + "\n\n-----------------------------------\n\n".join(outputs) + "\n\n--- END OF AGENT REPORTS ---"
+    
+    return combined_response
+
+orchestrator_agent = Agent(
+    name="AI Chat Orchestrator",
+    tools=[prompt_broadcaster],
+    instructions="""
+    You are the **AI Chat Orchestrator**. 
+    Your goal is to provide a comprehensive, multi-perspective answer by synthesizing data from specialized sub-agents.
+
+    **Workflow**:
+    1.  **Analyze Request**: Understand the user's question.
+    2.  **Determine Needs**: Decide which specialized agents are required.
+        *   **Finance**: For stock prices, market trends, company financials, or analyst ratings.
+        *   **News**: For recent events, headlines, or breaking news.
+        *   **Web Search**: For general knowledge, history, facts, or broad research.
+    3.  **Broadcast Query**: Call the `prompt_broadcaster` tool with the `query` and set the `include_*` flags to True/False accordingly.
+        *   *Optimization Tip*: efficiently select ONLY the necessary agents to reduce latency.
+    4.  **Synthesize Results**: Read the returned "Agent Reports".
+        *   Combine the financial data (prices, sentiment), news headlines, and general search context.
+        *   Compare and contrast findings if necessary.
+        *   Resolve conflicts by prioritizing specific data (e.g., Yahoo Finance for prices) over general text.
+    5.  **Final Response**: Generate a clear, professional, and well-structured summary for the user. Do not simply paste the individual reports.
+
+    **Final Response Structure**:
+    You should adapt the response structure based on the user's query type:
+
+    *   **For Market/Finance Queries**: Use the "Market Analysis" style with a Financial Snapshot (Price, Sentinel, Ratings), Key Developments, and Synthesis.
+    *   **For News/Research**: Use a clear "Executive Summary" followed by "Key Findings" and "Details".
+    *   **For General Chat**: Maintain a conversational but professional tone. Use markdown for clarity (bullet points, bold text).
+    *   **For Coding Requests**: Provide clear code blocks and explanations.
+
+    **Constraint**:
+    *   Do NOT try to answer based on your own knowledge if live data is needed/requested.
+    *   Use `prompt_broadcaster` when the query implies a need for external information.
+    *   If agents return "No data", explicitly state that in the relevant section.
+    """,
+    model=get_model_client(),
+)
+orchestrator_agent.description = "An intelligent orchestrator that queries Finance, News, and Search agents in parallel and synthesizes a comprehensive response."
+
+
+__all__ = ["orchestrator_agent"]

src/chatbot_v1/app.py

@@ -0,0 +1,317 @@
+import os
+import glob
+import uuid
+import asyncio
+# import trace_config
+import logging
+import streamlit as st
+from aagents.orchestrator_agent import orchestrator_agent
+from agents import Runner, trace, SQLiteSession
+from agents.exceptions import InputGuardrailTripwireTriggered
+# from langsmith import traceable
+
+from traceloop.sdk import Traceloop
+from opentelemetry.sdk.trace import Span
+
+# --- Monkeypatch to fix "Invalid type Omit" errors ---
+# This filters out 'NotGiven'/'Omit' values from OpenAI that crash the OTel exporter
+_original_set_attribute = Span.set_attribute
+
+def _safe_set_attribute(self, key, value):
+    # Check string representation of type to avoid importing specific internal types
+    type_str = str(type(value))
+    if "Omit" in type_str or "NotGiven" in type_str:
+        return self
+    return _original_set_attribute(self, key, value)
+
+Span.set_attribute = _safe_set_attribute
+# -----------------------------------------------------
+
+Traceloop.init(
+  disable_batch=True,
+  api_key="tl_1c19b8e8fcfd411fb9fcdb02d381faef"
+)
+
+# -----------------------------
+# Configuration & Utils
+# -----------------------------
+st.set_page_config(
+    page_title="AI Assistant",
+    layout="wide",
+    page_icon="🤖"
+)
+
+def load_prompts(folder="prompts"):
+    prompts = []
+    prompt_labels = []
+    if os.path.exists(folder):
+        for file_path in glob.glob(os.path.join(folder, "*.txt")):
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read().strip()
+                if content:
+                    prompts.append(content)
+
+            prompt_labels.append(os.path.basename(file_path).replace("_", " ").replace(".txt", "").title())
+    return prompts, prompt_labels
+
+prompts, prompt_labels = load_prompts()
+
+# -----------------------------
+# Session State
+# -----------------------------
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "ai_session_id" not in st.session_state:
+    st.session_state.ai_session_id = str(uuid.uuid4())
+
+# Persistent SQLite session
+if "ai_session" not in st.session_state:
+    st.session_state.ai_session = SQLiteSession(f"conversation_{st.session_state.ai_session_id}.db")
+
+session = st.session_state.ai_session
+
+# -----------------------------
+# Premium Styling
+# -----------------------------
+st.markdown("""
+<style>
+    /* ---------------------------------------------------------------------
+       1. GLOBAL & RESET
+       --------------------------------------------------------------------- */
+    * {
+        box-sizing: border-box;
+    }
+    
+    .stApp, [data-testid="stAppViewContainer"] {
+        /* Standard Streamlit background */
+        background-color: #f8f9fa;
+        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji';
+    }
+    
+    html {
+        -webkit-text-size-adjust: 100%; /* Prevent iOS font boosting */
+    }
+    
+    /* ---------------------------------------------------------------------
+       2. LAYOUT & HERO BANNER
+       --------------------------------------------------------------------- */
+    
+    /* Mobile font optimization */
+    @media (max-width: 768px) {
+        /* Target all markdown text specifically */
+        .stMarkdown p, .stMarkdown li, .stChatMessage p, .message-content, .stDataFrame, .stTable {
+            font-size: 16px !important;
+            line-height: 1.6 !important;
+            color: #1a1a1a !important;
+        }
+        
+        h1, h2, h3, h4, h5, h6 {
+            color: #1a1a1a !important;
+        }
+    }
+    
+    /* Desktop Layout */
+    @media (min-width: 769px) {
+        .block-container {
+            padding-top: 0 !important;
+            padding-bottom: 2rem !important;
+            padding-left: 5rem !important;
+            padding-right: 5rem !important;
+            max-width: 100% !important;
+        }
+        
+        .hero-container {
+            margin-top: -3rem;
+            margin-left: -5rem;
+            margin-right: -5rem;
+            /* Simple negative margins to pull edge-to-edge */
+            padding: 2.5rem 1rem 2rem 1rem; /* Compact desktop padding */
+        }
+    }
+    
+    /* Mobile Layout */
+    @media (max-width: 768px) {
+        .block-container {
+            padding-left: 1rem !important;
+            padding-right: 1rem !important;
+            padding-top: 0 !important;
+        }
+        
+        .hero-container {
+            margin-top: -2rem;
+            margin-left: -1rem;
+            margin-right: -1rem;
+            /* Break out of the 1rem padding */
+            padding: 2rem 1rem 1.5rem 1rem; /* Compact mobile padding */
+            border-radius: 0 0 12px 12px;
+        }
+        
+        /* Ensure font sizes are standard (Streamlit defaults is ~16px) */
+        /* We DO NOT override them to 17px/fixed, allowing system zoom to work. */
+    }
+
+    /* Hero Styling */
+    .hero-container {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        text-align: center;
+        border-radius: 0 0 16px 16px;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+        margin-bottom: 2rem;
+    }
+
+    .hero-title {
+        font-size: 2rem; /* Slightly smaller */
+        font-weight: 700;
+        margin-bottom: 0.25rem;
+        color: white !important;
+    }
+    .hero-subtitle {
+        font-size: 1rem;
+        opacity: 0.95;
+        font-weight: 400;
+        color: rgba(255,255,255,0.95) !important;
+    }
+    
+    /* Remove Header Decoration */
+    header[data-testid="stHeader"] {
+        background-color: transparent !important;
+        height: 0 !important;
+        z-index: 100;
+    }
+    div[data-testid="stDecoration"] { display: none; }
+    
+    /* ---------------------------------------------------------------------
+       3. COMPONENT STYLING (Healthcare-like)
+       --------------------------------------------------------------------- */
+    
+    /* Chat Bubbles - Clean & Readable */
+    .stChatMessage {
+        background-color: white;
+        border-radius: 12px;
+        border: 1px solid #e5e5e5;
+        box-shadow: 0 1px 2px rgba(0,0,0,0.05);
+        padding: 1rem;
+    }
+    
+    .stChatMessage[data-testid="stChatMessage"]:nth-of-type(odd) {
+         background-color: #f8f9fa;
+    }
+    
+    /* Input Fields */
+    .stTextInput input {
+        border-radius: 20px; /* Matching healthcare-assistant roundness */
+        border: 1px solid #ddd;
+        padding: 0.75rem 1rem;
+    }
+    
+    /* Buttons */
+    .stButton button {
+        border-radius: 20px; /* Matching healthcare-assistant */
+        min-height: 48px;
+        font-weight: 500;
+    }
+
+    /* Sidebar */
+    section[data-testid="stSidebar"] {
+        background-color: #ffffff;
+        border-right: 1px solid #eaeaea;
+    }
+    
+    /* Minimize Sidebar Top Padding */
+    section[data-testid="stSidebar"] .block-container {
+        padding-top: 0rem !important;
+        padding-bottom: 0rem !important;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# -----------------------------
+# Logic
+# -----------------------------
+# @traceable(name="chatbot")
+async def get_ai_response(prompt: str) -> str:
+    try:
+        agent = orchestrator_agent
+        # Ensure session is valid
+        current_session = st.session_state.ai_session
+        current_session = st.session_state.ai_session
+        with trace("Chatbot Agent Run"): # Keep existing custom trace wrapper
+            # Run agent
+            result = await Runner.run(agent, prompt, session=current_session)
+            return result.final_output
+    except InputGuardrailTripwireTriggered as e:
+        reasoning = getattr(e, "reasoning", None) \
+            or getattr(getattr(e, "output", None), "reasoning", None) \
+            or getattr(getattr(e, "guardrail_output", None), "reasoning", None) \
+            or "Guardrail triggered, but no reasoning provided."
+        return f"⚠️ **Guardrail Blocked Input**\n\n{reasoning}"
+    except Exception as e:
+        return f"❌ **Error**: {str(e)}"
+
+# -----------------------------
+# Sidebar - Quick Actions
+# -----------------------------
+with st.sidebar:
+    st.markdown("### ⚡ Quick Starters")
+    st.markdown("Select a prompt to start:")
+    
+    # We use a trick with st.button to act as input triggers
+    # If a button is clicked, we'll handle it in the main loop logic
+    selected_prompt = None
+    for idx, prompt_text in enumerate(prompts):
+        label = prompt_labels[idx] if idx < len(prompt_labels) else f"Prompt {idx+1}"
+        if st.button(label, key=f"sidebar_btn_{idx}", use_container_width=True):
+            # Reset conversation
+            st.session_state.messages = []
+            st.session_state.ai_session_id = str(uuid.uuid4())
+            # Recreate session object with new ID
+            st.session_state.ai_session = SQLiteSession(f"conversation_{st.session_state.ai_session_id}.db")
+            selected_prompt = prompt_text
+
+    st.markdown("---")
+    if st.button("🗑️ Clear Conversation", use_container_width=True):
+        st.session_state.messages = []
+        st.rerun()
+
+# -----------------------------
+# Main Content
+# -----------------------------
+
+# Hero Banner (Always visible & Sticky)
+st.markdown("""
+    <div class="hero-container" role="banner">
+        <div class="hero-title">🤖 AI Companion</div>
+        <div class="hero-subtitle">Your intelligent partner for research, analysis, and more.</div>
+    </div>
+""", unsafe_allow_html=True)
+
+# Display Chat History
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"], unsafe_allow_html=True)
+
+# Chat Input Handling
+# We handle both the chat input widget and the sidebar selection here
+if prompt := (st.chat_input("Type your message...") or selected_prompt):
+    # User Message
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Assistant Response
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response_text = asyncio.run(get_ai_response(prompt))
+            st.markdown(response_text, unsafe_allow_html=True)
+
+
+            
+    st.session_state.messages.append({"role": "assistant", "content": response_text})
+    
+    # If it was a sidebar click, we need to rerun to clear the selection state potentially, 
+    # but st.chat_input usually handles focus. With buttons, a rerun happens automatically 
+    # but we want to make sure the input box is cleared (which 'selected_prompt' doesn't use).
+    if selected_prompt:
+        st.rerun()

src/chatbot_v1/prompts/economic_news.txt

@@ -0,0 +1,27 @@
+##### Task
+Provide a concise update on **major economic indicators released recently** in USA.
+
+###### Include
+- **Interest Rates**: Latest central bank decisions, current policy rates, and forward guidance.
+- **Labor Market**: Unemployment rate, job creation figures, and key labor metrics (if available).
+- **Inflation**: CPI, PCE, or other inflation data with MoM and YoY changes.
+- **Growth Indicators**: GDP, PMIs, or industrial production released recently.
+- **Market Reaction**: Brief impact on equities, bonds, FX, and commodities.
+
+###### Guidelines
+- Compare results against forecasts and prior releases
+- Highlight notable surprises and their implications
+- Keep the summary brief, factual, and structured
+- **Always retrieve numerical data from primary or authoritative sources**
+
+###### Fallback
+- If no relevant data was released recently, explicitly state **“No major economic indicators were released during this period.”**
+- If data is partially unavailable, summarize what is available and clearly note missing indicators.
+- Do not infer or fabricate numbers under any circumstance.
+
+###### Output Style
+- Concise, factual, and well-structured
+- Use clear bullet points or short paragraphs
+- Avoid speculation unless explicitly labeled as interpretation
+- **Cite data sources clearly**
+- Use color and emoji to make it more engaging.

src/chatbot_v1/prompts/entertainment_updates.txt

@@ -0,0 +1,26 @@
+##### Task
+Provide the **top 5 recent movie or series updates** that are trending or newly released in USA.
+
+###### For each title, include:
+- **Title in bold** and optionally use **color or emojis** to make it fun (e.g., 🎬, 🍿, 🌟)  
+- A **short, 2–3 line snippet** that generates excitement or humor about the plot, cast, or vibe  
+- **Platform or source** where it can be watched (Netflix, Prime, Disney+, etc.)  
+- **Release date or premiere date**  
+
+###### Requirements / Guidelines
+- Focus on **recent releases** (last 2–4 weeks) or currently trending content  
+- Keep the tone **fun, witty, and engaging**, like a friend recommending a show  
+- Use **emojis liberally** to emphasize excitement, genre, or humor
+- Call out the **main actors and actresses** to build the interest  
+- Where possible, add a **light humorous quip or pun** about the movie/series  
+- If color is supported, use HTML span tags, e.g., `<span style="color:orange">Title</span>` for emphasis
+
+###### Fallback
+- If fewer than 5 titles are available, provide what is available and indicate:  
+  **“Only X recent releases found.”**  
+- Do not fabricate platforms or release dates — only use verified sources
+
+###### Output Style
+- List format (1–5) sorted by **popularity or release date**  
+- **Title + snippet + watch source + release date** per entry  
+- Use **color, emojis, and humor** to make the output visually appealing and fun to read

src/chatbot_v1/prompts/india_news.txt

@@ -0,0 +1,26 @@
+##### Task
+Tell me the **top 3 headlines from India**.
+
+###### For each headline, provide:
+- **Title in bold**
+- A **3‑line summary**
+- **Publish date and time**
+- A **link to the exact source URL**
+
+###### Requirements
+- Use authoritative news sources (e.g., major national/regional news outlets)
+- Headlines should be **recent (last 24 hours)**
+- Provide timestamps in **UTC**
+- If publish date/time is not available, indicate “Date/Time not provided”
+
+###### Fallback
+- If fewer than 3 headlines are found, provide what is available and state:
+  **“Only X recent headlines found for India.”**
+- Do not fabricate headlines, dates, or URLs
+
+###### Output Style
+- Structured list sorted by **most recent first**
+- Clear and concise formatting as requested
+- Use color and emoji to make it more engaging.
+- Use `<span style="color:...">` for coloring the title if the renderer supports it
+- Keep the output **concise, factual, and visually engaging**

src/chatbot_v1/prompts/market_sentiment.txt

@@ -0,0 +1,34 @@
+##### Task
+Act as a **Senior Market Analyst** and provide a concise market sentiment update for the **US Stock Market / S&P 500**.
+
+###### Steps
+1. **Data Gathering**: Search for the **top 5 financial news headlines** from the last 24 hours related to the [US Stock Market / S&P 500].
+2. **Market Check**: Retrieve the **current value** and **today’s percentage change** for:
+   - **S&P 500 (SPX)**
+   - **VIX (Volatility Index)**
+3. **Synthesis**: Based on the **tone of the news headlines** and the **index performance**, determine whether the **current market sentiment** is:
+   - **Bullish**
+   - **Bearish**
+   - **Neutral**
+4. **Output**: Provide a:
+   - **Sentiment Score (1–10)**
+   - **Top 3 key drivers** influencing this sentiment
+
+###### Guidelines
+- Prioritize **reliable financial news sources** (e.g., Bloomberg, Reuters, WSJ, CNBC)
+- Use **accurate, real-time market data** for indices
+- Base sentiment on both **news tone** and **market movement**
+- Avoid subjective or unsupported judgments
+
+###### Fallback
+- If no relevant financial headlines are found in the last 24 hours, clearly state:  
+  **“No significant market news available in the last 24 hours.”**
+- If either index value or change is unavailable, report available data and note missing values explicitly
+- Do not invent or estimate values — only use verified data
+
+###### Output Style
+- Concise, factual, and structured
+- Use clear bullet points or short paragraphs
+- Include numerical values and data timestamps
+- Provide **sources for headlines and index data**
+- Use color and emoji to make it more engaging.

src/chatbot_v1/prompts/news_headlines.txt

@@ -0,0 +1,28 @@
+##### Task
+Tell me the **top 3 USA headlines**. Use **emojis** and, where supported, **HTML color tags** to make the output engaging.
+
+###### For each headline, provide:
+- **Title in bold** and optionally in color, e.g., `<span style="color:blue">Title</span>`  
+- A **3-line summary** with an emoji indicating the type of news:
+  - 📰 Politics
+  - 💼 Business
+  - 🌎 World
+  - ⚡ Breaking news
+- **Publish date and time** (UTC)
+- A **link to the exact source URL**
+
+###### Requirements
+- Use **credible news sources** (Reuters, AP, BBC, Guardian, etc.)
+- Headlines should be **recent (last 24 hours)**
+- If publish time is unavailable, indicate **“Time not provided”**
+
+###### Fallback
+- If fewer than 3 headlines are found, state:  
+  **“Only X recent headlines found for the USA.”**
+- Do not fabricate headlines, dates, or URLs
+
+###### Output Style
+- Structured list sorted by **most recent first**
+- Use emojis consistently to indicate news type
+- Use `<span style="color:...">` for coloring the title if the renderer supports it
+- Keep the output **concise, factual, and visually engaging**

src/chatbot_v1/prompts/odia_news.txt

@@ -0,0 +1,26 @@
+##### Task
+Tell me the **top 3 headlines from Odisha**.
+
+###### For each headline, provide:
+- **Title in bold**
+- A **3‑line summary**
+- **Publish date and time**
+- A **link to the exact source URL**
+
+###### Requirements
+- Use authoritative news sources (e.g., major national/regional news outlets)
+- Headlines should be **recent (last 24 hours)**
+- Provide timestamps in **UTC**
+- If publish date/time is not available, indicate “Date/Time not provided”
+
+###### Fallback
+- If fewer than 3 headlines are found, provide what is available and state:
+  **“Only X recent headlines found for Odisha.”**
+- Do not fabricate headlines, dates, or URLs
+
+###### Output Style
+- Structured list sorted by **most recent first**
+- Clear and concise formatting as requested
+- Use color and emoji to make it more engaging.
+- Use `<span style="color:...">` for coloring the title if the renderer supports it
+- Keep the output **concise, factual, and visually engaging**

src/chatbot_v1/prompts/trade_recommendation.txt

@@ -0,0 +1,40 @@
+##### Task
+Recommend **three option spreads** with **>80% probability of profit**. Perform a thorough analysis of each underlying’s **3-month price trend** and current **market sentiment** before selecting spreads.
+
+###### Steps
+1. **Stock selection & analysis**
+   - Analyze the **last 3 months** of price action (trend, volatility, support/resistance).
+   - Assess market sentiment from the **last 7 days** of headlines and social/analyst tone.
+2. **Spread construction**
+   - For each of the **3 recommended spreads**, specify:
+     - **Underlying ticker**
+     - **Spread type** (e.g., bull put, bear call, iron condor)
+     - **Exact expiry date** (YYYY-MM-DD)
+     - **Each leg**: side (sell/buy), option type (put/call), **strike price**
+     - **Premium entry**: exact net credit/debit per share (use live bid/ask midpoint)
+     - **Position size guidance** (risk per trade as % of portfolio) — optional
+3. **Probability & rationale**
+   - Provide a **quantitative probability of profit (%)** (clearly state model/method used).
+   - Give a concise **rationale** linking 3-month trend, implied volatility, and sentiment to the spread choice.
+   - Show key supporting numbers: current spot, IV30, recent volatility, and relevant news headlines (with timestamps).
+
+###### Requirements / Guidelines
+- Target **>80% probability of profit** for each spread. Explain how the probability was computed (IV-based log-normal, normal approximation, or risk-neutral model).
+- **Always** use live option-chain quotes (bid/ask midpoint) and authoritative sources for prices/IV (e.g., exchange data, major market data providers).
+- Compare outcomes **vs. forecasts / recent range** and note any idiosyncratic risk (earnings, events).
+- Include **exact timestamps** (UTC) for all quoted prices.
+- Provide **sources** for price, IV, and headlines.
+
+###### Fallback
+- If live option-chain or price data is unavailable, state: **“Live market data unavailable — cannot generate exact strike/premium. Provide analysis based on most recent available snapshot.”**
+- If sentiment or 3-month history is incomplete, present what is available and **explicitly list missing items**.
+- **Do not fabricate** strikes, premiums, probabilities, or news — only use verified data.
+
+###### Output Style
+- For each spread, use a compact block with:
+  - Ticker — Spread type — Expiry (YYYY-MM-DD) — Net premium — PO P (%)
+  - Legs: bullet list of exact leg details (sell/buy, put/call, strike, premium)
+  - Rationale: 2–3 short sentences linking trend & sentiment to the trade
+  - Sources & timestamps
+- Keep language concise, factual, and machine/agent friendly for downstream parsing.
+- Use color and emoji to make it more engaging.

src/chatbot_v1/prompts/upcoming_earnings.txt

@@ -0,0 +1,27 @@
+##### Task
+Search for **upcoming critical earnings announcements** in the stock market.
+
+###### Include
+- **Ticker**
+- **Company name**
+- **Earnings date & time**
+- **Expected EPS & revenue consensus**
+- **Last quarter’s actual EPS & revenue**
+- **Implied volatility trend ahead of earnings**
+
+###### Requirements / Guidelines
+- Focus on **high‑impact names** (large cap, high volume, sector leaders)
+- Include **earnings expected within the next 7 calendar days**
+- Use **primary/authoritative sources** for earnings dates and estimates (e.g., exchange calendars, Bloomberg/Refinitiv/Estimize)
+- Show **timestamped data** (UTC)
+
+###### Fallback
+- If no critical earnings are found in the next 7 days, state:  
+  **“No upcoming critical earnings announcements found within the specified period.”**
+- If consensus estimates are unavailable, list the earnings date/time and note missing metrics.
+
+###### Output Style
+- Structured list sorted by **earnings date**
+- Use clear bullet points or short paragraphs
+- Provide **sources** for each item
+- Use color and emoji to make it more engaging.

src/chatbot_v1/trace_config.py

@@ -0,0 +1,37 @@
+# trace_config.py
+import openai
+from langsmith.wrappers import wrap_openai
+import os
+
+print("🔌 APPLYING LANGSMITH TRACE PATCH...")
+
+# 1. Save original classes
+_OriginalOpenAI = openai.OpenAI
+_OriginalAsyncOpenAI = openai.AsyncOpenAI
+
+# 2. Define the shim
+def PatchedOpenAI(*args, **kwargs):
+    print("✨ Creating Wrapped OpenAI Client (Sync)") # Debug print
+    client = _OriginalOpenAI(*args, **kwargs)
+    return wrap_openai(client)
+
+def PatchedAsyncOpenAI(*args, **kwargs):
+    print("✨ Creating Wrapped OpenAI Client (Async)") # Debug print
+    client = _OriginalAsyncOpenAI(*args, **kwargs)
+    return wrap_openai(client)
+
+# 3. Apply patch
+openai.OpenAI = PatchedOpenAI
+openai.AsyncOpenAI = PatchedAsyncOpenAI
+
+from langsmith import traceable
+
+# You can't decorate the class directly with @traceable, 
+# but you can use this helper to wrap all methods:
+
+def instrument_class(cls):
+    for attr_name, attr_value in cls.__dict__.items():
+        if callable(attr_value) and not attr_name.startswith("__"):
+            setattr(cls, attr_name, traceable(attr_value, run_type="tool"))
+    return cls
+

uv.lock

@@ -15,6 +15,10 @@ dependencies = [
     { name = "autogen-agentchat" },
     { name = "autogen-ext", extra = ["grpc", "mcp", "openai"] },
     { name = "azure-identity" },
+    { name = "azure-mgmt-compute" },
+    { name = "azure-mgmt-monitor" },
+    { name = "azure-mgmt-resource" },
+    { name = "azure-monitor-query" },
     { name = "beautifulsoup4" },
     { name = "chromadb" },
     { name = "datasets" },
@@ -61,11 +65,13 @@ dependencies = [
     { name = "opentelemetry-api" },
     { name = "opentelemetry-exporter-otlp" },
     { name = "opentelemetry-sdk" },
+    { name = "phidata" },
     { name = "pillow" },
     { name = "playwright" },
     { name = "plotly" },
     { name = "polygon-api-client" },
     { name = "psutil" },
+    { name = "pygithub" },
     { name = "pymupdf" },
     { name = "pypdf" },
     { name = "pypdf2" },
@@ -103,6 +109,10 @@ requires-dist = [
     { name = "autogen-agentchat", specifier = "==0.4.7" },
     { name = "autogen-ext", extras = ["grpc", "mcp", "ollama", "openai"], specifier = "==0.4.7" },
     { name = "azure-identity", specifier = ">=1.25.1" },
+    { name = "azure-mgmt-compute", specifier = ">=30.3.0" },
+    { name = "azure-mgmt-monitor", specifier = ">=6.0.2" },
+    { name = "azure-mgmt-resource", specifier = ">=23.0.1" },
+    { name = "azure-monitor-query", specifier = ">=1.2.0" },
     { name = "beautifulsoup4", specifier = ">=4.12.3" },
     { name = "chromadb", specifier = ">=0.4.0" },
     { name = "datasets", specifier = ">=4.4.1" },
@@ -149,11 +159,13 @@ requires-dist = [
     { name = "opentelemetry-api", specifier = ">=1.20.0" },
     { name = "opentelemetry-exporter-otlp", specifier = ">=1.20.0" },
     { name = "opentelemetry-sdk", specifier = ">=1.20.0" },
+    { name = "phidata", specifier = ">=2.0.0" },
     { name = "pillow" },
     { name = "playwright", specifier = ">=1.51.0" },
     { name = "plotly", specifier = ">=6.5.0" },
     { name = "polygon-api-client", specifier = ">=1.16.3" },
     { name = "psutil", specifier = ">=7.0.0" },
+    { name = "pygithub", specifier = ">=2.1.1" },
     { name = "pymupdf" },
     { name = "pypdf", specifier = ">=6.3.0" },
     { name = "pypdf2", specifier = ">=3.0.1" },
@@ -446,6 +458,15 @@ openai = [
     { name = "tiktoken" },
 ]
 
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+    { name = "isodate" },
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+]
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 version = "2.2.1"
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 ]
 
+[[package]]
+name = "isodate"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/54/4d/e940025e2ce31a8ce1202635910747e5a87cc3a6a6bb2d00973375014749/isodate-0.7.2.tar.gz", hash = "sha256:4cd1aa0f43ca76f4a6c6c0292a85f40b35ec2e43e315b59f06e6d32171a953e6", size = 29705, upload-time = "2024-10-08T23:04:11.5Z" }
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 name = "jedi"
 version = "0.19.2"
@@ -3594,6 +3693,28 @@ wheels = [
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 ]
 
+[[package]]
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+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "docstring-parser" },
+    { name = "gitpython" },
+    { name = "httpx" },
+    { name = "pydantic" },
+    { name = "pydantic-settings" },
+    { name = "python-dotenv" },
+    { name = "pyyaml" },
+    { name = "rich" },
+    { name = "tomli" },
+    { name = "typer" },
+    { name = "typing-extensions" },
+]
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 name = "pillow"
 version = "12.1.0"
@@ -3971,6 +4092,22 @@ wheels = [
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 ]
 
+[[package]]
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+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyjwt", extra = ["crypto"] },
+    { name = "pynacl" },
+    { name = "requests" },
+    { name = "typing-extensions" },
+    { name = "urllib3" },
+]
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