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common/aagents/google_agent.py

@@ -135,5 +135,6 @@ google_agent = Agent(
         - Respect timeouts and handle errors gracefully
         """,
 )
+google_agent.description = "A Google search agent that finds accurate, up-to-date information and recent news using Google Search."
 
 __all__ = ["google_agent", "google_search", "google_search_recent", "duckduckgo_search", "fetch_page_content", "current_datetime"]

common/aagents/healthcare_agent.py

@@ -6,7 +6,7 @@ from openai import AsyncOpenAI
 
 # Import tools
 from mcp.tools.rag_tool import rag_search, UserContext
-from mcp.tools.search_tools import duckduckgo_search
+from mcp.tools.search_tools import duckduckgo_search, fetch_page_content
 from mcp.tools.time_tools import current_datetime
 
 
@@ -34,7 +34,7 @@ groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=gro
 healthcare_agent = Agent[UserContext](
     name="HealthcareRAGAgent",
     model=gemini_model,
-    tools=[rag_search, duckduckgo_search],
+    tools=[rag_search, duckduckgo_search, fetch_page_content],
     instructions="""
         You are a healthcare information retrieval agent. You retrieve information from tools and synthesize it into well-formatted markdown responses.
         
@@ -96,5 +96,6 @@ healthcare_agent = Agent[UserContext](
         - Accept useless RAG results without calling web search
         """,
 )
+healthcare_agent.description = "A healthcare agent that combines RAG (Retrieval Augmented Generation) with web search to answer medical questions."
 
 __all__ = ["healthcare_agent"]

common/aagents/news_agent.py

@@ -3,7 +3,6 @@ import os
 from agents import Agent, OpenAIChatCompletionsModel
 from dotenv import load_dotenv
 from mcp.tools.news_tools import get_top_headlines, search_news, get_news_by_category
-from mcp.tools.search_tools import duckduckgo_search
 from mcp.tools.time_tools import current_datetime
 from openai import AsyncOpenAI
 
@@ -25,7 +24,7 @@ groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=gro
 news_agent = Agent(
     name="NewsAgent",
     model=gemini_model,
-    tools=[current_datetime, get_top_headlines, search_news, get_news_by_category, duckduckgo_search],
+    tools=[current_datetime, get_top_headlines, search_news, get_news_by_category],
     instructions="""
         You are a NewsAgent specialized in fetching and analyzing recent news articles and headlines.
         Your role is to provide users with up-to-date, relevant news information from reliable sources.
@@ -46,13 +45,8 @@ news_agent = Agent(
            - Categories: "business", "entertainment", "general", "health", "science", "sports", "technology"
            - Input: { "category": "business", "country": "us", "num_results": 5 }
 
-        **FALLBACK TOOL (DuckDuckGo Search):**
-        4. 'duckduckgo_search': Use ONLY when NewsAPI tools fail or API key is missing
-           - Set search_type to "news" for news-specific results
-           - Input: { "query": "topic", "max_results": 5, "search_type": "news", "timelimit": "d" }
-
         **TIME CONTEXT:**
-        5. 'current_datetime': Use to provide current date/time context in your responses
+        4. 'current_datetime': Use to provide current date/time context in your responses
            - Input: { "format": "natural" }
 
         ## Workflow
@@ -62,14 +56,11 @@ news_agent = Agent(
            - Topic-specific → use search_news
            - Category-specific → use get_news_by_category
         
-        2. **Try Primary Tools First**: Always attempt NewsAPI tools before fallback
-        
-        3. **Fallback if Needed**: If NewsAPI returns an error (missing API key, no results), 
-           use duckduckgo_search with search_type="news"
+        2. **Execute Search**: Use the appropriate NewsAPI tool.
         
-        4. **Include Time Context**: Use current_datetime to provide temporal context
+        3. **Include Time Context**: Use current_datetime to provide temporal context.
         
-        5. **Format Response**: Present news in a clear, organized format with:
+        4. **Format Response**: Present news in a clear, organized format with:
            - Headlines/titles
            - Sources
            - Publication dates
@@ -95,12 +86,12 @@ news_agent = Agent(
 
         - Always cite sources and include publication dates
         - Prioritize recent news (within last 7 days unless specified otherwise)
-        - If API key is missing, inform the user and use the fallback tool
         - Never fabricate news or sources
         - Present news objectively without bias
         - Include URLs so users can read full articles
         - Use current_datetime to ensure temporal accuracy
         """,
 )
+news_agent.description = "A news agent that fetches top headlines and searches for news articles by category or topic."
 
-__all__ = ["news_agent", "get_top_headlines", "search_news", "get_news_by_category", "duckduckgo_search", "current_datetime"]
+__all__ = ["news_agent", "get_top_headlines", "search_news", "get_news_by_category", "current_datetime"]

common/aagents/search_agent.py

@@ -0,0 +1,51 @@
+"""Search agent module for comprehensive web searches."""
+import os
+from agents import Agent, OpenAIChatCompletionsModel
+from openai import AsyncOpenAI
+from dotenv import load_dotenv
+from mcp.tools.search_tools import duckduckgo_search, fetch_page_content
+from mcp.tools.time_tools import current_datetime
+
+# ---------------------------------------------------------
+# Load environment variables
+# ---------------------------------------------------------
+load_dotenv()
+
+GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
+google_api_key = os.getenv('GOOGLE_API_KEY')
+gemini_client = AsyncOpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
+gemini_model = OpenAIChatCompletionsModel(model="gemini-2.0-flash-exp", openai_client=gemini_client)
+
+search_agent = Agent(
+    name="Web Search Agent",
+    model=gemini_model,
+    tools=[current_datetime, duckduckgo_search, fetch_page_content],
+    instructions="""
+        You are a highly efficient and specialized **Web Search Agent** 🌐. Your sole function is to retrieve and analyze information from the internet using the **duckduckgo_search** and **fetch_page_content** functions. You must act as a digital librarian and researcher, providing synthesized, cited, and up-to-date answers.
+
+        ## Core Directives & Priorities
+        1.  **Time Awareness First:** ALWAYS invoke **current_datetime** at the very beginning of your execution to establish the current temporal context. This is crucial for answering questions about "today", "yesterday", or recent events.
+        2.  **Search Strategy:**
+            *   Analyze the user's request and construct 1-3 targeted search queries.
+            *   Use **duckduckgo_search** to find relevant information. Use the 'news' type for current events.
+            *   **Mandatory Deep Dive:** You MUST select the **top 3** most relevant search results and use **fetch_page_content** to retrieve their full text. *Do not rely solely on the short search snippets.*
+        3.  **Synthesis & Answer Construction:**
+            *   Read the fetched content thoroughly.
+            *   Synthesize the information into a coherent answer.
+            *   **Conflict Resolution:** If sources disagree, note the discrepancy and favor the most recent or authoritative source.
+            *   **Citations:** You **must** cite your sources. At the end of your response, list the *Title* and *URL* of the pages you used.
+        4.  **Clarity:** Use professional, plain language. Use headings and bullet points for readability.
+        5.  **Data Gaps:** If you cannot find a conclusive answer after searching and fetching, state: **"A conclusive answer could not be verified by current web search results."**
+
+        ## Workflow Example
+        1.  Call `current_datetime()`.
+        2.  Call `duckduckgo_search(query="...")`.
+        3.  Loop through top 3 results: `fetch_page_content(url=...)`.
+        4.  Synthesize findings into final answer.
+
+        **Crucially, never fabricate information. Your answer must be grounded in the text you have fetched.**
+    """,
+)
+search_agent.description = "A web search agent that retrieves information using DuckDuckGo and fetches page content for detailed answers."
+
+__all__ = ["search_agent"]

common/aagents/weather_agent.py

@@ -32,7 +32,6 @@ groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=gro
 weather_agent = Agent(
     name="WeatherAgent",
     model=gemini_model, #"gpt-4o-mini",
-    # description="An agent that can perform web searches using DuckDuckGo.",
     tools=[current_datetime, get_weather_forecast, search_weather_fallback_ddgs, search_weather_fallback_bs],
     instructions="""
         You are a Weather Forecast agent who forecasts weather information ONLY.
@@ -61,9 +60,7 @@ weather_agent = Agent(
             }.
         ]
         """,
-    # output_type=AgentOutputSchema(list[searchResult], strict_json_schema=False),
-    # output_type=list[dict],  # safer than list[searchResult],    
-    # output_type=list[searchResult],
 )
+weather_agent.description = "A weather agent that provides current and forecasted weather information for specific cities."
 
 __all__ = ["weather_agent", "get_weather_forecast", "search_weather_fallback_ddgs", "search_weather_fallback_bs"]

common/aagents/web_research_agent.py

@@ -29,9 +29,7 @@ groq_client = AsyncOpenAI(base_url=GROQ_BASE_URL, api_key=groq_api_key)
 groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=groq_client)
 
 web_research_agent = Agent(
-    name="WebResearchAgent",
     model="gpt-4o-mini",
-    # description="An agent that can perform web searches using DuckDuckGo.",
     tools=[duckduckgo_search, fetch_page_content],
     instructions="""
 You are WebResearchAgent — an advanced internet research assistant with two core abilities:
@@ -79,5 +77,6 @@ IMPORTANT RULES
 """
     ,
 )
+web_research_agent.description = "A deep research agent that performs extensive web searches and content fetching for complex research queries."
 
 __all__ = ["web_research_agent", "duckduckgo_search", "fetch_page_content", "searchQuery", "searchResult"]

common/aagents/yf_agent.py

@@ -2,7 +2,7 @@
 import os
 from agents import Agent, OpenAIChatCompletionsModel
 from dotenv import load_dotenv
-from mcp.tools.yf_tools import get_summary, get_market_sentiment, get_history
+from mcp.tools.yf_tools import get_summary, get_market_sentiment, get_history, get_analyst_recommendations, get_earnings_calendar
 from mcp.tools.time_tools import current_datetime
 from openai import AsyncOpenAI
 
@@ -24,7 +24,7 @@ groq_model = OpenAIChatCompletionsModel(model="groq/compound", openai_client=gro
 yf_agent = Agent(
     name="YahooFinanceAgent",
     model=gemini_model,
-    tools=[current_datetime, get_summary, get_market_sentiment, get_history],
+    tools=[current_datetime, get_summary, get_market_sentiment, get_history, get_analyst_recommendations, get_earnings_calendar],
     instructions="""
         You are a specialized **Financial Analysis Agent** 💰, expert in market research, financial data retrieval, and market analysis. 
         Your primary role is to provide *actionable*, *data-driven*, and *concise* financial reports based on the available tools.
@@ -35,14 +35,16 @@ yf_agent = Agent(
            Financial data is extremely time-sensitive.
         
         2. **Financial Data Integrity:** Use the Yahoo Finance tools for specific stock/index data:
-           - 'get_summary': Get latest summary information and intraday price data for a ticker
-           - 'get_market_sentiment': Analyze recent price changes and provide market sentiment (Bullish/Bearish/Neutral)
-           - 'get_history': Fetch historical price data for a given ticker
+           - 'get_summary': Get latest summary information and intraday price data for a ticker.
+           - 'get_market_sentiment': Analyze recent price changes and provide market sentiment (Bullish/Bearish/Neutral).
+           - 'get_history': Fetch historical price data for a given ticker.
+           - 'get_analyst_recommendations': Fetch latest analyst ratings (Buy/Sell/Hold) for a symbol to provide **trading recommendations**.
+           - 'get_earnings_calendar': Fetch upcoming earnings dates for a symbol.
            
            Be precise about the date range and data source.
         
-        3. **Synthesis and Analysis:** Do not just list data. You must **synthesize** financial data (prices, volume, sentiment) 
-           to provide a complete analytical perspective (e.g., "Stock X is up 5% today driven by strong market momentum").
+        3. **Synthesis and Analysis:** Do not just list data. You must **synthesize** financial data (prices, volume, sentiment, recommendations) 
+           to provide a complete analytical perspective (e.g., "Stock X is up 5% today driven by strong market momentum and a generic 'Buy' rating from analysts").
         
         4. **Professional Clarity:** Present information in a clear, professional, and structured format. 
            Use numerical data and financial terminology correctly.
@@ -62,9 +64,12 @@ yf_agent = Agent(
         
         Tool: get_market_sentiment
         Input: { "symbol": "AAPL", "period": "1mo" }
-        
-        Tool: get_history
-        Input: { "symbol": "AAPL", "period": "1mo" }
+
+        Tool: get_analyst_recommendations
+        Input: { "symbol": "AAPL" }
+
+        Tool: get_earnings_calendar
+        Input: { "symbol": "AAPL" }
 
         ## Output Format Guidelines
         
@@ -74,5 +79,6 @@ yf_agent = Agent(
         * Always include a disclaimer: "This analysis is for informational purposes only and is not financial advice."
         """,
 )
+yf_agent.description = "A financial analysis agent that provides stock summaries, market sentiment, and historical data using Yahoo Finance."
 
-__all__ = ["yf_agent", "get_summary", "get_market_sentiment", "get_history", "current_datetime"]
+__all__ = ["yf_agent", "get_summary", "get_market_sentiment", "get_history", "get_analyst_recommendations", "get_earnings_calendar", "current_datetime"]

common/mcp/tools/time_tools.py

@@ -18,6 +18,7 @@ def current_datetime(format: str = "natural") -> str:
     Returns:
         str: Current date and time in the specified format
     """
+    print(f"[DEBUG] current_datetime called with format='{format}'")
     now = datetime.now()
     
     # Natural format options

common/mcp/tools/yf_tools.py

@@ -37,6 +37,7 @@ def get_summary(symbol: str, period: str = "1d", interval: str = "1h") -> str:
         - Volume
         - Period and interval used
     """
+    print(f"[DEBUG] get_summary called for symbol='{symbol}', period='{period}', interval='{interval}'")
     try:
         ticker = yf.Ticker(symbol)
 
@@ -109,6 +110,7 @@ def get_market_sentiment(symbol: str, period: str = "1mo") -> str:
     str
         A human-readable sentiment string including percentage change.
     """
+    print(f"[DEBUG] get_market_sentiment called for symbol='{symbol}', period='{period}'")
     try:
         ticker = yf.Ticker(symbol)
 
@@ -164,6 +166,7 @@ def get_history(symbol: str, period: str = "1mo") -> str:
     str
         A formatted string showing the last 5 rows of historical prices (Open, High, Low, Close, Volume).
     """
+    print(f"[DEBUG] get_history called for symbol='{symbol}', period='{period}'")
     try:
         ticker = yf.Ticker(symbol)
 
@@ -190,3 +193,65 @@ def get_history(symbol: str, period: str = "1mo") -> str:
 
     except Exception as e:
         return f"Error fetching historical data for '{symbol}': {e}"
+
+@function_tool
+def get_analyst_recommendations(symbol: str) -> str:
+    """
+    Fetch analyst recommendations for a given ticker.
+    
+    Parameters:
+    -----------
+    symbol : str
+        The ticker symbol.
+
+    Returns:
+    --------
+    str
+        Formatted string string of analyst recommendations.
+    """
+    print(f"[DEBUG] get_analyst_recommendations called for symbol='{symbol}'")
+    try:
+        ticker = yf.Ticker(symbol)
+        recs = ticker.recommendations
+        if recs is None or recs.empty:
+             return f"No analyst recommendations found for {symbol}."
+        
+        # Format the last few recommendations
+        latest = recs.tail(5)
+        return f"Analyst Recommendations for {symbol}:\n{latest.to_string()}"
+    except Exception as e:
+         return f"Error fetching recommendations for '{symbol}': {e}"
+
+@function_tool
+def get_earnings_calendar(symbol: str) -> str:
+    """
+    Fetch the next earnings date for a ticker.
+
+    Parameters:
+    -----------
+    symbol : str
+        The ticker symbol.
+
+    Returns:
+    --------
+    str
+        Next earnings date info.
+    """
+    print(f"[DEBUG] get_earnings_calendar called for symbol='{symbol}'")
+    try:
+        ticker = yf.Ticker(symbol)
+        calendar = ticker.calendar
+        if calendar is None:
+            return f"No earnings calendar found for {symbol}."
+        
+        # Handle dict (new yfinance) or DataFrame (old yfinance)
+        if isinstance(calendar, dict):
+             if not calendar:
+                 return f"No earnings calendar found for {symbol}."
+        elif hasattr(calendar, 'empty') and calendar.empty:
+             return f"No earnings calendar found for {symbol}."
+            
+        return f"Earnings Calendar for {symbol}:\n{calendar}"
+    except Exception as e:
+         return f"Error fetching earnings calendar for '{symbol}': {e}"
+

src/deep-research/app.py

@@ -28,70 +28,82 @@ st.set_page_config(page_title="Deep Research AI", layout="wide", page_icon="🧠
 st.markdown("""
 <style>
     /* Global Defaults */
-    .stApp {
+    .stApp, [data-testid="stAppViewContainer"] {
         background-color: #f8f9fa;
         font-family: 'Inter', sans-serif;
+        overflow-x: hidden !important; /* Force hide horizontal scroll */
     }
     
-    /* Remove default Streamlit top padding but add space for Fixed Header - Revert: Just remove top padding */
     .block-container {
-        padding-top: 1rem !important; /* Small buffer */
+        max-width: 1200px;
+        padding-top: 2rem !important;
+        padding-bottom: 2rem !important;
     }
     
-    /* Sticky Header */
-    header[data-testid="stHeader"] { display: none; } /* Hide default streamlit header */
+    /* Remove default header decoration */
+    header[data-testid="stHeader"] {
+        background-color: transparent !important;
+        z-index: 100 !important;
+    }
     
-    .header-container {
-        position: sticky;
-        top: 0;
-        z-index: 999;
-        
+    div[data-testid="stDecoration"] {
+        display: none;
+    }
+
+    /* Hero Section (Matching Chatbot Style) */
+    .hero-container {
+        position: relative;
+        width: 100vw;
+        left: 50%;
+        right: 50%;
+        margin-left: -50vw;
+        margin-right: -50vw;
+        margin-top: -6rem; /* Pull up to cover top padding */
+        padding: 4rem 1rem 2rem 1rem; /* Extra top padding for status bar area */
+        text-align: center;
+        margin-bottom: 2rem;
         background: linear-gradient(135deg, #0f2027 0%, #203a43 50%, #2c5364 100%);
-        color: #ffffff;
-        padding: 3rem 2rem;
-        display: flex;
-        justify-content: space-between;
-        align-items: center;
-        box-shadow: 0 4px 20px rgba(0,0,0,0.15);
-        
-        margin-top: -4rem; /* Pull up aggressively to cover top gap */
-        margin-left: -5rem;
-        margin-right: -5rem;
-        
-        border-bottom: none;
-        border-radius: 0 0 1rem 1rem;
+        color: white;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.1);
     }
-    
-    .app-brand {
-        font-family: 'Inter', sans-serif;
-        font-size: 1.6rem;
+
+    .hero-title {
+        font-size: 2rem;
+        margin-bottom: 0.5rem;
         font-weight: 700;
-        letter-spacing: -0.02em;
-        color: #ffffff;
-        display: flex;
-        gap: 0.75rem;
-        align-items: center;
+    }
+    .hero-subtitle {
+        font-size: 1rem;
+        opacity: 0.95;
+        font-weight: 400;
     }
 
     /* Centered Search Area */
     .search-wrapper {
         max-width: 800px;
-        margin: 4rem auto 2rem auto;
+        margin: 2rem auto;
         text-align: center;
+        padding: 0 1rem;
     }
     
     .search-headline {
-        font-size: 2.5rem;
+        font-size: 2rem;
         font-weight: 800;
         color: #111;
         margin-bottom: 0.5rem;
-        letter-spacing: -0.03em;
     }
     
     .search-subtext {
-        font-size: 1.1rem;
+        font-size: 1rem;
         color: #666;
-        margin-bottom: 2.5rem;
+        margin-bottom: 2rem;
+    }
+    
+    /* Mobile font sizes */
+    @media (max-width: 768px) {
+        .search-headline {
+            font-size: 1.75rem;
+        }
     }
 
     /* Input styling override */
@@ -101,11 +113,8 @@ st.markdown("""
         padding: 1rem !important;
         background: white !important;
         box-shadow: 0 4px 12px rgba(0,0,0,0.03) !important;
-        font-size: 1.1rem !important;
-    }
-    .stTextArea textarea:focus {
-        border-color: #667eea !important;
-        box-shadow: 0 4px 15px rgba(102, 126, 234, 0.1) !important;
+        font-size: 1rem !important; /* Proper reading size */
+        color: #333 !important;
     }
     
     /* Custom Button */
@@ -115,8 +124,9 @@ st.markdown("""
         border-radius: 30px !important;
         padding: 0.5rem 2rem !important;
         border: none !important;
-        box-shadow: 0 4px 10px rgba(0,0,0,0.2) !important;
         transition: transform 0.1s ease;
+        min-height: 48px; /* Large touch target */
+        white-space: nowrap !important; /* Prevent label wrapping */
     }
     .stButton button:hover {
         transform: scale(1.02);
@@ -127,11 +137,11 @@ st.markdown("""
         max-width: 850px;
         margin: 2rem auto;
         background: white;
-        padding: 4rem;
-        min-height: 800px;
-        box-shadow: 0 1px 3px rgba(0,0,0,0.1), 0 20px 40px rgba(0,0,0,0.05);
+        padding: 2rem;
+        min-height: 600px;
+        box-shadow: 0 1px 3px rgba(0,0,0,0.1);
         color: #2c3e50;
-        border: 1px solid #f0f0f0;
+        border-radius: 8px;
     }
 </style>
 """, unsafe_allow_html=True)
@@ -221,13 +231,9 @@ async def run_research(query: str):
 
 # Custom Header
 st.markdown("""
-<div class="header-container">
-    <div class="app-brand">
-        <span>🧠</span> Deep Research <i>(OpenAI Agentic)</i>
-    </div>
-    <div>
-        <!-- Could add profile or other links here -->
-    </div>
+<div class="hero-container">
+    <div class="hero-title">🧠 Deep Research</div>
+    <div class="hero-subtitle">OpenAI Agentic Research Assistant</div>
 </div>
 """, unsafe_allow_html=True)