Added new Demo
This commit is contained in:
ShubhamSaboo
parent
5a27fbcf71
commit
805507ef0c
19 changed files with 406 additions and 16 deletions
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GOOGLE_API_KEY=your_gemini_api_key_here
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@ -38,7 +38,7 @@ Follow these steps to set up and run the application:
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1. **Clone the Repository**:
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd ai_agent_tutorials/ai_mental_wellbeing_agent
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cd advanced_ai_agents/multi_agent_apps/ai_mental_wellbeing_agent
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```
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2. **Install Dependencies**:
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@ -12,7 +12,7 @@ This Streamlit app empowers you to research top stories and users on HackerNews
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd awesome-llm-apps/ai_agent_tutorials/multi_agent_researcher
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cd advanced_ai_agents/multi_agent_apps/multi_agent_researcher
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```
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2. Install the required dependencies:
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@ -0,0 +1,84 @@
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# 🚀 Product Launch Intelligence Agent
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A **streamlined intelligence hub** for Go-To-Market (GTM) & Product-Marketing teams.
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Built with **Agno + Firecrawl + Streamlit**, the app turns scattered public-web data into concise, actionable launch insights.
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## 🎯 Core Use-Cases
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| Tab | What You Get |
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|-----|--------------|
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| **Competitor Analysis** | GTM-focused breakdown of a rival's latest launches – key messaging, differentiators, pricing cues & launch channels |
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| **Market Sentiment** | Consolidated review themes & social chatter split by 🚀 *positive* / ⚠️ *negative* drivers |
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| **Launch Metrics** | Publicly available KPIs – press coverage, engagement numbers, qualitative "buzz" signals |
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Responses are neatly rendered in markdown with a two-step process:
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1. First, a concise bullet list of key findings
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2. Then, an expanded 1200-word analysis with executive summary, deep dive, and recommendations
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## 🛠️ Tech Stack
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| Layer | Details |
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|-------|---------|
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| Data | **Firecrawl** search + crawl (async, poll-based) |
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| Agent | **Agno** single-agent with FirecrawlTools & markdown output |
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| UI | **Streamlit** wide layout, custom CSS, tabbed workflow |
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| LLM | **OpenAI GPT-4o** for analysis and insights |
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### How to get Started?
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd advanced_ai_agents/multi_agent_apps/product_launch_intelligence_agent
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```
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. **Set up API Keys**
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You can provide your API keys in two ways:
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- **Environment Variables**: Add to `.env` file
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```ini
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OPENAI_API_KEY=sk-************************
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FIRECRAWL_API_KEY=fc-************************
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```
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- **UI Input**: Enter keys directly in the app's sidebar
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3. **Run**
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```bash
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streamlit run product_launch_intelligence_agent.py
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```
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4. **Navigate** to <http://localhost:8501> and start exploring.
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## 🕹️ Using the Application
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1. **Enter API Keys** in the sidebar if not set in environment variables
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2. Pick a tab (Competitor ▸ Sentiment ▸ Metrics)
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3. Enter the **company / product / hashtag** requested
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4. Hit **Analyze** – a spinner indicates data gathering
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5. Review the two-part analysis:
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- Initial bullet points for quick insights
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- Expanded report with detailed analysis
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## 📦 Output Structure
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The analysis is structured in two parts:
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1. **Quick Bullet Points** (max 10 bullets)
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- Concise key findings
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- Easy to scan and share
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2. **Expanded Analysis** (~1200 words)
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- Executive Summary (<120 words)
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- Deep Dive Analysis (with sub-headings)
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- Actionable Recommendations
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- Key Risks / Watch-outs
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import streamlit as st
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.tools.firecrawl import FirecrawlTools
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from dotenv import load_dotenv
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from datetime import datetime
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from textwrap import dedent
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import os
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# ---------------- Page Config & Styles ----------------
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st.set_page_config(page_title="Product Intelligence Agent", page_icon="🚀", layout="wide")
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st.markdown(
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"""
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<style>
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/* Custom CSS for a sleek look */
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.stButton>button {
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border-radius: 5px;
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height: 3em;
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font-weight: 600;
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}
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.analysis-box {
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padding: 1rem;
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border-radius: 0.5rem;
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background-color: #f9f9f9;
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border: 1px solid #e1e1e1;
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}
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div[data-testid="stExpander"] div[role="button"] p {
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font-size: 1.05rem;
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font-weight: 600;
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}
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</style>
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""",
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unsafe_allow_html=True,
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)
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# ---------------- Environment & Agent ----------------
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load_dotenv()
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# Add API key inputs in sidebar
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with st.sidebar.expander("🔑 API Keys", expanded=True):
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openai_key = st.text_input("OpenAI API Key", type="password", value=os.getenv("OPENAI_API_KEY", ""))
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firecrawl_key = st.text_input("Firecrawl API Key", type="password", value=os.getenv("FIRECRAWL_API_KEY", ""))
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# Set environment variables
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if openai_key:
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os.environ["OPENAI_API_KEY"] = openai_key
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if firecrawl_key:
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os.environ["FIRECRAWL_API_KEY"] = firecrawl_key
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# Initialize agent only if both keys are provided
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if openai_key and firecrawl_key:
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launch_analyst = Agent(
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name="Product Launch Analyst",
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description=dedent("""
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You are a senior Go-To-Market strategist who evaluates competitor product launches with a critical, evidence-driven lens.
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Your objective is to uncover:
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• How the product is positioned in the market
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• Which launch tactics drove success (strengths)
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• Where execution fell short (weaknesses)
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• Actionable learnings competitors can leverage
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Always cite observable signals (messaging, pricing actions, channel mix, timing, engagement metrics). Maintain a crisp, executive tone and focus on strategic value.
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"""),
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model=OpenAIChat(id="gpt-4o"),
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tools=[FirecrawlTools(search=True, crawl=True, limit=8, poll_interval=10)],
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show_tool_calls=True,
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markdown=True,
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exponential_backoff=True,
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delay_between_retries=2,
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)
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else:
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launch_analyst = None
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st.warning("⚠️ Please enter both API keys in the sidebar to use the application.")
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# ---------------- Helper to display response ----------------
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def display_agent_response(resp):
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"""Render different response structures nicely."""
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if hasattr(resp, "content") and resp.content:
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st.markdown(resp.content)
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elif hasattr(resp, "messages"):
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for m in resp.messages:
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if m.role == "assistant" and m.content:
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st.markdown(m.content)
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else:
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st.markdown(str(resp))
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# Helper to expand bullet summary into 1200-word general report
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def expand_insight(bullet_text: str, topic: str) -> str:
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if not launch_analyst:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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return ""
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prompt = (
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f"Using ONLY the bullet points below, craft an in-depth (~1200-word) launch analysis report on {topic}.\n"
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f"Structure:\n"
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f"1. Executive Summary (<120 words)\n"
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f"2. Strengths & Opportunities (what worked well)\n"
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f"3. Weaknesses & Gaps (what didn't work or could be improved)\n"
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f"4. Actionable Recommendations (bullet list)\n"
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f"5. Key Risks / Watch-outs\n\n"
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f"Bullet Points:\n{bullet_text}\n\n"
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f"Ensure analysis is objective, evidence-based and references the bullet insights. Keep paragraphs short (≤120 words)."
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)
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long_resp = launch_analyst.run(prompt)
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return long_resp.content if hasattr(long_resp, "content") else str(long_resp)
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# Helper to craft competitor-focused launch report for product managers
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def expand_competitor_report(bullet_text: str, competitor: str) -> str:
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if not launch_analyst:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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return ""
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prompt = (
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f"Transform the insight bullets below into a professional launch review for product managers analysing {competitor}.\n\n"
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f"Produce well-structured **Markdown** with a mix of tables, call-outs and concise bullet points — avoid long paragraphs.\n\n"
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f"=== FORMAT SPECIFICATION ===\n"
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f"# {competitor} – Launch Review\n\n"
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f"## 1. Market & Product Positioning\n"
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f"• Bullet point summary of how the product is positioned (max 6 bullets).\n\n"
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f"## 2. Launch Strengths\n"
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f"| Strength | Evidence / Rationale |\n|---|---|\n| … | … | (add 4-6 rows)\n\n"
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f"## 3. Launch Weaknesses\n"
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f"| Weakness | Evidence / Rationale |\n|---|---|\n| … | … | (add 4-6 rows)\n\n"
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f"## 4. Strategic Takeaways for Competitors\n"
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f"1. … (max 5 numbered recommendations)\n\n"
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f"=== SOURCE BULLETS ===\n{bullet_text}\n\n"
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f"Guidelines:\n"
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f"• Populate the tables with specific points derived from the bullets.\n"
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f"• Only include rows that contain meaningful data; omit any blank entries."
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)
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resp = launch_analyst.run(prompt)
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return resp.content if hasattr(resp, "content") else str(resp)
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# Helper to craft market sentiment report
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def expand_sentiment_report(bullet_text: str, product: str) -> str:
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if not launch_analyst:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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return ""
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prompt = (
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f"Use the tagged bullets below to create a concise market-sentiment brief for **{product}**.\n\n"
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f"### Positive Sentiment\n"
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f"• List each positive point as a separate bullet (max 6).\n\n"
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f"### Negative Sentiment\n"
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f"• List each negative point as a separate bullet (max 6).\n\n"
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f"### Overall Summary\n"
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f"Provide a short paragraph (≤120 words) summarising the overall sentiment balance and key drivers.\n\n"
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f"Tagged Bullets:\n{bullet_text}"
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)
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resp = launch_analyst.run(prompt)
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return resp.content if hasattr(resp, "content") else str(resp)
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# Helper to craft launch metrics report
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def expand_metrics_report(bullet_text: str, launch: str) -> str:
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if not launch_analyst:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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return ""
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prompt = (
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f"Convert the KPI bullets below into a launch-performance snapshot for **{launch}** suitable for an executive dashboard.\n\n"
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f"## Key Performance Indicators\n"
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f"| Metric | Value / Detail | Source |\n"
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f"|---|---|---|\n"
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f"| … | … | … | (include one row per KPI)\n\n"
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f"## Qualitative Signals\n"
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f"• Bullet list of notable qualitative insights (max 5).\n\n"
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f"## Summary & Implications\n"
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f"Brief paragraph (≤120 words) highlighting what the metrics imply about launch success and next steps.\n\n"
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f"KPI Bullets:\n{bullet_text}"
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)
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resp = launch_analyst.run(prompt)
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return resp.content if hasattr(resp, "content") else str(resp)
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# ---------------- UI ----------------
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st.title("🚀 Product Launch Intelligence Agent")
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st.caption("AI Agent powered insights for GTM, Product Marketing & Growth Teams")
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# Create tabs for analysis types
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analysis_tabs = st.tabs(["Competitor Analysis", "Market Sentiment", "Launch Metrics"])
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# Persistent storage for latest response
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if "analysis_response" not in st.session_state:
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st.session_state.analysis_response = None
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st.session_state.analysis_meta = {}
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# -------- Competitor Analysis Tab --------
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with analysis_tabs[0]:
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st.subheader("🔍 Competitor Launch Analysis")
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competitor_name = st.text_input("Competitor name", key="competitor_input")
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cols = st.columns([2, 1])
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with cols[0]:
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if st.button("Analyze", key="competitor_btn") and competitor_name:
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if not launch_analyst:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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else:
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with st.spinner("Gathering competitive insights..."):
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try:
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bullets = launch_analyst.run(
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f"Generate up to 16 evidence-based insight bullets about {competitor_name}'s most recent product launches.\n"
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f"Format requirements:\n"
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f"• Start every bullet with exactly one tag: Positioning | Strength | Weakness | Learning\n"
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f"• Follow the tag with a concise statement (max 30 words) referencing concrete observations: messaging, differentiation, pricing, channel selection, timing, engagement metrics, or customer feedback."
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)
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long_text = expand_competitor_report(
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bullets.content if hasattr(bullets, "content") else str(bullets),
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competitor_name
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)
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st.session_state.analysis_response = long_text
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st.session_state.analysis_meta = {
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"type": "Competitor Analysis",
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"query": competitor_name,
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"timestamp": datetime.utcnow().isoformat()
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}
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st.success("✅ Analysis ready")
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except Exception as e:
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st.error(f"❌ Error: {e}")
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if st.session_state.analysis_response and st.session_state.analysis_meta.get("type") == "Competitor Analysis":
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st.markdown("### 📊 Results")
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st.markdown(st.session_state.analysis_response)
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# -------- Market Sentiment Tab --------
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with analysis_tabs[1]:
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st.subheader("💬 Market Sentiment Analysis")
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product_name = st.text_input("Product name", key="sentiment_input")
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cols = st.columns([2, 1])
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with cols[0]:
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if st.button("Analyze", key="sentiment_btn") and product_name:
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if not launch_analyst:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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else:
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with st.spinner("Collecting market sentiment..."):
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try:
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bullets = launch_analyst.run(
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f"Summarize market sentiment for {product_name} in <=10 bullets. "
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f"Cover top positive & negative themes with source mentions (G2, Reddit, Twitter)."
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)
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long_text = expand_sentiment_report(
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bullets.content if hasattr(bullets, "content") else str(bullets),
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product_name
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)
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st.session_state.analysis_response = long_text
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st.session_state.analysis_meta = {
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"type": "Market Sentiment",
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"query": product_name,
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"timestamp": datetime.utcnow().isoformat()
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}
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st.success("✅ Sentiment analysis ready")
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except Exception as e:
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st.error(f"❌ Error: {e}")
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if st.session_state.analysis_response and st.session_state.analysis_meta.get("type") == "Market Sentiment":
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st.markdown("### 📈 Sentiment Insights")
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st.markdown(st.session_state.analysis_response)
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# -------- Launch Metrics Tab --------
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with analysis_tabs[2]:
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st.subheader("📈 Launch Performance Metrics")
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product_launch = st.text_input("Product name / Launch campaign", key="metrics_input")
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cols = st.columns([2, 1])
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with cols[0]:
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if st.button("Analyze", key="metrics_btn") and product_launch:
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if not launch_analyst:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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else:
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with st.spinner("Fetching launch performance data..."):
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try:
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bullets = launch_analyst.run(
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f"List (max 10 bullets) the most important publicly available KPIs & qualitative signals for {product_launch}. "
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f"Include engagement stats, press coverage and social traction if available."
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)
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long_text = expand_metrics_report(
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bullets.content if hasattr(bullets, "content") else str(bullets),
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product_launch
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)
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st.session_state.analysis_response = long_text
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st.session_state.analysis_meta = {
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"type": "Launch Metrics",
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"query": product_launch,
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"timestamp": datetime.utcnow().isoformat()
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}
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st.success("✅ Metrics analysis ready")
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except Exception as e:
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st.error(f"❌ Error: {e}")
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if st.session_state.analysis_response and st.session_state.analysis_meta.get("type") == "Launch Metrics":
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st.markdown("### 📊 Metric Highlights")
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st.markdown(st.session_state.analysis_response)
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# ---------------- Sidebar ----------------
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st.sidebar.header("ℹ️ About")
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st.sidebar.markdown(
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"""
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**Product Launch Intelligence Agent** helps GTM teams quickly:
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- Benchmark competitor launches
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- Monitor market sentiment pre/post-launch
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- Track launch performance signals
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Built with **Agno** & **Firecrawl**.
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"""
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)
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@ -0,0 +1,4 @@
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streamlit
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agno
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firecrawl
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|
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@ -13,7 +13,7 @@ This Streamlit app implements an AI-powered customer support agent for synthetic
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd awesome-llm-apps/ai_agent_tutorials/ai_customer_support_agent
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cd advanced_ai_agents/single_agent_apps/ai_customer_support_agent
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```
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2. Install the required dependencies:
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|
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|
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@ -29,7 +29,7 @@ A powerful research assistant that leverages OpenAI's Agents SDK and Firecrawl's
|
|||
1. Clone this repository:
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd ai_agent_tutorials/ai_deep_research_agent
|
||||
cd advanced_ai_agents/single_agent_apps/ai_deep_research_agent
|
||||
```
|
||||
|
||||
2. Install the required packages:
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ Before anything else, Please get a free Gemini API Key provided by Google AI her
|
|||
1. **Clone the Repository**:
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/ai_agent_tutorials/ai_health_fitness_agent
|
||||
cd advanced_ai_agents/single_agent_apps/ai_health_fitness_agent
|
||||
```
|
||||
|
||||
2. **Install the dependencies**
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ This Streamlit app is an AI-powered investment agent built with Agno's AI Agent
|
|||
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/ai_agent_tutorials/ai_investment_agent
|
||||
cd advanced_ai_agents/single_agent_apps/ai_investment_agent
|
||||
```
|
||||
2. Install the required dependencies:
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ This Streamlit app is an AI-powered journalist agent that generates high-quality
|
|||
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/ai_agent_tutorials/ai_journalist_agent
|
||||
cd advanced_ai_agents/single_agent_apps/ai_journalist_agent
|
||||
```
|
||||
2. Install the required dependencies:
|
||||
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ The AI Lead Generation Agent automates the process of finding and qualifying pot
|
|||
1. **Clone the repository**:
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd ai_agent_tutorials/ai_lead_generation_agent
|
||||
cd advanced_ai_agents/single_agent_apps/ai_lead_generation_agent
|
||||
```
|
||||
3. **Install the required packages**:
|
||||
```bash
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ This Streamlit application leverages multiple AI agents to create comprehensive
|
|||
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/ai_agent_tutorials/ai_meeting_agent
|
||||
cd advanced_ai_agents/single_agent_apps/ai_meeting_agent
|
||||
```
|
||||
2. Install the required dependencies:
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ This Streamlit app is an AI-powered movie production assistant that helps bring
|
|||
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/ai_agent_tutorials/ai_movie_production_agent
|
||||
cd advanced_ai_agents/single_agent_apps/ai_movie_production_agent
|
||||
```
|
||||
2. Install the required dependencies:
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ This Streamlit app is an AI-powered personal finance planner that generates pers
|
|||
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/ai_agent_tutorials/ai_personal_finance_agent
|
||||
cd advanced_ai_agents/single_agent_apps/ai_personal_finance_agent
|
||||
```
|
||||
2. Install the required dependencies:
|
||||
|
||||
|
|
|
|||
|
|
@ -39,7 +39,6 @@ An advanced web extraction and analysis tool built using Firecrawl's FIRE-1 agen
|
|||
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd advanced_ai_agents/single_agent_apps/ai_startup_insight_fire1_agent
|
||||
|
||||
```
|
||||
|
||||
# Install dependencies
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ An Agno agentic system that provides expert software architecture analysis and r
|
|||
```bash
|
||||
# Clone the repository
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/ai_agent_tutorials/ai_system_architect_r1
|
||||
cd advanced_ai_agents/single_agent_apps/ai_system_architect_r1
|
||||
|
||||
# Install dependencies
|
||||
pip install -r requirements.txt
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ LLM app with RAG to chat with GitHub Repo in just 30 lines of Python Code. The a
|
|||
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/chat_with_X_tutorials/chat_with_github
|
||||
cd advanced_llm_apps/chat_with_X_tutorials/chat_with_github
|
||||
```
|
||||
2. Install the required dependencies:
|
||||
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ LLM app with RAG to chat with Gmail in just 30 lines of Python Code. The app use
|
|||
|
||||
```bash
|
||||
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
||||
cd awesome-llm-apps/chat_with_X_tutorials/chat_with_gmail
|
||||
cd advanced_llm_apps/chat_with_X_tutorials/chat_with_gmail
|
||||
```
|
||||
2. Install the required dependencies
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue