Updated README
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ShubhamSaboo
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9 changed files with 50 additions and 46 deletions
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@ -47,7 +47,7 @@ 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_competitors_analysis_team
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cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_competitor_intelligence_agent_team
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```
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2. **Install the dependencies**:
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@ -15,7 +15,7 @@ This script demonstrates how to build a team of AI agents that work together as
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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_finance_agent_team
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cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_finance_agent_team
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```
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2. Install the required dependencies:
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@ -37,7 +37,7 @@ 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_game_design_team
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cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_game_design_agent_team
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```
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2. **Install Dependencies**:
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@ -26,7 +26,7 @@ A Streamlit application that simulates a full-service legal team using multiple
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```bash
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# Clone the repository
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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_legal_agent_team
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cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_legal_agent_team
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# Install dependencies
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pip install -r requirements.txt
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@ -36,8 +36,8 @@ A Streamlit application that simulates a full-service recruitment team using mul
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```bash
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# Clone the repository
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd ai_agent_tutorials/ai_recruitment_agent_team
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cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_recruitment_agent_team
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# Install dependencies
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pip install -r requirements.txt
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```
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@ -64,7 +64,7 @@ Before anything else, Please get your OpenAI API Key here: https://platform.open
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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 awesome-llm-apps/ai_agent_tutorials/ai_services_agency
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cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency
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```
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2. **Install the dependencies**:
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@ -36,7 +36,7 @@ Follow the steps below 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_coding_agent_o3-mini
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cd advanced_ai_agents/multi_agent_apps/agent_teams/multimodal_coding_agent_team
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```
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2. **Install the dependencies**
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@ -25,7 +25,7 @@ This application leverages multiple specialized AI agents to provide comprehensi
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```bash
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# Clone the repository
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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/multimodal_design_agent_team
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cd advanced_ai_agents/multi_agent_apps/agent_teams/multimodal_design_agent_team
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# Create and activate virtual environment (optional)
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python -m venv venv
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@ -1,69 +1,73 @@
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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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Built with **Streamlit + Agno (GPT-4o) + Firecrawl**, the app turns scattered public-web data into concise, actionable launch insights.
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## 🎯 Core Use-Cases
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## 3 Specialized Agents
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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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| **Competitor Analysis Agent** | Evidence-backed breakdown of a rival's latest launches – positioning, differentiators, pricing cues & channel mix |
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| **Market Sentiment Agent** | Consolidated social chatter & review themes split by 🚀 *positive* / ⚠️ *negative* drivers |
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| **Launch Metrics Agent** | Publicly available KPIs – adoption numbers, press coverage, qualitative "buzz" signals |
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Additional goodies:
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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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* 🔑 **Sidebar key input** – enter OpenAI & Firecrawl keys securely (type="password")
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* 🧠 **Specialised multi-agent core** – three expert agents collaborate for richer insight
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* 🔍 Product Launch Analyst (GTM strategist)
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* 💬 Market Sentiment Specialist (consumer-perception guru)
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* 📈 Launch Metrics Specialist (performance analyst)
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* ⚡ **Quick actions** – press **J/K/L** to trigger the three analyses without touching the UI
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* 📑 **Auto-formatted Markdown reports** – bullet summary first, then expanded deep-dive
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* 🛠️ **Sources section** – every report ends with the URLs that were crawled or searched
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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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| Data | **Firecrawl** async search + crawl API |
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| Agents | **Agno** (GPT-4o) with FirecrawlTools |
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| UI | **Streamlit** wide-layout, tabbed workflow |
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| LLM | **OpenAI GPT-4o** |
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## 🚀 Quick Start
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### How to get Started?
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1. Clone the GitHub repository
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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 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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2. **Install** 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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3. **Provide API keys** (choose either option)
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• **Environment variables** – create a `.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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• **In-app sidebar** – paste the keys into the secure text inputs
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4. **Navigate** to <http://localhost:8501> and start exploring.
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4. **Run the app**
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```bash
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streamlit run product_launch_intelligence_agent.py
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```
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5. **Browse** to <http://localhost:8501> – you should see three analysis tabs.
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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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1. Enter **API keys** in the sidebar (or ensure they are in your environment).
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2. Type a **company / product / hashtag** in the main input box.
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3. Pick a tab and hit the corresponding **Analyze** button – a spinner will appear while the agent works.
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4. Review the two-part analysis:
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* Bullet list of key findings
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* Expanded, richly-formatted report (tables, call-outs, recommendations)
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