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Merge pull request #198 from Madhuvod/firecrawl-fire-v1
Added new demo: AI Startup Insight with Firecrawl's FIRE 1 Agent
This commit is contained in:
Shubham Saboo
GitHub
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# 🔥 AI Startup Insight with Firecrawl FIRE-1 Agent
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An advanced web extraction and analysis tool built using Firecrawl's FIRE-1 agent + extract v1 endpoint and the Agno Agent framework to get details of a new startup instantly! This application automatically extracts structured data from startup websites and provides AI-powered business analysis, making it easy to gather insights about companies without manual research.
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## Features
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- 🌐 **Intelligent Web Extraction**:
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- Extract structured data from any company website
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- Automatically identify company information, mission, and product features
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- Process multiple websites in sequence
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- 🔍 **Advanced Web Navigation**:
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- Interact with buttons, links, and dynamic elements
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- Handle pagination and multi-step processes
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- Access information across multiple pages
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- 🧠 **AI Business Analysis**:
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- Generate insightful summaries of extracted company data
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- Identify unique value propositions and market opportunities
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- Provide actionable business intelligence
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- 📊 **Structured Data Output**:
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- Organize information in a consistent JSON schema
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- Extract company name, description, mission, and product features
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- Standardize output for further processing
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- 🎯 **Interactive UI**:
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- User-friendly Streamlit interface
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- Process multiple URLs in parallel
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- Clear presentation of extracted data and analysis
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## How to Run
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1. **Setup Environment**
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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 advanced_ai_agents/single_agent_apps/ai_startup_insight_fire1_agent
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```
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# Install dependencies
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```
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pip install -r requirements.txt
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```
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2. **Configure API Keys**
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- Get Firecrawl API key from [Firecrawl](https://firecrawl.dev)
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- Get OpenAI API key from [OpenAI Platform](https://platform.openai.com)
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3. **Run the Application**
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```bash
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streamlit run ai_startup_insight_fire1_agent.py
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```
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## Usage
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1. Launch the application using the command above
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2. Provide your Firecrawl and OpenAI API keys in the sidebar
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3. Enter one or more company website URLs in the text area (one per line)
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4. Click "🚀 Start Analysis" to begin the extraction and analysis process
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5. View the structured data and AI analysis for each website in the tabbed interface
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## Example Websites to Try
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- https://www.spurtest.com
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- https://cluely.com
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- https://www.harvey.ai
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## Technologies Used
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- **Firecrawl FIRE-1**: Advanced web extraction agent
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- **Agno Agent Framework**: For AI analysis capabilities
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- **OpenAI GPT Models**: For business insight generation
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- **Streamlit**: For the interactive web interface
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## Requirements
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- Python 3.8+
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- Firecrawl API key
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- OpenAI API key
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- Internet connection for web extraction
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@@ -0,0 +1,266 @@
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from firecrawl import FirecrawlApp
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import streamlit as st
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import os
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import json
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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st.set_page_config(
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page_title="Startup Info Extraction",
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page_icon="🔍",
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layout="wide"
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)
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st.title("AI Startup Insight with Firecrawl's FIRE-1 Agent")
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# Sidebar for API key
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with st.sidebar:
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st.header("API Configuration")
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firecrawl_api_key = st.text_input("Firecrawl API Key", type="password")
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openai_api_key = st.text_input("OpenAI API Key", type="password")
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st.caption("Your API keys are securely stored and not shared.")
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st.markdown("---")
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st.markdown("### About")
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st.markdown("This tool extracts company information from websites using Firecrawl's FIRE-1 agent and provides AI-powered business analysis.")
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st.markdown("### How It Works")
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st.markdown("1. 🔍 **FIRE - 1 Agent** extracts structured data from websites")
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st.markdown("2. 🧠 **Agno Agent** analyzes the data for business insights")
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st.markdown("3. 📊 **Results** are presented in an organized format")
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# Main content
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# Add information about Firecrawl's capabilities
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st.markdown("## 🔥 Firecrawl FIRE 1 Agent Capabilities")
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col1, col2 = st.columns(2)
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with col1:
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st.info("**Advanced Web Extraction**\n\nFirecrawl's FIRE 1 agent combined with the extract endpoint can intelligently navigate websites to extract structured data, even from complex layouts and dynamic content.")
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st.success("**Interactive Navigation**\n\nThe agent can interact with buttons, links, input fields, and other dynamic elements to access hidden information.")
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with col2:
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st.warning("**Multi-page Processing**\n\nFIRE can handle pagination and multi-step processes, allowing it to gather comprehensive data across entire websites.")
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st.error("**Intelligent Data Structuring**\n\nThe agent automatically structures extracted information according to your specified schema, making it immediately usable.")
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st.markdown("---")
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st.markdown("### 🌐 Enter Website URLs")
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st.markdown("Provide one or more company website URLs (one per line) to extract information.")
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website_urls = st.text_area("Website URLs (one per line)", placeholder="https://example.com\nhttps://another-company.com")
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# Define a JSON schema directly without Pydantic
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extraction_schema = {
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"type": "object",
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"properties": {
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"company_name": {
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"type": "string",
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"description": "The official name of the company or startup"
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},
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"company_description": {
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"type": "string",
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"description": "A description of what the company does and its value proposition"
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},
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"company_mission": {
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"type": "string",
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"description": "The company's mission statement or purpose"
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},
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"product_features": {
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"type": "array",
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"items": {
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"type": "string"
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},
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"description": "Key features or capabilities of the company's products/services"
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},
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"contact_phone": {
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"type": "string",
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"description": "Company's contact phone number if available"
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}
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},
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"required": ["company_name", "company_description", "product_features"]
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}
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# Custom CSS for better UI
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st.markdown("""
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<style>
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.stButton button {
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background-color: #FF4B4B;
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color: white;
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font-weight: bold;
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border-radius: 10px;
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padding: 0.5rem 1rem;
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border: none;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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transition: all 0.3s ease;
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}
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.stButton button:hover {
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background-color: #FF2B2B;
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box-shadow: 0 6px 8px rgba(0, 0, 0, 0.15);
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transform: translateY(-2px);
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}
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.css-1r6slb0 {
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border-radius: 10px;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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}
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</style>
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""", unsafe_allow_html=True)
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# Start extraction when button is clicked
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if st.button("🚀 Start Analysis", type="primary"):
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if not website_urls.strip():
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st.error("Please enter at least one website URL")
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else:
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try:
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with st.spinner("Extracting information from website..."):
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# Initialize the FirecrawlApp with the API key
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app = FirecrawlApp(api_key=firecrawl_api_key)
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# Parse the input URLs more robustly
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# Split by newline, strip whitespace from each line, and filter out empty lines
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urls = [url.strip() for url in website_urls.split('\n') if url.strip()]
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# Debug: Show the parsed URLs
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st.info(f"Attempting to process these URLs: {urls}")
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if not urls:
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st.error("No valid URLs found after parsing. Please check your input.")
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elif not openai_api_key:
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st.warning("Please provide an OpenAI API key in the sidebar to get AI analysis.")
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else:
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# Create tabs for each URL
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tabs = st.tabs([f"Website {i+1}: {url}" for i, url in enumerate(urls)])
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# Initialize the Agno agent once (outside the loop)
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if openai_api_key:
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agno_agent = Agent(
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model=OpenAIChat(id="gpt-4o", api_key=openai_api_key),
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instructions="""You are an expert business analyst who provides concise, insightful summaries of companies.
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You will be given structured data about a company including its name, description, mission, and product features.
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Your task is to analyze this information and provide a brief, compelling summary that highlights:
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1. What makes this company unique or innovative
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2. The core value proposition for customers
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3. The potential market impact or growth opportunities
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Keep your response under 150 words, be specific, and focus on actionable insights.
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""",
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markdown=True
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)
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# Process each URL one at a time
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for i, (url, tab) in enumerate(zip(urls, tabs)):
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with tab:
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st.markdown(f"### 🔍 Analyzing: {url}")
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st.markdown("<hr style='border: 2px solid #FF4B4B; border-radius: 5px;'>", unsafe_allow_html=True)
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with st.spinner(f"FIRE agent is extracting information from {url}..."):
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try:
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# Extract data for this single URL
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data = app.extract(
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[url], # Pass as a list with a single URL
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params={
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'prompt': '''
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Analyze this company website thoroughly and extract comprehensive information.
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1. Company Information:
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- Identify the official company name
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Explain: This is the legal name the company operates under.
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- Extract a detailed yet concise description of what the company does
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- Find the company's mission statement or purpose
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Explain: What problem is the company trying to solve? How do they aim to make a difference?
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2. Product/Service Information:
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- Identify 3-5 specific product features or service offerings
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Explain: What are the key things their product or service can do? Describe as if explaining to a non-expert.
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- Focus on concrete capabilities rather than marketing claims
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Explain: What does the product actually do, in simple terms, rather than how it's advertised?
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- Be specific about what the product/service actually does
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Explain: Give examples of how a customer might use this product or service in their daily life.
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3. Contact Information:
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- Find direct contact methods (phone numbers)
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Explain: How can a potential customer reach out to speak with someone at the company?
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- Only extract contact information that is explicitly provided
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Explain: We're looking for official contact details, not inferring or guessing.
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Important guidelines:
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- Be thorough but concise in your descriptions
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- Extract factual information, not marketing language
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- If information is not available, do not make assumptions
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- For each piece of information, provide a brief, simple explanation of what it means and why it's important
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- Include a layman's explanation of what the company does, as if explaining to someone with no prior knowledge of the industry or technology involved
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''',
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'schema': extraction_schema,
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'agent': {"model": "FIRE-1"}
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}
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)
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# Check if extraction was successful
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if data and data.get('data'):
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# Display extracted data
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st.subheader("📊 Extracted Information")
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company_data = data.get('data')
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# Display company name prominently
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if 'company_name' in company_data:
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st.markdown(f"{company_data['company_name']}")
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# Display other extracted fields
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for key, value in company_data.items():
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if key == 'company_name':
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continue # Already displayed above
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display_key = key.replace('_', ' ').capitalize()
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if value: # Only display if there's a value
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if isinstance(value, list):
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st.markdown(f"**{display_key}:**")
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for item in value:
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st.markdown(f"- {item}")
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elif isinstance(value, str):
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st.markdown(f"**{display_key}:** {value}")
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elif isinstance(value, bool):
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st.markdown(f"**{display_key}:** {str(value)}")
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else:
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st.write(f"**{display_key}:**", value)
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# Process with Agno agent
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if openai_api_key:
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with st.spinner("Generating AI analysis..."):
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# Run the agent with the extracted data
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agent_response = agno_agent.run(f"Analyze this company data and provide insights: {json.dumps(company_data)}")
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# Display the agent's analysis in a highlighted box
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st.subheader("🧠 AI Business Analysis")
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st.markdown(agent_response.content)
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# Show raw data in expander
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with st.expander("🔍 View Raw API Response"):
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st.json(data)
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# Add processing details
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with st.expander("ℹ️ Processing Details"):
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st.markdown("**FIRE Agent Actions:**")
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st.markdown("- 🔍 Scanned website content and structure")
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st.markdown("- 🖱️ Interacted with necessary page elements")
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st.markdown("- 📊 Extracted and structured data according to schema")
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st.markdown("- 🧠 Applied AI reasoning to identify relevant information")
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if 'status' in data:
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st.markdown(f"**Status:** {data['status']}")
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if 'expiresAt' in data:
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st.markdown(f"**Data Expires:** {data['expiresAt']}")
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else:
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st.error(f"No data was extracted from {url}. The website might be inaccessible, or the content structure may not match the expected format.")
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except Exception as e:
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st.error(f"Error processing {url}: {str(e)}")
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except Exception as e:
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st.error(f"Error during extraction: {str(e)}")
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@@ -0,0 +1,4 @@
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firecrawl-py
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streamlit
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agno
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openai
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