Extracting Enriched Product Knowledge Graphs from Product Hunt into Neo4j

Overview

In this tutorial, you’ll learn how to build a Hypermode Agent that automatically extracts product data from Product Hunt, enriches it with LinkedIn insights, and stores it as a knowledge graph in Neo4j. This powerful combination allows you to visualize relationships between products, founders, companies, and market trends.

What you’ll build

By the end of this tutorial, you’ll have an Agent that:

Scrapes Product Hunt’s homepage for trending products using web search
Enriches product data with founder and company information from LinkedIn
Transforms the data into a knowledge graph structure
Stores everything in Neo4j using Cypher queries

Prerequisites

A Hypermode Pro account
A Neo4j Sandbox or AuraDB instance (free tier works fine)
Basic understanding of graph databases (helpful but not required)

What’s a Hypermode Agent?

Hypermode Agents are domain specific AI-powered assistants created with natural language instructions that can understand instructions, interact with external services, and perform complex tasks on your behalf. Unlike traditional chatbots, Hypermode Agents can actually take actions—like scraping websites, querying databases, and transforming data.

Key features for this tutorial

Natural Language Understanding: Give instructions in plain English
Multiple Connections: Integrate with web search, LinkedIn, and Neo4j
Data Transformation: Convert unstructured web data into structured graph relationships
Flexible Output: Agents adapt their Cypher queries based on the data they find

Understanding the Technologies

Neo4j: the graph database

Neo4j is a graph database that stores data as nodes (entities) and relationships (connections between entities). Unlike traditional databases that use tables and rows, Neo4j excels at representing and querying interconnected data. Neo4j Banner

Product Hunt: the product discovery platform

Product Hunt is where makers launch new products daily - it’s a goldmine of data about:

Emerging products and startups
Founder networks and connections
Market trends and categories
User engagement metrics

Why combine them?

By extracting Product Hunt data into Neo4j, you can:

Discover patterns in successful product launches
Track founder networks and serial entrepreneurs
Identify trending categories and technologies
Analyze competitive landscapes

Step 1: Set up Neo4j Sandbox

Create a Neo4j Sandbox

First, you need to go to https://sandbox.neo4j.com/ and create an account. When you get to making a database, select a blank sandbox.

Neo4j Sandbox provides free temporary instances perfect for testing. For production use, consider Neo4j Aura or a self-hosted instance.

Get your connection details

Once created (it may take a moment), you can navigate to the HTTP tab and grab the URL. Note you will have to modify this to use the Neo4j HTTP v2 endpoint when adding the Neo4j connection in Hypermode Agents.

Remove /db/neo4j/tx/commit
Replace it with /db/neo4j/query/v2

For example:

Original: http://52.54.53.148:7474/db/neo4j/tx/commit
Modified: http://52.54.53.148:7474/db/neo4j/query/v2

The Neo4j connection in Hypermode Agents uses the Neo4j Query HTTP API v2 endpoint, not the now deprecated HTTP v1 endpoint, which is why we need to modify the URL. Refer to the Hypermode Agents Neo4j connection guide for more information.

Get authentication credentials

Save this URL and then move to the connection details to grab the username and password. Make sure to note these details - you’ll need them when setting up the connection in Hypermode.

Step 2: Create your knowledge graph Agent

Manual Agent creation

Let’s create an Agent specifically designed for knowledge graph extraction. Navigate to your Hypermode workspace and create a new agent with these settings:

Agent Name: KnowledgeGraphBuilder
Agent Title: Product Hunt Knowledge Graph Extractor
Description: Extracts product data from Product Hunt and builds knowledge graphs in Neo4j

System prompt

Use this comprehensive system prompt for your agent:

Identity:
You are GraphBuilder, a specialized agent for extracting product data from Product Hunt and constructing knowledge graphs in Neo4j.
Your role is to discover new products, enrich them with additional context, and maintain a comprehensive graph database of the product ecosystem.

Context:
You work with web search to discover trending products from Product Hunt,
LinkedIn to gather founder and company information, and Neo4j to store everything as an interconnected knowledge graph.
You understand both web scraping techniques and Cypher query language for Neo4j.

Core Responsibilities:
1. Extract product listings from Product Hunt including:
   - Product name, tagline, and description
   - Launch date and upvote count
   - Categories and tags
   - Maker information
   - Product URLs and media

2. Enrich data using LinkedIn:
   - Founder professional backgrounds
   - Company size and funding information
   - Team member connections
   - Industry positioning

3. Transform data into graph structures:
   - Create nodes for Products, People, Companies, Categories
   - Establish relationships like CREATED_BY, WORKS_AT, BELONGS_TO
   - Add properties with timestamps and metadata

4. Maintain data quality:
   - Avoid duplicate nodes using MERGE
   - Update existing records when found
   - Preserve historical data with timestamps

Workflow Process:
For each Product Hunt extraction:
1. First check if products already exist in Neo4j
2. Search Product Hunt homepage or specific pages
3. Parse product data and identify makers
4. Search LinkedIn for maker/company details
5. Generate Cypher queries to insert/update graph
6. Execute queries and verify data integrity
7. Report on new additions and updates

Cypher Query Guidelines:
- Always use MERGE to avoid duplicates
- Add timestamps to track data freshness
- Create appropriate indexes for performance
- Use descriptive relationship types
- Include relevant properties on both nodes and relationships

When generating Cypher queries, adapt the structure based on available data.
Not all products will have the same information, so create flexible queries that handle missing data gracefully.

Always maintain data accuracy and provide clear explanations of the graph structure you're creating.

Select your model

For this use case, we recommend Claude 4 Sonnet or GPT-4.1 as they excel at:

Understanding complex data structures
Writing accurate Cypher queries
Managing multi-step workflows

Step 3: Add connections

Your agent needs key connections to function properly:

Add Neo4j connection

Navigate to your agent’s connections tab
Click “Add connection”
Search for “Neo4j” and select it

Configure the Neo4j connection with your Sandbox details:

URL: Your modified HTTP v2 endpoint
Username: neo4j (or your custom username)
Password: Your Sandbox password

Add LinkedIn connection

For enriching founder and company data:

Add the “LinkedIn” connection
Complete the OAuth flow to authorize access
This enables the agent to gather professional information

Add Product Hunt connection

For direct Product Hunt API access (if available):

Add the “Product Hunt” connection if available in your workspace
This provides structured access to Product Hunt data

Step 4: Test the connection

Verify Neo4j connectivity

Start a new thread with your agent and test the Neo4j connection:

Can you connect to Neo4j and run a simple query to check if the database is empty?

Your agent should respond with a Cypher query and results showing the connection is working. Neo4j connection test

Test web search capabilities

Search for Product Hunt's trending products and tell me what the top 3 are today.

This verifies that your agent can access and parse Product Hunt data.

Step 5: Extract your first knowledge graph

Start with a simple extraction

Now let’s extract some real data! Try this prompt:

Extract the top 5 products from Product Hunt today. For each product:
1. Get the basic product information (name, description, upvotes, etc.)
2. Look up the founders on LinkedIn to get their background
3. Create a knowledge graph in Neo4j with nodes for:
   - Product
   - Person (founders/makers)
   - Company
   - Category
4. Create appropriate relationships between these entities

Show me the Cypher queries you generate and the final graph structure.

Example workflow

Your agent will follow this process:

Data Search: Search for Product Hunt trending products
Data Parsing: Extract product details, maker information
LinkedIn Enrichment: Search for founder profiles and company data
Graph Construction: Generate Cypher queries to create nodes and relationships
Data Storage: Execute queries in Neo4j
Verification: Query the graph to confirm data was stored correctly

Expected output structure

Your knowledge graph will have this structure:

// Products
(:Product {name: "ProductName", description: "...", upvotes: 150, launch_date: "2025-01-27"})

// People (founders/makers)
(:Person {name: "Founder Name", title: "CEO", linkedin_url: "..."})

// Companies
(:Company {name: "Company Name", size: "11-50", industry: "Technology"})

// Categories
(:Category {name: "AI Tools"})

// Relationships
(:Person)-[:FOUNDED]->(:Product)
(:Person)-[:WORKS_AT]->(:Company)
(:Product)-[:BELONGS_TO]->(:Category)
(:Company)-[:CREATED]->(:Product)

By instructing the agent to display the database queries we can verify the structure and content of the extracted graph before it is created in Neo4j. This gives us the opportunity to adjust the graph structure and relationships as needed.

Step 6: Visualize your knowledge graph

Open Neo4j Bloom

Once your agent has populated the database, you can visualize the results using Neo4j Bloom, Neo4j’s graph visualization tool.

Find your Neo4j Bloom: Go to your Sandbox console and click “Open with Neo4j Bloom”

You’ll need to authenticate with your Neo4j Sandbox credentials. Neo4j Bloom authentication

Generate a perspective

Once authenticated, you can generate a perspective to visualize your knowledge graph. Perspectives are Neo4j’s way of defining how to visualize graph data in Neo4j Bloom. Let’s generate a perspective from the graph data our agent has loaded into Neo4j. Neo4j Bloom generate perspective

Explore the graph

Once you’ve generated a perspective, you can explore the graph using Neo4j Bloom’s pattern matching search features by describing the patterns in the graph you want to visualize. Neo4j Bloom pattern matching search

Bloom will then display the graph data that matches your pattern and allow you to explore the graph interactively. Neo4j Bloom pattern matching results

Summary

You’ve successfully built a Hypermode Agent that can extract, enrich, and store Product Hunt data as a knowledge graph in Neo4j. This powerful combination enables you to discover patterns and insights that would be impossible to find manually. The beauty of Hypermode Agents is their flexibility - you can easily modify your agent’s behavior, add new data sources, or change the graph structure without writing any code. As your needs evolve, your agent can evolve with them. Keep experimenting with different queries, data sources, and analysis techniques. The knowledge graph you’ve built is a living system that becomes more valuable as you add more data and connections.

What’s next?

Knowledge graphs are a powerful tool for representing and analyzing complex data. They can be used for a variety of tasks, such as:

Enrich your knowledge graph

Add more data sources: Crunchbase for funding data, GitHub for technical metrics
Include temporal data: Track how products evolve over time
Add sentiment analysis: Analyze comments and reviews
Geographic data: Map where products and founders are located

Build applications

Recommendation engine: Suggest products based on founder networks
Trend analysis: Identify emerging categories and technologies
Investment insights: Find promising startups based on founder backgrounds
Competitive intelligence: Track competitor products and strategies

Once you’ve built a comprehensive knowledge graph, you can:

Export data for external analysis
Create automated reports and dashboards
Share insights with your team
Build APIs on top of your graph data

Expand your knowledge graph

You can expand what your knowledge graph agent can do for you. Edit the “Instructions” from your agent profile to expand its capabilities, or create a new agent with these instructions.

Add a second agent that analyzes emerging categories and technologies from your knowledge graph.

## Description
Analyzes market trends and emerging technologies from Product Hunt knowledge graph.

## Instructions

Identity:
You are TrendSpotter, a market intelligence assistant for {Company Name}.
Your job is to analyze the Product Hunt knowledge graph in Neo4j to identify emerging trends, popular categories, and technology patterns.

Context:
You have access to a Neo4j knowledge graph containing Product Hunt products, founders, companies, and categories.
When asked about trends, query the graph to find patterns in:
- Product launch frequency by category over time
- Founder backgrounds and their success patterns
- Technology keywords and their adoption rates
- Geographic distribution of successful products

Core Responsibilities:

1. Trend Identification
   - Query products launched in the last 30, 60, and 90 days
   - Group by categories to identify growth areas
   - Analyze upvote patterns and engagement metrics
   - Compare current trends to historical data

2. Technology Analysis
   - Extract technology keywords from product descriptions
   - Track emergence of new tech stacks and tools
   - Identify relationships between technologies and success metrics
   - Map technology adoption across different product categories

3. Founder Network Analysis
   - Identify serial entrepreneurs and their success patterns
   - Map connections between successful founders
   - Analyze founder backgrounds that correlate with product success
   - Track company-to-product relationships and growth patterns

4. Reporting
   - Generate weekly trend reports with visual Cypher queries
   - Create alerts for sudden category growth or new technology emergence
   - Provide competitive intelligence on specific market segments
   - Export trend data for external analysis tools

Output Format:
- Executive summary of key trends (3-5 bullet points)
- Category analysis with growth percentages
- Technology adoption timeline
- Founder success patterns
- Actionable insights for product strategy

Always provide the Cypher queries used for analysis and offer to dive deeper into specific trends or categories.

Read

Guide

Read the Neo4j connection guide to learn more about connecting your Hypermode agent to Neo4j for graph operations

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​Overview

​What you’ll build

​Prerequisites

​What’s a Hypermode Agent?

​Key features for this tutorial

​Understanding the Technologies

​Neo4j: the graph database

​Product Hunt: the product discovery platform

​Why combine them?

​Step 1: Set up Neo4j Sandbox

​Create a Neo4j Sandbox

​Get your connection details

​Get authentication credentials

​Step 2: Create your knowledge graph Agent

​Manual Agent creation

​System prompt

​Select your model

​Step 3: Add connections

​Add Neo4j connection

​Add LinkedIn connection

​Add Product Hunt connection

​Step 4: Test the connection

​Verify Neo4j connectivity

​Test web search capabilities

​Step 5: Extract your first knowledge graph

​Start with a simple extraction

​Example workflow

​Expected output structure

​Step 6: Visualize your knowledge graph

​Open Neo4j Bloom

​Generate a perspective

​Explore the graph

​Summary

​What’s next?

​Enrich your knowledge graph

​Build applications

​Export and share

​Expand your knowledge graph

Read

Guide

Bootcamp

Overview

What you’ll build

Prerequisites

What’s a Hypermode Agent?

Key features for this tutorial

Understanding the Technologies

Neo4j: the graph database

Product Hunt: the product discovery platform

Why combine them?

Step 1: Set up Neo4j Sandbox

Create a Neo4j Sandbox

Get your connection details

Get authentication credentials

Step 2: Create your knowledge graph Agent

Manual Agent creation

System prompt

Select your model

Step 3: Add connections

Add Neo4j connection

Add LinkedIn connection

Add Product Hunt connection

Step 4: Test the connection

Verify Neo4j connectivity

Test web search capabilities

Step 5: Extract your first knowledge graph

Start with a simple extraction

Example workflow

Expected output structure

Step 6: Visualize your knowledge graph

Open Neo4j Bloom

Generate a perspective

Explore the graph

Summary

What’s next?

Enrich your knowledge graph

Build applications

Export and share

Expand your knowledge graph