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OpenGraph

A Personal Intelligence System for Human Understanding


Overview

OpenGraph is an open-source system that transforms fragmented personal data into a structured, queryable graph.

It allows individuals to move beyond searching files and instead understand the relationships within their information. By automatically organizing data into entities and connections, OpenGraph provides a foundation for reasoning, exploration, and insight.

The goal is not to give people more data.
The goal is to help people understand what they already have.


The Problem

Modern life generates vast amounts of information:

  • documents
  • emails
  • notes
  • spreadsheets
  • links

Yet this information exists in isolation.

We can store it.
We can search it.
But we cannot truly reason over it.

Meanwhile, large institutions operate differently. They use advanced systems to:

  • unify fragmented data
  • resolve identities across sources
  • map relationships between entities
  • query complex systems in real time

This creates a quiet imbalance.

Organizations can understand systems.
Individuals can only navigate fragments.


Why This Matters

Knowledge without structure is noise.

Without the ability to connect information:

  • patterns remain invisible
  • insights are lost
  • decisions rely on incomplete context

OpenGraph exists to restore a fundamental capability:

The ability for individuals to see, connect, and reason over their own information.

This is not about power over others.
It is about clarity over one’s own data.


What OpenGraph Does

OpenGraph ingests personal data and converts it into a graph of entities and relationships.

It identifies:

  • people
  • organizations
  • documents
  • links between them

Then it enables users to explore and query this structure.

Instead of asking:

“Where is that file?”

You can ask:

“What connects these two people?”
“Where does this idea appear?”
“Who shows up most often in my work?”


Core Features

Data Ingestion

Import data from:

  • local files (PDF, TXT, Markdown)
  • email (read-only access)
  • structured data (CSV)

Entity Extraction

Automatically detects:

  • people
  • organizations
  • emails
  • URLs

Entity Resolution

Merges references to the same real-world entity
(e.g. “Sam Altman” and “S. Altman”)


Graph Construction

Builds a network of:

  • entities (nodes)
  • relationships (edges)

Query Engine

Ask questions over your data:

  • natural language queries
  • structured graph queries

Graph Exploration

Interactively explore relationships:

  • expand connections
  • filter entities
  • navigate through networks

What OpenGraph Is Not

  • Not a note-taking app
  • Not a dashboard tool
  • Not a chatbot wrapper
  • Not a surveillance system

OpenGraph is a reasoning layer over your own data.


Design Principles

1. Structure over volume

More data is not useful without organization.

2. Transparency over magic

Users should understand how relationships are formed.

3. Local-first by default

Your data remains under your control.

4. Useful before perfect

The system should provide value early, even if incomplete.


Example Use Cases

  • Map relationships across documents and emails
  • Trace how ideas evolve over time
  • Identify recurring people or organizations
  • Explore hidden connections in your work

Set Up

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

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