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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>LiteGraph | AI-Native Multi-Dimensional Database</title>
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<a href="#features">Features</a>
<a href="#benefits">Why LiteGraph</a>
<a href="#comparison">Comparison</a>
<a href="#use-cases">Use Cases</a>
<a href="#getting-started">Get Started</a>
<a href="#web-ui">Web UI</a>
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<!-- Hero Section -->
<section class="hero">
<div class="hero-content">
<h1 class="hero-title">
One Database.<br>
Every Dimension.<br>
<span class="gradient-text">AI-Native.</span>
</h1>
<p class="hero-subtitle">
The multi-dimensional graph database that speaks your AI's language.<br>
Relationships, metadata, vectors, and nested data—all in one unified layer.
</p>
<div class="hero-badges">
<span class="badge">Free & Open Source</span>
<span class="badge">MIT License</span>
<span class="badge">SQLite-Based</span>
<span class="badge">Zero Dependencies</span>
<span class="badge">Edge-Ready</span>
</div>
<div class="hero-actions">
<a href="#getting-started" class="btn btn-primary">Get Started</a>
<a href="https://litegraph.readme.io" target="_blank" class="btn btn-secondary">API Documentation</a>
<a href="https://github.com/litegraphdb/litegraph" target="_blank" class="btn btn-secondary">View on GitHub</a>
</div>
</div>
</section>
<!-- Key Features Section -->
<section id="features" class="features">
<div class="container">
<h2 class="section-title">Key Features</h2>
<div class="features-grid">
<div class="feature-card">
<h3>Unified Data Store for AI</h3>
<p>One powerful platform combining graph relationships, relational queries, and vector embeddings. Stop juggling multiple databases—your AI applications deserve integrated intelligence.</p>
</div>
<div class="feature-card">
<h3>Comprehensive Developer-Friendly API</h3>
<p>Intuitive SDKs for C#, JavaScript, and Python with full REST support. Build faster with clear documentation, predictable patterns, and thoughtful abstractions that just make sense.</p>
</div>
<div class="feature-card">
<h3>Find the Right Data Relationships, Fast</h3>
<p>Lightning-quick graph traversals, vector similarity search, and SQL-like queries in one engine. Navigate complex connections in milliseconds, not minutes.</p>
</div>
<div class="feature-card">
<h3>Flexible In-Memory or On-Disk Operation</h3>
<p>Choose your performance profile: blazing-fast RAM operations for real-time AI or persistent SQLite storage for durability. Switch modes without changing your code.</p>
</div>
<div class="feature-card">
<h3>Native MCP Server Integration</h3>
<p>Built-in Model Context Protocol (MCP) server lets AI assistants and LLMs interact with your graph data directly. Seamlessly integrate LiteGraph into your AI and RAG pipelines without custom glue code.</p>
</div>
</div>
</div>
</section>
<!-- Benefits Section -->
<section id="benefits" class="benefits">
<div class="container">
<h2 class="section-title">⚡ Why LiteGraph is Different</h2>
<div class="benefits-grid">
<div class="benefit-card">
<h3>Single Platform for Graph, Relational, and Vector</h3>
<p>Unifies three essential data paradigms in one lightweight engine. No more complex ETL pipelines or data synchronization nightmares.</p>
</div>
<div class="benefit-card">
<h3>Graph Intelligence at the Edge</h3>
<p>Combines graph structure, metadata semantics, and vector space reasoning—all embeddable in a 5MB binary.</p>
</div>
<div class="benefit-card">
<h3>Embedded or Server Mode</h3>
<p>Run as an embedded library for zero-latency access or deploy as a REST API server. Same data model, your choice of deployment.</p>
</div>
<div class="benefit-card">
<h3>SQLite Foundation</h3>
<p>Built on the world's most deployed database engine. Battle-tested reliability with zero operational overhead.</p>
</div>
<div class="benefit-card">
<h3>Blazing-Fast In-Memory Graphs</h3>
<p>Optional full in-RAM execution with controlled persistence enables high throughput applications.</p>
</div>
<div class="benefit-card">
<h3>Transparent & Source-Available</h3>
<p>Open-source with simple, understandable architecture—ideal for integration and auditing.</p>
</div>
<div class="benefit-card">
<h3>Built-in Web Dashboard</h3>
<p>Comprehensive web UI for exploring graphs, managing data, and visualizing relationships. Deploy alongside your database or run standalone.</p>
</div>
<div class="benefit-card">
<h3>Vector Search with HNSW Indexing</h3>
<p>Advanced vector similarity search with optional HNSW indexing for sub-millisecond semantic queries at scale.</p>
</div>
<div class="benefit-card">
<h3>First-Class MCP Server Support</h3>
<p>Native Model Context Protocol integration means your AI agents can query, traverse, and update graph data using standardized tooling—no middleware required.</p>
</div>
</div>
</div>
</section>
<!-- Comparison Section -->
<section id="comparison" class="comparison">
<div class="container">
<h2 class="section-title">Why Traditional Databases Fall Short</h2>
<div class="comparison-grid">
<div class="comparison-card">
<h3>Relational (SQL)</h3>
<p class="comparison-problem">Rigid schemas can't handle AI's dynamic needs. No native vector search. Painful for graph traversals. Your AI speaks embeddings, not tables.</p>
</div>
<div class="comparison-card">
<h3>Vector Databases</h3>
<p class="comparison-problem">Great for similarity search, blind to relationships. Can't track lineage, store metadata, or handle complex objects. It's one-dimensional thinking in a multi-dimensional AI world.</p>
</div>
<div class="comparison-card">
<h3>Graph Databases</h3>
<p class="comparison-problem">Excel at relationships but can't do semantic search. No vector operations means no AI-native queries. Missing the intelligence layer modern AI demands.</p>
</div>
<div class="comparison-card">
<h3>Object/BLOB Storage</h3>
<p class="comparison-problem">Unstructured dumping grounds with no intelligence. Can't query relationships, find similar content, or maintain context. It's storage without understanding.</p>
</div>
</div>
<div class="multi-tool-problem">
<h3>The Multi-Tool Problem</h3>
<p>Stitching together 4+ databases means complex pipelines, data synchronization nightmares, and queries that take days to write. Your AI moves at machine speed—your data infrastructure should too.</p>
</div>
</div>
</section>
<!-- AI Use Cases Section -->
<section id="use-cases" class="use-cases">
<div class="container">
<h2 class="section-title">Where LiteGraph Shines</h2>
<p class="section-subtitle">
From edge AI to enterprise knowledge graphs, LiteGraph powers applications that demand multi-dimensional intelligence.
</p>
<div class="use-case-grid">
<div class="use-case-card">
<div class="use-case-icon">🎯</div>
<h3>Embedded AI/ML Applications</h3>
<p>Power lightweight, local-first AI systems with graph-native metadata and vector embeddings—no external database required. Perfect for edge computing and privacy-sensitive applications.</p>
</div>
<div class="use-case-card">
<div class="use-case-icon">🚀</div>
<h3>RAG Beyond Vectors</h3>
<p>Combine vector similarity with graph relationships and metadata filters for more accurate, contextual retrieval that actually understands your data.</p>
</div>
<div class="use-case-card">
<div class="use-case-icon">🌐</div>
<h3>Knowledge Graphs for Edge Devices</h3>
<p>Run complex entity-relation and vector similarity lookups entirely in-process, even on constrained hardware. Bring intelligence to IoT and mobile applications.</p>
</div>
<div class="use-case-card">
<div class="use-case-icon">🏢</div>
<h3>Enterprise Data Relationship Mapping</h3>
<p>Build semantically rich models for identity graphs, asset tracking, and network topologies with full traceability. Track complex organizational relationships with ease.</p>
</div>
<div class="use-case-card">
<div class="use-case-icon">🔍</div>
<h3>Semantic Search for Unstructured Data</h3>
<p>Store structured metadata, JSON blobs, and vector embeddings together to support hybrid symbolic-vector search at scale. Query by meaning AND structure.</p>
</div>
<div class="use-case-card">
<div class="use-case-icon">🔗</div>
<h3>Custom Data Mesh/Lineage Engines</h3>
<p>Track data transformations across pipelines and teams with full visibility using labeled, versioned graph relationships. Know where your data comes from and goes.</p>
</div>
<div class="use-case-card">
<div class="use-case-icon">⚡</div>
<h3>In-Memory AI Prototyping</h3>
<p>Rapidly prototype graph/vector logic in-memory and flush to disk only when needed—ideal for LLM and RAG experiments. Iterate at the speed of thought.</p>
</div>
<div class="use-case-card">
<div class="use-case-icon">🏭</div>
<h3>Digital Twins & Simulation Graphs</h3>
<p>Model real-world systems (IoT, manufacturing, security) with mutable node/edge metadata, labels, and temporal graph states. Simulate complex systems with ease.</p>
</div>
<div class="use-case-card">
<div class="use-case-icon">🤖</div>
<h3>MCP-Powered AI Pipelines</h3>
<p>Connect AI assistants and LLMs directly to your graph data via the built-in MCP server. Enable agentic workflows, tool-calling, and autonomous data retrieval without custom integration code.</p>
</div>
</div>
</div>
</section>
<!-- Getting Started Section -->
<section id="getting-started" class="getting-started">
<div class="container">
<h2 class="section-title">Get Started in Minutes</h2>
<p class="section-subtitle">
Install LiteGraph using NuGet into your solution, or deploy a separate server using Docker
</p>
<div class="code-examples">
<div class="code-tabs">
<button class="code-tab active" data-lang="csharp-integrated">C# (Integrated)</button>
<button class="code-tab" data-lang="csharp-sdk">C# (SDK)</button>
<button class="code-tab" data-lang="javascript">JavaScript</button>
<button class="code-tab" data-lang="python">Python</button>
<button class="code-tab" data-lang="curl">cURL</button>
</div>
<div class="code-content">
<pre class="code-block active" data-lang="csharp-integrated"><code>using LiteGraph;
// Initialize SDK
var client = new LiteGraphClient(new SqliteRepository("ai.db"));
// Create a graph
var graph = client.CreateGraph(new Graph { Name = "AI Knowledge" });
// Create nodes
var user = client.CreateNode(new Node {
GraphGUID = graph.GUID,
Name = "User Profile",
Labels = new[] { "user" },
Tags = new NameValueCollection { { "type", "customer" } },
Vectors = new[] { new VectorMetadata {
Model = "ada-002",
Vectors = new[] { 0.1f, 0.2f, 0.3f }
}}
});
var pref = client.CreateNode(new Node {
GraphGUID = graph.GUID,
Name = "Prefers Technical",
Labels = new[] { "preference" }
});
// Connect them
var edge = client.CreateEdge(new Edge {
GraphGUID = graph.GUID,
From = user.GUID,
To = pref.GUID,
Name = "has_preference"
});</code></pre>
<pre class="code-block" data-lang="csharp-sdk"><code>using LiteGraph.Sdk;
// Initialize SDK
var sdk = new LiteGraphSdk("http://localhost:8701", "bearer-token");
// Create a graph
var graph = await sdk.Graph.Create(new Graph {
TenantGUID = tenantGuid,
Name = "AI Knowledge",
Labels = new List<string> { "knowledge", "ai" },
Tags = new NameValueCollection { { "domain", "customer-service" } }
});
// Create nodes
var user = await sdk.Node.Create(new Node {
TenantGUID = tenantGuid,
GraphGUID = graph.GUID,
Name = "User Profile",
Labels = new List<string> { "user" },
Tags = new NameValueCollection { { "type", "customer" } },
Vectors = new List<VectorMetadata> {
new VectorMetadata {
Model = "ada-002",
Dimensionality = 3,
Content = "customer profile data",
Vectors = new List<float> { 0.1f, 0.2f, 0.3f }
}
}
});
var pref = await sdk.Node.Create(new Node {
TenantGUID = tenantGuid,
GraphGUID = graph.GUID,
Name = "Prefers Technical",
Labels = new List<string> { "preference" },
Tags = new NameValueCollection { { "category", "communication-style" } }
});
// Connect them
var edge = await sdk.Edge.Create(new Edge {
TenantGUID = tenantGuid,
GraphGUID = graph.GUID,
From = user.GUID,
To = pref.GUID,
Name = "has_preference",
Labels = new List<string> { "relationship" }
});</code></pre>
<pre class="code-block" data-lang="javascript"><code>import { LiteGraphSdk } from 'litegraphdb';
// Initialize SDK
const sdk = new LiteGraphSdk('http://localhost:8701', 'tenant-id', 'key');
// Create a graph
const graph = await sdk.createGraph('graph-id', 'AI Knowledge');
// Create nodes
const user = await sdk.createNode({
GraphGUID: graph.GUID,
Name: 'User Profile',
Labels: ['user'],
Tags: { type: 'customer' },
Vectors: [{
Model: 'ada-002',
Vectors: [0.1, 0.2, 0.3]
}]
});
const pref = await sdk.createNode({
GraphGUID: graph.GUID,
Name: 'Prefers Technical',
Labels: ['preference']
});
// Connect them
const edge = await sdk.createEdge({
GraphGUID: graph.GUID,
From: user.GUID,
To: pref.GUID,
Name: 'has_preference'
});</code></pre>
<pre class="code-block" data-lang="python"><code>from litegraph_sdk import configure, Graph, Node, Edge
# Initialize SDK
configure(endpoint="http://localhost:8701", tenant_guid="id", access_key="key")
# Create a graph
graph = Graph.create(name="AI Knowledge")
# Create nodes
user = Node.create(
graph_guid=graph.guid,
name="User Profile",
labels=["user"],
tags={"type": "customer"},
vectors=[{
"Model": "ada-002",
"Vectors": [0.1, 0.2, 0.3]
}]
)
pref = Node.create(
graph_guid=graph.guid,
name="Prefers Technical",
labels=["preference"]
)
# Connect them
edge = Edge.create(
graph_guid=graph.guid,
from_node=user.guid,
to_node=pref.guid,
name="has_preference"
)</code></pre>
<pre class="code-block" data-lang="curl"><code># Create a graph
curl -X PUT http://localhost:8701/v1.0/tenants/${TENANT}/graphs \
-H "Authorization: Bearer ${TOKEN}" \
-H "Content-Type: application/json" \
-d '{"Name": "AI Knowledge"}'
# Create user node with vector
curl -X PUT http://localhost:8701/v1.0/tenants/${TENANT}/graphs/${GRAPH}/nodes \
-H "Authorization: Bearer ${TOKEN}" \
-H "Content-Type: application/json" \
-d '{
"Name": "User Profile",
"Labels": ["user"],
"Tags": {"type": "customer"},
"Vectors": [{
"Model": "ada-002",
"Vectors": [0.1, 0.2, 0.3]
}]
}'
# Create preference node
curl -X PUT http://localhost:8701/v1.0/tenants/${TENANT}/graphs/${GRAPH}/nodes \
-H "Authorization: Bearer ${TOKEN}" \
-H "Content-Type: application/json" \
-d '{"Name": "Prefers Technical", "Labels": ["preference"]}'
# Create edge
curl -X PUT http://localhost:8701/v1.0/tenants/${TENANT}/graphs/${GRAPH}/edges \
-H "Authorization: Bearer ${TOKEN}" \
-H "Content-Type: application/json" \
-d '{"From": "${USER_NODE}", "To": "${PREF_NODE}", "Name": "has_preference"}'</code></pre>
</div>
</div>
</div>
</section>
<!-- SDKs Section -->
<section id="sdks" class="sdks">
<div class="container">
<h2 class="section-title">Available SDKs</h2>
<div class="sdk-grid">
<div class="sdk-card">
<div class="sdk-header">
<h3>C# / .NET</h3>
<span class="sdk-badge">.NET 8.0+</span>
</div>
<p>Full-featured SDK for .NET applications with embedded and server modes.</p>
<div class="sdk-stats">
<img src="https://img.shields.io/nuget/v/LiteGraph.svg?style=flat" alt="NuGet Version">
<img src="https://img.shields.io/nuget/dt/LiteGraph.svg" alt="NuGet Downloads">
</div>
<a href="https://www.nuget.org/packages/LiteGraph/" target="_blank" class="sdk-link">Install via NuGet →</a>
</div>
<div class="sdk-card">
<div class="sdk-header">
<h3>JavaScript</h3>
<span class="sdk-badge">Node.js 18.20.4+</span>
</div>
<p>Modern JavaScript SDK with TypeScript support and comprehensive API coverage.</p>
<div class="sdk-code">
<code>npm i litegraphdb</code>
</div>
<a href="https://www.npmjs.com/package/litegraphdb" target="_blank" class="sdk-link">View on npm →</a>
</div>
<div class="sdk-card">
<div class="sdk-header">
<h3>Python</h3>
<span class="sdk-badge">Python 3.8+</span>
</div>
<p>Pythonic SDK with async support and comprehensive error handling.</p>
<div class="sdk-code">
<code>pip install litegraph_sdk</code>
</div>
<a href="https://pypi.org/project/litegraph-sdk/" target="_blank" class="sdk-link">View on PyPI →</a>
</div>
<div class="sdk-card">
<div class="sdk-header">
<h3>REST API</h3>
<span class="sdk-badge">HTTP/JSON</span>
</div>
<p>RESTful API with comprehensive endpoints for all graph operations.</p>
<div class="sdk-links">
<a href="https://litegraph.readme.io" target="_blank">API Documentation →</a>
<a href="https://github.com/litegraphdb/litegraph/blob/main/LiteGraph.postman_collection.json" target="_blank">Postman Collection →</a>
</div>
</div>
</div>
</div>
</section>
<!-- Docker Deployment Section -->
<section class="docker-deployment">
<div class="container">
<h2 class="section-title">Rapid Deployment with Docker</h2>
<p class="section-subtitle">
Get LiteGraph up and running in seconds with our official Docker images—including the MCP server for AI integration.
</p>
<div class="docker-content">
<div class="docker-example">
<h3>LiteGraph Server</h3>
<pre class="docker-code"><code># Pull and run LiteGraph
docker run -d --name litegraph -p 8701:8701 -v $(pwd)/data:/data litegraphdb/litegraph:latest
# Or use Docker Compose
curl -O https://raw.githubusercontent.com/litegraphdb/litegraph/main/docker-litegraph/compose.yaml
docker compose up -d</code></pre>
</div>
<div class="docker-example">
<h3>MCP Server for AI Integration</h3>
<pre class="docker-code"><code># Run the MCP server for AI/LLM integration
docker run -d --name litegraph-mcp -p 8000:8000 \
-e LITEGRAPH_ENDPOINT=http://localhost:8701 \
-e LITEGRAPH_TENANT_GUID=your-tenant-guid \
-e LITEGRAPH_ACCESS_KEY=your-access-key \
jchristn/litegraph-mcp:latest
# Or use Docker Compose
curl -O https://raw.githubusercontent.com/litegraphdb/litegraph/main/docker-mcp/compose.yaml
docker compose up -d</code></pre>
<p class="docker-note">The MCP server exposes LiteGraph operations as standardized tools for AI assistants, enabling direct graph queries, traversals, and updates from your RAG pipelines and agentic workflows.</p>
</div>
<div class="docker-features">
<h3>Docker Features</h3>
<ul>
<li>Pre-configured with sensible defaults</li>
<li>Persistent storage with volume mounting</li>
<li>Environment variable configuration</li>
<li>Health checks included</li>
<li>MCP server for seamless AI integration</li>
</ul>
<div class="docker-links">
<a href="https://hub.docker.com/r/jchristn/litegraph" target="_blank" class="btn btn-primary">LiteGraph on Docker Hub</a>
<a href="https://hub.docker.com/r/jchristn/litegraph-mcp" target="_blank" class="btn btn-primary">MCP Server on Docker Hub</a>
<a href="https://github.com/litegraphdb/litegraph/tree/main/Docker" target="_blank" class="btn btn-secondary">Docker Scripts</a>
</div>
</div>
</div>
</div>
</section>
<!-- Web UI Section -->
<section id="web-ui" class="web-ui">
<div class="container">
<h2 class="section-title">Web Dashboard & Management UI</h2>
<p class="section-subtitle">
Explore, visualize, and manage your LiteGraph databases with our comprehensive web interface.
</p>
<div class="web-ui-content">
<div class="web-ui-features">
<h3>Dashboard Features</h3>
<ul>
<li><strong>Graph Management</strong> - Create and manage graph definitions</li>
<li><strong>Node & Edge Operations</strong> - Add, edit, and organize graph nodes and edges</li>
<li><strong>Labels & Tags</strong> - Organize graph elements with labels and tags</li>
<li><strong>Vector Management</strong> - Manage vector embeddings associated with graph elements</li>
<li><strong>Graph Visualization</strong> - 2D and 3D interactive graph rendering</li>
<li><strong>Multi-tenant Support</strong> - Tenant isolation and user management</li>
<li><strong>Authentication</strong> - User login and admin controls</li>
<li><strong>Data Backup</strong> - Interface for backup management operations</li>
</ul>
</div>
<div class="web-ui-deployment">
<h3>Quick Deploy</h3>
<pre class="docker-code"><code># Run the web UI with Docker
docker run -d --name litegraph-ui -p 8080:8080 \
-e LITEGRAPH_SERVER=http://localhost:8701 \
jchristn/litegraph-ui:v1.0.0
# Or download and run from source
git clone https://github.com/litegraphdb/ui
cd ui
npm install && npm start</code></pre>
<div class="web-ui-links">
<a href="https://github.com/litegraphdb/ui" target="_blank" class="btn btn-primary">View Source Code</a>
<a href="https://hub.docker.com/repository/docker/jchristn/litegraph-ui/general" target="_blank" class="btn btn-secondary">Docker Hub</a>
</div>
</div>
</div>
<div class="web-ui-note">
<p><strong>Note:</strong> The web UI connects to your LiteGraph server instance and provides a database administration interface for managing graphs, nodes, edges, and metadata. Built with React/Next.js and designed for development and database management tasks.</p>
</div>
</div>
</section>
<!-- Footer -->
<footer class="footer">
<div class="container">
<div class="footer-content">
<div class="footer-section">
<img src="https://raw.githubusercontent.com/litegraphdb/litegraph/main/assets/favicon.png" alt="LiteGraph" class="footer-logo">
<p>The AI-native multi-dimensional database.</p>
<p>Graph, vector, relational, and metadata in a single database.</p>
<p class="footer-license">Free & Open Source • MIT License</p>
</div>
<div class="footer-section">
<h4>Resources</h4>
<ul>
<li><a href="https://github.com/litegraphdb" target="_blank">GitHub Repositories</a></li>
<li><a href="https://github.com/litegraphdb/ui" target="_blank">Web Dashboard</a></li>
<li><a href="https://litegraph.readme.io" target="_blank">API Documentation</a></li>
<li><a href="https://github.com/litegraphdb/litegraph/blob/main/REST_API.md" target="_blank">REST API Docs</a></li>
<li><a href="https://github.com/litegraphdb/litegraph/issues" target="_blank">Report Issues</a></li>
</ul>
</div>
<div class="footer-section">
<h4>Packages</h4>
<ul>
<li><a href="https://www.nuget.org/packages/LiteGraph/" target="_blank">NuGet (C#)</a></li>
<li><a href="https://www.npmjs.com/package/litegraphdb" target="_blank">npm (JavaScript)</a></li>
<li><a href="https://pypi.org/project/litegraph-sdk/" target="_blank">PyPI (Python)</a></li>
<li><a href="https://hub.docker.com/r/jchristn/litegraph" target="_blank">Docker (Server)</a></li>
<li><a href="https://hub.docker.com/r/jchristn/litegraph-mcp" target="_blank">Docker (MCP Server)</a></li>
<li><a href="https://hub.docker.com/r/jchristn/litegraph-ui" target="_blank">Docker (UI)</a></li>
</ul>
</div>
</div>
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<p>© 2025 LiteGraph and Community</p>
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