What is LiteGraph?

LiteGraph is a property graph database with support for graph relationships, tags, labels, metadata, data, and vectors. LiteGraph is intended to be a unified database for providing persistence and retrieval for knowledge and artificial intelligence applications.

LiteGraph provides a lightweight yet powerful solution that combines the best of multiple database paradigms - graph databases for relationship modeling, vector databases for AI/ML embeddings, and document stores for flexible metadata storage.

Key Features

• Multi-modal Database: Supports graph structures, relational data, vectors, and metadata in a single system
• Property Graph Model: Full support for nodes, edges, labels, tags, and custom data properties
• Vector Support: Native vector storage and similarity search capabilities for AI applications
• Flexible Deployment: Can be run in-process (using LiteGraphClient) or as a standalone RESTful server (using LiteGraph.Server)
• In-Memory Operation: Optional in-memory mode with controlled flushing to disk
• Multi-tenancy: Built-in support for multiple tenants and graph isolation
• Export Capabilities: Export graphs to GEXF format for visualization
• No Dependencies: Leverages SQLite for a zero-configuration embedded database

Use Cases

Knowledge Management

• Build knowledge graphs for organizing complex information
• Create semantic networks for content relationships
• Implement recommendation systems based on graph traversal

Artificial Intelligence Applications

• Store and query vector embeddings from LLMs
• Build RAG (Retrieval-Augmented Generation) systems
• Implement similarity search for semantic content matching
• Create hybrid search combining graph relationships and vector similarity

Application Development

• Embed a graph database directly into applications without external dependencies
• Build networks with detailed, rich relationships
• Model organizational hierarchies and permissions
• Track dependencies and relationships in complex systems

Research and Analysis

• Network analysis and graph algorithms
• Pattern detection in connected data
• Path finding and route optimization
• Community detection and clustering

Comparing LiteGraph

Vs Traditional Relational Databases

• Native Graph Support: First-class support for nodes, edges, and graph traversal
• Flexible Schema: No rigid table structures; properties can be added dynamically
• Relationship-Centric: Optimized for querying connected data rather than tabular data
• Multi-modal: Combines relational, graph, and vector capabilities

Vs Vector Databases

• Graph Context: Vectors are enriched with graph relationships and metadata
• Unified Storage: No need for separate graph and vector databases
• Flexible Filtering: Use labels, tags, and expressions (data filters) when performing a vector search
• Lightweight: Built on SQLite rather than requiring specialized infrastructure

Vs Metadata/Feature Stores

• Relationship Modeling: Goes beyond key-value storage to model complex relationships
• Type Flexibility: The Data property is an object and can be attached to any Graph, Node, or Edge. Data supports any object serializable to JSON
• Built-in Search: Native support for graph traversal and vector similarity search
• Version History: Timestamps for creation and updates on all entities

Vs Traditional Graph Databases

• Embedded Operation: No separate server required; runs in-process with your application
• SQLite Foundation: Leverages proven SQLite reliability and performance
• Vector Integration: Native vector support without extensions or plugins
• Simplified Deployment: Single file database with no complex configuration

Getting Started

Install via NuGet:

dotnet add package LiteGraph

Basic Usage

using LiteGraph;

LiteGraphClient graph = new LiteGraphClient(new SqliteRepository("litegraph.db"));
graph.InitializeRepository();

// Create a tenant
TenantMetadata tenant = graph.CreateTenant(new TenantMetadata { Name = "My tenant" });

// Create a graph
Graph graph = graph.CreateGraph(new Graph { TenantGUID = tenant.GUID, Name = "This is my graph!" });

// Create nodes
Node node1 = graph.CreateNode(new Node { TenantGUID = tenant.GUID, GraphGUID = graph.GUID, Name = "node1" });
Node node2 = graph.CreateNode(new Node { TenantGUID = tenant.GUID, GraphGUID = graph.GUID, Name = "node2" });
Node node3 = graph.CreateNode(new Node { TenantGUID = tenant.GUID, GraphGUID = graph.GUID, Name = "node3" });

// Create edges
Edge edge1 = graph.CreateEdge(new Edge { TenantGUID = tenant.GUID, GraphGUID = graph.GUID, From = node1.GUID, To = node2.GUID, Name = "Node 1 to node 2" });
Edge edge2 = graph.CreateEdge(new Edge { TenantGUID = tenant.GUID, GraphGUID = graph.GUID, From = node2.GUID, To = node3.GUID, Name = "Node 2 to node 3" });

// Find routes
foreach (RouteDetail route in graph.GetRoutes(
	SearchTypeEnum.DepthFirstSearch,
  tenant.GUID,
	graph.GUID,
	node1.GUID,
	node2.GUID))
{
  Console.WriteLine(...);
}

Getting Started with Docker

Using Docker Hub Image A Docker image is available in Docker Hub under jchristn/litegraph.

Quick Start with Docker Compose

Navigate to the Docker directory in the repository Use the Docker Compose start (compose-up.sh and compose-up.bat) and stop (compose-down.sh and compose-down.bat) scripts

Manual Docker Setup

docker pull jchristn/litegraph

Run with volume mounts for database and configuration

docker run -d \
-p 8701:8701 \
-v /path/to/litegraph.db:/app/litegraph.db \
-v /path/to/litegraph.json:/app/litegraph.json \
jchristn/litegraph

Ensure that you have a valid database file (e.g. litegraph.db) and configuration file (e.g. litegraph.json) exposed into your container.

REST API Server

By default, LiteGraph.Server (and by consequence, the Docker image) listens on http://localhost:8701 and is only accessible to localhost. Modify the litegraph.json file to change settings including hostname and port.