Loro

Reimagine state management with CRDTs 🦜
Make your app state synchronized and collaborative effortlessly.

Documentation | Getting Started | Rust Doc

https://github.com/loro-dev/loro/assets/18425020/fe246c47-a120-44b3-91d4-1e7232a5b4ac

⚠️ Notice: The current API and encoding schema of Loro are experimental and subject to change. You should not use it in production.

Loro is a CRDTs(Conflict-free Replicated Data Types) library that makes building local-first apps easier.

Loro is currently available for JavaScript (via WASM) and Rust developers.

Explore our vision in our blog: Reimagine State Management with CRDTs.

Features

Supported CRDT Algorithms

• Common Data Structures: Includes List for ordered collections, LWW (Last Write Win) Map for key-value pairs, Tree for hierarchical data, and Text for rich text manipulation, enabling various applications.
• Text Editing with Fugue: Loro integrates Fugue, a CRDT algorithm designed to minimize interleaving anomalies in concurrent text editing.
• Peritext-like Rich Text CRDT: Drawing inspiration from Peritext, Loro manages rich text CRDTs that excel at merging concurrent rich text style edits, maintaining the original intent of users input as much as possible. Learn more in our blog Introduction to Loro's Rich Text CRDT.
• Moveable Tree: For applications requiring directory-like data manipulation, Loro utilizes the algorithm from A Highly-Available Move Operation for Replicated Trees, which simplifies the process of moving hierarchical data structures.
• Moveable List: Both List and MovableList utilize Fugue to achieve maximal noninterleaving. Additionally, MovableList uses the algorithm from Moving Elements in List CRDTs to implement the move operation.

Advanced Features in Loro

• Preserve Editing History
  • With Loro, you can track changes effortlessly as it records the editing history with low overhead.
  • This feature is useful for audit trails, undo/redo functionality, and version control.
• Time Travel Through History
  • It allows users to compare and merge manually when needed, although CRDTs typically resolve conflicts well.
• High Performance
  • See benchmarks.

Build time travel feature easily for large documents.

https://github.com/loro-dev/loro/assets/18425020/ec2d20a3-3d8c-4483-a601-b200243c9792

Features Provided by CRDTs

• Decentralized Synchronization: Loro allows your app's state synced via p2p connections.
• Automatic Merging: CRDTs guarantee strong eventual consistency by automating the merging of concurrent changes.
• Local Availability: Data can be persisted on users' devices, supporting offline functionality and real-time responsiveness.
• Scalability: Effortlessly scale your application horizontally thanks to the inherently distributed nature of CRDTs.
• Delta Updates

Example

import { expect, test } from 'vitest';
import { Loro, LoroList } from 'loro-crdt';

/**
 * Demonstrates synchronization of two documents with two rounds of exchanges.
 */
// Initialize document A
const docA = new Loro();
const listA: LoroList = docA.getList('list');
listA.insert(0, 'A');
listA.insert(1, 'B');
listA.insert(2, 'C');

// Export the state of document A as a byte array
const bytes: Uint8Array = docA.exportFrom();

// Simulate sending `bytes` across the network to another peer, B
const docB = new Loro();
// Peer B imports the updates from A
docB.import(bytes);

// Verify that B's state matches A's state
expect(docB.toJSON()).toStrictEqual({
  list: ['A', 'B', 'C'],
});

// Get the current operation log version of document B
const version = docB.oplogVersion();

// Simulate editing at B: delete item 'B'
const listB: LoroList = docB.getList('list');
listB.delete(1, 1);

// Export the updates from B since the last synchronization point
const bytesB: Uint8Array = docB.exportFrom(version);

// Simulate sending `bytesB` back across the network to A
// A imports the updates from B
docA.import(bytesB);

// Verify that the list at A now matches the list at B after merging
expect(docA.toJSON()).toStrictEqual({
  list: ['A', 'C'],
});

Credits

Loro draws inspiration from the innovative work of the following projects and individuals:

• Ink & Switch: The principles of Local-first Software have greatly influenced this project. The Peritext project has also shaped our approach to rich text CRDTs.
• Diamond-types: The Replayable Event Graph (REG) algorithm from @josephg has been adapted to reduce the computation and space usage of CRDTs.
• Automerge: Their use of columnar encoding for CRDTs has informed our strategies for efficient data encoding.
• Yjs: We have incorporated a similar algorithm for effectively merging collaborative editing operations, thanks to their pioneering works.
• Matthew Weidner: His work on the Fugue algorithm has been invaluable, enhancing our text editing capabilities.
• Martin Kleppmann: His work on CRDTs has significantly influenced our comprehension of the field.