collaborative-text-editor

name	collaborative-text-editor
description	Build a collaborative text editor backend with Yjs CRDTs and Rivet Actors: per-document actors relay sync and awareness updates and persist snapshots.

Collaborative Text Editor

IMPORTANT: Before doing anything, you MUST read BASE_SKILL.md in this skill's directory. It contains essential guidance on debugging, error handling, state management, deployment, and project setup. Those rules and patterns apply to all RivetKit work. Everything below assumes you have already read and understood it.

Working Examples

If you need a reference implementation, read the raw working example code in these templates:

• collaborative-document

Patterns for building a Yjs server on RivetKit: CRDT document sync, presence and cursors, and snapshot persistence, with one Rivet Actor per document acting as a relay.

Starter Code

Start with the working example on GitHub and adapt it to your editor. It ships a React frontend with a plain textarea, remote cursor overlays, and a workspace document index.

Use Case	Starter Code	Common Examples
Shared document editing	GitHub	Notion-style docs, shared notes, pair-writing tools, form co-editing

CRDT vs OT

Two families of algorithms solve concurrent text editing. The choice decides what your server has to do.

Dimension	CRDT (Yjs)	Operational Transformation
Conflict resolution model	Commutative merges. Updates apply in any order on any peer and converge to the same result.	Server transforms each operation against every concurrent operation. Correctness depends on a central sequencer.
Offline support	Strong. Clients keep editing locally and merge buffered updates on reconnect.	Weak. Long-lived divergence makes transformation chains complex and fragile.
Server role	Relay plus persistence. The server applies opaque updates and rebroadcasts them. It never needs to understand document semantics.	Authoritative transformer. The server must implement transformation logic for every operation type.
Library maturity	Yjs is mature and widely deployed, with bindings for ProseMirror, CodeMirror, Monaco, and others.	Production-grade implementations are mostly proprietary (Google Docs) or aging (ShareDB).

The example uses Yjs because CRDTs let the server stay a relay-style Rivet Actor. The actor applies each incoming update to a server-side Y.Doc so it can persist the merged state and serve late joiners, but it never transforms operations or arbitrates conflicts. Ordering does not matter because Yjs merges are commutative.

Document Actor Model

Topic	Summary
Topology	One document[workspaceId, documentId] actor per document plus one documentList[workspaceId] coordinator per workspace.
Sync model	Each client holds a local Y.Doc. The document actor relays incremental Yjs updates as broadcast events and keeps a server-side merged copy in vars.
Persistence	Full merged Yjs snapshot overwritten in one binary actor KV key (yjs:doc) on every sync update. Document metadata lives in JSON state.
Queues	None. The example is purely actions plus broadcast events.
Presence	Yjs Awareness relayed through the same applyUpdate action. Per-connection connState tracks asserted awareness clientIds for disconnect cleanup.

The two-actor split follows the coordinator pattern from Design Patterns: the coordinator owns discovery and creation, and each document actor owns one document's realtime state. Multi-part keys scope both actors to a workspace.

Actors

• Key: document[workspaceId, documentId]

• Responsibility: Applies incoming sync and awareness updates to a server-side Y.Doc and Awareness, persists the merged Yjs snapshot to actor KV, and broadcasts updates to all connected collaborators.

• Actions

  • getContent
  • applyUpdate
  • getAwareness

• Queues

  • None

• State

  • JSON metadata only: title, createdAt, updatedAt
  • Binary KV key yjs:doc holding the full merged Yjs snapshot
  • Ephemeral vars: the live Y.Doc and Awareness, created in createVars and rehydrated from KV on actor start
  • Per-connection connState: clientIds of awareness clients asserted by that connection

• Key: documentList[workspaceId]

• Responsibility: Coordinator for one workspace. Creates document actors through the actor-to-actor client and maintains the index of document summaries.

• Actions

  • createDocument
  • listDocuments
  • deleteDocument

• Queues

  • None

• State

  • JSON
  • documents array of DocumentSummary entries (id, title, createdAt, updatedAt)

The coordinator's createDocument generates a UUID, then explicitly creates the document actor with c.client<typeof registry>() and passes { title, createdAt } as creation input, which the document actor's createState consumes. See Communicating Between Actors for the actor-to-actor client.

Update Relay

A single applyUpdate(update, kind, clientId?) action handles both update kinds. Updates cross the action boundary as number[] byte arrays and are converted back to Uint8Array on each side.

Kind	Server Applies To	Persists	Broadcasts
"sync"	c.vars.doc via Y.applyUpdate with origin "client"	Full merged snapshot to KV key yjs:doc, then bumps updatedAt	sync event carrying the incremental update
"awareness"	c.vars.awareness via applyAwarenessUpdate with origin "client"	Nothing. Presence is ephemeral.	awareness event carrying the update

Note the asymmetry on the sync branch: the broadcast carries only the small incremental update, while the KV write stores the full merged document re-encoded with Y.encodeStateAsUpdate.

Yjs origin tags are the echo guards that keep the relay loop-free:

Origin Tag	Set Where	Effect
"local"	Client edits inside doc.transact(..., "local")	The client's update listener fires and sends applyUpdate to the actor.
"client"	Server applying an incoming update to its Y.Doc or Awareness	Marks the change as client-originated on the server copy.
"remote"	Client applying broadcast events or initial sync data	Update listeners early-return on "remote", so a client never re-sends its own echo.

On connect or reconnect, the client calls getContent and getAwareness, then applies both results to its local Y.Doc and Awareness with origin "remote". After that, every change flows through applyUpdate and the broadcast events.

Awareness And Presence

Presence (user names, colors, cursor positions) rides on the Yjs Awareness protocol instead of actor state:

• Clients set presence with awareness.setLocalStateField for the user and cursor fields. The awareness update listener encodes the change and sends applyUpdate(update, "awareness", awareness.clientID).
• The actor records each asserted clientId in that connection's connState.clientIds, applies the update to the server-side Awareness, and broadcasts the awareness event to all peers. See Connections for per-connection state.
• onDisconnect reads the connection's clientIds, calls removeAwarenessStates on the server-side Awareness, and broadcasts the encoded removal so every remaining client drops the departed user's cursor. See Lifecycle for the hook.

Because the actor tracks which awareness clientIds belong to which connection, presence cleanup is automatic on disconnect with no client cooperation required.

Persistence And Compaction

The example persists with a full-snapshot overwrite: on every "sync" update, the actor re-encodes the entire merged document with Y.encodeStateAsUpdate and overwrites the single binary KV key yjs:doc. There is no append-only update log and no separate compaction job. Compaction is implicit because Y.encodeStateAsUpdate emits one compact merged representation of the document, so Yjs merge semantics keep the stored blob compact on their own.

Property	Full-Snapshot Overwrite (the example)
Write cost	One full-document KV write per sync update, so every keystroke rewrites the whole blob.
Read cost	One binary KV read in createVars rehydrates the document on actor start.
Crash safety	The last completed applyUpdate is durable. No log replay needed.
Sweet spot	Small to medium documents where simplicity beats write amplification.

Recommended extension (not in the example): for large documents or very high edit rates, switch to appending incremental updates to a KV update log and writing a merged snapshot only periodically (for example every N updates). Boot becomes snapshot plus log replay, and the snapshot write becomes the explicit compaction step that truncates the log. Adopt this only when full-snapshot writes become the measured bottleneck or the blob approaches KV value size limits.

Lifecycle

sequenceDiagram
	participant A as Client A
	participant B as Client B
	participant DL as documentList
	participant D as document

	A->>DL: listDocuments()
	A->>DL: createDocument(title)
	DL->>D: create([workspaceId, documentId], input)
	DL-->>A: DocumentSummary
	A->>D: connect
	B->>D: connect
	Note over D: createVars rehydrates Y.Doc from KV "yjs:doc"
	A->>D: getContent() + getAwareness()
	D-->>A: encoded doc + awareness state
	Note over A: local edit with origin "local"
	A->>D: applyUpdate(update, "sync")
	Note over D: apply with origin "client", overwrite KV snapshot
	D-->>B: sync event (incremental update)
	Note over B: apply with origin "remote", no echo
	A->>D: applyUpdate(update, "awareness", clientId)
	D-->>B: awareness event
	B-->>D: disconnect
	Note over D: onDisconnect removes B's awareness clientIds
	D-->>A: awareness event (removal)

Security Checklist

The example ships with no authentication or authorization. Harden it with this baseline before production. None of these are implemented in the example.

• Authenticate before connect: Anyone who knows or guesses a workspace ID can connect, and because useActor implicitly getOrCreates, connecting with a nonexistent workspace ID silently creates a blank documentList coordinator. Add connection auth so unauthenticated clients never reach an actor. See Authentication.
• Per-document access control: Validate that the authenticated user is allowed to access the specific [workspaceId, documentId] key, not just any document.
• Cap and rate limit applyUpdate: Update payloads are unvalidated number[] arrays with no size limit, and the example client sends one action per keystroke and per cursor move with zero throttling. Enforce payload size caps and per-connection rate limits on the server, and debounce on the client.
• Do not trust client-asserted awareness clientIds: The clientId argument to applyUpdate is client-supplied and trusted as-is. Derive or verify presence identity from connection-scoped server state instead.
• Destroy actors and KV on delete: deleteDocument only filters the entry out of the coordinator's index. The document actor and its KV snapshot are orphaned. On delete, also destroy the document actor and its storage, with a permission check on who may delete.

Reference Map

Actors

• Access Control
• Actions
• Actor Keys
• Actor Runtime Socket
• Actor Scheduling
• Actor Statuses
• Authentication
• Cloudflare Workers Quickstart
• Communicating Between Actors
• Connections
• Custom Inspector Tabs
• Debugging
• Design Patterns
• Destroying Actors
• Effect.ts Quickstart (Beta)
• Errors
• Fetch and WebSocket Handler
• Helper Types
• Icons & Names
• In-Memory State
• Input Parameters
• Lifecycle
• Limits
• Low-Level HTTP Request Handler
• Low-Level KV Storage
• Low-Level WebSocket Handler
• Metadata
• Next.js Quickstart
• Node.js & Bun Quickstart
• Queues & Run Loops
• React Quickstart
• Realtime
• Rust Quickstart (Beta)
• Scaling & Concurrency
• Sharing and Joining State
• SQLite
• SQLite + Drizzle
• Supabase Functions Quickstart
• Testing
• Troubleshooting
• Types
• Vanilla HTTP API
• Versions & Upgrades
• Workflows

Cli

• CLI

Clients

• Node.js & Bun
• React
• Rust (Beta)
• Swift
• SwiftUI

Cookbook

• AI Agent
• Chat Room
• Collaborative Text Editor
• Cron Jobs and Scheduled Tasks
• Database per Tenant
• Deploying Rivet in a VPC or Air-Gapped Network
• Live Cursors and Presence
• Multiplayer Game

Deploy

• Container Runner
• Deploy To Amazon Web Services Lambda
• Deploying to AWS ECS
• Deploying to Cloudflare Workers
• Deploying to Freestyle
• Deploying to Google Cloud Run
• Deploying to Hetzner
• Deploying to Kubernetes
• Deploying to Railway
• Deploying to Rivet Compute
• Deploying to Supabase Functions
• Deploying to Vercel
• Deploying to VMs & Bare Metal

General

• Actor Configuration
• Architecture
• Cross-Origin Resource Sharing
• Documentation for LLMs & AI
• Edge Networking
• Endpoints
• Environment Variables
• HTTP Server
• Logging
• Pool Configuration
• Production Checklist
• Registry Configuration
• Runtime Modes
• WASM vs Native SDK

Self Hosting

• Configuration
• Docker Compose
• Docker Container
• File System
• FoundationDB (Enterprise)
• Installing Rivet Engine
• Kubernetes
• Multi-Region
• PostgreSQL
• Production Checklist
• Railway Deployment
• Render Deployment
• TLS & Certificates