LangChain Open Canvas: A Self-Hosted Alternative to OpenAI Canvas

Introduction

In today’s rapidly evolving AI landscape, efficiently leveraging Large Language Models (LLMs) for writing and programming has become a crucial challenge. LangChain Open Canvas, as an open-source AI collaboration platform, offers excellent user experience and rich features, making AI-assisted creation simpler and more efficient.

This article will detail the core features of LangChain Open Canvas and guide you through private deployment.

💡 If you’re looking to build your own AI-assisted writing and programming platform, LangChain Open Canvas is an ideal choice.

Why Choose LangChain Open Canvas?

LangChain Open Canvas offers these outstanding advantages:

• 🔓 Fully open-source, MIT licensed
• 💭 Built-in memory system
• 📝 Support for starting from existing documents
• 🔄 Version control support
• 🛠️ Custom quick actions
• 📊 Real-time Markdown rendering
• 💻 Code and document editing support

Core Features

System Requirements

Requirements for deploying Open Canvas:

Online Demo

LangChain Open Canvas

Basic Installation

1. Clone Repository

git clone https://github.com/langchain-ai/open-canvas.git
cd open-canvas

2. Install Dependencies

yarn install

3. Environment Setup

Create .env file:

cp .env.example .env

Configure essential environment variables:

# LLM API Keys
OPENAI_API_KEY=sk-xxx
ANTHROPIC_API_KEY=sk-xxx

# Supabase Configuration
NEXT_PUBLIC_SUPABASE_URL=your-project-url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key

# LangGraph Configuration
LANGSMITH_API_KEY=ls-xxx

Authentication Setup

1. Supabase Configuration

1. Create a Supabase project
2. Configure authentication providers:
   • Enable Email authentication
   • Optionally configure GitHub/Google login
3. Copy project URL and API keys

2. Verify Configuration

# Start development server
yarn dev

# Visit http://localhost:3000 to test login

LangGraph Service Setup

1. Install LangGraph CLI

Follow the installation guide in the LangGraph documentation.

2. Start Service

LANGSMITH_API_KEY="your-key" langgraph up --watch --port 54367

3. Verify Status

Visit http://localhost:54367/docs to check API documentation.

LLM Model Configuration

Open Canvas supports various LLM models:

1. Online Models

# Supported Models
- Anthropic Claude 3 Haiku
- Fireworks Llama 3 70B
- OpenAI GPT-4

2. Local Ollama Models

Enable local model support:

# .env configuration
NEXT_PUBLIC_OLLAMA_ENABLED=true
OLLAMA_API_URL=http://host.docker.internal:11434

Practical Features Configuration

1. Custom Quick Actions

// Add in src/config/quickActions.ts
export const customQuickActions = [
  {
    name: "Optimize Code",
    prompt: "Please optimize this code for performance and readability:",
    type: "code"
  }
];

2. Memory System Configuration

memory:
  enabled: true
  maxTokens: 2000
  relevanceThreshold: 0.8

Performance Optimization Tips

1. Use production-grade LLM APIs
2. Configure appropriate caching strategies
3. Optimize frontend resource loading
4. Use CDN for static assets

Summary

Open Canvas provides a feature-complete AI-assisted creation platform. Through this deployment guide, you can quickly set up your own private environment. Its open-source nature and rich features make it an ideal choice for team collaboration.

References

• Open Canvas GitHub Repository
• LangChain Documentation
• Supabase Documentation