svelte-langgraph

Opinionated SvelteKit-based LLM frontend for LangGraph server.

Demo

https://svelte-langgraph-demo.synergyai.nl/

Architecture

• Backend: Python 3.12 + LangGraph server for AI workflow management
• Frontend: SvelteKit + TypeScript with Tailwind CSS and shadcn/bits-ui components
• Authentication: Generic OIDC (OpenID Connect) integration
• Internationalization: Paraglide-JS for multi-language support

Prerequisites

• Install moonrepo, which installs all other dependencies:
  • Python 3.12
  • Node.js 24 LTS
  • uv (Python package manager)
  • pnpm (Node.js package manager)

Configuration

The monorepo uses a single .env file at the root to configure both frontend and backend. Copy the example file and update it with your values:

cp .env.example .env

Environment Variables

The .env file is organized into sections:

Common Variables:

• AUTH_OIDC_ISSUER - Your OIDC provider's issuer URL (e.g., http://localhost:8080 for local mock)

Backend Variables:

• OPENAI_API_KEY - Your OpenAI-compatible API key (e.g., OpenAI, OpenRouter)
• OPENAI_BASE_URL - OpenAI-compatible API base URL (optional, defaults to OpenAI)
• CHAT_MODEL_NAME - OpenAI-compatible model to use (defaults to gpt-4o-mini)
• LANGSMITH_API_KEY - Your LangSmith API key for tracing (optional)
• LANGSMITH_ENDPOINT - LangSmith endpoint URL (optional, defaults to EU region)

Frontend Variables:

• AUTH_TRUST_HOST - Enable auth trust host (set to true for development)
• AUTH_OIDC_CLIENT_ID - Your OIDC client ID (e.g., svelte-langgraph)
• AUTH_OIDC_CLIENT_SECRET - Your OIDC client secret
• AUTH_SECRET - Random string for session encryption (generate with npx auth secret)
• PUBLIC_LANGGRAPH_API_URL - URL of your LangGraph server (typically http://localhost:2024)
• PUBLIC_SENTRY_DSN - Public DSN for Sentry error tracking (optional)

AI Provider Configuration

This application supports multiple OpenAI-compatible providers. Configure your preferred provider using the environment variables above.

Using OpenAI (Default)

To use OpenAI directly (no additional configuration needed):

# .env
OPENAI_API_KEY=your_openai_api_key
# OPENAI_BASE_URL not needed for OpenAI (uses default)
CHAT_MODEL_NAME=gpt-4o-mini  # Default OpenAI model

Popular OpenAI models:

• gpt-4o-mini - Fast, cost-effective model (default)
• gpt-4o - Most capable model
• gpt-3.5-turbo - Legacy model

Using OpenRouter

OpenRouter provides access to multiple AI models including free options:

# .env
OPENAI_API_KEY=your_openrouter_api_key
OPENAI_BASE_URL=https://openrouter.ai/api/v1
CHAT_MODEL_NAME=x-ai/grok-4-fast:free  # Free Grok model

Popular OpenRouter models:

• x-ai/grok-4-fast:free - Free Grok model
• meta-llama/llama-3.2-3b-instruct:free - Free Llama model
• anthropic/claude-3.5-sonnet - Claude 3.5 Sonnet (paid)

Using Ollama

For local AI models using Ollama:

# .env
OPENAI_API_KEY=ollama  # Can be any value for local usage
OPENAI_BASE_URL=http://localhost:11434/v1
CHAT_MODEL_NAME=llama3.2  # Your local Ollama model

Getting Started

Local Development with OIDC Mock Provider

For local development and testing, the project includes a mock OIDC provider using oidc-provider-mock. This lightweight Python-based mock server simulates a real OIDC provider, allowing you to develop and test authentication flows without needing to set up a full OAuth2/OIDC provider.

What it does:

• Provides a complete OIDC discovery endpoint (.well-known/openid-configuration)
• Issues JWT tokens with configurable user claims
• Supports the authorization code flow with PKCE
• No client registration required - accepts any client ID/secret
• Automatically started with moon :dev for seamless development

Configuration:

• Issuer: http://localhost:8080
• Client ID: Any value (e.g., svelte-langgraph)
• Client Secret: Any value (e.g., secret)
• Test User: test-user (subject claim in JWT)

Start dev servers

The following command ensures dependencies are installed and starts dev servers for frontend, backend, and OIDC mock provider, with hot reload:

moon :dev :oidc-mock

This automatically starts:

• Frontend dev server at http://localhost:5173
• Backend LangGraph server at http://localhost:2024
• OIDC mock provider at http://localhost:8080 (for local authentication)

Make sure to configure your .env file to point to the OIDC mock provider (see Configuration section above).

Run local checks

Run all checks (linting, type checking, formatting, building, unit and E2E tests):

moon check --all

This currently requires Docker to be running for the LangGraph server build.

Tooling

Backend Development

The backend uses LangGraph for AI workflow orchestration with the following key dependencies:

• LangChain with OpenAI-compatible integration (OpenRouter, OpenAI, etc.)
• Authlib for OIDC/JWT authentication
• Python-dotenv for environment management

Frontend Development

The frontend is built with modern web technologies:

• SvelteKit for the application framework
• Tailwind CSS for styling
• shadcn/bits-ui for UI components
• Playwright for end-to-end testing
• Vitest for unit testing

End to end testing

End to end tests are written using Playwright with fixtures and page object models. They live in their own project in e2e/.

They can be run with:

moon e2e:test

Or, interactively, with:

moon e2e:test-ui

Production

To run a Docker build of the project, use Docker Compose:

docker compose build

To run it:

docker compose up [--build]

For now, we will not be running the backend in Docker, so to test with the dev backend, it's required to make it available to the Docker container and inform the Docker container of your IP:

moon backend:dev -- --host 0.0.0.0

And in a different terminal:

PUBLIC_LANGGRAPH_API_URL=http://host.docker.internal:2024 docker compose up --build

Internationalization with Paraglide

This project uses Paraglide-JS for type-safe internationalization. Paraglide offers a developer-friendly approach where you can:

• Add or modify translations without touching code - just edit JSON files
• Add new languages by creating a new JSON file and updating one config line
• Change locale names (e.g., "English" → "Inglés") without any code changes

Message Files

All translations are stored in apps/frontend/messages/:

Each file contains key-value pairs for all UI text:

{
  "$schema": "https://inlang.com/schema/inlang-message-format",
  "hello_world": "Hello, {name}!",
  "local_name": "English"
}

The local_name key is special - it defines how each language refers to itself in the language switcher.

Adding a New Language

1. Create a new JSON file in apps/frontend/messages/ (e.g., fr.json for French)
2. Copy the structure from en.json and translate all values
3. Add the locale code to apps/frontend/project.inlang/settings.json:

   {
     "locales": ["en", "nl", "hi", "fr"]
   }
   

That's it! The language will automatically appear in the language switcher with the name specified in local_name.