Next Steps

Next Steps

Choose a learning path based on your goals and background.

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Learning Paths

I want to build AI-powered applications

Path: AI Integration

1. byLLM Quickstart - Basic LLM integration
2. Structured Outputs - Type-safe AI responses
3. Agentic AI - Tool calling and ReAct patterns

Key concept: The by llm() syntax lets you delegate function bodies to AI models. The compiler generates prompts from function names, parameter names, and type signatures. Use sem for additional semantic context.

def summarize(text: str) -> str by llm();
sem summarize = "Summarize the article in 2-3 sentences.";

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I want to build full-stack web apps

Path: Full-Stack Development

1. Project Setup - Create a Jac web project
2. React-Style Components - Build UI with JSX
3. State & Effects - Reactive state management
4. Backend Integration - Connect frontend to walkers
5. Authentication - Add user login
6. Routing - Multi-page apps

Key concept: Write frontend and backend in one file. The cl { } block marks client-side code.

# Backend
walker get_data {
    can fetch with Root entry {
        report {"message": "Hello from backend"};
    }
}

# Frontend
cl {
    def:pub app() -> JsxElement {
        data = root spawn get_data();
        return <div>{data}</div>;
    }
}

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I want to learn the Jac language deeply

Path: Core Language

1. Jac Basics - Syntax and fundamentals
2. Object-Spatial Programming - Nodes, edges, walkers
3. Testing - Write and run tests

Key concept: Jac supersets Python and JavaScript, adding graphs as first-class citizens and walkers for graph traversal.

node Person { has name: str; }
edge Knows { has since: int; }

walker find_friends {
    can search with Person entry {
        friends = [here ->:Knows:->];
        report friends;
    }
}

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I want to deploy to production

Path: Production Deployment

1. Local API Server - Run as HTTP server
2. Deploy to Kubernetes - Scale with jac-scale

Key concept: One command transforms your Jac code into a production API with auto-provisioned infrastructure.

# Local development
jac start app.jac

# Production Kubernetes
jac start app.jac --scale

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By Background

Coming from Python

You’ll feel at home. Jac supersets Python.

What’s different:

• Braces { } instead of indentation
• Semicolons ; required
• Type annotations encouraged
• New keywords: node, edge, walker, has, can

Start here: Jac Basics

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Coming from JavaScript/TypeScript

Jac’s frontend syntax will look familiar (JSX-style).

What’s familiar:

• Braces and semicolons
• JSX for components
• React-like patterns (useState, useEffect)

What’s different:

• Python-based syntax for logic
• No const/let - just variable assignment
• Type annotations use : not TypeScript syntax

Start here: Full-Stack Setup

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Coming from Other Languages

Key concepts to learn:

1. Python ecosystem - Jac uses Python libraries
2. Graph thinking - Model data as nodes and edges
3. Walker pattern - Computation that moves through data

Start here: Hello World → Build Your First App

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Reference Documentation

When you need details:

Resource	Use For
Language Reference	Complete syntax and semantics
CLI Reference	All jac commands
Configuration	jac.toml settings
byLLM Reference	AI integration details
jac-client Reference	Frontend framework
jac-scale Reference	Production deployment

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Examples Gallery

Learn by studying complete applications:

Example	Description	Difficulty
LittleX	Twitter clone in 200 lines	Intermediate
EmailBuddy	AI email assistant	Intermediate
RAG Chatbot	Document Q&A with MCP	Advanced
RPG Generator	AI-generated game levels	Advanced

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Get Help

• Discord: Join our community for real-time help
• JacGPT: jac-gpt.jaseci.org - AI assistant for Jac questions
• GitHub Issues: Report bugs
• Playground: Try Jac in browser