Stop wrestling with prompts. Start shipping AI features.

Ax brings DSPy's approach to TypeScript – describe what you want, and let the framework handle the rest. Production-ready, type-safe, works with all major LLMs.

Building with LLMs is painful. You write prompts, test them, they break. You switch providers, everything needs rewriting. You add validation, error handling, retries – suddenly you're maintaining infrastructure instead of shipping features.

Define what goes in and what comes out. Ax handles the rest.

import { ai, ax } from "@ax-llm/ax"; const llm = ai({ name: "openai", apiKey: process.env.OPENAI_APIKEY }); const classifier = ax( 'review:string -> sentiment:class "positive, negative, neutral"', ); const result = await classifier.forward(llm, { review: "This product is amazing!", }); console.log(result.sentiment); // "positive"

No prompt engineering. No trial and error. Works with GPT-4, Claude, Gemini, or any LLM.

Write once, run anywhere. Switch between OpenAI, Anthropic, Google, or 15+ providers with one line. No rewrites.

Ship faster. Stop tweaking prompts. Define inputs and outputs. The framework generates optimal prompts automatically.

Production-ready. Built-in streaming, validation, error handling, observability. Used in production handling millions of requests.

Gets smarter. Train your programs with examples. Watch accuracy improve automatically. No ML expertise needed.

const extractor = ax(`  customerEmail:string, currentDate:datetime ->   priority:class "high, normal, low",  sentiment:class "positive, negative, neutral",  ticketNumber?:number,  nextSteps:string[],  estimatedResponseTime:string `); const result = await extractor.forward(llm, { customerEmail: "Order #12345 hasn't arrived. Need this resolved immediately!", currentDate: new Date(), });

import { f, ax } from "@ax-llm/ax"; const productExtractor = f() .input("productPage", f.string()) .output("product", f.object({ name: f.string(), price: f.number(), specs: f.object({ dimensions: f.object({ width: f.number(), height: f.number() }), materials: f.array(f.string()) }), reviews: f.array(f.object({ rating: f.number(), comment: f.string() })) })) .build(); const generator = ax(productExtractor); const result = await generator.forward(llm, { productPage: "..." }); // Full TypeScript inference console.log(result.product.specs.dimensions.width); console.log(result.product.reviews[0].comment);

const userRegistration = f() .input("userData", f.string()) .output("user", f.object({ username: f.string().min(3).max(20), email: f.string().email(), age: f.number().min(18).max(120), password: f.string().min(8).regex("^(?=.*[A-Za-z])(?=.*\\d)", "Must contain letter and digit"), bio: f.string().max(500).optional(), website: f.string().url().optional(), })) .build();

Available constraints: .min(n), .max(n), .email(), .url(), .date(), .datetime(), .regex(pattern, description), .optional()

Validation runs on both input and output. Automatic retry with corrections on validation errors.

const assistant = ax( "question:string -> answer:string", { functions: [ { name: "getCurrentWeather", func: weatherAPI }, { name: "searchNews", func: newsAPI }, ], }, ); const result = await assistant.forward(llm, { question: "What's the weather in Tokyo and any news about it?", });

const analyzer = ax(`  image:image, question:string ->  description:string,  mainColors:string[],  category:class "electronics, clothing, food, other",  estimatedPrice:string `);

npm install @ax-llm/ax

Additional packages:

# AWS Bedrock provider npm install @ax-llm/ax-ai-aws-bedrock # Vercel AI SDK v5 integration npm install @ax-llm/ax-ai-sdk-provider # Tools: MCP stdio transport, JS interpreter npm install @ax-llm/ax-tools

• 15+ LLM Providers – OpenAI, Anthropic, Google, Mistral, Ollama, and more
• Type-safe – Full TypeScript support with auto-completion
• Streaming – Real-time responses with validation
• Multi-modal – Images, audio, text in the same signature
• Optimization – Automatic prompt tuning with MiPRO, ACE, GEPA
• Observability – OpenTelemetry tracing built-in
• Workflows – Compose complex pipelines with AxFlow
• RAG – Multi-hop retrieval with quality loops
• Agents – Tools and multi-agent collaboration
• Zero dependencies – Lightweight, fast, reliable

Get Started

• Quick Start Guide – Set up in 5 minutes
• Examples Guide – Comprehensive examples
• DSPy Concepts – Understanding the approach
• Signatures Guide – Type-safe signature design

Deep Dives

• AI Providers – All providers, AWS Bedrock, Vercel AI SDK
• AxFlow Workflows – Build complex AI systems
• Optimization (MiPRO, ACE, GEPA) – Make programs smarter
• Advanced RAG – Production search and retrieval

OPENAI_APIKEY=your-key npm run tsx ./src/examples/[example-name].ts

Core examples: extract.ts, react.ts, agent.ts, streaming1.ts, multi-modal.ts

Production patterns: customer-support.ts, food-search.ts, ace-train-inference.ts, ax-flow-enhanced-demo.ts

View all 70+ examples

• Twitter – Updates
• Discord – Help and discussion
• GitHub – Star the project
• DeepWiki – AI-powered docs

• Battle-tested in production
• Stable minor versions
• Comprehensive test coverage
• OpenTelemetry built-in
• TypeScript first

• Author: @dosco
• GEPA and ACE optimizers: @monotykamary

Apache 2.0

npm install @ax-llm/ax