Ax Examples Guide

A comprehensive collection of examples showcasing Ax framework capabilities, from basic signatures to production-ready patterns.

Getting Started
Core Concepts
Advanced Features
Production Patterns
Optimization & Training
Multi-Modal & Vision
Agent Systems
Workflow Orchestration

Getting Started

1. Basic Signature - Email Classification

The simplest way to start with Ax - define input → output, get type-safe results.

import { ai, ax } from '@ax-llm/ax';

// Define your signature
const classifier = ax(
 'email:string -> category:class "spam, important, normal" "Email category"'
);

// Choose your LLM
const llm = ai({ 
 name: 'openai', 
 apiKey: process.env.OPENAI_APIKEY!
});

// Get results
const result = await classifier.forward(llm, {
 email: "URGENT: You've won 1,000,000ドル! Click here now!"
});

console.log(result.category); // "spam" - Type-safe!

Key Concepts:

Signatures define the contract: input -> output
class type ensures output is one of the specified values
Full TypeScript type inference

2. Structured Extraction

Extract multiple structured fields from unstructured text in one call.

import { ax, ai } from '@ax-llm/ax';

const extractor = ax(`
 customerEmail:string, currentDate:datetime -> 
 subject:string "Email subject",
 priority:class "high, normal, low",
 sentiment:class "positive, negative, neutral",
 ticketNumber?:number "Optional ticket number",
 nextSteps:string[] "Action items",
 estimatedResponseTime:string
`);

const result = await extractor.forward(ai({ name: 'openai' }), {
 customerEmail: `
 Subject: Order #12345 hasn't arrived

 I ordered 2 weeks ago and still haven't received my package.
 This is unacceptable! I need this resolved immediately or I want a refund.
 The tracking shows it's been stuck for days.
 `,
 currentDate: new Date()
});

console.log(result);
// {
// subject: "Order #12345 hasn't arrived",
// priority: "high",
// sentiment: "negative",
// ticketNumber: 12345,
// nextSteps: ["Check tracking status", "Contact shipping carrier", "Offer refund or replacement"],
// estimatedResponseTime: "Within 24 hours"
// }

3. Adding Validation with Assertions

Ensure outputs meet your business rules with assertions. Assertions provide multiple ways to signal failures:

import { ax, ai } from '@ax-llm/ax';

const gen = ax('startNumber:number -> next10Numbers:number[], summary:string');

// Method 1: Return false with a fallback message
gen.addAssert(
 ({ next10Numbers }) => next10Numbers?.length === 10,
 'Must generate exactly 10 numbers'
);

// Method 2: Return a custom error string (recommended)
gen.addAssert(({ next10Numbers }) => {
 if (!next10Numbers) return undefined; // Skip validation if undefined
 if (next10Numbers.length !== 10) {
 return `Generated ${next10Numbers.length} numbers, expected exactly 10`;
 }
 return true; // Pass validation
});

// Method 3: Throw AxAssertionError to trigger retry
import { AxAssertionError } from '@ax-llm/ax';

gen.addAssert(({ next10Numbers }) => {
 if (next10Numbers?.some(n => n <= 0)) {
 throw new AxAssertionError({
 message: `Invalid numbers found: ${next10Numbers.filter(n => n <= 0)}`
 });
 }
 return true;
});

// Method 4: Throw standard Error for immediate failure (no retries)
gen.addAssert(({ next10Numbers }) => {
 if (!next10Numbers) {
 throw new Error('Critical: Numbers missing entirely!');
 }
 return true;
});

// Method 5: Conditional validation with undefined return
gen.addAssert(({ summary }) => {
 if (!summary) return undefined; // Skip if summary not provided
 return summary.length>= 20; // Only validate if present
}, 'Summary must be at least 20 characters when provided');

// Ax will automatically retry if assertions fail (up to maxRetries)
const result = await gen.forward(ai({ name: 'openai' }), {
 startNumber: 1
});

Assertion Return Values:

true: Assertion passes, continue generation
false: Assertion fails, triggers retry with provided message
string: Assertion fails, triggers retry with returned string as error message
undefined: Skip this assertion (useful for conditional validation)
throw new AxAssertionError(...): Assertion fails, triggers retry
throw new Error(...): Immediate failure (crashes program, no retries)

Streaming Assertions:

const streamingGen = ax('topic:string -> article:string, title:string');

// Validate streaming content as it's generated
streamingGen.addStreamingAssert('article', (content, done) => {
 // Only validate complete content
 if (!done) return undefined;

 if (content.length < 100) {
 return 'Article must be at least 100 characters long';
 }

 return true;
});

// Stream with validation
for await (const chunk of streamingGen.streamingForward(ai({ name: 'openai' }), {
 topic: 'TypeScript best practices'
})) {
 console.log(chunk.article || chunk.title || '');
}

3.5. Field Validation & Constraints (New!)

Ensure data quality with built-in Zod-like validation constraints. These run automatically on both inputs and outputs.

Data Quality with Built-in Validators

import { ax, f, ai } from '@ax-llm/ax';

const userRegistration = f()
 .input('formData', f.string('Raw registration form data'))
 .output('user', f.object({
 username: f.string('Username').min(3).max(20),
 email: f.string('Email address').email(),
 age: f.number('User age').min(18).max(120),
 password: f.string('Password').min(8).regex('^(?=.*[A-Za-z])(?=.*\\d)'),
 bio: f.string('User biography').max(500).optional(),
 website: f.string('Personal website').url().optional(),
 tags: f.string('Interest tag').min(2).max(30).array()
 }))
 .build();

const llm = ai({ name: 'openai', apiKey: process.env.OPENAI_APIKEY! });
const generator = ax(userRegistration);

const result = await generator.forward(llm, {
 formData: `
 Name: johndoe
 Email: john@example.com
 Age: 25
 Password: secure123
 Bio: Software developer passionate about TypeScript and AI
 Website: https://johndoe.dev
 Tags: typescript, ai, web development
 `
});

console.log(result.user);
// {
// username: "johndoe",
// email: "john@example.com",
// age: 25,
// password: "secure123",
// bio: "Software developer passionate about TypeScript and AI",
// website: "https://johndoe.dev",
// tags: ["typescript", "ai", "web development"]
// }

// All constraints are validated:
// ✅ username: 3-20 characters
// ✅ email: valid email format
// ✅ age: between 18-120
// ✅ password: min 8 chars with letter and number
// ✅ website: valid URL format
// ✅ tags: each 2-30 characters

Available Validators:

.min(n) / .max(n) - String length or number range
.email() - Email format (or use f.email())
.url() - URL format (or use f.url())
.date() - Date format (or use f.date())
.datetime() - DateTime format (or use f.datetime())
.regex(pattern, description) - Custom regex pattern
.optional() - Make field optional

Note: For email, url, date, and datetime, you can use either the validator syntax (f.string().email()) or the dedicated type syntax (f.email()). Both work consistently everywhere!

Contact Form with Regex Patterns

import { ax, f } from '@ax-llm/ax';

const contactFormParser = f()
 .input('formSubmission', f.string('Raw form data'))
 .output('contact', f.object({
 fullName: f.string('Full name').min(2).max(100),
 email: f.string('Email address').email(),
 phone: f.string('Phone number').regex('^\\+?[1-9]\\d{1,14}$'),
 subject: f.string('Subject line').min(5).max(200),
 message: f.string('Message content').min(20).max(2000),
 urgency: f.string('Urgency level').optional()
 }))
 .build();

const result = await ax(contactFormParser).forward(llm, {
 formSubmission: `
 Name: Jane Smith
 Email: jane.smith@company.com
 Phone: +1234567890
 Subject: Product inquiry about Enterprise plan
 Message: I'm interested in learning more about your Enterprise plan for our team of 50 developers. Could you provide pricing and feature details?
 Urgency: High
 `
});

// Validation ensures:
// ✅ Phone matches international format
// ✅ Email is properly formatted
// ✅ Message has sufficient detail (20+ chars)
// ✅ Subject is descriptive (5-200 chars)

E-Commerce Product Validation

import { ax, f } from '@ax-llm/ax';

const productExtractor = f()
 .input('productPage', f.string('Product page HTML'))
 .output('product', f.object({
 name: f.string('Product name').min(1).max(200),
 price: f.number('Price in USD').min(0),
 specifications: f.object({
 dimensions: f.object({
 width: f.number('Width in cm').min(0),
 height: f.number('Height in cm').min(0),
 depth: f.number('Depth in cm').min(0)
 }),
 weight: f.number('Weight in kg').min(0),
 materials: f.string('Material name').min(1).array()
 }),
 availability: f.object({
 inStock: f.boolean('Stock status'),
 quantity: f.number('Available quantity').min(0),
 restockDate: f.string('Restock date').optional()
 }),
 images: f.object({
 url: f.string('Image URL').url(),
 alt: f.string('Alt text').min(1).max(100)
 }).array(),
 reviews: f.object({
 rating: f.number('Rating').min(1).max(5),
 comment: f.string('Review text').min(10).max(1000),
 verified: f.boolean('Verified purchase')
 }).array()
 }))
 .build();

const result = await ax(productExtractor).forward(llm, {
 productPage: '<html>...</html>' // Real product page HTML
});

// Deep validation ensures:
// ✅ All dimensions are non-negative numbers
// ✅ Rating is between 1-5
// ✅ Image URLs are valid
// ✅ Review comments have meaningful length
// ✅ Nested object structure is correct

Key Features:

Automatic Input Validation: Validates before sending to LLM
Automatic Output Validation: Validates LLM responses
Auto-Retry: ValidationError triggers retry with corrections
Streaming Support: Incremental validation during streaming
Nested Validation: Works recursively through objects and arrays
TypeScript Safety: Full compile-time + runtime validation

Core Concepts

4. Function Calling (ReAct Pattern)

Let your AI use tools to answer questions - the ReAct (Reasoning + Acting) pattern.

import { ax, ai, type AxFunction } from '@ax-llm/ax';

// Define available functions
const functions: AxFunction[] = [
 {
 name: 'getCurrentWeather',
 description: 'Get current weather for a location',
 parameters: {
 type: 'object',
 properties: {
 location: { type: 'string', description: 'City name' },
 units: { type: 'string', enum: ['celsius', 'fahrenheit'] }
 },
 required: ['location']
 },
 func: async ({ location, units }) => {
 // Real API call would go here
 return { temp: 72, condition: 'sunny', location };
 }
 },
 {
 name: 'searchNews',
 description: 'Search for recent news',
 parameters: {
 type: 'object',
 properties: {
 query: { type: 'string' },
 limit: { type: 'number', default: 5 }
 },
 required: ['query']
 },
 func: async ({ query, limit }) => {
 return [`Breaking: ${query} news item 1`, `Update: ${query} item 2`];
 }
 }
];

// Create signature with functions
const assistant = ax(
 'question:string -> answer:string "Detailed answer using available tools"',
 { functions }
);

const result = await assistant.forward(
 ai({ name: 'openai' }), 
 { question: "What's the weather like in Tokyo and any news about it?" }
);

// AI will automatically call both functions and combine results
console.log(result.answer);
// "The current weather in Tokyo is 72°F and sunny. Recent news about Tokyo includes..."

4.5. Parallel Function Calling

Execute multiple tools in parallel for complex queries.

import { ax, ai, type AxFunction, AxAIGoogleGeminiModel } from '@ax-llm/ax';

const functions: AxFunction[] = [
 {
 name: 'getCurrentWeather',
 description: 'get the current weather for a location',
 func: async ({ location }) => ({ temperature: '22C', condition: 'Sunny' }),
 parameters: {
 type: 'object',
 properties: {
 location: { type: 'string' }
 },
 required: ['location']
 }
 },
 {
 name: 'getCurrentTime',
 description: 'get the current time for a location',
 func: async ({ location }) => ({ time: '14:30' }),
 parameters: {
 type: 'object',
 properties: {
 location: { type: 'string' }
 },
 required: ['location']
 }
 }
];

const agent = ax(
 'query:string -> report:string "Comprehensive report"',
 { functions }
);

const result = await agent.forward(
 ai({ name: 'google-gemini', config: { model: AxAIGoogleGeminiModel.Gemini15Pro } }),
 { query: "Compare the weather and time in Tokyo, New York, and London." }
);

// The AI will call weather and time functions for all 3 cities in parallel
console.log(result.report);

5. Streaming Responses

Stream responses for real-time user feedback.

import { ax, ai } from '@ax-llm/ax';

const gen = ax('topic:string -> article:string "500 word article"');

// Enable streaming
const stream = await gen.streamingForward(
 ai({ name: 'openai' }),
 { topic: 'The future of TypeScript' }
);

// Process chunks as they arrive
for await (const chunk of stream) {
 if (chunk.article) {
 process.stdout.write(chunk.article); // Real-time output
 }
}

6. Multi-Step Reasoning with Examples

Improve accuracy by providing examples - few-shot learning made simple.

import { AxGen, ai } from '@ax-llm/ax';

const analyzer = new AxGen<
 { code: string },
 { hasVulnerability: boolean; type?: string; severity?: string; suggestion?: string }
>('code:string -> hasVulnerability:boolean, type?:string, severity?:string, suggestion?:string');

// Add examples to guide the AI
analyzer.setExamples([
 {
 code: 'const password = "admin123"',
 hasVulnerability: true,
 type: 'Hardcoded Credentials',
 severity: 'critical',
 suggestion: 'Use environment variables for sensitive data'
 },
 {
 code: 'const add = (a: number, b: number) => a + b',
 hasVulnerability: false
 }
]);

const result = await analyzer.forward(
 ai({ name: 'openai' }),
 { code: 'eval(userInput)' }
);
// { hasVulnerability: true, type: "Code Injection", severity: "critical", ... }

Advanced Features

Process images and text together seamlessly.

import { ax, ai, image, AxAIOpenAIModel } from '@ax-llm/ax';

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

const result = await analyzer.forward(
 ai({ name: 'openai', config: { model: AxAIOpenAIModel.GPT4O } }),
 {
 image: image('./product.jpg'),
 question: 'What product is this and what can you tell me about it?'
 }
);

8. Smart Document Processing with Chain of Thought

Process complex documents with automatic reasoning steps.

import { ax, ai, AxAIAnthropicModel } from '@ax-llm/ax';

const processor = ax(`
 document:string "Full document text",
 instructions:string ->
 thinking:string "Step-by-step analysis",
 summary:string "Executive summary",
 keyInsights:string[] "Main takeaways",
 risks:string[] "Identified risks",
 opportunities:string[] "Identified opportunities",
 recommendedActions:string[] "Concrete next steps",
 confidence:number "0-100 confidence score"
`);

const result = await processor.forward(
 ai({ name: 'anthropic', config: { model: AxAIAnthropicModel.Claude35Sonnet } }),
 {
 document: businessPlan,
 instructions: "Analyze this business plan for investment potential"
 }
);

// Access the reasoning process
console.log('Analysis:', result.thinking);
console.log('Summary:', result.summary);
console.log('Confidence:', result.confidence);

Production Patterns

9. Customer Support Agent

Complete customer support system with routing, prioritization, and response generation.

import { ax, ai } from '@ax-llm/ax';

const supportAgent = ax(`
 customerMessage:string,
 customerHistory?:string "Previous interactions",
 knowledgeBase?:string "Relevant KB articles" ->
 intent:class "question, complaint, feedback, request",
 department:class "billing, technical, sales, general",
 priority:class "urgent, high, normal, low",
 sentiment:number "0-10 scale",
 suggestedResponse:string,
 internalNotes:string "For support team",
 requiresHumanReview:boolean,
 tags:string[]
`);

// Add validation rules
supportAgent.addAssert(
 ({ priority, sentiment }) =>
 !(priority === 'low' && sentiment !== undefined && sentiment < 3),
 'Low sentiment should not be low priority'
);

const result = await supportAgent.forward(
 ai({ name: 'openai' }),
 {
 customerMessage: "I've been charged twice for my subscription and need a refund immediately!",
 customerHistory: "Premium customer since 2020, previous billing issue in March"
 }
);

console.log(`Route to: ${result.department} (Priority: ${result.priority})`);
console.log(`Response: ${result.suggestedResponse}`);

10. Restaurant Recommendation System

Multi-criteria recommendation with function calling.

import { ax, ai, type AxFunction } from '@ax-llm/ax';

const searchRestaurants: AxFunction = {
 name: 'searchRestaurants',
 description: 'Search restaurants by criteria',
 parameters: {
 type: 'object',
 properties: {
 cuisine: { type: 'string' },
 priceRange: { type: 'string', enum: ['$', '$$', '$$$', '$$$$'] },
 location: { type: 'string' },
 features: { 
 type: 'array', 
 items: { type: 'string' },
 description: 'outdoor seating, delivery, etc'
 }
 }
 },
 func: async (params) => {
 // Database query would go here
 return mockRestaurantData.filter(r =>
 r.cuisine === params.cuisine &&
 r.priceRange === params.priceRange
 );
 }
};

const recommender = ax(`
 preferences:string "User's dining preferences",
 occasion:string,
 groupSize:number,
 location:string ->
 thinking:string "Analysis of preferences",
 recommendations:object[] "Top 3 restaurants with reasons",
 bestMatch:object "Single best recommendation",
 alternativeOptions:string "Other cuisines to consider"
`, { 
 functions: [searchRestaurants, getWeather, checkAvailability] 
});

const result = await recommender.forward(
 ai({ name: 'openai' }),
 {
 preferences: "I love spicy food and outdoor dining",
 occasion: "anniversary dinner",
 groupSize: 2,
 location: "San Francisco"
 }
);

Optimization & Training

11. Automatic Prompt Optimization

Use Bootstrap Few-Shot optimization to improve accuracy automatically.

import { ax, ai, AxBootstrapFewShot, type AxMetricFn } from '@ax-llm/ax';

// Define your task
const classifier = ax(
 'email:string -> category:class "spam, important, normal", confidence:number'
);

// Provide training examples
const trainingData = [
 { email: "Meeting at 3pm", category: "normal", confidence: 0.9 },
 { email: "WINNER! Claim prize!", category: "spam", confidence: 0.95 },
 { email: "Server is down", category: "important", confidence: 0.85 },
 // ... more examples
];

// Define success metric
const metric: AxMetricFn = ({ prediction, example }) => {
 const correct = prediction.category === example.category;
 const confidentAndCorrect = correct && prediction.confidence > 0.8;
 return confidentAndCorrect ? 1 : correct ? 0.5 : 0;
};

// Run optimization
const optimizer = new AxBootstrapFewShot({
 studentAI: ai({ name: 'openai' }),
 teacherAI: ai({ name: 'anthropic' }), // Optional: use stronger model as teacher
 metric,
 options: {
 maxRounds: 5,
 maxDemos: 3,
 maxExamples: 100
 }
});

const optimized = await optimizer.compile(classifier, trainingData);
console.log(`Improved accuracy from 65% to ${optimized.bestScore * 100}%`);

// Use optimized program
const result = await optimized.program.forward(ai({ name: 'openai' }), {
 email: "System maintenance tonight"
});

Agent Systems

12. Multi-Agent Collaboration

Build systems where specialized agents work together.

import { AxAgent, ai } from '@ax-llm/ax';

// Specialized researcher agent
const researcher = new AxAgent({
 name: 'Researcher',
 description: 'Expert at finding and analyzing information',
 signature: 'question:string -> research:string "Detailed findings", sources:string[]'
});

// Specialized writer agent 
const writer = new AxAgent({
 name: 'Writer',
 description: 'Expert at creating engaging content',
 signature: 'research:string, tone:string -> article:string, title:string'
});

// Specialized editor agent
const editor = new AxAgent({
 name: 'Editor',
 description: 'Expert at improving clarity and correctness',
 signature: 'article:string -> editedArticle:string, changes:string[]'
});

// Coordinator agent that orchestrates others
const coordinator = new AxAgent({
 name: 'Content Creator',
 description: 'Creates high-quality articles using specialized agents',
 signature: 'topic:string, style:string -> finalArticle:string, metadata:object',
 agents: [researcher, writer, editor]
});

// The coordinator will automatically delegate to the right agents
const result = await coordinator.forward(
 ai({ name: 'openai' }),
 {
 topic: 'The future of TypeScript',
 style: 'technical but accessible'
 }
);

console.log(result.finalArticle); // Fully researched, written, and edited article

13. Agent with Memory and Tools

Build stateful agents that remember context and use tools.

import { AxAgent, AxMemory, ai, type AxFunction } from '@ax-llm/ax';

// Create memory store
const memory = new AxMemory();

// Define agent tools
const tools: AxFunction[] = [
 {
 name: 'saveNote',
 description: 'Save important information for later',
 parameters: {
 type: 'object',
 properties: {
 category: { type: 'string' },
 content: { type: 'string' }
 }
 },
 func: async ({ category, content }) => {
 await memory.add(category, content);
 return 'Saved to memory';
 }
 },
 {
 name: 'recall',
 description: 'Recall previously saved information',
 parameters: {
 type: 'object',
 properties: {
 category: { type: 'string' },
 query: { type: 'string' }
 }
 },
 func: async ({ category, query }) => {
 return await memory.search(category, query);
 }
 }
];

const assistant = new AxAgent({
 name: 'Personal Assistant',
 description: 'Helps manage tasks and remember important information',
 signature: 'message:string, userId:string -> response:string, actionsTaken:string[]',
 functions: tools,
 memory
});

// First interaction
await assistant.forward(ai({ name: 'openai' }), {
 message: "Remember that my favorite color is blue",
 userId: "user123"
});

// Later interaction - agent remembers
const result = await assistant.forward(ai({ name: 'openai' }), {
 message: "What's my favorite color?",
 userId: "user123"
});
// "Your favorite color is blue"

Workflow Orchestration

14. AxFlow - Complex Pipeline

Build sophisticated data processing pipelines with AxFlow.

import { AxFlow, AxFlow, ai } from '@ax-llm/ax';

// Create a content moderation pipeline
const pipeline = new AxFlow()
 // Step 1: Analyze content
 .addNode('analyzer', ax(`
 content:string ->
 hasPII:boolean "Contains personal information",
 hasProfanity:boolean,
 toxicityScore:number "0-100",
 topics:string[]
 `))

 // Step 2: Redact sensitive info (only if needed)
 .addNode('redactor', ax(`
 content:string,
 hasPII:boolean ->
 redactedContent:string,
 redactedItems:string[]
 `))

 // Step 3: Generate moderation decision
 .addNode('moderator', ax(`
 content:string,
 toxicityScore:number,
 hasProfanity:boolean ->
 decision:class "approve, flag, reject",
 reason:string,
 suggestedAction:string
 `))

 // Define the flow
 .flow(({ content }) => ({
 analyzer: { content },
 redactor: { 
 content, 
 hasPII: '{{analyzer.hasPII}}'
 },
 moderator: {
 content: '{{redactor.redactedContent}}',
 toxicityScore: '{{analyzer.toxicityScore}}',
 hasProfanity: '{{analyzer.hasProfanity}}'
 }
 }));

const result = await pipeline.run(
 ai({ name: 'openai' }),
 { content: "John Smith (SSN: 123-45-6789) posted offensive content" }
);

console.log(result.moderator.decision); // "reject"
console.log(result.redactor.redactedItems); // ["SSN: XXX-XX-XXXX"]

15. Parallel Processing with Map-Reduce

Process multiple items in parallel and aggregate results.

import { AxFlow, ax, ai } from '@ax-llm/ax';

const flow = new AxFlow()
 // Map: Process each item in parallel
 .map('processor', ax(`
 item:object ->
 processed:object,
 quality:number,
 issues:string[]
 `))

 // Reduce: Aggregate all results
 .reduce('aggregator', ax(`
 results:object[] ->
 summary:string,
 totalQuality:number,
 allIssues:string[],
 recommendations:string[]
 `));

const items = [
 { id: 1, data: 'Item 1 data' },
 { id: 2, data: 'Item 2 data' },
 { id: 3, data: 'Item 3 data' }
];

const result = await flow.run(
 ai({ name: 'openai' }),
 { items }
);

console.log(`Processed ${items.length} items`);
console.log(`Average quality: ${result.totalQuality / items.length}`);

Running Examples

All examples are in the src/examples/ directory. To run any example:

# Set your API key
export OPENAI_APIKEY=your-key-here
# Or for other providers:
export ANTHROPIC_APIKEY=your-key
export GOOGLE_APIKEY=your-key

# Run an example
npm run tsx ./src/examples/summarize.ts

Complete Examples Reference

Below is a comprehensive list of all available examples in src/examples/, organized by category.

Basic Concepts

chat.ts - Simple chat interface demonstrating basic conversation flow
simple-classify.ts - Basic classification example
extract.ts - Extract structured data from unstructured text
extract-test.ts - Testing extraction capabilities
summarize.ts - Document summarization with key insights
marketing.ts - Marketing content generation
embed.ts - Text embeddings and vector operations

Signatures & Type Safety

fluent-signature-example.ts - Using the fluent API for signature definition
signature-tool-calling.ts - Combining signatures with tool calling
structured_output.ts - Complex structured output with validation
debug_schema.ts - Debugging JSON schema generation

Function Calling & Tools

function.ts - Basic function calling (ReAct pattern)
food-search.ts - Restaurant search with multi-step reasoning
smart-home.ts - Smart home control with multiple devices
stop-function.ts - Controlling function execution flow
function-result-formatter.ts - Formatting function call results
function-result-picker.ts - Selecting best function results
result-picker.ts - Advanced result selection strategies

Streaming

streaming.ts - Basic streaming responses
streaming-asserts.ts - Streaming with real-time validation

Assertions & Validation

asserts.ts - Using assertions for output validation
sample-count.ts - Controlling sampling and retries

Agent Systems

agent.ts - Basic agent implementation
agent-migration-example.ts - Migrating to the agent pattern
customer-support.ts - Complete customer support agent
meetings.ts - Meeting assistant with scheduling

Workflow Orchestration (AxFlow)

ax-flow.ts - Comprehensive AxFlow demonstration
ax-flow-enhanced-demo.ts - Advanced flow patterns
ax-flow-async-map.ts - Parallel processing with map
ax-flow-auto-parallel.ts - Automatic parallelization
ax-flow-map-merge-test.ts - Map-reduce patterns
ax-flow-signature-inference.ts - Type inference in flows
ax-flow-to-function.ts - Converting flows to functions
fluent-flow-example.ts - Fluent API for flows
flow-type-inference-demo.ts - Type safety in flows
flow-type-safe-output.ts - Type-safe flow outputs
flow-logging-simple.ts - Simple flow logging
flow-verbose-logging.ts - Detailed flow debugging

Optimization & Training

teacher-student-optimization.ts - MiPRO teacher-student optimization
mipro-python-optimizer.ts - MiPRO with Python backend
gepa.ts - GEPA optimizer basics
gepa-flow.ts - GEPA with workflows
gepa-train-inference.ts - GEPA training and inference
gepa-quality-vs-speed-optimization.ts - Multi-objective optimization
ace-train-inference.ts - ACE optimizer demonstration
simple-optimizer-test.ts - Basic optimizer testing
optimizer-metrics.ts - Optimization metrics tracking
use-examples.ts - Using examples for few-shot learning

multi-modal.ts - Basic multi-modal processing
multi-modal-abstraction.ts - Advanced multi-modal patterns
image-arrays-test.ts - Processing multiple images
image-arrays-multi-provider-test.ts - Multi-provider image handling
audio-arrays-test.ts - Audio processing

RAG & Document Processing

rag-docs.ts - Basic RAG implementation
advanced-rag.ts - Advanced RAG patterns
vectordb.ts - Vector database integration
codingWithMemory.ts - Code generation with memory

Provider-Specific Examples

Anthropic (Claude)

anthropic-thinking-function.ts - Extended thinking with function calls
anthropic-thinking-separation.ts - Separating thinking from output
anthropic-web-search.ts - Web search with Claude
test-anthropic-cache.ts - Prompt caching with Anthropic

Google Gemini

gemini-file-support.ts - File uploads with Gemini
gemini-google-maps.ts - Google Maps integration
gemini-empty-params-function.ts - Functions without parameters
vertex-auth-example.ts - Vertex AI authentication

OpenAI

openai-responses.ts - OpenAI response handling
openai-web-search.ts - Web search with OpenAI
reasoning-o3-example.ts - O3 reasoning model

Other Providers

grok-live-search.ts - Grok with live search
openrouter.ts - OpenRouter integration

MCP (Model Context Protocol)

mcp-client-memory.ts - MCP memory server integration
mcp-client-blender.ts - Blender MCP integration
mcp-client-pipedream.ts - Pipedream MCP integration
mcp-client-notion-http-oauth.ts - Notion MCP with HTTP OAuth
mcp-client-notion-sse-oauth.ts - Notion MCP with SSE OAuth

Advanced Patterns

react.ts - ReAct (Reasoning + Acting) pattern
prime.ts - Prime number generation with reasoning
fibonacci.ts - Fibonacci sequence generation
show-thoughts.ts - Displaying model reasoning
checkpoint-recovery.ts - Checkpointing and recovery
balancer.ts - Load balancing across models
ax-multiservice-router.ts - Routing between multiple AI services

Monitoring & Debugging

debug-logging.ts - Debug logging configuration
telemetry.ts - Telemetry and observability
metrics-export.ts - Exporting metrics

Abort & Control Flow

abort-simple.ts - Simple abort handling
abort-patterns.ts - Advanced abort patterns

Web & Browser

web-chat.html - Browser-based chat interface
webllm-chat.html - WebLLM browser integration
cors-proxy.js - CORS proxy for browser usage

Deployment

docker.ts - Docker deployment example

Best Practices

Start Simple: Begin with basic signatures, add complexity as needed
Use Types: Leverage TypeScript’s type system for safety
Add Assertions: Validate outputs to ensure quality
Provide Examples: Few-shot examples dramatically improve accuracy
Optimize When Needed: Use BootstrapFewShot for production accuracy
Handle Errors: Always wrap in try-catch for production
Stream for UX: Use streaming for better user experience
Monitor Performance: Use built-in telemetry for observability

Next Steps

Read the DSPy Concepts to understand the theory
Explore the API Reference for detailed documentation
Join our Discord for help and discussions
Star us on GitHub if you find Ax useful!

Ax Examples Guide

Table of Contents

Getting Started

1. Basic Signature - Email Classification

2. Structured Extraction

3. Adding Validation with Assertions

3.5. Field Validation & Constraints (New!)

Data Quality with Built-in Validators

Contact Form with Regex Patterns

E-Commerce Product Validation

Core Concepts

4. Function Calling (ReAct Pattern)

4.5. Parallel Function Calling

5. Streaming Responses

6. Multi-Step Reasoning with Examples

Advanced Features

7. Multi-Modal Processing

8. Smart Document Processing with Chain of Thought

Production Patterns

9. Customer Support Agent

10. Restaurant Recommendation System

Optimization & Training

11. Automatic Prompt Optimization

Agent Systems

12. Multi-Agent Collaboration

13. Agent with Memory and Tools

Workflow Orchestration

14. AxFlow - Complex Pipeline

15. Parallel Processing with Map-Reduce

Running Examples

Complete Examples Reference

Basic Concepts

Signatures & Type Safety

Function Calling & Tools

Streaming

Assertions & Validation

Agent Systems

Workflow Orchestration (AxFlow)

Optimization & Training

Multi-Modal & Vision

RAG & Document Processing

Provider-Specific Examples

Anthropic (Claude)

Google Gemini

OpenAI

Other Providers

MCP (Model Context Protocol)

Advanced Patterns

Monitoring & Debugging

Abort & Control Flow

Web & Browser

Deployment

Best Practices

Next Steps