Ax LLM

// Prefer factory functions: ax(), ai(), agent(), flow(); avoid class constructors.
import { ax, ai, f, s, fn, agent, flow, AxMemory, AxMCPClient } from '@ax-llm/ax';
import { z } from 'zod'; // optional — any Standard Schema v1 library works

// AI provider
const llm = ai({ name: 'openai', apiKey: process.env.OPENAI_APIKEY });

// Generator (from string signature)
const gen = ax('question:string -> answer:string');

// Generator (from fluent signature)
const gen = ax(
  f()
    .input('question', f.string('User question'))
    .output('answer', f.string('AI response'))
    .build()
);

// Generator (from zod — Standard Schema v1, also works with valibot/arktype)
const zodGen = ax(
  f()
    .input(z.object({ question: z.string().describe('User question') }))
    .output(z.object({ answer: z.string().describe('AI response') }))
    .build()
);

// Reusable signature
const sig = s('question:string, context:string[] -> answer:string');

// Agent
const myAgent = agent('userInput:string -> response:string', {
  name: 'helper',
  description: 'A helpful assistant',
});

// Flow
const wf = flow<{ input: string }, { output: string }>()
  .node('step1', 'input:string -> output:string')
  .execute('step1', (state) => ({ input: state.input }))
  .returns((state) => ({ output: state.step1Result.output }));

// Function tool — native fluent
const tool = fn('search')
  .description('Search the web')
  .arg('query', f.string('Search query'))
  .returns(f.string('Search results'))
  .handler(({ query }) => searchWeb(query))
  .build();

// Function tool — zod schema (Standard Schema v1: also works with valibot, arktype)
const zodTool = fn('calculateTax')
  .description('Calculate tax for an amount')
  .arg(z.object({
    amount: z.number().positive().describe('Pre-tax amount in USD'),
    region: z.enum(['US', 'EU', 'UK']).describe('Tax region'),
  }))
  .returns(z.object({ tax: z.number(), total: z.number() }))
  .handler(async ({ amount }) => ({ tax: amount * 0.1, total: amount * 1.1 }))
  .build();

// Forward (blocking)
const result = await gen.forward(llm, { question: 'What is 2+2?' });

// Streaming
for await (const chunk of gen.streamingForward(llm, { question: 'Tell a story' })) {
  if (chunk.delta.answer) process.stdout.write(chunk.delta.answer);
}

import { axGlobals, axCreateDefaultColorLogger } from '@ax-llm/ax';
import { metrics, trace } from '@opentelemetry/api';

axGlobals.rateLimiter = async (next, info) => next();
axGlobals.tracer = trace.getTracer('my-app');
axGlobals.meter = metrics.getMeter('my-app');
axGlobals.debug = true;
axGlobals.logger = axCreateDefaultColorLogger();

import { AxMemory } from '@ax-llm/ax';

const memory = new AxMemory();

// Multi-turn conversation
await gen.forward(llm, { userMessage: 'My name is Alice' }, { mem: memory });
const r = await gen.forward(llm, { userMessage: 'What is my name?' }, { mem: memory });

const classifier = ax('reviewText:string -> sentiment:class "positive, negative, neutral"');

classifier.setExamples([
  { reviewText: 'I love this!', sentiment: 'positive' },
  { reviewText: 'Terrible.', sentiment: 'negative' },
  { reviewText: 'It works.', sentiment: 'neutral' },
]);

const classifier = ax(
  f()
    .input('text', f.string())
    .output('category', f.class(['spam', 'ham', 'uncertain']))
    .output('confidence', f.number().min(0).max(1))
    .build()
);

const extractor = ax(
  f()
    .input('text', f.string())
    .output('entities', f.object({
      people: f.string().array(),
      organizations: f.string().array(),
      locations: f.string().array()
    }))
    .build()
);

const analyzer = ax(
  f()
    .input('image', f.image('Image to analyze'))
    .input('question', f.string('Question').optional())
    .output('description', f.string())
    .output('objects', f.string().array())
    .build()
);

const result = await analyzer.forward(llm, {
  image: { mimeType: 'image/jpeg', data: base64Data },
  question: 'What objects are in this image?'
});

const researcher = ax('topic:string -> research:string, keyFacts:string[]');
const writer = ax('research:string, keyFacts:string[] -> article:string');

const research = await researcher.forward(llm, { topic: 'AGI' });
const draft = await writer.forward(llm, { research: research.research, keyFacts: research.keyFacts });

import { AxGenerateError, AxAIServiceError, AxAIServiceAbortedError } from '@ax-llm/ax';

try {
  const result = await gen.forward(llm, { input: 'test' });
} catch (error) {
  if (error instanceof AxGenerateError) {
    console.error('Generation failed:', error.details.model, error.details.signature);
  } else if (error instanceof AxAIServiceAbortedError) {
    console.log('Request was aborted');
  } else if (error instanceof AxAIServiceError) {
    console.error('AI service error:', error.message);
  }
}

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

const result = await gen.forward(llm, { input: 'test' }, {
  debug: true,
  logger: axCreateDefaultColorLogger(),
  // OpenTelemetry
  tracer: openTelemetryTracer,
  meter: openTelemetryMeter,
});

// Or set live app-wide defaults for future calls:
axGlobals.tracer = openTelemetryTracer;
axGlobals.meter = openTelemetryMeter;

import { AxMCPClient, agent } from '@ax-llm/ax';
import { AxMCPStdioTransport } from '@ax-llm/ax-tools';

// Stdio transport (local MCP server)
const transport = new AxMCPStdioTransport({
  command: 'npx',
  args: ['-y', '@modelcontextprotocol/server-memory'],
});

const mcpClient = new AxMCPClient(transport, { namespace: 'memory' });

// Native MCP context is initialized once and inherited by all agent stages.
const myAgent = agent('userMessage:string -> response:string', {
  mcp: mcpClient,
  functionDiscovery: true,
  contextFields: [],
});

const result = await myAgent.forward(llm, { userMessage: 'Remember this.' });
await mcpClient.close(); // caller-owned clients remain caller-owned