ax-gen

name	ax-gen
description	This skill helps an LLM generate correct AxGen code using @ax-llm/ax. Use when the user asks about ax(), AxGen, generators, forward(), streamingForward(), validation, assertions, streaming assertions, field processors, step hooks, self-tuning, or structured outputs.
version	23.0.0

AxGen Codegen Rules (@ax-llm/ax)

Use this skill to generate AxGen code. Prefer short, modern, copyable patterns. Do not write tutorial prose unless the user explicitly asks for explanation.

Use These Defaults

• Use ax(...) factory, not new AxGen(...).
• Always pass an AI instance from ai(...) as the first argument to forward().
• Streaming uses streamingForward(), not forward() with a stream option.
• Use schema validation for field shape and constraints.
• Use addAssert(...) for whole-output hard invariants with correction retries.
• Use addStreamingAssert(...) for partial streaming hard invariants with fail-fast per-attempt correction retries.
• Use bestOfN(...) / refine(...) for reward-scored complete outputs.
• Step hook mutations are applied at the next step boundary (pending pattern).
• stopFunction accepts a string or string[] for multiple stop functions.
• Multi-step continues until: all outputs filled, stop function called, or maxSteps reached.

Canonical Pattern

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

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

// Inline signature
const gen = ax('input:string -> output:string, reasoning:string');

// Reusable signature
const sig = s('question:string, context:string[] -> answer:string');
const gen2 = ax(sig);

// With options
const gen3 = ax('input -> output', {
  description: 'A helpful assistant',
  maxRetries: 3,
  maxSteps: 10,
  temperature: 0.7,
});

const result = await gen.forward(llm, { input: 'Hello world' });
console.log(result.output);

Signatures from zod / valibot / arktype

ax() accepts any signature built with f(), and f().input() / .output() accept Standard Schema v1 validators directly — per-field or a whole z.object({...}):

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

const gen = ax(
  f()
    .input(z.object({
      productName: z.string(),
      buyerProfile: z.string(),
    }))
    .output(z.object({
      headline: z.string(),
      recommendation: z.enum(['buy', 'wait', 'skip']),
    }))
    .build()
);

Constraints (.min(), .email(), .regex()) and custom logic (.refine(), .transform(), .superRefine()) execute in the normal validation/retry pipeline — at parse time on complete field values, including at field boundaries during streaming. For cache/internal hints pass companion options: .input('ctx', z.string(), { cache: true }) or .output('reasoning', z.string(), { internal: true }).

Define tool functions with zod the same way — fn().arg() / .returns() accept per-argument or whole-object schemas and infer the handler's argument type:

import { z } from 'zod';
import { ax, fn } from '@ax-llm/ax';

const lookupProduct = fn('lookupProduct')
  .description('Look up a product by name')
  .arg(z.object({
    productName: z.string().min(1),
    includeSpecs: z.boolean().optional(),
  }))
  .returns(z.object({
    price: z.number(),
    inStock: z.boolean(),
    rating: z.number().min(1).max(5),
  }))
  .handler(async ({ productName, includeSpecs }) => ({
    price: 79.99,
    inStock: true,
    rating: 4.3,
  }))
  .build();

const result = await gen.forward(llm, { ... }, { functions: [lookupProduct] });

Running AxGen

forward()

const result = await gen.forward(llm, { input: '...' });

// With options
const result = await gen.forward(llm, { input: '...' }, {
  maxRetries: 5,
  model: 'gpt-5.4-mini',
  modelConfig: { temperature: 0.9, maxTokens: 1000 },
  debug: true,
});

Live Global Defaults

AxGen respects axGlobals for app-wide runtime defaults:

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

const responseCache = new Map<string, any>();

axGlobals.tracer = trace.getTracer('my-app');
axGlobals.debug = true;
axGlobals.cachingFunction = async (key, value?) => {
  if (value !== undefined) {
    responseCache.set(key, value);
    return;
  }
  return responseCache.get(key);
};

Rules:

• Tracing/logging precedence is: forward options, then generator options, then AI service options, then current axGlobals, then built-in defaults.
• abortSignal from axGlobals is merged with local forward signals.
• customLabels merge from globals to AI service to forward options.
• cachingFunction and functionResultFormatter also fall back to current axGlobals when local options do not provide them.

streamingForward()

const stream = gen.streamingForward(llm, { input: 'Write a long story' });
for await (const chunk of stream) {
  if (chunk.delta.output) process.stdout.write(chunk.delta.output);
}

Stopping And Cancellation

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

const timer = setTimeout(() => gen.stop(), 3_000);

try {
  const result = await gen.forward(llm, { topic: 'Long document' }, {
    abortSignal: AbortSignal.timeout(10_000),
  });
} catch (err) {
  if (err instanceof AxAIServiceAbortedError) console.log('Aborted');
}

Rules:

• gen.stop() gracefully stops multi-step execution at the next step boundary.
• abortSignal cancels the underlying AI service call immediately.
• Catch AxAIServiceAbortedError when using either mechanism.

Validation, Selection, And Guards

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

// Schema validation: output shape and field validity.
const gen = ax(
  f()
    .input('topic', z.string().min(1))
    .output('summary', z.string().min(50))
    .build()
);

// bestOfN: choose the best complete candidate.
const selected = bestOfN(gen, {
  n: 4,
  rewardFn: ({ prediction }) => prediction.summary.length,
});

// Whole-output assertion: retries with correction feedback.
gen.addAssert(
  (output) => output.summary.includes(topic) || 'Summary must mention the topic.'
);

// Streaming assertion: fail fast on unsafe partial output.
gen.addStreamingAssert(
  'summary',
  (text) => !text.includes('forbidden'),
  'Output contains forbidden text'
);

Rules:

• Schema validation retries with parser/constraint feedback.
• addAssert(...) checks the complete parsed output after validation/processors and retries with correction feedback on failure.
• bestOfN(...) scores complete candidates and returns the highest reward or first threshold hit.
• refine(...) runs rounds and can feed reward-derived advice into instruction components between rounds.
• addStreamingAssert(...) targets a string/code output field and receives partial text so far.
• Streaming assertions abort the current stream attempt by throwing AxStreamingAssertionError, then feed correction feedback into AxGen retries.

Field Processors

// Post-processing after generation
gen.addFieldProcessor('summary', (value, context) => value.toUpperCase());

// Streaming field processor (called on each chunk)
gen.addStreamingFieldProcessor('content', (partialValue, context) => {
  console.log(`Received ${partialValue.length} chars`);
  return partialValue;
});

Rules:

• addFieldProcessor runs once after the field is fully generated.
• addStreamingFieldProcessor runs on each streaming chunk for the target field.
• Both must return the (possibly transformed) value.

Function Calling

const result = await gen.forward(llm, { question: '...' }, {
  functions: tools,
  functionCallMode: 'auto',
  stopFunction: 'finalAnswer',
});

Rules:

• functionCallMode can be 'auto', 'none', or a specific function name to force.
• stopFunction accepts a string or string[] to halt multi-step on specific function calls.
• Multi-step continues until all outputs filled, stop function called, or maxSteps reached.

Caching

Response Caching

const gen = ax('question:string -> answer:string', {
  cachingFunction: async (key, value?) => {
    if (value !== undefined) {
      await cache.set(key, value);
      return;
    }
    return await cache.get(key);
  },
});

Context Caching

const result = await gen.forward(llm, { question: '...' }, {
  contextCache: { cacheBreakpoint: 'after-examples' },
});

Rules:

• cachingFunction acts as a get/set: called with (key) to read, (key, value) to write.
• contextCache enables AI provider-level prompt caching for long context.

Sampling And Result Picker

const result = await gen.forward(llm, { question: '...' }, {
  sampleCount: 3,
  resultPicker: async (samples) => {
    // Evaluate each sample and return the index of the best one
    return bestIndex;
  },
});

Rules:

• sampleCount generates multiple completions in parallel.
• resultPicker receives all samples and must return the index of the chosen result.

Extended Thinking

const result = await gen.forward(llm, { question: '...' }, {
  thinkingTokenBudget: 'medium',
  showThoughts: true,
});
console.log(result.thought);

Rules:

• thinkingTokenBudget can be 'low', 'medium', 'high', or a number.
• Set showThoughts: true to include the model's reasoning in result.thought.

Structured Outputs

const sig = f()
  .input('text', f.string())
  .output('summary', f.string())
  .output('metadata', f.json().optional())
  .useStructured()
  .build();

Rules:

• .useStructured() asks providers with native support, including OpenAI, Anthropic, and Gemini, for schema-constrained JSON.
• Native structured-output schemas list every object property in required, set additionalProperties: false on objects, and express optional fields as nullable types.
• Flexible json fields and unshaped object fields are sent as JSON-encoded strings for native structured outputs, then parsed back into normal JavaScript values.

Step Hooks

const result = await gen.forward(llm, values, {
  stepHooks: {
    beforeStep: (ctx) => {
      if (ctx.functionsExecuted.has('complexanalysis')) {
        ctx.setModel('smart');
        ctx.setThinkingBudget('high');
      }
    },
    afterStep: (ctx) => {
      console.log(`Usage: ${ctx.usage.totalTokens} tokens`);
    },
  },
});

AxStepContext Read-Only Properties

• stepIndex - current step number
• maxSteps - configured maximum steps
• isFirstStep - whether this is the first step
• functionsExecuted - Set<string> of function names called so far
• lastFunctionCalls - array of the most recent function call results
• usage - token usage statistics
• state - current step state

AxStepContext Mutators

• setModel(model) - change the model for the next step
• setThinkingBudget(budget) - adjust thinking budget
• setTemperature(temp) - adjust temperature
• setMaxTokens(max) - adjust max output tokens
• setOptions(opts) - set arbitrary forward options
• addFunctions(fns) - add functions for the next step
• removeFunctions(names) - remove functions by name
• stop() - stop multi-step execution

Rules:

• All mutations are pending and applied at the next step boundary.
• beforeStep runs before each LLM call; afterStep runs after.
• Use afterFunctionExecution to react to specific function results.

Self-Tuning

// Simple: enable all self-tuning
const result = await gen.forward(llm, values, { selfTuning: true });

// Granular: pick what to tune
const result = await gen.forward(llm, values, {
  selfTuning: {
    model: true,
    thinkingBudget: true,
    functions: [searchWeb, calculate],
  },
});

Rules:

• selfTuning: true enables automatic model and parameter selection.
• Granular config allows tuning specific aspects independently.
• selfTuning.functions provides a pool of functions the tuner may add or remove per step.

Error Handling

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

try {
  const result = await gen.forward(llm, { input: '...' });
} catch (error) {
  if (error instanceof AxGenerateError) {
    console.log(error.details.model, error.details.signature);
  }
}

Rules:

• AxGenerateError includes details with model and signature for debugging.
• AxAIServiceAbortedError is thrown on cancellation via stop() or abortSignal.

Chat Log and Usage

getChatLog()

After any .forward() or streamingForward() call, gen.getChatLog() returns the full normalized chat history — every ai.chat() round-trip, including the system prompt, all messages, and the model response. The log is reset at the start of each .forward() call. Multi-step generators (with function calls) produce one entry per step.

await gen.forward(llm, { question: 'What is 2+2?' });

for (const entry of gen.getChatLog()) {
  console.log('model:', entry.model);
  for (const msg of entry.messages) {
    console.log(`[${msg.role}]`, msg.content);
  }
  console.log('tokens:', entry.modelUsage?.tokens);
}

Message roles: system, user, assistant, tool. Assistant content uses inline XML:

• <think>...</think> — reasoning/thinking tokens
• <tool_call>\n{...}\n</tool_call> — tool invocations

The system message includes a <tools> JSON block when functions are present.

type AxChatLogMessage =
  | { role: 'system'; content: string }
  | { role: 'user'; content: string }
  | { role: 'assistant'; content: string }
  | { role: 'tool'; name: string; content: string };

type AxChatLogEntry = {
  name?: string;
  model: string;
  messages: AxChatLogMessage[];
  modelUsage?: AxProgramUsage;
};

gen.getChatLog(): readonly AxChatLogEntry[]

getUsage()

Returns token usage aggregated by (ai, model) across all steps. When a provider reports prompt-cache usage, promptTokens is the uncached input portion and cacheReadTokens / cacheCreationTokens carry the cache counters. Reset with resetUsage().

const usage = gen.getUsage(); // AxProgramUsage[]
console.log(usage[0]?.tokens?.promptTokens);
gen.resetUsage();

AxAgent and AxFlow also return flat AxChatLogEntry[] logs; composite programs set entry.name so callers can filter by node/stage.

Examples

Fetch these for full working code:

• Streaming — field-by-field streaming
• Best Of N — reward-scored sample selection
• Refine — retry rounds with generated feedback
• Streaming Assert — fail-fast partial-output correction
• Structured Output — fluent API with validation
• Debug Logging — debug mode and step hooks
• Stop Function — stop functions
• Fibonacci — streaming with thinking
• Extraction — information extraction
• Multi-Sampling — sample count usage

Do Not Generate

• Do not use new AxGen(...) for new code unless explicitly required.
• Do not pass raw API keys or config objects where an ai(...) instance is expected.
• Do not use forward() for streaming; use streamingForward().
• Do not use streaming assertions as reward/refine mechanisms; they enforce hard partial-output invariants and retry with correction.
• Do not mutate step hook context expecting immediate effect; mutations are pending until the next step.
• Do not assume multi-step stops after one LLM call; it continues until outputs are filled, a stop function fires, or maxSteps is reached.