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ax-llm/ax

Source: https://github.com/ax-llm/ax

Ax — DSPy for TypeScript

Automatic prompt generation, RLM agents, and a single API across 15+ providers. Production-tested.

What Ax is

• A small, dependency-free TypeScript library that brings the DSPy programming model to JS/TS runtimes.
• You declare a signature (string DSL, fluent f() builder, or any Standard Schema v1 validator — Zod, Valibot, ArkType). Ax compiles it to a prompt at runtime, runs the call, parses the output, and gives you back a fully typed value.
• The same signatures plug into agents, workflows, optimizers, and a sandboxed JS runtime — without rewriting prompts.
• Works in NodeJS, Bun, Deno and in all browsers.

flowchart LR
  S["Signature (string, f, zod)"] --> P["Prompt"]
  P --> AI["AI"]
  AI --> R["Streaming parser"]
  R --> O["Typed output"]
  X["GEPA / ACE optimizer"] --> P

30 seconds

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

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

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

const { sentiment } = await classify.forward(llm, {
  review: "This product is amazing!",
});
// sentiment: "positive" — typed as the literal union

No prompt engineering. Switch name: "openai" to "anthropic", "google-gemini", "mistral", "ollama", etc. — same signature, same code.

Provider-Native Speed

Ax is designed to stay in the same latency class as direct provider calls while adding typed outputs, validation, retries, tools, tracing, and memory. The hot path is intentionally thin: render the signature, call the provider, parse the result, and return a typed value.

Streaming is the default because it lets Ax do useful work before the model finishes: parse fields as they arrive, run streaming assertions, fail early, cancel the in-flight stream, and start correction without spending tokens on an output that is already known to be invalid. When you only want a final object, forward() still gives you one; when you want live output, streamingForward() exposes the stream directly.

The repo includes a live benchmark for checking overhead on your own providers and models:

AX_STREAM_BENCH_PROVIDER=anthropic AX_STREAM_BENCH_MODEL=claude-sonnet-4-5-20250929 AX_STREAM_BENCH_RUNS=2 AX_STREAM_BENCH_WARMUP_RUNS=0 npm run tsx src/examples/streaming-latency.ts
AX_STREAM_BENCH_PROVIDER=google-gemini AX_STREAM_BENCH_MODEL=gemini-2.5-flash AX_STREAM_BENCH_RUNS=2 AX_STREAM_BENCH_WARMUP_RUNS=0 npm run tsx src/examples/streaming-latency.ts

Recent runs on Claude Haiku/Sonnet and Gemini Flash/Flash Lite show provider queueing and model generation dominate total latency; AxGen stays close to the raw ai.chat() path while providing the structured-output control loop that direct SDK calls leave to application code.

Examples

Structured extraction

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

const result = await extract.forward(llm, {
  customerEmail: "Order #12345 hasn't arrived. Need this resolved immediately!",
  currentDate: new Date(),
});

Nested objects with f()

import { ax, f } 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 gen = ax(productExtractor);
const { product } = await gen.forward(llm, { productPage: "..." });
// product.specs.dimensions.width is typed end-to-end

Standard Schema v1 (Zod / Valibot / ArkType)

Any Standard Schema v1 validator works wherever f.* is accepted — at field level, whole-object level, or on a fn() tool. Same retry pipeline, same type inference, no adapter.

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

// (1) Per-field zod — mix freely with f.* fields
const reviewSentiment = ax(
  f()
    .input("productName", z.string().describe("Reviewed product"))
    .input("reviewText", z.string().min(10))
    .output("sentiment", z.enum(["positive", "neutral", "negative"]))
    .output("score", z.number().min(1).max(10))
    .output("keyPoints", z.array(z.string()))
    .build(),
);

// (2) Whole-object zod — declare once, decomposed into ordered fields
const productSummary = ax(
  f()
    .input(z.object({ productName: z.string(), buyerProfile: z.string() }))
    .output(z.object({
      headline: z.string(),
      pros: z.array(z.string()),
      cons: z.array(z.string()),
      recommendation: z.enum(["buy", "wait", "skip"]),
    }))
    .build(),
);

// (3) Whole-object zod on fn() — typed tool definition
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 }) => ({ price: 79.99, inStock: true, rating: 4.3 }))
  .build();

.min(), .max(), .email(), .url(), .regex() feed the normal retry pipeline; .refine(), .transform(), and .superRefine() execute at parse time on complete field values, in both streaming and non-streaming. Cache breakpoints and internal reasoning fields use companion options: { cache: true }, { internal: true }. Multimodal inputs (image, audio, file) still use f.*.

Runnable: src/examples/standard-schema.ts.

Tools (ReAct)

const assistant = ax("question:string -> answer:string", {
  functions: [
    { name: "getCurrentWeather", func: weatherAPI },
    { name: "searchNews", func: newsAPI },
  ],
});

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

Multi-modal

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

Conversational audio

Audio input/output for bounded .chat() turns is supported directly on AI services. Generated audio lives on result.audio, not in DSPy signature output fields.

OpenAI supports both request-based audio chat (gpt-audio, gpt-audio-mini) and realtime voice/transcription models (gpt-realtime-2, gpt-realtime-whisper). Gemini native audio uses the Live API under the same .chat() shape.

import WebSocket from "ws";
import {
  ai,
  axAIOpenAIRealtimeDefaultConfig,
  axAIOpenAIRealtimeTranscriptionDefaultConfig,
} from "@ax-llm/ax";

const voice = ai({
  name: "openai",
  apiKey: process.env.OPENAI_APIKEY!,
  config: axAIOpenAIRealtimeDefaultConfig(), // gpt-realtime-2
});

const stream = await voice.chat(
  { chatPrompt: [{ role: "user", content: "Say hello out loud." }] },
  { stream: true, webSocket: WebSocket },
);

for await (const chunk of stream) {
  const audio = chunk.results[0]?.audio;
  if (audio?.isDelta) {
    // base64 pcm16 audio bytes
    process.stdout.write(".");
  }
}

const transcriber = ai({
  name: "openai",
  apiKey: process.env.OPENAI_APIKEY!,
  config: axAIOpenAIRealtimeTranscriptionDefaultConfig(), // gpt-realtime-whisper
});

Runnable: src/examples/audio-chat.ts.

AxAgent

AxAgent is a three-stage pipeline that turns a signature into a long-running, tool-using actor. Each forward() call runs distiller → executor → responder.

flowchart LR
  IN["inputs"] --> D["Distiller"]
  D --> E["Executor (RLM loop)"]
  E --> RT["AxJSRuntime sandbox"]
  E --> FN["functions / child agents"]
  E --> M["recall - memories"]
  E --> SK["consult - skills"]
  E --> RES["Responder"]
  RES --> OUT["typed output"]

import { agent, AxJSRuntime } from "@ax-llm/ax";

const analyzer = agent(
  "context:string, query:string -> answer:string, evidence:string[]",
  {
    agentIdentity: {
      name: "documentAnalyzer",
      description: "Analyze long documents with iterative code + sub-queries",
    },
    contextFields: ["context"],
    runtime: new AxJSRuntime(),
    maxTurns: 20,
    maxRuntimeChars: 2_000,
    contextPolicy: { preset: "checkpointed", budget: "balanced" },
    executorOptions: { model: "gpt-4o-mini" },
  },
);

const result = await analyzer.forward(llm, {
  context: veryLongDocument,
  query: "What are the main arguments and supporting evidence?",
});

The recursive runtime (RLM) keeps long context out of the root prompt: the executor runs JS in a persistent sandboxed session, narrows context with llmQuery(...) sub-calls, and uses checkpointed replay so older turns collapse into summaries instead of growing the prompt unbounded.

Runnable: src/examples/rlm-agent-controlled.ts, src/examples/rlm-discovery.ts.

Memories, skills, sandboxed runtime

Three orthogonal options on agent(...). Opt in to what the task needs.

Memories — vector / BM25 / KV lookup the actor controls via await recall([...]). Results land on inputs.memories for the next turn. Lifetime is one .forward(); persist externally to carry across calls.

const myAgent = agent("task:string -> plan:string", {
  onMemoriesSearch: async (searches, alreadyLoaded) => {
    const skip = new Set(alreadyLoaded.map((m) => m.id));
    return (await myVectorDB.searchBatch(searches, { topK: 3 }))
      .filter((m) => !skip.has(m.id));
  },
  onUsedMemories: (results) => console.log("[memories]", results.map((r) => r.id)),
});

Skills — guidance / runbook bodies the actor pulls in on demand via await consult([...]). Loaded skills render under “Loaded Skills” in the executor system prompt and persist across .forward() calls.

const myAgent = agent("task:string -> plan:string", {
  onSkillsSearch: async (searches) =>
    mySkillStore.searchBatch(searches, { topK: 2 }),
  // Or preload statically — `consult()` not required:
  skills: [{ name: "release-checklist", content: "1. Bump version\n2. ..." }],
});

Sandboxed JS runtime — AxJSRuntime is the default; it is hardened by default and portable across Node, Bun (smol: true workers), Deno, and the browser. Capabilities are opt-in via permissions.

import { AxJSRuntime, AxJSRuntimePermission } from "@ax-llm/ax";

const runtime = new AxJSRuntime({
  permissions: [AxJSRuntimePermission.NETWORK], // grant fetch only
});

Defaults: import() blocked, intrinsics frozen, ShadowRealm locked, worker IPC locked, and on Node 20+ the OS Permission Model auto-engages as a second defense layer. Add FILESYSTEM, STORAGE, CHILD_PROCESS, etc. only as the task requires.

Runnable: src/examples/rlm-memories-and-skills.ts.

AxFlow + optimization

AxFlow is a typed, chainable workflow runner — define nodes, wire state through execute, finalize with map. State types evolve as you add nodes, so the final mapper is fully type-checked.

import { AxAI, AxAIOpenAIModel, AxGEPA, flow } from "@ax-llm/ax";

const emailFlow = flow<{ emailText: string }>()
  .description("Email Priority", "Classify priority and write a one-line rationale.")
  .n("classifier", 'emailText:string -> priority:class "high, normal, low"')
  .n("rationale", "emailText:string, priority:string -> rationale:string")
  .e("classifier", (s) => ({ emailText: s.emailText }))
  .e("rationale", (s) => ({ emailText: s.emailText, priority: s.classifierResult.priority }))
  .m((s) => ({
    priority: s.classifierResult.priority,
    rationale: s.rationaleResult.rationale,
  }));

Tune the whole flow with GEPA (multi-objective Pareto optimizer). Define a metric that returns one or more named scores; GEPA explores the prompt space and returns a Pareto front.

const student = new AxAI({ name: "openai", apiKey: process.env.OPENAI_APIKEY!,
  config: { model: AxAIOpenAIModel.GPT4OMini } });
const teacher = new AxAI({ name: "openai", apiKey: process.env.OPENAI_APIKEY!,
  config: { model: AxAIOpenAIModel.GPT4O } });

const optimizer = new AxGEPA({
  studentAI: student,
  teacherAI: teacher,
  numTrials: 16,
  minibatch: true,
  minibatchSize: 6,
  seed: 42,
});

const result = await optimizer.compile(
  emailFlow,
  trainSet,
  async ({ prediction, example }) => ({
    accuracy: prediction.priority === example.priority ? 1 : 0,
    brevity: (prediction.rationale?.length ?? 0) <= 60 ? 1 : 0.4,
  }),
  { auto: "medium", validationExamples: valSet, maxMetricCalls: 240 },
);
// result.paretoFront, result.hypervolume, result.paretoFrontSize

ACE (Automatic Curriculum Extraction) works the same way via new AxACE({...}).compile(...) — playbook-based iterative refinement. See src/examples/ace-train-inference.ts and src/examples/gepa-flow.ts.

Capabilities

Install

npm install @ax-llm/ax

Optional packages:

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

Documentation

Get started

• Quick Start
• Examples
• DSPy concepts
• Signatures

Deep dives

• AI providers
• Audio I/O
• AxFlow workflows
• Optimization (GEPA, ACE)
• AxAgent & RLM
• Advanced RAG

Run examples

OPENAI_APIKEY=your-key npm run tsx ./src/examples/<name>.ts

Highlights: extract.ts, react.ts, agent.ts, streaming1.ts, multi-modal.ts, audio-chat.ts, standard-schema.ts, rlm-memories-and-skills.ts, rlm-discovery.ts, gepa-flow.ts, ace-train-inference.ts, ax-flow-enhanced-demo.ts. Browse all 70+ examples →

Community

• Discord — questions and discussion
• Twitter — updates
• GitHub — source and issues
• DeepWiki — AI-generated docs

Contributors

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

License

Apache 2.0

Joshua Gu (@astrogu_): Recent agentic systems (Claude Code, Codex, RLM, etc.) push context out of the prompt and into the environment (e.g., as files). This helps them maintain long-term knowledge about their goals and functionality.

🚨 While this is a good idea, we show a surprising result: systems