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
`);

Audio

Batch speech APIs live on AI services: ai.transcribe({ audio }) turns audio into text, and ai.speak({ text }) turns text into an audio artifact. Signature audio outputs are scripted artifacts: the model writes the text for speech:audio, then Ax synthesizes it after parsing.

const say = ax("question:string -> speech:audio, summary:string");
const res = await say.forward(llm, { question: "Greet the team." }, {
  speech: { speak: { voice: "alloy", format: "mp3" } },
});

console.log(res.speech.data);       // base64 audio
console.log(res.speech.transcript); // generated script

Agents transcribe :audio inputs before the planner/executor/responder stages, so tools and memory receive stable text rather than base64 payloads. Native conversational audio is still available through .chat().

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; Grok Voice uses the realtime voice endpoint.

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 streams realtime audio, saves a WAV, and plays it when a local player is available. src/examples/audio-batch-and-agent.ts writes generated MP3 artifacts under src/examples/output/ and plays them immediately.

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.

Context map, memories, skills, sandboxed runtime

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

Context map — a small persistent orientation cache for repeated questions over the same long context. When configured, Ax shows it to the distiller and updates it once after each successful completed run. By default the map keeps evolving forever; set infiniteEvolve: false with evolveSteps on the map object to do a finite warmup and then reuse a frozen map. Use onUpdate to save the new snapshot wherever your app stores state.

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

const map = new AxAgentContextMap(savedSnapshot, {
  maxChars: 4000,
  infiniteEvolve: false,
  evolveSteps: 10,
});

const analyzer = agent("context:string, query:string -> answer:string", {
  contextFields: ["context"],
  contextMap: {
    map,
    onUpdate: ({ map }) => saveSnapshot(map.snapshot()),
  },
});

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

Capability	Entrypoint	Notes
String signature DSL	`ax`, `s`	`'review:string -> sentiment:class "..."'`
Fluent signature builder	`f`	typed nesting, constraints, retry on validation error
Standard Schema v1	`f`, `fn`	Zod, Valibot, ArkType — per-field or whole-object
Tools / function calling	`fn`, `functions:` option	typed args, typed return, async handler
Streaming + validation	`.streamingForward()`	parses at field boundaries
Multi-modal	`f.image`, `f.audio`, `.chat({ audio })`	OpenAI, Gemini, Anthropic
Batch STT/TTS	`ai.transcribe`, `ai.speak`	OpenAI, xAI, Gemini, Groq, Mistral, Together where provider endpoints exist
Signature audio artifacts	`speech:audio` outputs + `speech` options	model emits script text, Ax synthesizes audio after parsing
Conversational audio	`.chat()` + `result.audio`	OpenAI `gpt-audio*`, `gpt-realtime-2`, `gpt-realtime-whisper`; Gemini Live native audio; Grok Voice
Workflows	`flow`, `AxFlow`	typed DAG, parallelism, branching, sub-contexts
Optimization	`AxGEPA`, `AxACE`, `AxBootstrapFewShot`	Pareto front, playbook curriculum, few-shot
Agent loop	`agent`, `AxAgent`	distiller → executor → responder
Context map	`contextMap`, `AxAgentContextMap`	persistent orientation cache for recurring long context
Memories	`onMemoriesSearch`, `recall(...)`	vector/BM25-backed context loader
Skills	`onSkillsSearch`, `consult(...)`	on-demand prompt-section loader
Sandboxed JS	`AxJSRuntime`, `AxJSRuntimePermission`	Node, Bun, Deno, browser
Recursive runtime (RLM)	`agent({ runtime, contextFields })`	long-context REPL with checkpointed replay
Providers	`ai({ name: ... })`	OpenAI, Anthropic, Gemini, Mistral, Cohere, Groq, Together, Ollama, OpenRouter, Bedrock (separate pkg), Reka, DeepSeek, Grok, HuggingFace, WebLLM
Observability	OpenTelemetry, `executorTurnCallback`, `onFunctionCall`	per-turn telemetry, tool-call tracing
RAG	`AxDBManager`, `AxDefaultResultReranker`	multi-hop retrieval with quality loops
MCP	`AxMCPClient`, `AxMCPHTTPSSETransport`, `AxMCPStreambleHTTPTransport`	use any MCP server as a tool source

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

Deep dives

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, audio-batch-and-agent.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

Name		Name	Last commit message	Last commit date
Latest commit History 1,484 Commits
.circleci		.circleci
.claude		.claude
.cspell		.cspell
.github		.github
.vscode		.vscode
docs		docs
media		media
notes		notes
scripts		scripts
src		src
.editorconfig		.editorconfig
.gitignore		.gitignore
.node-version		.node-version
.prettierrc		.prettierrc
.release-it.json		.release-it.json
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
ax.pdf		ax.pdf
biome.jsonc		biome.jsonc
cspell.json		cspell.json
index.ts		index.ts
lefthook.yml		lefthook.yml
package-lock.json		package-lock.json
package.json		package.json
tsconfig.json		tsconfig.json
typedoc.json		typedoc.json

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Ax — DSPy for TypeScript

What Ax is

30 seconds

Provider-Native Speed

Examples

Structured extraction

Nested objects with `f()`

Standard Schema v1 (Zod / Valibot / ArkType)

Tools (ReAct)

Multi-modal

Audio

AxAgent

Context map, memories, skills, sandboxed runtime

AxFlow + optimization

Capabilities

Install

Documentation

Run examples

Community

Contributors

License

About

Uh oh!

Releases 417

Uh oh!

Contributors

Uh oh!

Languages

Folders and files

Latest commit

History

Repository files navigation

Ax — DSPy for TypeScript

What Ax is

30 seconds

Provider-Native Speed

Examples

Structured extraction

Nested objects with f()

Standard Schema v1 (Zod / Valibot / ArkType)

Tools (ReAct)

Multi-modal

Audio

AxAgent

Context map, memories, skills, sandboxed runtime

AxFlow + optimization

Capabilities

Install

Documentation

Run examples

Community

Contributors

License

About

Topics

Resources

License

Contributing

Security policy

Uh oh!

Stars

Watchers

Forks

Releases 417

Uh oh!

Contributors

Uh oh!

Languages

Nested objects with `f()`