Ax — DSPy for TypeScript / Python / Java / C++ / Go / Rust and more

One programming model for building with LLMs across TypeScript, Python, Java,
C++, Go, and Rust.

Ax is TypeScript-first and ships today as @ax-llm/ax. The same signatures,
provider mappings, agents, flows, runtime contracts, and optimizers are also
compiled into verified generated Python, Java, C++, Go, and Rust libraries.

💬 Follow @dosco on X for new releases and to chat about the project.

What Ax is

Signatures for typed structured generation: string DSL, fluent f()
builder, or any Standard Schema v1 validator — Zod, Valibot, ArkType.
Provider abstraction across OpenAI-compatible endpoints, OpenAI
Responses, Anthropic, Gemini, Grok/xAI, Mistral, Cohere, Reka, DeepSeek,
Azure OpenAI, audio, and realtime event streams.
Agents with runtime execution, context budgets, checkpoints, action-log
replay, discovery, memory, skills, and delegation.
Flows as typed program graphs with branches, loops, feedback, cache
behavior, parallel execution, and .returns(...) projection.
Optimizers including GEPA, few-shot bootstrapping, portable optimizer
artifacts, and evaluation/apply flows.
One semantic core compiled into TypeScript, Python, Java, C++, Go, and
Rust library shapes, so the same Ax program model can move across runtime
stacks.

Language Matrix

Ecosystem	Package / import	Status
TypeScript / JavaScript	`@ax-llm/ax`<br>`import { ai, ax, agent, flow } from "@ax-llm/ax"`	Published on npm
Python	`axllm`<br>`from axllm import ai, ax, agent, flow`	Published on PyPI
Java	`dev.axllm:ax`<br>`import dev.axllm.ax.*`	Published on Maven Central
C++	`axllm::axllm`<br>`#include <axllm/axllm.hpp>`	CMake `FetchContent` (source build)
Go	`github.com/ax-llm/ax/packages/go`<br>`import ax "github.com/ax-llm/ax/packages/go"`	Installable with `go get`; opt-in `runtime/goja` actor runtime
Rust	`axllm`<br>`use axllm::{ai, ax, agent, flow};`	Published on crates.io; protocol-first code runtime

mermaid
flowchart LR
  S["Signature (string, f, Standard Schema)"] --> G["AxGen typed generation"]
  G --> P["Provider descriptors / AI clients"]
  G --> A["AxAgent"]
  G --> F["AxFlow"]
  G --> O["GEPA / optimizer artifacts"]
  C["Shared Ax semantics"] --> TS["TypeScript"]
  C --> PY["Python"]
  C --> JV["Java"]
  C --> CP["C++"]
  C --> GO["Go"]
  C --> RS["Rust"]

30 seconds

The TypeScript package is the source implementation and the current published
package:

typescript
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", "deepseek", "grok", etc. — same signature, same code.

Same idea in every language

The generated Python, Java, C++, Go, and Rust libraries expose the same top-level Ax
ideas in native package shapes. Their generated source is checked in under
packages/<language> so the supported APIs are easy to inspect. The repo
runner uses those committed packages and runs examples without asking you to
remember compiler commands:

bash
npm run example -- list
npm run example -- python src/examples/python/generation/axgen-openai.py
npm run example -- java src/examples/java/generation/BasicGenerationExample.java
npm run example -- cpp src/examples/cpp/generation/basic_generation.cpp
npm run example -- go src/examples/go/generation/basic_generation.go
npm run example -- rust src/examples/rust/generation/basic_generation.rs

See src/examples/README.md for runnable examples,
docs/RELEASE.md for package/release shape, and
docs/COMPILER.md for how the language-agnostic Ax
compiler works. When AxIR changes, run npm run axir:generate-packages to
refresh the checked-in packages.

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 incremental output, streamingForward() exposes the stream directly.

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

bash
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

typescript
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()`

typescript
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.

typescript
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)

typescript
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?",
});

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

Audio

Batch speech APIs are exposed by 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.

typescript
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.

typescript
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.

mermaid
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"]

typescript
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.

typescript
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.

typescript
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.

typescript
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.

typescript
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.

Security model: the runtime is defense-in-depth for LLM-authored code, not a container or VM boundary. Host callbacks and the permissions you grant remain the authority boundary; keep durable secrets and privileged effects in host-side functions.

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

AxFlow + optimization

AxFlow is a typed, chainable workflow runner — define nodes, wire state through execute, and finalize outputs with returns. State types evolve as you add nodes, so the final output mapper is fully type-checked. Independent node executes are planned as a safe DAG optimization when their metadata reads and writes do not conflict.

typescript
import { ai, 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 }))
  .r((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.

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

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, Mistral 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`	typed program graphs, branching, loops, parallelism, `.returns(...)`
Optimization	`AxGEPA`, `AxBootstrapFewShot`	Pareto front, few-shot, portable optimizer artifacts
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 runtime	`AxJSRuntime`, `AxJSRuntimePermission`	TypeScript runtime for Node, Bun, Deno, browser
Recursive runtime (RLM)	`agent({ runtime, contextFields })`	long-context REPL with checkpointed replay
Providers	`ai({ name: ... })`	OpenAI, OpenAI Responses, Azure OpenAI, Anthropic, Gemini, Mistral, Cohere, Reka, DeepSeek, Grok/xAI, Bedrock (separate pkg)
OpenAI-compatible endpoints	`ai({ name: "openai", apiURL, apiKey, models })`	one path for custom OpenAI-compatible gateways
Observability	OpenTelemetry, `actorTurnCallback`, `onFunctionCall`	per-turn telemetry, tool-call tracing
MCP	`AxMCPClient`, `AxMCPStreamableHTTPTransport`, `AxMCPStdioTransport`	use any MCP server as a tool source

Install

TypeScript / JavaScript:

bash
npm install @ax-llm/ax

The generated Python, Java, C++, Go, and Rust libraries are checked in under packages/,
verified in this repo, and installable from each language's package manager (all Apache-2.0):

Python: pip install axllm
Rust: cargo add axllm
Go: go get github.com/ax-llm/ax/packages/go
Java: dev.axllm:ax on Maven Central (Gradle / Maven snippet in packages/java)
C++: CMake FetchContent (GIT_REPOSITORY https://github.com/ax-llm/ax,
SOURCE_SUBDIR packages/cpp), then link axllm::axllm

Optional packages:

bash
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

bash
OPENAI_APIKEY=your-key npm run tsx ./src/examples/<name>.ts
npm run example -- list
npm run example -- list --json
npm run example -- ts src/examples/typescript/generation/axgen-openai.ts
npm run example -- python src/examples/python/short-agents/agent-openai.py
npm run example -- java src/examples/java/flows/SequentialFlowExample.java
npm run example -- cpp src/examples/cpp/audio/speech_audio.cpp
npm run example -- go src/examples/go/optimization/axgen_optimization.go
npm run example -- rust src/examples/rust/generation/basic_generation.rs

npm run example -- list shows public provider-backed examples for TypeScript,
Python, Java, C++, Go, and Rust. Public examples live under
src/examples/<language>/<group>/, use ax-example metadata headers, call real
providers, and read credentials from .env. Internal generated-package fixtures
remain under packages/<language>/examples for AxIR verification, but the public
catalog and website are generated only from src/examples/<language>/.

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, openai-compatible.ts, ax-flow-enhanced-demo.ts. Browse all examples →

Community

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

Contributing

Ax is TypeScript-first. Most contributors and coding agents should focus on the
TypeScript source change they are making and should not try to update every
generated language backend by hand.

When a PR changes portable behavior under src/ax/ai/, src/ax/dsp/,
src/ax/agent/, src/ax/flow/, or src/ax/mcp/, CI will ask for either
AxIR/conformance updates or an AxIR backlog entry. If you are not already
working in AxIR, use the backlog path:

bash
npm run axir:backlog -- add --title "..." --surface axai --impact "..." --paths src/ax/ai/...
npm run axir:backlog:validate

That keeps normal TypeScript PRs small while giving AxIR maintainers and coding
agents a precise queue for migrating the behavior into Python, Java, C++, Go,
and future generated backends later.