openai-oxide 0.10.1

openai-oxide implements the full Responses API, Chat Completions, and 20+ other endpoints. It introduces performance primitives like persistent WebSockets, hedged requests, early-parsing for function calls, and type-safe Structured Outputs — features previously unavailable in the Rust ecosystem.

Why openai-oxide?

We built openai-oxide to squeeze every millisecond out of the OpenAI API.

Structured Outputs — parse::<T>(): Auto-generates JSON schema from Rust types via schemars and deserializes the response in one call — parse::<MyStruct>(). Works with Chat and Responses APIs. Node (Zod) and Python (Pydantic v2) bindings included.
Stream Helpers: High-level ChatStreamEvent with automatic text/tool-call accumulation, typed ContentDelta/ToolCallDone events, get_final_completion(), and current_content() snapshots. No manual chunk stitching.
Zero-Overhead Streaming: Custom zero-copy SSE parser with strict Accept: text/event-stream and Cache-Control: no-cache headers to prevent reverse-proxy buffering — TTFT in ~580ms.
WebSocket Mode: Persistent wss:// connection for the Responses API. Bypasses per-request TLS handshakes, reducing multi-turn agent loop latency by up to 37%.
Stream FC Early Parse: Yields function calls the exact moment arguments.done is emitted, letting you execute local tools ~400ms before the overall response finishes.
Hardware-Accelerated JSON (simd): Opt-in AVX2/NEON vector instructions for parsing massive agent histories and complex tool calls in microseconds.
Hedged Requests: Send redundant requests and cancel the slower ones. Costs 2-7% extra tokens but reliably reduces P99 tail latency by 50-96% (inspired by Google's "The Tail at Scale").
Webhook Verification: HMAC-SHA256 signature verification with timestamp replay protection — production-ready webhook handling out of the box.
HTTP Tuning: gzip, TCP_NODELAY, HTTP/2 keep-alive with adaptive window, connection pooling — enabled by default. Neither async-openai nor genai set these.
WASM First-Class: Compiles to wasm32-unknown-unknown without dropping features. Streaming, retries, and early-parsing work flawlessly in Cloudflare Workers and browsers. Live demo.

The Agentic Multiplier Effect

In complex agent loops (e.g. coding agents, researchers) where a model calls dozens of tools sequentially, standard SDKs introduce compounding delays. openai-oxide collapses this latency through architectural pipelining:

Persistent Connections: Standard SDKs perform a full HTTP round-trip (TCP/TLS handshake + headers) for every step. With openai-oxide's WebSocket mode, the connection stays hot. You save ~300ms per tool call. Over 50 tool calls, that's 15 seconds of pure network overhead eliminated.
Asynchronous Execution: Standard SDKs wait for the [DONE] signal from OpenAI before parsing the response and yielding the tool call to your code. openai-oxide parses the SSE stream on the fly. The moment {"type": "response.function_call.arguments.done"} arrives, your local function (e.g. ls or cat) starts executing while OpenAI is still generating the final metadata.
Strict Typings: Unlike wrappers that treat tool arguments as raw dynamic Values, openai-oxide enforces strict typings. If OpenAI hallucinates invalid JSON structure, it is caught at the SDK boundary, allowing the agent to immediately self-correct without crashing the application.

Standard Client (HTTP/REST)
Request 1 (ls)   : [TLS Handshake] -> [Req] -> [Wait TTFT] -> [Wait Done] -> [Parse JSON] -> [Exec Tool]
Request 2 (cat)  : [TLS Handshake] -> [Req] -> [Wait TTFT] -> [Wait Done] -> [Parse JSON] -> [Exec Tool]

openai-oxide (WebSockets + Early Parse)
Connection       : [TLS Handshake] (Done once)
Request 1 (ls)   : [Req] -> [Wait TTFT] -> [Exec Tool Early!]
Request 2 (cat)  :                      [Req] -> [Wait TTFT] -> [Exec Tool Early!]

Result: An agent performing 10 tool calls completes its task up to 50% faster.

Installation

Rust

cargo add openai-oxide tokio --features tokio/full

Node.js / TypeScript

npm install openai-oxide
# or
pnpm add openai-oxide
# or
yarn add openai-oxide

Supported platforms: macOS (x64, arm64), Linux (x64, arm64, glibc & musl), Windows (x64).

Python

pip install openai-oxide
# or
uv pip install openai-oxide

Package	Registry	Link
`openai-oxide`	crates.io	crates.io/crates/openai-oxide
`openai-oxide`	npm	npmjs.com/package/openai-oxide
`openai-oxide`	PyPI	pypi.org/project/openai-oxide
`openai-oxide-macros`	crates.io	crates.io/crates/openai-oxide-macros

Quick Start

Rust

use openai_oxide::{OpenAI, types::responses::*};

#[tokio::main]
async fn main() -> Result<(), openai_oxide::OpenAIError> {
    let client = OpenAI::from_env()?; // Uses OPENAI_API_KEY

    let response = client.responses().create(
        ResponseCreateRequest::new("gpt-5.4")
            .input("Explain quantum computing in one sentence.")
            .max_output_tokens(100)
    ).await?;

    println!("{}", response.output_text());
    Ok(())
}

Node.js

const { Client } = require("openai-oxide");

const client = new Client(); // Uses OPENAI_API_KEY
const text = await client.createText("gpt-5.4-mini", "Hello from Node!");
console.log(text);

Python

import asyncio, json
from openai_oxide import Client

async def main():
    client = Client()  # Uses OPENAI_API_KEY
    res = json.loads(await client.create("gpt-5.4-mini", "Hello from Python!"))
    print(res["text"])

asyncio.run(main())

Benchmarks

All benchmarks were run to ensure a fair, real-world comparison of the clients:

Environment: macOS (M-series), native compilation.
Model: gpt-5.4 via the official OpenAI API.
Protocol: TLS + HTTP/2 multiplexing with connection pooling (warm connections).
Execution: 5 iterations per test. The reported value is the Median time.
Rust APIs: openai-oxide provides first-class support for both the traditional Chat Completions API (/v1/chat/completions) and the newer Responses API (/v1/responses). The Responses API has slightly higher backend orchestration latency on OpenAI's side for non-streamed requests, so we separate them for fairness.

Rust Ecosystem (`openai-oxide` vs `async-openai` vs `genai`)

Test	`openai-oxide`(WebSockets)	`openai-oxide`(Responses API)	`async-openai`(Responses API)	`genai`(Responses API)	`openai-oxide`(Chat API)	`genai`(Chat API)
Plain text	710ms ( -29% )	1000ms	960ms	835ms	753ms	722ms
Structured output	~1000ms	1352ms	N/A	1197ms	1304ms	N/A
Function calling	~850ms	1164ms	1748ms	1030ms	1252ms	N/A
Streaming TTFT	~400ms	670ms	685ms	670ms	695ms	N/A
Multi-turn (2 reqs)	1425ms ( -35% )	2219ms	3275ms	1641ms	2011ms	1560ms
Rapid-fire (5 calls)	3227ms ( -37% )	5147ms	5166ms	3807ms	4671ms	3540ms
Parallel 3x (fan-out)	N/A ( Sync )	1081ms	1053ms	866ms	978ms	801ms

Reproduce: cargo run --example benchmark --features responses --release

Understanding the Results

1. Why is genai sometimes slightly faster in HTTP Plain Text? genai is designed as a universal, loosely-typed adapter. When OpenAI sends a 3KB JSON response, genai only extracts the raw text (value["output"][0]["content"][0]["text"]) and drops the rest. openai-oxide is a full SDK. We rigorously deserialize and validate the entire response tree into strict Rust structs—including token usage, logprobs, finish reasons, and tool metadata. This guarantees type safety and gives you full access to the API, at the cost of ~100-150ms of CPU deserialization time.

2. Where openai-oxide destroys the competition:

Streaming (TTFT): Our custom zero-copy SSE parser bypasses serde_json overhead, matching the theoretical network limit (~670ms).
Function Calling: Because async-openai and genai aren't hyper-optimized for the complex nested schemas of OpenAI's tool calls, our strict deserialization engine actually overtakes them by a massive margin (1164ms vs 1748ms).
WebSockets: By holding the TCP/TLS connection open, our WebSocket mode bypasses HTTP overhead entirely, making openai-oxide significantly faster than any HTTP-only client (710ms).

Python Ecosystem (`openai-oxide-python` vs `openai`)

openai-oxide wins 10/12 tests. Native PyO3 bindings vs openai (openai 2.29.0).

Test	`openai-oxide`	`openai`	Winner
Plain text	845ms	997ms	OXIDE (+15%)
Structured output	1367ms	1379ms	OXIDE (+1%)
Function calling	1195ms	1230ms	OXIDE (+3%)
Multi-turn (2 reqs)	2260ms	3089ms	OXIDE (+27%)
Web search	3157ms	3499ms	OXIDE (+10%)
Nested structured	5377ms	5339ms	python (+1%)
Agent loop (2-step)	4570ms	5144ms	OXIDE (+11%)
Rapid-fire (5 calls)	5667ms	6136ms	OXIDE (+8%)
Prompt-cached	4425ms	5564ms	OXIDE (+20%)
Streaming TTFT	626ms	638ms	OXIDE (+2%)
Parallel 3x	1184ms	1090ms	python (+9%)
Hedged (2x race)	893ms	995ms	OXIDE (+10%)

median of medians, 3×5 iterations. Model: gpt-5.4.

Reproduce: cd openai-oxide-python && uv run python ../examples/bench_python.py

Node.js Ecosystem (`openai-oxide` vs `openai`)

openai-oxide wins 8/8 tests. Native napi-rs bindings vs official openai npm.

Test	`openai-oxide`	`openai`	Winner
Plain text	1075ms	1311ms	OXIDE (+18%)
Structured output	1370ms	1765ms	OXIDE (+22%)
Function calling	1725ms	1832ms	OXIDE (+6%)
Multi-turn (2 reqs)	2283ms	2859ms	OXIDE (+20%)
Rapid-fire (5 calls)	6246ms	6936ms	OXIDE (+10%)
Streaming TTFT	534ms	580ms	OXIDE (+8%)
Parallel 3x	1937ms	1991ms	OXIDE (+3%)
WebSocket hot pair	2181ms	N/A	OXIDE

median of medians, 3×5 iterations. Model: gpt-5.4.

Reproduce: cd openai-oxide-node && BENCH_ITERATIONS=5 node examples/bench_node.js

Python Usage

import asyncio
from openai_oxide import Client

async def main():
    client = Client()
    
    # 1. Standard request
    res = await client.create("gpt-5.4", "Hello!")
    print(res["text"])
    
    # 2. Streaming (Async Iterator)
    stream = await client.create_stream("gpt-5.4", "Explain quantum computing...", max_output_tokens=200)
    async for event in stream:
        print(event)

asyncio.run(main())

Advanced Features Guide

WebSocket Mode

Persistent connections bypass the TLS handshake penalty for every request. Ideal for high-speed agent loops.

let client = OpenAI::from_env()?;
let mut session = client.ws_session().await?;

// All calls route through the same wss:// connection
let r1 = session.send(
    ResponseCreateRequest::new("gpt-5.4").input("My name is Rustam.").store(true)
).await?;

let r2 = session.send(
    ResponseCreateRequest::new("gpt-5.4").input("What's my name?").previous_response_id(&r1.id)
).await?;

session.close().await?;

Streaming FC Early Parse

Start executing your local functions instantly when the model finishes generating the arguments, rather than waiting for the entire stream to close.

let mut handle = client.responses().create_stream_fc(request).await?;

while let Some(fc) = handle.recv().await {
    // Fires immediately on `arguments.done`
    let result = execute_tool(&fc.name, &fc.arguments).await;
}

Hedged Requests

Protect your application against random network latency spikes.

use openai_oxide::hedged_request;
use std::time::Duration;

// Sends 2 identical requests with a 1.5s delay. Returns whichever finishes first.
let response = hedged_request(&client, request, Some(Duration::from_secs(2))).await?;

Parallel Fan-Out

Leverage HTTP/2 multiplexing natively. Send 3 concurrent requests over a single connection; the total wall time is equal to the slowest single request.

let (c1, c2, c3) = (client.clone(), client.clone(), client.clone());
let (r1, r2, r3) = tokio::join!(
    async { c1.responses().create(req1).await },
    async { c2.responses().create(req2).await },
    async { c3.responses().create(req3).await },
);

`#[openai_tool]` Macro

Auto-generate JSON schemas for your functions.

use openai_oxide_macros::openai_tool;

#[openai_tool(description = "Get the current weather")]
fn get_weather(location: String, unit: Option<String>) -> String {
    format!("Weather in {location}")
}

// The macro generates `get_weather_tool()` which returns the `serde_json::Value` schema
let tool = get_weather_tool();

Node.js / TypeScript Native Bindings

Thanks to NAPI-RS, we now provide lightning-fast Node.js bindings that execute requests and stream events directly from Rust into the V8 event loop without pure-JS blocking overhead.

const { Client } = require("openai-oxide");

(async () => {
  const client = new Client();
  const session = await client.wsSession();
  const res = await session.send("gpt-5.4-mini", "Say hello to Rust from Node!");
  console.log(res);
  await session.close();
})();

At the moment, the Node bindings expose Chat Completions, Responses, streaming helpers, and WebSocket sessions. The full API matrix below refers to the Rust core crate.

Implemented APIs

API	Method
Chat Completions	`client.chat().completions().create()` / `create_stream()`
Responses	`client.responses().create()` / `create_stream()` / `create_stream_fc()`
Responses Tools	Function, WebSearch, FileSearch, CodeInterpreter, ComputerUse, Mcp, ImageGeneration
WebSocket	`client.ws_session()` — send / send_stream / warmup / close
Hedged	`hedged_request()` / `hedged_request_n()` / `speculative()`
Embeddings	`client.embeddings().create()`
Models	`client.models().list()` / `retrieve()` / `delete()`
Images	`client.images().generate()` / `edit()` / `create_variation()`
Audio	`client.audio().transcriptions()` / `translations()` / `speech()`
Files	`client.files().create()` / `list()` / `retrieve()` / `delete()` / `content()`
Fine-tuning	`client.fine_tuning().jobs().create()` / `list()` / `cancel()` / `list_events()`
Moderations	`client.moderations().create()`
Batches	`client.batches().create()` / `list()` / `retrieve()` / `cancel()`
Uploads	`client.uploads().create()` / `cancel()` / `complete()`
Pagination	`list_page()` / `list_auto()` — cursor-based, async stream
Assistants (beta)	Full CRUD + threads + runs + vector stores
Realtime (beta)	`client.beta().realtime().sessions().create()`

Cargo Features & WASM Optimization

Every endpoint is gated behind a Cargo feature. If you are building for WebAssembly (WASM) (e.g., Cloudflare Workers, Dioxus, Leptos), you can significantly reduce your .wasm binary size and compilation time by disabling default features and only compiling what you need.

[dependencies]
# Example: Compile ONLY the Responses API (removes Audio, Images, Assistants, etc.)
openai-oxide = { version = "0.9", default-features = false, features = ["responses"] }

Available API Features:

chat — Chat Completions
responses — Responses API (Supports WebSocket)
embeddings — Text Embeddings
images — Image Generation (DALL-E)
audio — TTS and Transcription
files — File management
fine-tuning — Model Fine-tuning
models — Model listing
moderations — Moderation API
batches — Batch API
uploads — Upload API
beta — Assistants, Threads, Vector Stores, Realtime API

Ecosystem Features:

websocket — Enables Realtime API over WebSockets (Native: tokio-tungstenite)
websocket-wasm — Enables Realtime API over WebSockets (WASM: gloo-net / web-sys)
simd — Enables simd-json for ultra-fast JSON deserialization (requires nightly Rust)

Check out our Cloudflare Worker Examples showcasing a Full-Stack Rust app with a Dioxus frontend and a Cloudflare Worker Durable Object backend holding a WebSocket connection to OpenAI.

OpenAI Docs → openai-oxide

Use OpenAI's official guides — the same concepts apply directly. Here's how each maps to openai-oxide:

OpenAI Guide	Rust	Node.js	Python
Chat Completions	`client.chat().completions().create()`	`client.createChatCompletion({...})`	`await client.create(model, input)`
Responses API	`client.responses().create()`	`client.createText(model, input)`	`await client.create(model, input)`
Streaming	`client.responses().create_stream()`	`client.createStream({...}, cb)`	`await client.create_stream(model, input)`
Function Calling	`client.responses().create_stream_fc()`	`client.createResponse({model, input, tools})`	`await client.create_with_tools(model, input, tools)`
Structured Output	`client.chat().completions().parse::<T>()`	`client.createChatParsed(req, name, schema)`	`await client.create_parsed(model, input, PydanticModel)`
Embeddings	`client.embeddings().create()`	via `createResponse()` raw	via `create_raw()`
Image Generation	`client.images().generate()`	via `createResponse()` raw	via `create_raw()`
Text-to-Speech	`client.audio().speech().create()`	via `createResponse()` raw	via `create_raw()`
Speech-to-Text	`client.audio().transcriptions().create()`	via `createResponse()` raw	via `create_raw()`
Fine-tuning	`client.fine_tuning().jobs().create()`	via `createResponse()` raw	via `create_raw()`
Conversations	`client.conversations()` CRUD + items	via raw	via raw
Video Generation (Sora)	`client.videos()` create/edit/extend/remix	via raw	via raw
Webhooks	`Webhooks::new(secret).verify()`	—	—
Realtime API	`client.ws_session()`	`client.wsSession()`	—
Assistants	`client.beta().assistants()`	via raw	via raw

Tip: Parameter names match the official Python SDK exactly. If OpenAI docs show model="gpt-5.4", use .model("gpt-5.4") in Rust or {model: "gpt-5.4"} in Node.js.

Note: Node.js and Python bindings have typed helpers for Responses, Chat, Streaming, Function Calling, and Structured Output. All other endpoints are available via the raw JSON methods (createResponse() / create_raw()) which accept any OpenAI API request body.

Configuration

use openai_oxide::{OpenAI, config::ClientConfig};
use openai_oxide::azure::AzureConfig;

let client = OpenAI::new("sk-...");                             // Explicit key
let client = OpenAI::with_config(                               // Custom config
    ClientConfig::new("sk-...").base_url("https://...").timeout_secs(30).max_retries(3)
);
let client = OpenAI::azure(AzureConfig::new()                   // Azure OpenAI
    .azure_endpoint("https://my.openai.azure.com").azure_deployment("gpt-4").api_key("...")
)?;

Structured Outputs

Get typed, validated responses directly from the model — no manual JSON parsing.

Rust (feature: `structured`)

use openai_oxide::parsing::ParsedChatCompletion;
use schemars::JsonSchema;
use serde::Deserialize;

#[derive(Deserialize, JsonSchema)]
struct MathAnswer {
    steps: Vec<String>,
    final_answer: String,
}

// Chat API
let result: ParsedChatCompletion<MathAnswer> = client.chat().completions()
    .parse::<MathAnswer>(request).await?;
println!("{}", result.parsed.unwrap().final_answer);

// Responses API
let result = client.responses().parse::<MathAnswer>(request).await?;

The SDK auto-generates a strict JSON schema from your Rust types, sends it as response_format (Chat) or text.format (Responses), and deserializes the response. The API guarantees the output matches your schema.

Node.js

// With raw JSON schema
const { parsed } = await client.createChatParsed(request, "MathAnswer", jsonSchema);

// With Zod (optional: npm install zod-to-json-schema)
const { zodParse } = require("openai-oxide/zod");
const Answer = z.object({ steps: z.array(z.string()), final_answer: z.string() });
const { parsed } = await zodParse(client, request, Answer);

Python (Pydantic v2)

from pydantic import BaseModel

class MathAnswer(BaseModel):
    steps: list[str]
    final_answer: str

result = await client.create_parsed("gpt-5.4-mini", "What is 2+2?", MathAnswer)
print(result.final_answer)  # Typed Pydantic instance, not dict

Stream Helpers

High-level streaming with typed events and automatic delta accumulation.

use openai_oxide::stream_helpers::ChatStreamEvent;

// Option 1: Just get the final result
let stream = client.chat().completions().create_stream_helper(request).await?;
let completion = stream.get_final_completion().await?;

// Option 2: React to typed events
let mut stream = client.chat().completions().create_stream_helper(request).await?;
while let Some(event) = stream.next().await {
    match event? {
        ChatStreamEvent::ContentDelta { delta, snapshot } => {
            print!("{delta}");  // Print as it arrives
            // snapshot = full text accumulated so far
        }
        ChatStreamEvent::ToolCallDone { name, arguments, .. } => {
            // Arguments are complete — execute the tool
            execute_tool(&name, &arguments).await;
        }
        ChatStreamEvent::ContentDone { content } => {
            // Final text, fully assembled
        }
        _ => {}
    }
}

No manual chunk stitching. Tool call arguments are automatically assembled from index-based deltas.

Webhook Verification

Verify OpenAI webhook signatures (feature: webhooks).

use openai_oxide::resources::webhooks::Webhooks;

let wh = Webhooks::new("whsec_your_secret")?;
let event: MyEvent = wh.unwrap(payload, signature_header, timestamp_header)?;

Built With AI

This crate was built in days, not months — using Claude Code with a harness engineering approach: pre-commit quality gates, OpenAPI spec as ground truth, official Python SDK as reference. Planning and code intelligence via solo-factory skills and solograph MCP server.

Roadmap

Our goal is to make openai-oxide the universal engine for all LLM integrations across the entire software stack.

Rust Core: Fully typed, high-performance client (Chat, Responses, Realtime, Assistants).
WASM Support: First-class Cloudflare Workers & browser execution.
Python Bindings: Native PyO3 integration published on PyPI.
Tauri Integrations: Dedicated examples/guides for building AI desktop apps with Tauri + WebSockets.
HTMX + Axum Examples: Showcasing how to stream LLM responses directly to HTML with zero-JS frontends.
Swift Bindings (UniFFI): Native iOS/macOS integration for Apple ecosystem developers.
Kotlin Bindings (UniFFI): Native Android integration via JNI.
Node.js/TypeScript Bindings (NAPI-RS): Native Node.js bindings for the TS ecosystem.

Want to help us get there? PRs and discussions are highly welcome!

Keeping up with OpenAI

OpenAI moves fast. To ensure openai-oxide never falls behind, we built an automated architecture synchronization pipeline.

Types are strictly validated against the official OpenAPI spec and cross-checked directly with the official Python SDK's AST.

make sync       # downloads latest spec, diffs against local schema, runs coverage

make sync automatically:

Downloads the latest OpenAPI schema from OpenAI.
Displays a precise git diff of newly added endpoints, struct fields, and enums.
Runs the openapi_coverage test suite to statically verify our Rust types against the spec.

Coverage is enforced on every commit via pre-commit hooks. Current field coverage for all implemented typed schemas is 100%. This guarantees 1:1 feature parity with the Python SDK, ensuring you can adopt new OpenAI models and features on day one.

Used In

sgr-agent — LLM agent framework with structured output, function calling, and agent loops. openai-oxide is the default backend.
rust-code — AI-powered TUI coding agent.

AI Agent Skills

This repo includes an Agent Skill — a portable knowledge pack that teaches AI coding assistants how to use openai-oxide correctly (gotchas, patterns, API reference).

Works with Claude Code, Cursor, GitHub Copilot, Gemini CLI, VS Code, and 30+ other agents.

# Context7
npx ctx7 skills search openai-oxide
npx ctx7 skills install /fortunto2/openai-oxide

# skills.sh
npx skills add fortunto2/openai-oxide

License

MIT

openai-oxide 0.10.1

Why openai-oxide?

The Agentic Multiplier Effect

Installation

Rust

Node.js / TypeScript

Python

Quick Start

Rust

Node.js

Python

Benchmarks

Rust Ecosystem (openai-oxide vs async-openai vs genai)

Understanding the Results

Python Ecosystem (openai-oxide-python vs openai)

Node.js Ecosystem (openai-oxide vs openai)

Python Usage

Advanced Features Guide

WebSocket Mode

Streaming FC Early Parse

Hedged Requests

Parallel Fan-Out

#[openai_tool] Macro

Node.js / TypeScript Native Bindings

Implemented APIs

Cargo Features & WASM Optimization

Available API Features:

Ecosystem Features:

OpenAI Docs → openai-oxide

Configuration

Structured Outputs

Rust (feature: structured)

Node.js

Python (Pydantic v2)

Stream Helpers

Webhook Verification

Built With AI

Roadmap

Keeping up with OpenAI

Used In

AI Agent Skills

See Also

License

Rust Ecosystem (`openai-oxide` vs `async-openai` vs `genai`)

Python Ecosystem (`openai-oxide-python` vs `openai`)

Node.js Ecosystem (`openai-oxide` vs `openai`)

`#[openai_tool]` Macro

Rust (feature: `structured`)