openai-oxide 0.11.1

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.
Streaming: SSE parser with anti-buffering headers (Accept: text/event-stream, Cache-Control: no-cache). On mock benchmarks, per-chunk processing is 2.6x faster than the official JS SDK (283µs vs 742µs for 114 chunks, p<0.001).
WebSocket Mode: Persistent wss:// connection for the Responses API. Measured 29-44% faster on multi-turn benchmarks vs HTTP (warm connections).
Stream FC Early Parse: Yields function calls the exact moment arguments.done is emitted, letting you start executing local tools 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. Trades extra tokens for lower tail latency (technique from 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.

WebSocket Mode for Agent Loops

In multi-turn agent loops, WebSocket mode avoids per-request HTTP/2 framing and header overhead. Both HTTP and WebSocket reuse the same TCP+TLS connection (no per-request handshake), but WebSocket eliminates HTTP/2 frame negotiation.

Standard Client (HTTP/2, warm connection)
Request 1 (ls)   : [HTTP/2 frames] -> [Wait TTFT] -> [Wait Done] -> [Parse] -> [Exec Tool]
Request 2 (cat)  : [HTTP/2 frames] -> [Wait TTFT] -> [Wait Done] -> [Parse] -> [Exec Tool]

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

Measured savings on warm connections (gpt-5.4, median of medians):

Plain text: 710ms WS vs 1011ms HTTP (29% faster)
Multi-turn (2 reqs): 1425ms vs 2362ms (40% faster)
Rapid-fire (5 calls): 3227ms vs 5807ms (44% faster)

The gap likely reflects both reduced framing overhead and different server-side routing for the WebSocket endpoint. Early parse (yielding tool calls before [DONE]) provides additional savings in streaming mode.

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

Environment: macOS (M-series), release mode.
Model: gpt-5.4 via the official OpenAI API.
Protocol: TLS + HTTP/2 with connection pooling (warm connections).
Methodology: 5 iterations per test, median. Date: 2026-03-29.

Rust Ecosystem

openai-oxide vs async-openai 0.34 vs genai 0.6-beta. All via Responses API (genai uses Chat API — it's a multi-provider adapter).

Test	`openai-oxide`	`async-openai`	`genai`
Plain text	1068ms	995ms	845ms
Structured output	1430ms	N/A	N/A
Function calling	1153ms	1108ms	N/A
Multi-turn (2 reqs)	2266ms	1866ms	N/A
Streaming TTFT	584ms	N/A	N/A
Parallel 3x (fan-out)	1057ms	N/A	N/A

On single HTTP calls, all three SDKs are within API variance — server latency (800-1100ms) is 100-1000x larger than SDK overhead. genai is slightly faster on plain text because it skips full response deserialization (extracts text only).

Where oxide stands out is features, not single-call speed:

Feature	`openai-oxide`	`async-openai` 0.34	`genai` 0.6
Streaming SSE (584ms TTFT)	yes	no	no
WebSocket Responses API	yes	no	no
Structured `parse::<T>()`	yes	no	no
Parallel fan-out (1057ms)	yes	manual	manual
WASM support	yes	no	no
Node.js / Python bindings	yes	no	no
Hedged requests	yes	no	no
Stream FC early parse	yes	no	no

Reproduce: cd benchmarks/rust-compare && cargo run --release

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. Date: 2026-03-24. Not re-measured since — results may have shifted.

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

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

Live API results are noisy — single-call differences are within API variance (n=10). Neither SDK consistently wins on single requests. The real advantages show on streaming, parallel, and WebSocket workloads.

Test	`openai-oxide`	`openai`	Notes
Plain text	~1050ms	~1000ms	within noise
Structured output	~1400ms	~1350ms	within noise
Function calling	~1220ms	~1210ms	within noise
Multi-turn (2 reqs)	~2200ms	~2500ms	oxide +12% (varies)
Rapid-fire (5 calls)	~4900ms	~4800ms	within noise
Streaming TTFT	~600ms	~670ms	oxide consistently faster
Parallel 3x	~1000ms	~1060ms	oxide consistently faster
WebSocket hot pair	~2300ms	N/A	no official equivalent

10 iterations, median. Model: gpt-5.4. Date: 2026-03-29. Results vary between runs.

Reproduce: cd openai-oxide-node && BENCH_ITERATIONS=10 node examples/bench_node.js

SDK Overhead (synthetic, Node.js)

The live benchmarks above include network latency and model inference, which adds noise. To isolate pure SDK overhead, we also run a synthetic benchmark with a localhost mock server (zero network, zero inference). Fixtures are captured from a real coding agent session (320 messages, 42 tools, 718KB request body).

Test	`openai-oxide`	`openai` npm	oxide faster	sig
Tiny req → Tiny resp	112µs	431µs	+74%	***
Tiny req → Structured 5KB	172µs	393µs	+56%	***
Medium 150KB → Tool call	839µs	1.2ms	+29%	***
Heavy 657KB → Real agent resp	2.3ms	2.7ms	+16%	***
SSE stream (114 real chunks)	312µs	783µs	+60%	***
Agent 20x sequential (tiny)	1.9ms	4.6ms	+58%	***
Agent 10x sequential (heavy)	22.7ms	26.0ms	+13%	***

50 iterations, 20 warmup, --expose-gc, Welch's t-test — all p<0.001. Date: 2026-03-29.

oxide wins 12/12 mock tests (was 10/12 before auto fast-path wrapper).

The wrapper auto-detects large payloads (>8KB) and routes through JSON.stringify → Rust, bypassing the napi object→Value copy. This fixed the heavy-payload regression: 657KB went from -7% (slower) to +16% (faster).

Where oxide is faster: everything on mock — 13-74% depending on payload size. SSE streaming 60% faster (zero-copy parser). Agent loops compound: 20 tiny calls save 2.7ms, 10 heavy calls save 3.3ms.

Where it doesn't matter: single API calls to OpenAI with 200ms-2s latency. SDK overhead (0.1-3ms) is <1% of wall time. Live benchmarks show no consistent winner on single requests — API variance dominates. Real advantages are streaming TTFT, parallel fan-out, and WebSocket mode.

Reproduce: node --expose-gc benchmarks/bench_science.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 0.11.1

Why openai-oxide?

WebSocket Mode for Agent Loops

Installation

Rust

Node.js / TypeScript

Python

Quick Start

Rust

Node.js

Python

Benchmarks

Rust Ecosystem

Python Ecosystem (openai-oxide-python vs openai)

Node.js Ecosystem (openai-oxide vs openai)

SDK Overhead (synthetic, Node.js)

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

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

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

`#[openai_tool]` Macro

Rust (feature: `structured`)