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Deep-Live-Cam

Tue, 31 Mar 2026 16:14:42 +0800

hacksider/Deep-Live-Cam

Deep-Live-Cam 2.1

Real-time face swap and video deepfake with a single click and only a single image.

Disclaimer

This deepfake software is designed to be a productive tool for the AI-generated media industry. It can assist artists in animating custom characters, creating engaging content, and even using models for clothing design.

We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to the law and ethics. We may shut down the project or add watermarks if legally required.

Ethical Use: Users are expected to use this software responsibly and legally. If using a real person’s face, obtain their consent and clearly label any output as a deepfake when sharing online.
Content Restrictions: The software includes built-in checks to prevent processing inappropriate media, such as nudity, graphic content, or sensitive material.
Legal Compliance: We adhere to all relevant laws and ethical guidelines. If legally required, we may shut down the project or add watermarks to the output.
User Responsibility: We are not responsible for end-user actions. Users must ensure their use of the software aligns with ethical standards and legal requirements.

By using this software, you agree to these terms and commit to using it in a manner that respects the rights and dignity of others.

Users are expected to use this software responsibly and legally. If using a real person’s face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.

Exclusive v2.7 beta Quick Start - Pre-built (Windows/Mac Silicon/CPU)

This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU, CPU or Mac Silicon, And you’ll receive special priority support. 2.7 beta is the best you can have with 30+ extra features than the open source version.

These Pre-builts are perfect for non-technical users or those who don’t have time to, or can’t manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually.

TLDR; Live Deepfake in just 3 Clicks

Select a face
Select which camera to use
Press live!

Features & Uses - Everything is in real-time

Mouth Mask

Retain your original mouth for accurate movement using Mouth Mask

Face Mapping

Use different faces on multiple subjects simultaneously

Your Movie, Your Face

Watch movies with any face in real-time

Live Show

Run Live shows and performances

Memes

Create Your Most Viral Meme Yet

_{Created using Many Faces feature in Deep-Live-Cam}

Omegle

Surprise people on Omegle

Installation (Manual)

Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the quickstart version.

Click to see the process

Installation

This is more likely to work on your computer but will be slower as it utilizes the CPU.

1. Set up Your Platform

Python (3.11 recommended)

pip

git

ffmpeg - iex (irm ffmpeg.tc.ht)
Visual Studio 2022 Runtimes (Windows)

2. Clone the Repository

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git clone https://github.com/hacksider/Deep-Live-Cam.git
cd Deep-Live-Cam

3. Download the Models

Place these files in the “models” folder.

4. Install Dependencies

We highly recommend using a venv to avoid issues.

For Windows:

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python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt

For Linux:

# Ensure you use the installed Python 3.10
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

For macOS:

Apple Silicon (M1/M2/M3) requires specific setup:

# Install Python 3.11 (specific version is important)
brew install python@3.11

# Install tkinter package (required for the GUI)
brew install python-tk@3.10

# Create and activate virtual environment with Python 3.11
python3.11 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

** In case something goes wrong and you need to reinstall the virtual environment **

# Deactivate the virtual environment
rm -rf venv

# Reinstall the virtual environment
python -m venv venv
source venv/bin/activate

# install the dependencies again
pip install -r requirements.txt

# gfpgan and basicsrs issue fix
pip install git+https://github.com/xinntao/BasicSR.git@master
pip uninstall gfpgan -y
pip install git+https://github.com/TencentARC/GFPGAN.git@master

Run: If you don’t have a GPU, you can run Deep-Live-Cam using python run.py. Note that initial execution will download models (~300MB).

GPU Acceleration

CUDA Execution Provider (Nvidia)

Install CUDA Toolkit 12.8.0
Install cuDNN v8.9.7 for CUDA 12.x (required for onnxruntime-gpu):
- Download cuDNN v8.9.7 for CUDA 12.x
- Make sure the cuDNN bin directory is in your system PATH
Install dependencies:

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pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip uninstall onnxruntime onnxruntime-gpu
pip install onnxruntime-gpu==1.21.0

Usage:

`1`	`python run.py --execution-provider cuda`

CoreML Execution Provider (Apple Silicon)

Apple Silicon (M1/M2/M3) specific installation:

Make sure you’ve completed the macOS setup above using Python 3.10.
Install dependencies:

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pip uninstall onnxruntime onnxruntime-silicon
pip install onnxruntime-silicon==1.13.1

Usage (important: specify Python 3.10):

`1`	`python3.10 run.py --execution-provider coreml`

Important Notes for macOS:

You must use Python 3.10, not newer versions like 3.11 or 3.13
Always run with python3.10 command not just python if you have multiple Python versions installed
If you get error about _tkinter missing, reinstall the tkinter package: brew reinstall python-tk@3.10
If you get model loading errors, check that your models are in the correct folder

If you encounter conflicts with other Python versions, consider uninstalling them:

# List all installed Python versions
brew list | grep python

# Uninstall conflicting versions if needed
brew uninstall --ignore-dependencies python@3.11 python@3.13

# Keep only Python 3.11
brew cleanup

CoreML Execution Provider (Apple Legacy)

Install dependencies:

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pip uninstall onnxruntime onnxruntime-coreml
pip install onnxruntime-coreml==1.21.0

Usage:

`1`	`python run.py --execution-provider coreml`

DirectML Execution Provider (Windows)

Install dependencies:

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pip uninstall onnxruntime onnxruntime-directml
pip install onnxruntime-directml==1.21.0

Usage:

`1`	`python run.py --execution-provider directml`

OpenVINO™ Execution Provider (Intel)

Install dependencies:

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pip uninstall onnxruntime onnxruntime-openvino
pip install onnxruntime-openvino==1.21.0

Usage:

`1`	`python run.py --execution-provider openvino`

Usage

1. Image/Video Mode

Execute python run.py.
Choose a source face image and a target image/video.
Click “Start”.
The output will be saved in a directory named after the target video.

2. Webcam Mode

Execute python run.py.
Select a source face image.
Click “Live”.
Wait for the preview to appear (10-30 seconds).
Use a screen capture tool like OBS to stream.
To change the face, select a new source image.

Download all models in this huggingface link

Download models here

Command Line Arguments (Unmaintained)

options:
  -h, --help                                               show this help message and exit
  -s SOURCE_PATH, --source SOURCE_PATH                     select a source image
  -t TARGET_PATH, --target TARGET_PATH                     select a target image or video
  -o OUTPUT_PATH, --output OUTPUT_PATH                     select output file or directory
  --frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...]  frame processors (choices: face_swapper, face_enhancer, ...)
  --keep-fps                                               keep original fps
  --keep-audio                                             keep original audio
  --keep-frames                                            keep temporary frames
  --many-faces                                             process every face
  --map-faces                                              map source target faces
  --mouth-mask                                             mask the mouth region
  --video-encoder {libx264,libx265,libvpx-vp9}             adjust output video encoder
  --video-quality [0-51]                                   adjust output video quality
  --live-mirror                                            the live camera display as you see it in the front-facing camera frame
  --live-resizable                                         the live camera frame is resizable
  --max-memory MAX_MEMORY                                  maximum amount of RAM in GB
  --execution-provider {cpu} [{cpu} ...]                   available execution provider (choices: cpu, ...)
  --execution-threads EXECUTION_THREADS                    number of execution threads
  -v, --version                                            show program's version number and exit

Looking for a CLI mode? Using the -s/–source argument will make the run program in cli mode.

Press

Ars Technica - “Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger”
Yahoo! - “OK, this viral AI live stream software is truly terrifying”
CNN Brasil - “AI can clone faces on webcam; understand how it works”
Bloomberg Technoz - “Get to know Deep Live Cam technology, it can be used as a tool for deception.”
TrendMicro - “AI vs AI: DeepFakes and eKYC”
PetaPixel - “Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo”
SomeOrdinaryGamers - “That’s Crazy, Oh God. That’s Fucking Freaky Dude… That’s So Wild Dude”
IShowSpeed - “Alright look look look, now look chat, we can do any face we want to look like chat”
TechLinked (Linus Tech Tips) - “They do a pretty good job matching poses, expression and even the lighting”
IShowSpeed - “What the F**! Why do I look like Vinny Jr? I look exactly like Vinny Jr!? No, this shit is crazy! Bro This is F*** Crazy!”*

Credits

ffmpeg: for making video-related operations easy
Henry: One of the major contributor in this repo
deepinsight: for their insightface project which provided a well-made library and models. Please be reminded that the use of the model is for non-commercial research purposes only.
havok2-htwo: for sharing the code for webcam
GosuDRM: for the open version of roop
pereiraroland26: Multiple faces support
vic4key: For supporting/contributing to this project
kier007: for improving the user experience
qitianai: for multi-lingual support
laurigates: Decoupling stuffs to make everything faster!
and all developers behind libraries used in this project.
Footnote: Please be informed that the base author of the code is s0md3v
All the wonderful users who helped make this project go viral by starring the repo ❤️

Contributions

Stars to the Moon 🚀

openai-node

Mon, 11 Aug 2025 15:35:37 +0800

openai/openai-node

OpenAI TypeScript and JavaScript API Library

This library provides convenient access to the OpenAI REST API from TypeScript or JavaScript.

It is generated from our OpenAPI specification with Stainless.

To learn how to use the OpenAI API, check out our API Reference and Documentation.

Installation

`1`	`npm install openai`

Installation from JSR

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deno add jsr:@openai/openai
npx jsr add @openai/openai

These commands will make the module importable from the @openai/openai scope. You can also import directly from JSR without an install step if you’re using the Deno JavaScript runtime:

`1`	`import OpenAI from 'jsr:@openai/openai';`

Usage

The full API of this library can be found in api.md file along with many code examples.

The primary API for interacting with OpenAI models is the Responses API. You can generate text from the model with the code below.

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

const response = await client.responses.create({
  model: 'gpt-4o',
  instructions: 'You are a coding assistant that talks like a pirate',
  input: 'Are semicolons optional in JavaScript?',
});

console.log(response.output_text);

The previous standard (supported indefinitely) for generating text is the Chat Completions API. You can use that API to generate text from the model with the code below.

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

const completion = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [
    { role: 'developer', content: 'Talk like a pirate.' },
    { role: 'user', content: 'Are semicolons optional in JavaScript?' },
  ],
});

console.log(completion.choices[0].message.content);

Streaming responses

We provide support for streaming responses using Server Sent Events (SSE).

import OpenAI from 'openai';

const client = new OpenAI();

const stream = await client.responses.create({
  model: 'gpt-4o',
  input: 'Say "Sheep sleep deep" ten times fast!',
  stream: true,
});

for await (const event of stream) {
  console.log(event);
}

File uploads

Request parameters that correspond to file uploads can be passed in many different forms:

File (or an object with the same structure)
a fetch Response (or an object with the same structure)
an fs.ReadStream
the return value of our toFile helper

import fs from 'fs';
import OpenAI, { toFile } from 'openai';

const client = new OpenAI();

// If you have access to Node `fs` we recommend using `fs.createReadStream()`:
await client.files.create({ file: fs.createReadStream('input.jsonl'), purpose: 'fine-tune' });

// Or if you have the web `File` API you can pass a `File` instance:
await client.files.create({ file: new File(['my bytes'], 'input.jsonl'), purpose: 'fine-tune' });

// You can also pass a `fetch` `Response`:
await client.files.create({ file: await fetch('https://somesite/input.jsonl'), purpose: 'fine-tune' });

// Finally, if none of the above are convenient, you can use our `toFile` helper:
await client.files.create({
  file: await toFile(Buffer.from('my bytes'), 'input.jsonl'),
  purpose: 'fine-tune',
});
await client.files.create({
  file: await toFile(new Uint8Array([0, 1, 2]), 'input.jsonl'),
  purpose: 'fine-tune',
});

Webhook Verification

Verifying webhook signatures is optional but encouraged.

For more information about webhooks, see the API docs.

Parsing webhook payloads

For most use cases, you will likely want to verify the webhook and parse the payload at the same time. To achieve this, we provide the method client.webhooks.unwrap(), which parses a webhook request and verifies that it was sent by OpenAI. This method will throw an error if the signature is invalid.

Note that the body parameter must be the raw JSON string sent from the server (do not parse it first). The .unwrap() method will parse this JSON for you into an event object after verifying the webhook was sent from OpenAI.

import { headers } from 'next/headers';
import OpenAI from 'openai';

const client = new OpenAI({
  webhookSecret: process.env.OPENAI_WEBHOOK_SECRET, // env var used by default; explicit here.
});

export async function webhook(request: Request) {
  const headersList = headers();
  const body = await request.text();

  try {
    const event = client.webhooks.unwrap(body, headersList);

    switch (event.type) {
      case 'response.completed':
        console.log('Response completed:', event.data);
        break;
      case 'response.failed':
        console.log('Response failed:', event.data);
        break;
      default:
        console.log('Unhandled event type:', event.type);
    }

    return Response.json({ message: 'ok' });
  } catch (error) {
    console.error('Invalid webhook signature:', error);
    return new Response('Invalid signature', { status: 400 });
  }
}

Verifying webhook payloads directly

In some cases, you may want to verify the webhook separately from parsing the payload. If you prefer to handle these steps separately, we provide the method client.webhooks.verifySignature() to only verify the signature of a webhook request. Like .unwrap(), this method will throw an error if the signature is invalid.

Note that the body parameter must be the raw JSON string sent from the server (do not parse it first). You will then need to parse the body after verifying the signature.

import { headers } from 'next/headers';
import OpenAI from 'openai';

const client = new OpenAI({
  webhookSecret: process.env.OPENAI_WEBHOOK_SECRET, // env var used by default; explicit here.
});

export async function webhook(request: Request) {
  const headersList = headers();
  const body = await request.text();

  try {
    client.webhooks.verifySignature(body, headersList);

    // Parse the body after verification
    const event = JSON.parse(body);
    console.log('Verified event:', event);

    return Response.json({ message: 'ok' });
  } catch (error) {
    console.error('Invalid webhook signature:', error);
    return new Response('Invalid signature', { status: 400 });
  }
}

Handling errors

When the library is unable to connect to the API, or if the API returns a non-success status code (i.e., 4xx or 5xx response), a subclass of APIError will be thrown:

const job = await client.fineTuning.jobs
  .create({ model: 'gpt-4o', training_file: 'file-abc123' })
  .catch(async (err) => {
    if (err instanceof OpenAI.APIError) {
      console.log(err.request_id);
      console.log(err.status); // 400
      console.log(err.name); // BadRequestError
      console.log(err.headers); // {server: 'nginx', ...}
    } else {
      throw err;
    }
  });

Error codes are as follows:

Status Code	Error Type
400	`BadRequestError`
401	`AuthenticationError`
403	`PermissionDeniedError`
404	`NotFoundError`
422	`UnprocessableEntityError`
429	`RateLimitError`
>=500	`InternalServerError`
N/A	`APIConnectionError`

Request IDs

For more information on debugging requests, see these docs

All object responses in the SDK provide a _request_id property which is added from the x-request-id response header so that you can quickly log failing requests and report them back to OpenAI.

const completion = await client.chat.completions.create({
  messages: [{ role: 'user', content: 'Say this is a test' }],
  model: 'gpt-4o',
});
console.log(completion._request_id); // req_123

You can also access the Request ID using the .withResponse() method:

const { data: stream, request_id } = await openai.chat.completions
  .create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Say this is a test' }],
    stream: true,
  })
  .withResponse();

Realtime API Beta

The Realtime API enables you to build low-latency, multi-modal conversational experiences. It currently supports text and audio as both input and output, as well as function calling through a WebSocket connection.

import { OpenAIRealtimeWebSocket } from 'openai/beta/realtime/websocket';

const rt = new OpenAIRealtimeWebSocket({ model: 'gpt-4o-realtime-preview-2024-12-17' });

rt.on('response.text.delta', (event) => process.stdout.write(event.delta));

For more information see realtime.md.

Microsoft Azure OpenAI

To use this library with Azure OpenAI, use the AzureOpenAI class instead of the OpenAI class.

[!IMPORTANT] The Azure API shape slightly differs from the core API shape which means that the static types for responses / params won’t always be correct.

import { AzureOpenAI } from 'openai';
import { getBearerTokenProvider, DefaultAzureCredential } from '@azure/identity';

const credential = new DefaultAzureCredential();
const scope = 'https://cognitiveservices.azure.com/.default';
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

const openai = new AzureOpenAI({ azureADTokenProvider });

const result = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Say hello!' }],
});

console.log(result.choices[0]!.message?.content);

Retries

Certain errors will be automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors will all be retried by default.

You can use the maxRetries option to configure or disable this:

// Configure the default for all requests:
const client = new OpenAI({
  maxRetries: 0, // default is 2
});

// Or, configure per-request:
await client.chat.completions.create({ messages: [{ role: 'user', content: 'How can I get the name of the current day in JavaScript?' }], model: 'gpt-4o' }, {
  maxRetries: 5,
});

Timeouts

Requests time out after 10 minutes by default. You can configure this with a timeout option:

// Configure the default for all requests:
const client = new OpenAI({
  timeout: 20 * 1000, // 20 seconds (default is 10 minutes)
});

// Override per-request:
await client.chat.completions.create({ messages: [{ role: 'user', content: 'How can I list all files in a directory using Python?' }], model: 'gpt-4o' }, {
  timeout: 5 * 1000,
});

On timeout, an APIConnectionTimeoutError is thrown.

Note that requests which time out will be retried twice by default.

Request IDs

For more information on debugging requests, see these docs

All object responses in the SDK provide a _request_id property which is added from the x-request-id response header so that you can quickly log failing requests and report them back to OpenAI.

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const response = await client.responses.create({ model: 'gpt-4o', input: 'testing 123' });
console.log(response._request_id); // req_123

You can also access the Request ID using the .withResponse() method:

const { data: stream, request_id } = await openai.responses
  .create({
    model: 'gpt-4o',
    input: 'Say this is a test',
    stream: true,
  })
  .withResponse();

Auto-pagination

List methods in the OpenAI API are paginated. You can use the for await … of syntax to iterate through items across all pages:

async function fetchAllFineTuningJobs(params) {
  const allFineTuningJobs = [];
  // Automatically fetches more pages as needed.
  for await (const fineTuningJob of client.fineTuning.jobs.list({ limit: 20 })) {
    allFineTuningJobs.push(fineTuningJob);
  }
  return allFineTuningJobs;
}

Alternatively, you can request a single page at a time:

let page = await client.fineTuning.jobs.list({ limit: 20 });
for (const fineTuningJob of page.data) {
  console.log(fineTuningJob);
}

// Convenience methods are provided for manually paginating:
while (page.hasNextPage()) {
  page = await page.getNextPage();
  // ...
}

Realtime API Beta

import { OpenAIRealtimeWebSocket } from 'openai/beta/realtime/websocket';

const rt = new OpenAIRealtimeWebSocket({ model: 'gpt-4o-realtime-preview-2024-12-17' });

rt.on('response.text.delta', (event) => process.stdout.write(event.delta));

For more information see realtime.md.

Microsoft Azure OpenAI

To use this library with Azure OpenAI, use the AzureOpenAI class instead of the OpenAI class.

[!IMPORTANT] The Azure API shape slightly differs from the core API shape which means that the static types for responses / params won’t always be correct.

import { AzureOpenAI } from 'openai';
import { getBearerTokenProvider, DefaultAzureCredential } from '@azure/identity';

const credential = new DefaultAzureCredential();
const scope = 'https://cognitiveservices.azure.com/.default';
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

const openai = new AzureOpenAI({
  azureADTokenProvider,
  apiVersion: '<The API version, e.g. 2024-10-01-preview>',
});

const result = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Say hello!' }],
});

console.log(result.choices[0]!.message?.content);

For more information on support for the Azure API, see azure.md.

Advanced Usage

Accessing raw Response data (e.g., headers)

The “raw” Response returned by fetch() can be accessed through the .asResponse() method on the APIPromise type that all methods return. This method returns as soon as the headers for a successful response are received and does not consume the response body, so you are free to write custom parsing or streaming logic.

You can also use the .withResponse() method to get the raw Response along with the parsed data. Unlike .asResponse() this method consumes the body, returning once it is parsed.

const client = new OpenAI();

const httpResponse = await client.responses
  .create({ model: 'gpt-4o', input: 'say this is a test.' })
  .asResponse();

// access the underlying web standard Response object
console.log(httpResponse.headers.get('X-My-Header'));
console.log(httpResponse.statusText);

const { data: modelResponse, response: raw } = await client.responses
  .create({ model: 'gpt-4o', input: 'say this is a test.' })
  .withResponse();
console.log(raw.headers.get('X-My-Header'));
console.log(modelResponse);

Logging

[!IMPORTANT] All log messages are intended for debugging only. The format and content of log messages may change between releases.

Log levels

The log level can be configured in two ways:

Via the OPENAI_LOG environment variable
Using the logLevel client option (overrides the environment variable if set)

import OpenAI from 'openai';

const client = new OpenAI({
  logLevel: 'debug', // Show all log messages
});

Available log levels, from most to least verbose:

'debug' - Show debug messages, info, warnings, and errors
'info' - Show info messages, warnings, and errors
'warn' - Show warnings and errors (default)
'error' - Show only errors
'off' - Disable all logging

At the 'debug' level, all HTTP requests and responses are logged, including headers and bodies. Some authentication-related headers are redacted, but sensitive data in request and response bodies may still be visible.

Custom logger

By default, this library logs to globalThis.console. You can also provide a custom logger. Most logging libraries are supported, including pino, winston, bunyan, consola, signale, and @std/log. If your logger doesn’t work, please open an issue.

When providing a custom logger, the logLevel option still controls which messages are emitted, messages below the configured level will not be sent to your logger.

import OpenAI from 'openai';
import pino from 'pino';

const logger = pino();

const client = new OpenAI({
  logger: logger.child({ name: 'OpenAI' }),
  logLevel: 'debug', // Send all messages to pino, allowing it to filter
});

Making custom/undocumented requests

This library is typed for convenient access to the documented API. If you need to access undocumented endpoints, params, or response properties, the library can still be used.

Undocumented endpoints

To make requests to undocumented endpoints, you can use client.get, client.post, and other HTTP verbs. Options on the client, such as retries, will be respected when making these requests.

await client.post('/some/path', {
  body: { some_prop: 'foo' },
  query: { some_query_arg: 'bar' },
});

Undocumented request params

To make requests using undocumented parameters, you may use // @ts-expect-error on the undocumented parameter. This library doesn’t validate at runtime that the request matches the type, so any extra values you send will be sent as-is.

client.chat.completions.create({
  // ...
  // @ts-expect-error baz is not yet public
  baz: 'undocumented option',
});

For requests with the GET verb, any extra params will be in the query, all other requests will send the extra param in the body.

If you want to explicitly send an extra argument, you can do so with the query, body, and headers request options.

Undocumented response properties

To access undocumented response properties, you may access the response object with // @ts-expect-error on the response object, or cast the response object to the requisite type. Like the request params, we do not validate or strip extra properties from the response from the API.

Customizing the fetch client

If you want to use a different fetch function, you can either polyfill the global:

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import fetch from 'my-fetch';

globalThis.fetch = fetch;

Or pass it to the client:

import OpenAI from 'openai';
import fetch from 'my-fetch';

const client = new OpenAI({ fetch });

Fetch options

If you want to set custom fetch options without overriding the fetch function, you can provide a fetchOptions object when instantiating the client or making a request. (Request-specific options override client options.)

import OpenAI from 'openai';

const client = new OpenAI({
  fetchOptions: {
    // `RequestInit` options
  },
});

Configuring proxies

To modify proxy behavior, you can provide custom fetchOptions that add runtime-specific proxy options to requests:

Node ^[docs]

import OpenAI from 'openai';
import * as undici from 'undici';

const proxyAgent = new undici.ProxyAgent('http://localhost:8888');
const client = new OpenAI({
  fetchOptions: {
    dispatcher: proxyAgent,
  },
});

Bun ^[docs]

import OpenAI from 'openai';

const client = new OpenAI({
  fetchOptions: {
    proxy: 'http://localhost:8888',
  },
});

Deno ^[docs]

import OpenAI from 'npm:openai';

const httpClient = Deno.createHttpClient({ proxy: { url: 'http://localhost:8888' } });
const client = new OpenAI({
  fetchOptions: {
    client: httpClient,
  },
});

Frequently Asked Questions

Semantic versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

Changes that only affect static types, without breaking runtime behavior.
Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals.)
Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an issue with questions, bugs, or suggestions.

Requirements

TypeScript >= 4.9 is supported.

The following runtimes are supported:

Node.js 20 LTS or later (non-EOL) versions.
Deno v1.28.0 or higher.
Bun 1.0 or later.
Cloudflare Workers.
Vercel Edge Runtime.
Jest 28 or greater with the "node" environment ("jsdom" is not supported at this time).
Nitro v2.6 or greater.
Web browsers: disabled by default to avoid exposing your secret API credentials. Enable browser support by explicitly setting dangerouslyAllowBrowser to true'.
More explanation

Why is this dangerous?
Enabling the dangerouslyAllowBrowser option can be dangerous because it exposes your secret API credentials in the client-side code. Web browsers are inherently less secure than server environments, any user with access to the browser can potentially inspect, extract, and misuse these credentials. This could lead to unauthorized access using your credentials and potentially compromise sensitive data or functionality.

When might this not be dangerous?
In certain scenarios where enabling browser support might not pose significant risks:
- Internal Tools: If the application is used solely within a controlled internal environment where the users are trusted, the risk of credential exposure can be mitigated.
- Public APIs with Limited Scope: If your API has very limited scope and the exposed credentials do not grant access to sensitive data or critical operations, the potential impact of exposure is reduced.
- Development or debugging purpose: Enabling this feature temporarily might be acceptable, provided the credentials are short-lived, aren’t also used in production environments, or are frequently rotated.

Note that React Native is not supported at this time.

If you are interested in other runtime environments, please open or upvote an issue on GitHub.

Contributing

See the contributing documentation.

openai-python

Sun, 10 Aug 2025 15:28:32 +0800

openai/openai-python

OpenAI Python API library

The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.8+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.

It is generated from our OpenAPI specification with Stainless.

Documentation

The REST API documentation can be found on platform.openai.com. The full API of this library can be found in api.md.

Installation

1
2

# install from PyPI
pip install openai

Usage

The full API of this library can be found in api.md.

The primary API for interacting with OpenAI models is the Responses API. You can generate text from the model with the code below.

import os
from openai import OpenAI

client = OpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)

response = client.responses.create(
    model="gpt-4o",
    instructions="You are a coding assistant that talks like a pirate.",
    input="How do I check if a Python object is an instance of a class?",
)

print(response.output_text)

The previous standard (supported indefinitely) for generating text is the Chat Completions API. You can use that API to generate text from the model with the code below.

from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "developer", "content": "Talk like a pirate."},
        {
            "role": "user",
            "content": "How do I check if a Python object is an instance of a class?",
        },
    ],
)

print(completion.choices[0].message.content)

While you can provide an api_key keyword argument, we recommend using python-dotenv to add OPENAI_API_KEY="My API Key" to your .env file so that your API key is not stored in source control. Get an API key here.

Vision

With an image URL:

prompt = "What is in this image?"
img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d5/2023_06_08_Raccoon1.jpg/1599px-2023_06_08_Raccoon1.jpg"

response = client.responses.create(
    model="gpt-4o-mini",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"{img_url}"},
            ],
        }
    ],
)

With the image as a base64 encoded string:

import base64
from openai import OpenAI

client = OpenAI()

prompt = "What is in this image?"
with open("path/to/image.png", "rb") as image_file:
    b64_image = base64.b64encode(image_file.read()).decode("utf-8")

response = client.responses.create(
    model="gpt-4o-mini",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"data:image/png;base64,{b64_image}"},
            ],
        }
    ],
)

Async usage

Simply import AsyncOpenAI instead of OpenAI and use await with each API call:

import os
import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)


async def main() -> None:
    response = await client.responses.create(
        model="gpt-4o", input="Explain disestablishmentarianism to a smart five year old."
    )
    print(response.output_text)


asyncio.run(main())

Functionality between the synchronous and asynchronous clients is otherwise identical.

With aiohttp

By default, the async client uses httpx for HTTP requests. However, for improved concurrency performance you may also use aiohttp as the HTTP backend.

You can enable this by installing aiohttp:

1
2

# install from PyPI
pip install openai[aiohttp]

Then you can enable it by instantiating the client with http_client=DefaultAioHttpClient():

import asyncio
from openai import DefaultAioHttpClient
from openai import AsyncOpenAI


async def main() -> None:
    async with AsyncOpenAI(
        api_key="My API Key",
        http_client=DefaultAioHttpClient(),
    ) as client:
        chat_completion = await client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": "Say this is a test",
                }
            ],
            model="gpt-4o",
        )


asyncio.run(main())

Streaming responses

We provide support for streaming responses using Server Side Events (SSE).

from openai import OpenAI

client = OpenAI()

stream = client.responses.create(
    model="gpt-4o",
    input="Write a one-sentence bedtime story about a unicorn.",
    stream=True,
)

for event in stream:
    print(event)

The async client uses the exact same interface.

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI()


async def main():
    stream = await client.responses.create(
        model="gpt-4o",
        input="Write a one-sentence bedtime story about a unicorn.",
        stream=True,
    )

    async for event in stream:
        print(event)


asyncio.run(main())

Realtime API beta

Under the hood the SDK uses the websockets library to manage connections.

The Realtime API works through a combination of client-sent events and server-sent events. Clients can send events to do things like update session configuration or send text and audio inputs. Server events confirm when audio responses have completed, or when a text response from the model has been received. A full event reference can be found here and a guide can be found here.

Basic text based example:

import asyncio
from openai import AsyncOpenAI

async def main():
    client = AsyncOpenAI()

    async with client.beta.realtime.connect(model="gpt-4o-realtime-preview") as connection:
        await connection.session.update(session={'modalities': ['text']})

        await connection.conversation.item.create(
            item={
                "type": "message",
                "role": "user",
                "content": [{"type": "input_text", "text": "Say hello!"}],
            }
        )
        await connection.response.create()

        async for event in connection:
            if event.type == 'response.text.delta':
                print(event.delta, flush=True, end="")

            elif event.type == 'response.text.done':
                print()

            elif event.type == "response.done":
                break

asyncio.run(main())

However the real magic of the Realtime API is handling audio inputs / outputs, see this example TUI script for a fully fledged example.

Realtime error handling

Whenever an error occurs, the Realtime API will send an error event and the connection will stay open and remain usable. This means you need to handle it yourself, as no errors are raised directly by the SDK when an error event comes in.

client = AsyncOpenAI()

async with client.beta.realtime.connect(model="gpt-4o-realtime-preview") as connection:
    ...
    async for event in connection:
        if event.type == 'error':
            print(event.error.type)
            print(event.error.code)
            print(event.error.event_id)
            print(event.error.message)

Using types

Nested request parameters are TypedDicts. Responses are Pydantic models which also provide helper methods for things like:

Serializing back into JSON, model.to_json()
Converting to a dictionary, model.to_dict()

Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set python.analysis.typeCheckingMode to basic.

Pagination

List methods in the OpenAI API are paginated.

This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:

from openai import OpenAI

client = OpenAI()

all_jobs = []
# Automatically fetches more pages as needed.
for job in client.fine_tuning.jobs.list(
    limit=20,
):
    # Do something with job here
    all_jobs.append(job)
print(all_jobs)

Or, asynchronously:

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI()


async def main() -> None:
    all_jobs = []
    # Iterate through items across all pages, issuing requests as needed.
    async for job in client.fine_tuning.jobs.list(
        limit=20,
    ):
        all_jobs.append(job)
    print(all_jobs)


asyncio.run(main())

Alternatively, you can use the .has_next_page(), .next_page_info(), or .get_next_page() methods for more granular control working with pages:

first_page = await client.fine_tuning.jobs.list(
    limit=20,
)
if first_page.has_next_page():
    print(f"will fetch next page using these details: {first_page.next_page_info()}")
    next_page = await first_page.get_next_page()
    print(f"number of items we just fetched: {len(next_page.data)}")

# Remove `await` for non-async usage.

Or just work directly with the returned data:

first_page = await client.fine_tuning.jobs.list(
    limit=20,
)

print(f"next page cursor: {first_page.after}")  # => "next page cursor: ..."
for job in first_page.data:
    print(job.id)

# Remove `await` for non-async usage.

Nested params

Nested parameters are dictionaries, typed using TypedDict, for example:

from openai import OpenAI

client = OpenAI()

response = client.chat.responses.create(
    input=[
        {
            "role": "user",
            "content": "How much ?",
        }
    ],
    model="gpt-4o",
    response_format={"type": "json_object"},
)

File uploads

Request parameters that correspond to file uploads can be passed as bytes, or a PathLike instance or a tuple of (filename, contents, media type).

from pathlib import Path
from openai import OpenAI

client = OpenAI()

client.files.create(
    file=Path("input.jsonl"),
    purpose="fine-tune",
)

The async client uses the exact same interface. If you pass a PathLike instance, the file contents will be read asynchronously automatically.

Webhook Verification

Verifying webhook signatures is optional but encouraged.

For more information about webhooks, see the API docs.

Parsing webhook payloads

from openai import OpenAI
from flask import Flask, request

app = Flask(__name__)
client = OpenAI()  # OPENAI_WEBHOOK_SECRET environment variable is used by default


@app.route("/webhook", methods=["POST"])
def webhook():
    request_body = request.get_data(as_text=True)

    try:
        event = client.webhooks.unwrap(request_body, request.headers)

        if event.type == "response.completed":
            print("Response completed:", event.data)
        elif event.type == "response.failed":
            print("Response failed:", event.data)
        else:
            print("Unhandled event type:", event.type)

        return "ok"
    except Exception as e:
        print("Invalid signature:", e)
        return "Invalid signature", 400


if __name__ == "__main__":
    app.run(port=8000)

Verifying webhook payloads directly

In some cases, you may want to verify the webhook separately from parsing the payload. If you prefer to handle these steps separately, we provide the method client.webhooks.verify_signature() to only verify the signature of a webhook request. Like .unwrap(), this method will raise an error if the signature is invalid.

Note that the body parameter must be the raw JSON string sent from the server (do not parse it first). You will then need to parse the body after verifying the signature.

import json
from openai import OpenAI
from flask import Flask, request

app = Flask(__name__)
client = OpenAI()  # OPENAI_WEBHOOK_SECRET environment variable is used by default


@app.route("/webhook", methods=["POST"])
def webhook():
    request_body = request.get_data(as_text=True)

    try:
        client.webhooks.verify_signature(request_body, request.headers)

        # Parse the body after verification
        event = json.loads(request_body)
        print("Verified event:", event)

        return "ok"
    except Exception as e:
        print("Invalid signature:", e)
        return "Invalid signature", 400


if __name__ == "__main__":
    app.run(port=8000)

Handling errors

When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of openai.APIConnectionError is raised.

When the API returns a non-success status code (that is, 4xx or 5xx response), a subclass of openai.APIStatusError is raised, containing status_code and response properties.

All errors inherit from openai.APIError.

import openai
from openai import OpenAI

client = OpenAI()

try:
    client.fine_tuning.jobs.create(
        model="gpt-4o",
        training_file="file-abc123",
    )
except openai.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
except openai.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except openai.APIStatusError as e:
    print("Another non-200-range status code was received")
    print(e.status_code)
    print(e.response)

Error codes are as follows:

Status Code	Error Type
400	`BadRequestError`
401	`AuthenticationError`
403	`PermissionDeniedError`
404	`NotFoundError`
422	`UnprocessableEntityError`
429	`RateLimitError`
>=500	`InternalServerError`
N/A	`APIConnectionError`

Request IDs

For more information on debugging requests, see these docs

All object responses in the SDK provide a _request_id property which is added from the x-request-id response header so that you can quickly log failing requests and report them back to OpenAI.

response = await client.responses.create(
    model="gpt-4o-mini",
    input="Say 'this is a test'.",
)
print(response._request_id)  # req_123

Note that unlike other properties that use an _ prefix, the _request_id property is public. Unless documented otherwise, all other _ prefix properties, methods and modules are private.

[!IMPORTANT]
If you need to access request IDs for failed requests you must catch the APIStatusError exception

import openai

try:
    completion = await client.chat.completions.create(
        messages=[{"role": "user", "content": "Say this is a test"}], model="gpt-4"
    )
except openai.APIStatusError as exc:
    print(exc.request_id)  # req_123
    raise exc

Retries

Certain errors are automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors are all retried by default.

You can use the max_retries option to configure or disable retry settings:

from openai import OpenAI

# Configure the default for all requests:
client = OpenAI(
    # default is 2
    max_retries=0,
)

# Or, configure per-request:
client.with_options(max_retries=5).chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "How can I get the name of the current day in JavaScript?",
        }
    ],
    model="gpt-4o",
)

Timeouts

By default requests time out after 10 minutes. You can configure this with a timeout option, which accepts a float or an httpx.Timeout object:

from openai import OpenAI

# Configure the default for all requests:
client = OpenAI(
    # 20 seconds (default is 10 minutes)
    timeout=20.0,
)

# More granular control:
client = OpenAI(
    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)

# Override per-request:
client.with_options(timeout=5.0).chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "How can I list all files in a directory using Python?",
        }
    ],
    model="gpt-4o",
)

On timeout, an APITimeoutError is thrown.

Note that requests that time out are retried twice by default.

Advanced

Logging

We use the standard library logging module.

You can enable logging by setting the environment variable OPENAI_LOG to info.

`1`	`$ export OPENAI_LOG=info`

Or to debug for more verbose logging.

How to tell whether `None` means `null` or missing

In an API response, a field may be explicitly null, or missing entirely; in either case, its value is None in this library. You can differentiate the two cases with .model_fields_set:

if response.my_field is None:
  if 'my_field' not in response.model_fields_set:
    print('Got json like {}, without a "my_field" key present at all.')
  else:
    print('Got json like {"my_field": null}.')

Accessing raw response data (e.g. headers)

The “raw” Response object can be accessed by prefixing .with_raw_response. to any HTTP method call, e.g.,

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.with_raw_response.create(
    messages=[{
        "role": "user",
        "content": "Say this is a test",
    }],
    model="gpt-4o",
)
print(response.headers.get('X-My-Header'))

completion = response.parse()  # get the object that `chat.completions.create()` would have returned
print(completion)

These methods return a LegacyAPIResponse object. This is a legacy class as we’re changing it slightly in the next major version.

For the sync client this will mostly be the same with the exception of content & text will be methods instead of properties. In the async client, all methods will be async.

A migration script will be provided & the migration in general should be smooth.

`.with_streaming_response`

The above interface eagerly reads the full response body when you make the request, which may not always be what you want.

To stream the response body, use .with_streaming_response instead, which requires a context manager and only reads the response body once you call .read(), .text(), .json(), .iter_bytes(), .iter_text(), .iter_lines() or .parse(). In the async client, these are async methods.

As such, .with_streaming_response methods return a different APIResponse object, and the async client returns an AsyncAPIResponse object.

with client.chat.completions.with_streaming_response.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
    model="gpt-4o",
) as response:
    print(response.headers.get("X-My-Header"))

    for line in response.iter_lines():
        print(line)

The context manager is required so that the response will reliably be closed.

Making custom/undocumented requests

This library is typed for convenient access to the documented API.

If you need to access undocumented endpoints, params, or response properties, the library can still be used.

Undocumented endpoints

To make requests to undocumented endpoints, you can make requests using client.get, client.post, and other http verbs. Options on the client will be respected (such as retries) when making this request.

import httpx

response = client.post(
    "/foo",
    cast_to=httpx.Response,
    body={"my_param": True},
)

print(response.headers.get("x-foo"))

Undocumented request params

If you want to explicitly send an extra param, you can do so with the extra_query, extra_body, and extra_headers request options.

Undocumented response properties

To access undocumented response properties, you can access the extra fields like response.unknown_prop. You can also get all the extra fields on the Pydantic model as a dict with response.model_extra.

Configuring the HTTP client

You can directly override the httpx client to customize it for your use case, including:

Support for proxies
Custom transports
Additional advanced functionality

import httpx
from openai import OpenAI, DefaultHttpxClient

client = OpenAI(
    # Or use the `OPENAI_BASE_URL` env var
    base_url="http://my.test.server.example.com:8083/v1",
    http_client=DefaultHttpxClient(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

You can also customize the client on a per-request basis by using with_options():

`1`	`client.with_options(http_client=DefaultHttpxClient(...))`

Managing HTTP resources

By default the library closes underlying HTTP connections whenever the client is garbage collected. You can manually close the client using the .close() method if desired, or with a context manager that closes when exiting.

from openai import OpenAI

with OpenAI() as client:
  # make requests here
  ...

# HTTP client is now closed

Microsoft Azure OpenAI

To use this library with Azure OpenAI, use the AzureOpenAI class instead of the OpenAI class.

[!IMPORTANT] The Azure API shape differs from the core API shape which means that the static types for responses / params won’t always be correct.

from openai import AzureOpenAI

# gets the API Key from environment variable AZURE_OPENAI_API_KEY
client = AzureOpenAI(
    # https://learn.microsoft.com/azure/ai-services/openai/reference#rest-api-versioning
    api_version="2023-07-01-preview",
    # https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
    azure_endpoint="https://example-endpoint.openai.azure.com",
)

completion = client.chat.completions.create(
    model="deployment-name",  # e.g. gpt-35-instant
    messages=[
        {
            "role": "user",
            "content": "How do I output all files in a directory using Python?",
        },
    ],
)
print(completion.to_json())

In addition to the options provided in the base OpenAI client, the following options are provided:

azure_endpoint (or the AZURE_OPENAI_ENDPOINT environment variable)
azure_deployment
api_version (or the OPENAI_API_VERSION environment variable)
azure_ad_token (or the AZURE_OPENAI_AD_TOKEN environment variable)
azure_ad_token_provider

An example of using the client with Microsoft Entra ID (formerly known as Azure Active Directory) can be found here.

Versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

Changes that only affect static types, without breaking runtime behavior.
Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals.)
Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an issue with questions, bugs, or suggestions.

Determining the installed version

If you’ve upgraded to the latest version but aren’t seeing any new features you were expecting then your python environment is likely still using an older version.

You can determine the version that is being used at runtime with:

1
2

import openai
print(openai.__version__)

Requirements

Python 3.8 or higher.

Contributing

See the contributing documentation.

nakama

Sat, 10 May 2025 15:26:57 +0800

heroiclabs/nakama

Distributed server for social and realtime games and apps.

Features

Users - Register/login new users via social networks, email, or device ID.
Storage - Store user records, settings, and other objects in collections.
Social - Users can connect with friends, and join groups. Builtin social graph to see how users can be connected.
Chat - 1-on-1, group, and global chat between users. Persist messages for chat history.
Multiplayer - Realtime, or turn-based active and passive multiplayer.
Leaderboards - Dynamic, seasonal, get top members, or members around a user. Have as many as you need.
Tournaments - Invite players to compete together over prizes. Link many together to create leagues.
Parties - Add team play to a game. Users can form a party and communicate with party members.
Purchase Validation - Validate in-app purchases and subscriptions.
In-App Notifications - Send messages and notifications to connected client sockets.
Runtime code - Extend the server with custom logic written in Lua, TypeScript/JavaScript, or native Go code.
Matchmaker, dashboard, metrics, and more.

Build scalable games and apps with a production ready server used by ambitious game studios and app developers all around the world. Have a look at the documentation and join the developer community for more info.

Getting Started

The server is simple to setup and run for local development and can be deployed to any cloud provider. See the deployment notes for recommendations on how to deploy the project for production. Nakama server requires CockroachDB or another Postgres wire-compatible server as it’s database.

Docker

The fastest way to run the server and the database is with Docker. Setup Docker and start the daemon.

Set up a docker-compose file and place it in a folder for your project.
Run docker-compose -f ./docker-compose.yml up to download container images and run the servers.

For more detailed instructions have a look at our Docker quickstart guide.

Nakama Docker images are maintained on Docker Hub and prerelease images are occasionally published for cutting edge features of the server.

Binaries

You can run the servers with native binaries for your platform.

Download the server from our releases page and the database.
Follow the database instructions to start it.

Run a migration which will setup or upgrade the database schema:

`1`	`nakama migrate up --database.address "root@127.0.0.1:26257"`

Start Nakama and connect to the database:

`1`	`nakama --database.address "root@127.0.0.1:26257"`

When connected you’ll see server output which describes all settings the server uses for configuration.

{“level”:“info”,“ts”:“2018-04-29T10:14:41.249+0100”,“msg”:“Node”,“name”:“nakama”,“version”:“2.0.0+7e18b09”,“runtime”:“go1.10.1”,“cpu”:4}
{“level”:“info”,“ts”:“2018-04-29T10:14:41.249+0100”,“msg”:“Database connections”,“dsns”:[“root@127.0.0.1:26257”]}
…

Usage

Nakama supports a variety of protocols optimized for various gameplay or app use cases. For request/response it can use GRPC or the HTTP1.1+JSON fallback (REST). For realtime communication you can use WebSockets or rUDP.

For example with the REST API to authenticate a user account with a device identifier.

1
2
3

curl "127.0.0.1:7350/v2/account/authenticate/device?create=true" \
  --user "defaultkey:" \
  --data '{"id": "someuniqueidentifier"}'

Response:

{
“token”:“eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJleHAiOjE1MjQ5OTU2NDksInVpZCI6Ijk5Y2Q1YzUyLWE5ODgtNGI2NC04YThhLTVmMTM5YTg4MTgxMiIsInVzbiI6InhBb1RxTUVSdFgifQ.-3_rXNYx3Q4jKuS7RkxeMWBzMNAm0vl93QxzRI8p_IY”
}

There’s a number of official client libraries available on GitHub with documentation. The current platform/language support includes: .NET (in C#), Unity engine, JavaScript, Java (with Android), Unreal engine, Godot, Defold, and Swift (with iOS). If you’d like to contribute a client or request one let us know.

Nakama Console

The server provides a web UI which teams can use to inspect various data stored through the server APIs, view lightweight service metrics, manage player data, update storage objects, restrict access to production with permission profiles, and gain visibility into realtime features like active multiplayer matches. There is no separate installation required as it is embedded as part of the single server binary.

You can navigate to it on your browser on http://127.0.0.1:7351.

Deployment

Nakama can be deployed to any cloud provider such as Google Cloud, Azure, AWS, Digital Ocean, Heroku, or your own private cloud. You should setup and provision separate nodes for Nakama and CockroachDB.

The recommended minimum production infrastructure for CockroachDB is outlined in these docs and Nakama can be run on instance types as small as “g1-small” on Google Cloud although we recommend a minimum of “n1-standard-1” in production. The specific hardware requirements will depend on what features of the server are used. Reach out to us for help and advice on what servers to run.

Heroic Cloud

You can support development, new features, and maintainance of the server by using the Heroic Labs’ Heroic Cloud for deployment. This service handles the uptime, replication, backups, logs, data upgrades, and all other tasks involved with production server environments.

Have a look at our Heroic Cloud service for more details.

Contribute

The development roadmap is managed as GitHub issues and pull requests are welcome. If you’re interested to add a feature which is not mentioned on the issue tracker please open one to create a discussion or drop in and discuss it in the community forum.

Simple Builds

All dependencies required for a build are vendored as part of the Go project. We recommend a modern release of the Go toolchain and do not store the codebase in the old GOPATH.

Download the source tree.

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git clone "https://github.com/heroiclabs/nakama" nakama
cd nakama

Build the project from source.

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go build -trimpath -mod=vendor
./nakama --version

Full Source Builds

The codebase uses Protocol Buffers, GRPC, GRPC-Gateway, and the OpenAPI spec as part of the project. These dependencies are generated as sources and committed to the repository to simplify builds for contributors.

To build the codebase and generate all sources follow these steps.

Install the toolchain.

go install \
    "google.golang.org/protobuf/cmd/protoc-gen-go" \
    "google.golang.org/grpc/cmd/protoc-gen-go-grpc" \
    "github.com/grpc-ecosystem/grpc-gateway/v2/protoc-gen-grpc-gateway" \
    "github.com/grpc-ecosystem/grpc-gateway/v2/protoc-gen-openapiv2"

If you’ve made changes to the embedded Console.
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cd console/ui ng serve
Re-generate the protocol buffers, gateway code and console UI.
1

env PATH="$HOME/go/bin:$PATH" go generate -x ./...
Build the codebase.
1

go build -trimpath -mod=vendor

Testing

In order to run all the unit and integration tests run:

`1`	`docker-compose -f ./docker-compose-tests.yml up --build --abort-on-container-exit; docker-compose -f ./docker-compose-tests.yml down -v`

This will create an isolated environment with Nakama and database instances, run all the tests, and drop the environment afterwards.

License

This project is licensed under the Apache-2 License.

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