API to convert OBJ, FBX and COLLADA files to glTF or GLB
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Fixed reference to glTF api website
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glTF converter API

Brought to you by Headjack

API to convert OBJ, FBX and COLLADA files to glTF or GLB, based on the ClayGL converter.


To convert a 3D model to glTF, go to the glTF API website and upload your 3D model as OBJ, FBX or COLLADA. Zipped files are also accepted.

Make sure your models use relative texture paths, else the model viewer on the page will not be able to preview your converted model.

If you want to use the API directly instead of the web interface, use a POST request to the /models endpoint. You can POST either a binary file.

import requests
url = 'https://gltfapi.co/v1/models'
file = open('test.fbx', 'rb')
requests.post(url=url, files={'file': file})

Or you can POST an url to a hosted file.

import requests
url = 'https://gltfapi.co/v1/models'
source_path = 'https://example.com/test.fbx'
requests.post(url=url, data={'source_path': source_path})

After uploading, you can use the /models/{id} endpoint to GET information about a single model.

import requests
url = 'https://gltfapi.co/v1/models/1234567890'

The response contains the following information:

  • model_id The unique ID of the model
  • filename Original filename of the model
  • created_at The upload timestamp
  • source_file Url to original upload
  • processed_file Url to the converted model (glTF or GLB)
  • downloadable_file Url to a download of the converted model (ZIP or GLB)
  • compressed Boolean indicating whether compression was applied


To protect the server, the API rate limit is currently set to 200 a day, 50 per hour.

Getting Started

These instructions will tell you how to get a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.


Download and install Docker.


Use command line to change to the project root directory, which contains the Dockerfile.

cd /path/to/project

Next, build the Docker image, using --tag to give it a name.

docker build --tag headjack/gltf:0.0.1 .

Now run the Docker container to start the API.

docker run -p 5022:5022 --name gltf --mount source=modelsvol,target=/var/www/static/models --mount source=modelsdb,target=/var/www/app/database.db headjack/gltf:0.0.1

As you can see, the run command contains several parameters:

  • -p maps port 5022 inside your Docker container to localhost port 5022. Change this if you want to use a different port to access the glTF API.
  • --name gives the Docker container an easy to reference name. Change to whatever you like.
  • --mount is used to create persistent storage for the uploaded models, as well as the database, so that they are not discarded when the container is restarted.

Now that the container is running, you can go to localhost:5022 and you should see the upload page for the glTF converter.

The following endpoints exist:

  • /:GET HTML5 upload form for the glTF converter
  • /v1/models:GET Retrieves a list of uploaded models (protected)
  • /v1/models:POST Post an FBX, ZIP or OBJ file to the converter
  • /v1/models:DELETE Delete all models older than hours_old parameter (protected)
  • /v1/models/{id}:GET Retrieve information about a single model
  • /v1/models/{id}:DELETE Delete a single model (protected)

The protected endpoints require you to pass a key parameter in the request, of which the value can be set using the API_KEY variable in api.py.


In the folder /tests you find the file test-api.py, which performs several requests to see if the API is working correctly. You will have to first change the filenames and urls used in the file to ones that you would like to use in your tests, then simply run the script.

python test-api.py


The glTF API runs on an Amazon EC2 instance. I'll briefly explain the deployment process, using OSX as the development platform.

First, install the AWS CLI.

brew install awscli

Check if the install succeeded by typing aws --version.

Next, sign in to the AWS console and create a new user with EC2 permissions, and write down the access key and and secret key in a secure location. Then configure the AWS CLI with these credentials.

aws configure

It is also possible to manually create the AWS credential files, in which case the AWS CLI is not required.

You can now start a "Dockerized" EC2 instance using the docker-machine command.

docker-machine create --driver amazonec2 --amazonec2-open-port 5022 --amazonec2-region us-east-1 gltf-api

You might have to run an eval command to make the instance active.

docker-machine env gltf-api
eval $(docker-machine env gltf-api)

You can also choose to use the AWS CLI to start an EC2 instance and then install Docker manually, but we will not cover this here.

Now copy all glTF API project files to the EC2 instance.

docker-machine scp -r -d /path/to/gltf-api/ gltf-api:/home/ubuntu/

Once all files are copied, connect with ssh to your EC2 instance.

docker-machine ssh gltf-api

Change to your project folder.

cd /home/ubuntu/project-folder

Next, build the Docker image, using --tag to give it a name.

sudo docker build --tag headjack/gltf:0.0.1 .

Now run the Docker container to start the API.

sudo docker run -p 5022:80 --name gltf --mount source=modelsvol,target=/var/www/static/models --mount source=modelsdb,target=/var/www/app/database.db headjack/gltf:0.0.1

Congratulations, you have just successfully deployed your API to EC2!

If you want to access the API through a domain instead of an IP address, you can use Amazon Route53 to link your domain to your instance.

To create persistent storage, you would need to integrate Amazon EBS as well, but that is outside the scope of this document.

Built With


This project is licensed under the MIT License - see the LICENSE.md file for details