)
The ImageKit Python SDK provides convenient access to the ImageKit REST API from any Python 3.9+ application. It offers powerful tools for URL generation and transformation, signed URLs for secure content delivery, webhook verification, file uploads, and more. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.
The REST API documentation can be found on imagekit.io. The full API of this library can be found in api.md.
- Installation
- Requirements
- Usage
- URL generation
- Authentication parameters for client-side uploads
- Webhook verification
- Advanced Usage
- Versioning
- Contributing
# install from PyPI
pip install imagekitioThe full API of this library can be found in api.md.
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"), # This is the default and can be omitted
)
# Upload a filewithopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
response=client.files.upload(
file=file_data,
file_name="uploaded-image.jpg",
)
print(response.file_id)
print(response.url)While you can provide a private_key keyword argument,
we recommend using python-dotenv
to add IMAGEKIT_PRIVATE_KEY="My Private Key" to your .env file
so that your Private Key is not stored in source control.
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.
Nested parameters are dictionaries, typed using TypedDict, for example:
fromimagekitioimportImageKitclient=ImageKit()
# Read file into memory and uploadwithopen("/path/to/file.jpg", "rb") asf:
file_data=f.read()
response=client.files.upload(
file=file_data,
file_name="fileName",
transformation={
"post": [
{
"type": "thumbnail",
"value": "w-150,h-150",
},
{
"protocol": "dash",
"type": "abs",
"value": "sr-240_360_480_720_1080",
},
]
},
)
print(response.file_id)Simply import AsyncImageKit instead of ImageKit and use await with each API call:
importosimportasynciofromimagekitioimportAsyncImageKitclient=AsyncImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"), # This is the default and can be omitted
)
asyncdefmain() ->None:
# Read file into memory and uploadwithopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
response=awaitclient.files.upload(
file=file_data,
file_name="file-name.jpg",
)
print(response.file_id)
print(response.url)
asyncio.run(main())Functionality between the synchronous and asynchronous clients is otherwise identical.
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:
# install from PyPI
pip install imagekitio[aiohttp]Then you can enable it by instantiating the client with http_client=DefaultAioHttpClient():
importosimportasynciofromimagekitioimportDefaultAioHttpClientfromimagekitioimportAsyncImageKitasyncdefmain() ->None:
asyncwithAsyncImageKit(
private_key=os.environ.get(
"IMAGEKIT_PRIVATE_KEY"
), # This is the default and can be omittedhttp_client=DefaultAioHttpClient(),
) asclient:
# Read file into memory and uploadwithopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
response=awaitclient.files.upload(
file=file_data,
file_name="file-name.jpg",
)
print(response.file_id)
print(response.url)
asyncio.run(main())The ImageKit SDK provides a powerful helper.build_url() method for generating optimized image and video URLs with transformations. Here are examples ranging from simple URLs to complex transformations with overlays and signed URLs.
Generate a simple URL without any transformations:
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"),
)
# Basic URL without transformationsurl=client.helper.build_url(
url_endpoint="https://ik.imagekit.io/your_imagekit_id",
src="/path/to/image.jpg",
)
print(url)
# Result: https://ik.imagekit.io/your_imagekit_id/path/to/image.jpgApply common transformations like resizing, cropping, and format conversion:
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"),
)
# URL with basic transformationsurl=client.helper.build_url(
url_endpoint="https://ik.imagekit.io/your_imagekit_id",
src="/path/to/image.jpg",
transformation=[
{
"width": 400,
"height": 300,
"crop": "maintain_ratio",
"quality": 80,
"format": "webp",
}
],
)
print(url)
# Result: https://ik.imagekit.io/your_imagekit_id/path/to/image.jpg?tr=w-400,h-300,c-maintain_ratio,q-80,f-webpAdd image overlays to your base image:
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"),
)
# URL with image overlayurl=client.helper.build_url(
url_endpoint="https://ik.imagekit.io/your_imagekit_id",
src="/path/to/base-image.jpg",
transformation=[
{
"width": 500,
"height": 400,
"overlay": {
"type": "image",
"input": "/path/to/overlay-logo.png",
"position": {
"x": 10,
"y": 10,
},
"transformation": [
{
"width": 100,
"height": 50,
}
],
},
}
],
)
print(url)
# Result: URL with image overlay positioned at x:10, y:10Add customized text overlays:
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"),
)
# URL with text overlayurl=client.helper.build_url(
url_endpoint="https://ik.imagekit.io/your_imagekit_id",
src="/path/to/base-image.jpg",
transformation=[
{
"width": 600,
"height": 400,
"overlay": {
"type": "text",
"text": "Sample Text Overlay",
"position": {
"x": 50,
"y": 50,
"focus": "center",
},
"transformation": [
{
"font_size": 40,
"font_family": "Arial",
"font_color": "FFFFFF",
"typography": "b", # bold
}
],
},
}
],
)
print(url)
# Result: URL with bold white Arial text overlay at center positionCombine multiple overlays for complex compositions:
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"),
)
# URL with multiple overlays (text + image)url=client.helper.build_url(
url_endpoint="https://ik.imagekit.io/your_imagekit_id",
src="/path/to/base-image.jpg",
transformation=[
{
"width": 800,
"height": 600,
"overlay": {
"type": "text",
"text": "Header Text",
"position": {
"x": 20,
"y": 20,
},
"transformation": [
{
"font_size": 30,
"font_color": "000000",
}
],
},
},
{
"overlay": {
"type": "image",
"input": "/watermark.png",
"position": {
"focus": "bottom_right",
},
"transformation": [
{
"width": 100,
"opacity": 70,
}
],
},
},
],
)
print(url)
# Result: URL with text overlay at top-left and semi-transparent watermark at bottom-rightGenerate signed URLs that expire after a specified time for secure content delivery:
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"),
)
# Generate a signed URL that expires in 1 hour (3600 seconds)url=client.helper.build_url(
url_endpoint="https://ik.imagekit.io/your_imagekit_id",
src="/private/secure-image.jpg",
transformation=[
{
"width": 400,
"height": 300,
"quality": 90,
}
],
signed=True,
expires_in=3600, # URL expires in 1 hour
)
print(url)
# Result: URL with signature parameters (?ik-t=timestamp&ik-s=signature)# Generate a signed URL that doesn't expirepermanent_signed_url=client.helper.build_url(
url_endpoint="https://ik.imagekit.io/your_imagekit_id",
src="/private/secure-image.jpg",
signed=True,
# No expires_in means the URL won't expire
)
print(permanent_signed_url)
# Result: URL with signature parameter (?ik-s=signature)ImageKit frequently adds new transformation parameters that might not yet be documented in the SDK. You can use the raw parameter to access these features or create custom transformation strings:
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"),
)
# Using Raw transformation for undocumented or new parametersurl=client.helper.build_url(
url_endpoint="https://ik.imagekit.io/your_imagekit_id",
src="/path/to/image.jpg",
transformation=[
{
# Combine documented transformations with raw parameters"width": 400,
"height": 300,
},
{
# Use raw for undocumented transformations or complex parameters"raw": "something-new",
},
],
)
print(url)
# Result: https://ik.imagekit.io/your_imagekit_id/path/to/image.jpg?tr=w-400,h-300:something-newGenerate authentication parameters for secure client-side file uploads:
importosfromimagekitioimportImageKitclient=ImageKit(
private_key=os.environ.get("IMAGEKIT_PRIVATE_KEY"),
)
# Generate authentication parameters for client-side uploadsauth_params=client.helper.get_authentication_parameters()
print(auth_params)
# Result: {'expire': <timestamp>, 'signature': '<hmac-signature>', 'token': '<uuid-token>'}# Generate with custom token and expirycustom_auth_params=client.helper.get_authentication_parameters(
token="my-custom-token",
expire=1800
)
print(custom_auth_params)
# Result: {'expire': 1800, 'signature': '<hmac-signature>', 'token': 'my-custom-token'}These authentication parameters can be used in client-side upload forms to securely upload files without exposing your private API key.
The ImageKit SDK provides utilities to verify webhook signatures for secure event handling. This ensures that webhook requests are actually coming from ImageKit and haven't been tampered with.
For detailed information about webhook setup, signature verification, and handling different webhook events, refer to the ImageKit webhook documentation.
Request parameters that correspond to file uploads can be passed as bytes, a PathLike instance, an IO[bytes] file object, or a tuple of (filename, contents, media type).
Here are common file upload patterns:
frompathlibimportPathfromimagekitioimportImageKitimportioclient=ImageKit()
# Method 1: Upload from bytes# Read file into memory first, then uploadwithopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
response=client.files.upload(
file=file_data,
file_name="uploaded-image.jpg",
)
# Method 2: Upload from file stream (for large files)# Pass file object directly - SDK reads itwithopen("/path/to/your/image.jpg", "rb") asfile_stream:
response=client.files.upload(
file=file_stream,
file_name="uploaded-image.jpg",
)
# Method 3: Upload using Path object (SDK reads automatically)response=client.files.upload(
file=Path("/path/to/file.jpg"),
file_name="fileName.jpg",
)
# Method 4: Upload from BytesIO (for programmatically generated content)content=b"your binary data"bytes_io=io.BytesIO(content)
response=client.files.upload(
file=bytes_io,
file_name="binary-upload.jpg",
)
# Method 5: Upload with custom content type using tuple formatimage_data=b"your binary data"response=client.files.upload(
file=("custom.jpg", image_data, "image/jpeg"),
file_name="custom-upload.jpg",
)The async client uses the exact same interface. If you pass a PathLike instance, the file contents will be read asynchronously automatically.
Note: URL strings (e.g., "https://example.com/image.jpg") are not supported by the Python SDK. To upload from a URL, download the content first:
importurllib.request# Download from URL and upload to ImageKiturl="https://example.com/image.jpg"withurllib.request.urlopen(url) asresponse:
url_content=response.read()
# Upload the downloaded contentupload_response=client.files.upload(
file=url_content,
file_name="downloaded-image.jpg",
)When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of imagekitio.APIConnectionError is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of imagekitio.APIStatusError is raised, containing status_code and response properties.
All errors inherit from imagekitio.APIError.
importimagekitiofromimagekitioimportImageKitclient=ImageKit()
try:
# Read file into memory and uploadwithopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
response=client.files.upload(
file=file_data,
file_name="file-name.jpg",
)
exceptimagekitio.APIConnectionErrorase:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.exceptimagekitio.RateLimitErrorase:
print("A 429 status code was received; we should back off a bit.")
exceptimagekitio.APIStatusErrorase:
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 |
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:
fromimagekitioimportImageKit# Configure the default for all requests:client=ImageKit(
# default is 2max_retries=0,
)
# Or, configure per-request:withopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
client.with_options(max_retries=5).files.upload(
file=file_data,
file_name="file-name.jpg",
)By default requests time out after 1 minute. You can configure this with a timeout option,
which accepts a float or an httpx.Timeout object:
fromimagekitioimportImageKit# Configure the default for all requests:client=ImageKit(
# 20 seconds (default is 1 minute)timeout=20.0,
)
# More granular control:client=ImageKit(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:withopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
client.with_options(timeout=5.0).files.upload(
file=file_data,
file_name="file-name.jpg",
)On timeout, an APITimeoutError is thrown.
Note that requests that time out are retried twice by default.
We use the standard library logging module.
You can enable logging by setting the environment variable IMAGE_KIT_LOG to info.
$ export IMAGE_KIT_LOG=infoOr to debug for more verbose logging.
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:
ifresponse.my_fieldisNone:
if'my_field'notinresponse.model_fields_set:
print('Got json like {}, without a "my_field" key present at all.')
else:
print('Got json like {"my_field": null}.')The "raw" Response object can be accessed by prefixing .with_raw_response. to any HTTP method call, e.g.,
fromimagekitioimportImageKitclient=ImageKit()
# Read file into memory and uploadwithopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
response=client.files.with_raw_response.upload(
file=file_data,
file_name="file-name.jpg",
)
print(response.headers.get('X-My-Header'))
file=response.parse() # get the object that `files.upload()` would have returnedprint(file.file_id)These methods return an APIResponse object.
The async client returns an AsyncAPIResponse with the same structure, the only difference being awaitable methods for reading the response content.
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.
# Read file into memory and uploadwithopen("/path/to/your/image.jpg", "rb") asf:
file_data=f.read()
withclient.files.with_streaming_response.upload(
file=file_data,
file_name="file-name.jpg",
) asresponse:
print(response.headers.get("X-My-Header"))
forlineinresponse.iter_lines():
print(line)The context manager is required so that the response will reliably be closed.
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.
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.
importhttpxresponse=client.post(
"/foo",
cast_to=httpx.Response,
body={"my_param": True},
)
print(response.headers.get("x-foo"))If you want to explicitly send an extra param, you can do so with the extra_query, extra_body, and extra_headers request
options.
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.
You can directly override the httpx client to customize it for your use case, including:
- Support for proxies
- Custom transports
- Additional advanced functionality
importhttpxfromimagekitioimportImageKit, DefaultHttpxClientclient=ImageKit(
# Or use the `IMAGE_KIT_BASE_URL` env varbase_url="http://my.test.server.example.com:8083",
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():
client.with_options(http_client=DefaultHttpxClient(...))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.
fromimagekitioimportImageKitwithImageKit() asclient:
# make requests here
...
# HTTP client is now closedThis 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.
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:
importimagekitioprint(imagekitio.__version__)Python 3.9 or higher.