lithic 0.11.0

Client library for the lithic API

Lithic Python API Library

Migration Guide

We've made some major improvements to how you pass arguments to methods which will require migrating your existing code.

If you want to migrate to the new patterns incrementally you can do so by installing v0.5.0. This release contains both the new and old patterns with a backwards compatibility layer.

You can find a guide to migrating in this document.

The Lithic Python library provides convenient access to the Lithic REST API from any Python 3.7+ application. It includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.

Documentation

The API documentation can be found here.

Installation

pip install lithic

Usage

from lithic import Lithic

lithic = Lithic(
    # defaults to os.environ.get("LITHIC_API_KEY")
    api_key="my api key",
    # defaults to "production".
    environment="sandbox",
)

card = lithic.cards.create(
    type="SINGLE_USE",
)
print(card.token)

While you can provide an api_key keyword argument, we recommend using python-dotenv and adding LITHIC_API_KEY="my api key" to your .env file so that your API Key is not stored in source control.

Async Usage

Simply import AsyncLithic instead of Lithic and use await with each API call:

from lithic import AsyncLithic

lithic = AsyncLithic(
    # defaults to os.environ.get("LITHIC_API_KEY")
    api_key="my api key",
    # defaults to "production".
    environment="sandbox",
)


async def main():
    card = await lithic.cards.create(
        type="SINGLE_USE",
    )
    print(card.token)


asyncio.run(main())

Functionality between the synchronous and asynchronous clients is otherwise identical.

Using Types

Nested request parameters are TypedDicts, while responses are Pydantic models. This helps 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 Lithic API are paginated.

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

import lithic

lithic = Lithic()

all_cards = []
# Automatically fetches more pages as needed.
for card in lithic.cards.list():
    # Do something with card here
    all_cards.append(card)
print(all_cards)

Or, asynchronously:

import asyncio
import lithic

lithic = AsyncLithic()


async def main() -> None:
    all_cards = []
    # Iterate through items across all pages, issuing requests as needed.
    async for card in lithic.cards.list():
        all_cards.append(card)
    print(all_cards)


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 lithic.cards.list()
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 lithic.cards.list()

print(f"page number: {first_page.page}")  # => "page number: 1"
for card in first_page.data:
    print(card.created)

# Remove `await` for non-async usage.

Nested params

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

from lithic import Lithic

lithic = Lithic()

lithic.cards.create(
    foo={
        "bar": True
    },
)

Webhook Verification

We provide helper methods for verifying that a webhook request came from Lithic, and not a malicious third party.

You can use lithic.webhooks.verify_signature(body, headers, secret?) -> None or lithic.webhooks.unwrap(body, headers, secret?) -> Payload like so:

@app.post('/my-webhook-handler')
async def handler(request: Request):
    body = await request.body()
    secret = os.environ['LITHIC_WEBHOOK_SECRET']  # env var used by default; explicit here.
    payload = client.webhooks.unwrap(body, request.headers, secret)
    print(payload)

This example is written for FastAPI, but usage is similar no matter what web framework you use.

Handling errors

When the library is unable to connect to the API (e.g., due to network connection problems or a timeout), a subclass of lithic.APIConnectionError is raised.

When the API returns a non-success status code (i.e., 4xx or 5xx response), a subclass of lithic.APIStatusError will be raised, containing status_code and response properties.

All errors inherit from lithic.APIError.

from lithic import Lithic

lithic = Lithic()

try:
    lithic.cards.create(
        type="an_incorrect_type",
    )
except lithic.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
except lithic.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except lithic.APIStatusError as e:
    print("Another non-200-range status code was received")
    print(e.status_code)
    print(e.response)

Error codes are as followed:

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

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), 409 Conflict, 429 Rate Limit, and >=500 Internal errors will all be retried by default.

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

from lithic import Lithic

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

# Or, configure per-request:
lithic.with_options(max_retries=5).cards.list(
    page_size=10,
)

Timeouts

Requests time out after 60 seconds by default. You can configure this with a timeout option, which accepts a float or an httpx.Timeout:

from lithic import Lithic

# Configure the default for all requests:
lithic = Lithic(
    # default is 60s
    timeout=20.0,
)

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

# Override per-request:
lithic.with_options(timeout=5 * 1000).cards.list(
    page_size=10,
)

On timeout, an APITimeoutError is thrown.

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

Advanced: Configuring custom URLs, proxies, and transports

You can configure the following keyword arguments when instantiating the client:

import httpx
from lithic import Lithic

lithic = Lithic(
    # Use a custom base URL
    base_url="http://my.test.server.example.com:8083",
    proxies="http://my.test.proxy.example.com",
    transport=httpx.HTTPTransport(local_address="0.0.0.0"),
)

See the httpx documentation for information about the proxies and transport keyword arguments.

Migration guide

This section outlines the features that were deprecated in v0.5.0, and subsequently removed in v0.6.0 and how to migrate your code.

Breaking changes

TypedDict → keyword arguments

The way you pass arguments to methods has been changed from a single TypedDict to individual arguments. For example, this snippet:

card = await client.cards.create({"type": "VIRTUAL"})

Now becomes:

card = await client.cards.create(type="VIRTUAL")

Migrating

The easiest way to make your code compatible with this change is to add **{, for example:

- card = await client.cards.create({'type': 'VIRTUAL'})
+ card = await client.cards.create(**{'type': 'VIRTUAL'})

However, it is highly recommended to completely switch to explicit keyword arguments:

- card = await client.cards.create({'type': 'VIRTUAL'})
+ card = await client.cards.create(type='VIRTUAL')

Named path arguments

All but the last path parameter must now be passed as named arguments instead of positional arguments, for example, for a method that calls the endpoint /account_holders/{account_holder_token}/documents/{document_token} you would've been able to call the method like this:

card = await client.account_holders.retrieve(
    "account_holder_token", "my_document_token"
)

But now you must call the method like this:

card = await client.account_holders.retrieve(
    "my_document_token", account_holder_token="account_holder_token"
)

If you have type checking enabled in your IDE it will tell you which parts of your code need to be updated.

Request options

You used to be able to set request options on a per-method basis, now you can only set them on the client. There are two methods that you can use to make this easy, with_options and copy.

If you need to make multiple requests with changed options, you can use .copy() to get a new client object with those options. This can be useful if you need to set a custom header for multiple requests, for example:

copied = client.copy(default_headers={"X-My-Header": "Foo"})
card = await copied.cards.create(type="VIRTUAL")
await copied.cards.provision(card.token, digital_wallet="GOOGLE_PAY")

If you just need to override one of the client options for one request, you can use .with_options(), for example:

await client.with_options(timeout=None).cards.create(type="VIRTUAL")

It should be noted that the .with_options() method is simply an alias to .copy(), you can use them interchangeably.

You can pass nearly every argument that is supported by the Client __init__ method to the .copy() method, except for proxies and transport.

copied = client.copy(
    api_key="...",
    environment="sandbox",
    timeout=httpx.Timeout(read=10),
    max_retries=5,
    default_headers={
        "X-My-Header": "value",
    },
    default_query={
        "my_default_param": "value",
    },
)

New features

Pass custom headers

If you need to add additional headers to a request you can easily do so with the extra_headers argument:

card = await client.cards.create(
    type="VIRTUAL",
    extra_headers={
        "X-Foo": "my header",
    },
)

Pass custom JSON properties

You can add additional properties to the JSON request body that are not included directly in the method definition through the extra_body argument. This can be useful when there are in new properties in the API that are in beta and aren't in the SDK yet.

card = await client.cards.create(
    type="VIRTUAL",
    extra_body={
        "special_prop": "foo",
    },
)
# sends this to the API:
# {"type": "VIRTUAL", "special_prop": "foo"}

Pass custom query parameters

You can add additional query parameters that aren't specified in the method definition through the extra_query argument. This can be useful when there are any new/beta query parameters that are not yet in the SDK.

card = await client.cards.create(
    type="VIRTUAL",
    extra_query={
        "special_param": "bar",
    },
)
# makes the request to this URL:
# https://api.lithic.com/v1/cards?special_param=bar

Rich date and datetime types

We've improved the types for response fields / request params that correspond to date or datetime values!

Previously they were just raw strings but now response fields will be instances of date or datetime.

This means that if you're working with these fields and parsing them into datetime instances manually you will have to remove any code that performs said parsing.

card = client.cards.retrieve('<token>')
- created = datetime.fromisoformat(card.created_at)
+ created = card.created_at
print(created.month)

For request params you can continue to pass in strings if you want to use a datetime library other than the standard library version but if you were writing code that looked like this:

dt = datetime(...)
for card in client.cards.list(begin=dt.isoformat()):
  ...

You can remove the explicit call to isoformat!

dt = datetime(...)
- for card in client.cards.list(begin=dt.isoformat()):
+ for card in client.cards.list(begin=dt):
  ...

Status

This package is in beta. Its internals and interfaces are not stable and subject to change without a major semver bump; please reach out if you rely on any undocumented behavior.

We are keen for your feedback; please email us at [e-mail] or open an issue with questions, bugs, or suggestions.

Requirements

Python 3.7 or higher.