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Python library for the DeepL API.

Project description

DeepL Python Library

PyPI version Supported Python versions License: MIT

The DeepL API is a language translation API that allows other computer programs to send texts and documents to DeepL's servers and receive high-quality translations. This opens a whole universe of opportunities for developers: any translation product you can imagine can now be built on top of DeepL's best-in-class translation technology.

The DeepL Python library offers a convenient way for applications written in Python to interact with the DeepL API. We intend to support all API functions with the library, though support for new features may be added to the library after they’re added to the API.

Getting an authentication key

To use the DeepL Python Library, you'll need an API authentication key. To get a key, please create an account here. You can translate up to 500,000 characters/month for free.

After you have created an account, you can find your API authentication key on your DeepL Pro Account.


The library can be installed from PyPI using pip:

pip install --upgrade deepl

If you need to modify this source code, install the dependencies using poetry:

poetry install


The library is tested with Python versions 3.6 to 3.10.

The requests module is used to perform HTTP requests; the minimum is version 2.0.


import deepl
import os

# Create a Translator object providing your DeepL API authentication key.
# To avoid writing your key in source code, you can set it in an environment
# variable DEEPL_AUTH_KEY, then read the variable in your Python code:
translator = deepl.Translator(os.getenv("DEEPL_AUTH_KEY"))

# Translate text into a target language, in this case, French
result = translator.translate_text("Hello, world!", target_lang="FR")
print(result)  # "Bonjour, le monde !"
# Note: printing or converting the result to a string uses the output text

# Translate multiple texts into British English
result = translator.translate_text(["お元気ですか?", "¿Cómo estás?"], target_lang="EN-GB")
print(result[0].text)  # "How are you?"
print(result[0].detected_source_lang)  # "JA"
print(result[1].text)  # "How are you?"
print(result[1].detected_source_lang)  # "ES"

# Translate a formal document from English to German 
        "Instruction Manual.docx",
except deepl.DocumentTranslationException as error:
    # If an error occurs during translate_document_from_filepath() or
    # translate_document() and after the document was already uploaded, a 
    # DocumentTranslationException is raised. The document_handle property
    # contains the document handle to later retrieve the document or contact
    # DeepL support.
    doc_id =
    doc_key = error.document_handle.key
    print(f"Error after uploading document ${error}, id: ${doc_id} key: ${doc_key}")
except deepl.DeepLException as error:
    # Errors during upload raise a DeepLException

# Glossaries allow you to customize your translations
glossary_en_to_de = translator.create_glossary(
    "My glossary",
    entries={"artist": "Maler", "prize": "Gewinn"},

with_glossary = translator.translate_text_with_glossary(
    "The artist was awarded a prize.", glossary_en_to_de
print(with_glossary)  # "Der Maler wurde mit einem Gewinn ausgezeichnet."

without_glossary = translator.translate_text(
    "The artist was awarded a prize.", target_lang="DE"
print(without_glossary)  # "Der Künstler wurde mit einem Preis ausgezeichnet."

# Check account usage
usage = translator.get_usage()
if usage.character.limit_exceeded:
    print("Character limit exceeded.")
    print(f"Character usage: {usage.character.count} of {usage.character.limit}")

# Source and target languages
print("Source languages:")
for language in translator.get_source_languages():
    print(f"{language.code} ({})")  # Example: "DE (German)"

print("Target languages:")
for language in translator.get_target_languages():
    if language.supports_formality:
        print(f"{language.code} ({}) supports formality")
        print(f"{language.code} ({})")


All module functions may raise deepl.DeepLException or one of its subclasses. If invalid arguments are provided, they may raise the standard exceptions ValueError and TypeError.



Logging can be enabled to see the HTTP-requests sent and responses received by the library. Enable and control logging using Python's logging module, for example:

import logging

Proxy configuration

You can configure a proxy by specifying the proxy argument when creating a deepl.Translator:

proxy = "http://user:pass@"
translator = deepl.Translator(..., proxy=proxy)

The proxy argument is passed to the underlying requests session, see the documentation here; a dictionary of schemes to proxy URLs is also accepted.

Command Line Interface

The library can be run on the command line supporting all API functions. Use the --help option for usage information:

python3 -m deepl --help

The CLI requires your DeepL authentication key specified either as the DEEPL_AUTH_KEY environment variable, or using the --auth-key option, for example:

python3 -m deepl --auth-key=YOUR_AUTH_KEY usage

Note that the --auth-key argument must appear before the command argument. The recognized commands are:

Command Description
text translate text(s)
document translate document(s)
usage print usage information for the current billing period
languages print available languages
glossary create, list, and remove glossaries

For example, to translate text:

python3 -m deepl --auth-key=YOUR_AUTH_KEY text --to=DE "Text to be translated."

Wrap text arguments in quotes to prevent the shell from splitting sentences into words.


If you experience problems using the library, or would like to request a new feature, please open an issue.


We are currently unable to accept Pull Requests. If you would like to suggest changes, please open an issue instead.


Execute the tests using pytest. The tests communicate with the DeepL API using the auth key defined by the DEEPL_AUTH_KEY environment variable.

Be aware that the tests make DeepL API requests that contribute toward your API usage.

The test suite may instead be configured to communicate with the mock-server provided by deepl-mock. Although most test cases work for either, some test cases work only with the DeepL API or the mock-server and will be otherwise skipped. The test cases that require the mock-server trigger server errors and test the client error-handling. To execute the tests using deepl-mock, run it in another terminal while executing the tests. Execute the tests using pytest with the DEEPL_MOCK_SERVER_PORT and DEEPL_SERVER_URL environment variables defined referring to the mock-server.

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