The RunwayML Python library provides convenient access to the RunwayML 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 with Stainless.
The REST API documentation can be found on dev.runwayml.com. The full API of this library can be found in api.md.
# install from PyPI
pip install runwaymlThe full API of this library can be found in api.md.
import os
from runwayml import RunwayML
client = RunwayML(
api_key=os.environ.get("RUNWAYML_API_SECRET"), # This is the default and can be omitted
)
image_to_video = client.image_to_video.create(
model="gen3a_turbo",
prompt_image="https://example.com/assets/bunny.jpg",
prompt_text="The bunny is eating a carrot",
)
print(image_to_video.id)While you can provide a api_key keyword argument,
we recommend using python-dotenv
to add RUNWAYML_API_SECRET="My API Key" to your .env file
so that your API Key is not stored in source control.
Simply import AsyncRunwayML instead of RunwayML and use await with each API call:
import os
import asyncio
from runwayml import AsyncRunwayML
client = AsyncRunwayML(
api_key=os.environ.get("RUNWAYML_API_SECRET"), # This is the default and can be omitted
)
async def main() -> None:
image_to_video = await client.image_to_video.create(
model="gen3a_turbo",
prompt_image="https://example.com/assets/bunny.jpg",
prompt_text="The bunny is eating a carrot",
)
print(image_to_video.id)
asyncio.run(main())Functionality between the synchronous and asynchronous clients is otherwise identical.
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.
When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of runwayml.APIConnectionError is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of runwayml.APIStatusError is raised, containing status_code and response properties.
All errors inherit from runwayml.APIError.
import runwayml
from runwayml import RunwayML
client = RunwayML()
try:
client.image_to_video.create(
model="gen3a_turbo",
prompt_image="https://example.com/assets/bunny.jpg",
prompt_text="The bunny is eating a carrot",
)
except runwayml.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
except runwayml.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
except runwayml.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 |
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 runwayml import RunwayML
# Configure the default for all requests:
client = RunwayML(
# default is 2
max_retries=0,
)
# Or, configure per-request:
client.with_options(max_retries=5).image_to_video.create(
model="gen3a_turbo",
prompt_image="https://example.com/assets/bunny.jpg",
prompt_text="The bunny is eating a carrot",
)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:
from runwayml import RunwayML
# Configure the default for all requests:
client = RunwayML(
# 20 seconds (default is 1 minute)
timeout=20.0,
)
# More granular control:
client = RunwayML(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
client.with_options(timeout=5.0).image_to_video.create(
model="gen3a_turbo",
prompt_image="https://example.com/assets/bunny.jpg",
prompt_text="The bunny is eating a carrot",
)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 RUNWAYML_LOG to info.
$ export RUNWAYML_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:
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}.')The "raw" Response object can be accessed by prefixing .with_raw_response. to any HTTP method call, e.g.,
from runwayml import RunwayML
client = RunwayML()
response = client.image_to_video.with_raw_response.create(
model="gen3a_turbo",
prompt_image="https://example.com/assets/bunny.jpg",
prompt_text="The bunny is eating a carrot",
)
print(response.headers.get('X-My-Header'))
image_to_video = response.parse() # get the object that `image_to_video.create()` would have returned
print(image_to_video.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.
with client.image_to_video.with_streaming_response.create(
model="gen3a_turbo",
prompt_image="https://example.com/assets/bunny.jpg",
prompt_text="The bunny is eating a carrot",
) 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.
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) will be respected when making this
request.
import httpx
response = 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
from runwayml import RunwayML, DefaultHttpxClient
client = RunwayML(
# Or use the `RUNWAYML_BASE_URL` env var
base_url="http://my.test.server.example.com:8083",
http_client=DefaultHttpxClient(
proxies="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.
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.
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:
import runwayml
print(runwayml.__version__)Python 3.8 or higher.