)
The Writer Python library provides access to the Writer REST API from any Python 3.9+ application. It includes a set of tools and utilities that make it easy to integrate the capabilities of Writer into your projects.
It is generated with Stainless.
Use the Writer MCP Server to enable AI assistants to interact with this API, allowing them to explore endpoints, make test requests, and use documentation to help integrate this SDK into your application.
Note: You may need to set environment variables in your MCP client.
The REST API documentation can be found on dev.writer.com. The full API of this library can be found in api.md.
To install the package from PyPI, use pip:
# install from PyPI
pip install --pre writer-sdkBefore you begin, ensure you have:
- Python 3.8 or higher
- A Writer API key
To authenticate with the Writer API, set the WRITER_API_KEY environment variable.
$ export WRITER_API_KEY="my-api-key"The Writer class automatically infers your API key from the WRITER_API_KEY environment variable.
fromwriteraiimportWriterclient=Writer() # The API key will be inferred from the `WRITER_API_KEY` environment variableYou can also explicitly set the API key with the api_key parameter:
fromwriteraiimportWriterclient=Writer(api_key="my-api-key")Never hard-code your API keys in source code or commit them to version control systems like GitHub. We recommend adding
WRITER_API_KEY="My API Key"to your.envfile so that your API Key is not stored in source control.
You can find the full API for this library in api.md.
The Writer Python library supports both synchronous and asynchronous usage. With synchronous usage, you call the API methods directly:
fromwriteraiimportWriterclient=Writer()
chat_completion=client.chat.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
)
print(chat_completion.choices[0].message.content)With asynchronous usage, you import AsyncWriter instead of Writer and use await with each API call:
importasynciofromwriteraiimportAsyncWriterclient=AsyncWriter()
asyncdefmain() ->None:
chat_completion=awaitclient.chat.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
)
print(chat_completion.choices[0].message.content)
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 --pre writer-sdk[aiohttp]Then you can enable it by instantiating the client with http_client=DefaultAioHttpClient():
importosimportasynciofromwriteraiimportDefaultAioHttpClientfromwriteraiimportAsyncWriterasyncdefmain() ->None:
asyncwithAsyncWriter(
api_key=os.environ.get("WRITER_API_KEY"), # This is the default and can be omittedhttp_client=DefaultAioHttpClient(),
) asclient:
chat_completion=awaitclient.chat.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
)
print(chat_completion.id)
asyncio.run(main())The Writer Python library provides support for streaming responses using Server Side Events (SSE).
To use streaming, set the stream parameter to True when calling an API method. You can then iterate over the stream to get the response data:
fromwriteraiimportWriterclient=Writer()
stream=client.chat.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
stream=True,
)
output_text=""forchunkinstream:
ifchunk.choices[0].delta.content:
output_text+=chunk.choices[0].delta.contentelse:
continueprint(output_text)The async client uses the same interface.
importasynciofromwriteraiimportAsyncWriterclient=AsyncWriter()
stream=awaitclient.chat.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
stream=True,
)
output_text=""asyncforchunkinstream:
ifchunk.choices[0].delta.content:
output_text+=chunk.choices[0].delta.contentelse:
continueprint(output_text)For non-streaming responses, the library returns a single response object.
The SDK also includes helpers to process streams and handle incoming events.
withclient.chat.stream(
model="palmyra-x5",
messages=[{"role": "user", "content": prompt}],
) asstream:
foreventinstream:
ifevent.type=="content.delta":
print(event.delta, flush=True, end="")More information on streaming helpers can be found in the dedicated documentation: helpers.md
List methods in the Writer API are paginated.
This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:
fromwriteraiimportWriterclient=Writer()
all_graphs= []
# Automatically fetches more pages as needed.forgraphinclient.graphs.list():
# Do something with graph hereall_graphs.append(graph)
print(all_graphs)Or, asynchronously:
importasynciofromwriteraiimportAsyncWriterclient=AsyncWriter()
asyncdefmain() ->None:
all_graphs= []
# Iterate through items across all pages, issuing requests as needed.asyncforgraphinclient.graphs.list():
all_graphs.append(graph)
print(all_graphs)
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=awaitclient.graphs.list() # Remove `await` for non-async usage.iffirst_page.has_next_page():
print(f"will fetch next page using these details: {first_page.next_page_info()}")
next_page=awaitfirst_page.get_next_page()
print(f"number of items we just fetched: {len(next_page.data)}")You can also work directly with the returned data:
first_page=awaitclient.graphs.list() # Remove `await` for non-async usage.print(f"next page cursor: {first_page.after}") # => "next page cursor: ..."forgraphinfirst_page.data:
print(graph.id)Nested parameters are dictionaries, typed using TypedDict, for example:
fromwriteraiimportWriterclient=Writer()
chat_completion=client.chat.chat(
messages=[{"role": "user"}],
model="model",
response_format={"type": "text"},
)
print(chat_completion.response_format)You can pass file upload parameters as bytes, a PathLike instance or a tuple of (filename, contents, media type).
The content_type parameter is the MIME type of the file being uploaded. The file upload supports txt, doc, docx, ppt, pptx, jpg, png, eml, html, pdf, srt, csv, xls, and xlsx file extensions.
frompathlibimportPathfromwriteraiimportWriterclient=Writer()
client.files.upload(
content=Path("/path/to/file/example.pdf"),
content_disposition="attachment; filename='example.pdf'",
content_type="application/pdf",
)The async client uses the exact same interface. If you pass a PathLike instance, the file contents will be read asynchronously automatically.
When the library is unable to connect to the API (for example, due to network connection problems, a timeout, or a firewall that doesn't allow the connection), a subclass of writerai.APIConnectionError is raised.
If you are behind a firewall, you may need to configure it to allow connections to the Writer API at
https://api.writer.com/v1.
When the API returns a non-success status code - 4xx or 5xx - a subclass of writerai.APIStatusError is raised, containing status_code and response properties.
All errors inherit from writerai.APIError.
importwriteraifromwriteraiimportWriterclient=Writer()
try:
client.chat.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
)
exceptwriterai.APIConnectionErrorase:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.exceptwriterai.RateLimitErrorase:
print("A 429 status code was received; we should back off a bit.")
exceptwriterai.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 7 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:
fromwriteraiimportWriter# Configure the default for all requests:client=Writer(
# default is 2max_retries=0,
)
# Or, configure per request:client.with_options(max_retries=5).chat.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
)By default requests time out after three minutes. You can configure this with a timeout option,
which accepts a float or an httpx.Timeout object:
importhttpxfromwriteraiimportWriter# Configure the default for all requests:client=Writer(
# 20 seconds (default is 3 minutes)timeout=20.0,
)
# More granular control:client=Writer(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per request:client.with_options(timeout=5.0).chat.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
)On timeout, an APITimeoutError is thrown.
Note that requests that time out are retried twice by default.
We use the standard logging module.
You can enable logging by setting the environment variable WRITER_LOG to info.
$ export WRITER_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('Result was {}.')
else:
print('Result was{"my_field": null}.')You can access the raw Response object by prefixing .with_raw_response. to any HTTP method call.
fromwriteraiimportWriterclient=Writer()
response=client.chat.with_raw_response.chat(
messages=[{
"content": "Write a haiku about programming",
"role": "user",
}],
model="palmyra-x5",
)
print(response.headers.get('X-My-Header'))
chat=response.parse() # get the object that `chat.chat()` would have returnedprint(chat.id)Calling a method with .with_raw_response returns an APIResponse object.
The async client returns an AsyncAPIResponse with the same structure, the only difference being awaitable methods for reading the response content.
To stream the raw response body, use .with_streaming_response, 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.
withclient.chat.with_streaming_response.chat(
messages=[
{
"content": "Write a haiku about programming",
"role": "user",
}
],
model="palmyra-x5",
) 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, parameters, or response properties, you can still use the library.
To make requests to undocumented endpoints, use client.get, client.post, and other
http verbs. Options on the client (such as retries) are respected when making these requests.
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 parameter, 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
importhttpxfromwriteraiimportWriter, DefaultHttpxClientclient=Writer(
# Or use the `WRITER_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.
fromwriteraiimportWriterwithWriter() 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:
importwriteraiprint(writerai.__version__)We welcome feedback! Please open an issue with questions, bugs, or suggestions.