The Anthropic Python library provides convenient access to the Anthropic REST API from any Python 3.8+ application. It 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 docs.anthropic.com. The full API of this library can be found in api.md.
# install from PyPI
pip install anthropicThe full API of this library can be found in api.md.
import os
from anthropic import Anthropic
client = Anthropic(
api_key=os.environ.get("ANTHROPIC_API_KEY"), # This is the default and can be omitted
)
message = client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
print(message.content)While you can provide an api_key keyword argument,
we recommend using python-dotenv
to add ANTHROPIC_API_KEY="my-anthropic-api-key" to your .env file
so that your API Key is not stored in source control.
Simply import AsyncAnthropic instead of Anthropic and use await with each API call:
import os
import asyncio
from anthropic import AsyncAnthropic
client = AsyncAnthropic(
api_key=os.environ.get("ANTHROPIC_API_KEY"), # This is the default and can be omitted
)
async def main() -> None:
message = await client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
print(message.content)
asyncio.run(main())Functionality between the synchronous and asynchronous clients is otherwise identical.
We provide support for streaming responses using Server Side Events (SSE).
from anthropic import Anthropic
client = Anthropic()
stream = client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
stream=True,
)
for event in stream:
print(event.type)The async client uses the exact same interface.
from anthropic import AsyncAnthropic
client = AsyncAnthropic()
stream = await client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
stream=True,
)
async for event in stream:
print(event.type)This library provides several conveniences for streaming messages, for example:
import asyncio
from anthropic import AsyncAnthropic
client = AsyncAnthropic()
async def main() -> None:
async with client.messages.stream(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Say hello there!",
}
],
model="claude-3-opus-20240229",
) as stream:
async for text in stream.text_stream:
print(text, end="", flush=True)
print()
message = await stream.get_final_message()
print(message.to_json())
asyncio.run(main())Streaming with client.messages.stream(...) exposes various helpers for your convenience including accumulation & SDK-specific events.
Alternatively, you can use client.messages.create(..., stream=True) which only returns an async iterable of the events in the stream and thus uses less memory (it does not build up a final message object for you).
To get the token count for a message without creating it you can use the client.beta.messages.count_tokens() method. This takes the same messages list as the .create() method.
count = client.beta.messages.count_tokens(
model="claude-3-5-sonnet-20241022",
messages=[
{"role": "user", "content": "Hello, world"}
]
)
count.input_tokens # 10You can also see the exact usage for a given request through the usage response property, e.g.
message = client.messages.create(...)
message.usage
# Usage(input_tokens=25, output_tokens=13)This SDK provides beta support for the Message Batches API under the client.beta.messages.batches namespace.
Message Batches take the exact same request params as the standard Messages API:
await client.beta.messages.batches.create(
requests=[
{
"custom_id": "my-first-request",
"params": {
"model": "claude-3-5-sonnet-20240620",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Hello, world"}],
},
},
{
"custom_id": "my-second-request",
"params": {
"model": "claude-3-5-sonnet-20240620",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Hi again, friend"}],
},
},
]
)Once a Message Batch has been processed, indicated by .processing_status === 'ended', you can access the results with .batches.results()
result_stream = await client.beta.messages.batches.results(batch_id)
async for entry in result_stream:
if entry.result.type == "succeeded":
print(entry.result.message.content)This SDK provides support for tool use, aka function calling. More details can be found in the documentation.
This library also provides support for the Anthropic Bedrock API if you install this library with the bedrock extra, e.g. pip install -U anthropic[bedrock].
You can then import and instantiate a separate AnthropicBedrock class, the rest of the API is the same.
from anthropic import AnthropicBedrock
client = AnthropicBedrock()
message = client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello!",
}
],
model="anthropic.claude-3-sonnet-20240229-v1:0",
)
print(message)The bedrock client supports the following arguments for authentication
AnthropicBedrock(
aws_profile='...',
aws_region='us-east'
aws_secret_key='...',
aws_access_key='...',
aws_session_token='...',
)For a more fully fledged example see examples/bedrock.py.
This library also provides support for the Anthropic Vertex API if you install this library with the vertex extra, e.g. pip install -U anthropic[vertex].
You can then import and instantiate a separate AnthropicVertex/AsyncAnthropicVertex class, which has the same API as the base Anthropic/AsyncAnthropic class.
from anthropic import AnthropicVertex
client = AnthropicVertex()
message = client.messages.create(
model="claude-3-sonnet@20240229",
max_tokens=100,
messages=[
{
"role": "user",
"content": "Hello!",
}
],
)
print(message)For a more complete example see examples/vertex.py.
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.
List methods in the Anthropic API are paginated.
This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:
from anthropic import Anthropic
client = Anthropic()
all_batches = []
# Automatically fetches more pages as needed.
for batch in client.beta.messages.batches.list(
limit=20,
):
# Do something with batch here
all_batches.append(batch)
print(all_batches)Or, asynchronously:
import asyncio
from anthropic import AsyncAnthropic
client = AsyncAnthropic()
async def main() -> None:
all_batches = []
# Iterate through items across all pages, issuing requests as needed.
async for batch in client.beta.messages.batches.list(
limit=20,
):
all_batches.append(batch)
print(all_batches)
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 client.beta.messages.batches.list(
limit=20,
)
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 client.beta.messages.batches.list(
limit=20,
)
print(f"next page cursor: {first_page.last_id}") # => "next page cursor: ..."
for batch in first_page.data:
print(batch.id)
# Remove `await` for non-async usage.When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of anthropic.APIConnectionError is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of anthropic.APIStatusError is raised, containing status_code and response properties.
All errors inherit from anthropic.APIError.
import anthropic
from anthropic import Anthropic
client = Anthropic()
try:
client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
except anthropic.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
except anthropic.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
except anthropic.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 |
For more information on debugging requests, see these docs
All object responses in the SDK provide a _request_id property which is added from the request-id response header so that you can quickly log failing requests and report them back to Anthropic.
message = client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
print(message._request_id) # req_018EeWyXxfu5pfWkrYcMdjWGNote that unlike other properties that use an _ prefix, the _request_id property
is public. Unless documented otherwise, all other _ prefix properties,
methods and modules are private.
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 anthropic import Anthropic
# Configure the default for all requests:
client = Anthropic(
# default is 2
max_retries=0,
)
# Or, configure per-request:
client.with_options(max_retries=5).messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)By default requests time out after 10 minutes. You can configure this with a timeout option,
which accepts a float or an httpx.Timeout object:
from anthropic import Anthropic
# Configure the default for all requests:
client = Anthropic(
# 20 seconds (default is 10 minutes)
timeout=20.0,
)
# More granular control:
client = Anthropic(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
client.with_options(timeout=5.0).messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)On timeout, an APITimeoutError is thrown.
Note that requests that time out are retried twice by default.
We automatically send the anthropic-version header set to 2023-06-01.
If you need to, you can override it by setting default headers per-request or on the client object.
Be aware that doing so may result in incorrect types and other unexpected or undefined behavior in the SDK.
from anthropic import Anthropic
client = Anthropic(
default_headers={"anthropic-version": "My-Custom-Value"},
)We use the standard library logging module.
You can enable logging by setting the environment variable ANTHROPIC_LOG to info.
$ export ANTHROPIC_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 anthropic import Anthropic
client = Anthropic()
response = client.messages.with_raw_response.create(
max_tokens=1024,
messages=[{
"role": "user",
"content": "Hello, Claude",
}],
model="claude-3-opus-20240229",
)
print(response.headers.get('X-My-Header'))
message = response.parse() # get the object that `messages.create()` would have returned
print(message.content)These methods return an LegacyAPIResponse object. This is a legacy class as we're changing it slightly in the next major version.
For the sync client this will mostly be the same with the exception
of content & text will be methods instead of properties. In the
async client, all methods will be async.
A migration script will be provided & the migration in general should be smooth.
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.
As such, .with_streaming_response methods return a different APIResponse object, and the async client returns an AsyncAPIResponse object.
with client.messages.with_streaming_response.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
) 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) 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 anthropic import Anthropic, DefaultHttpxClient
client = Anthropic(
# Or use the `ANTHROPIC_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 anthropic
print(anthropic.__version__)Python 3.8 or higher.
