The Anthropic Python library provides convenient access to the Anthropic 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.
In v0.3.0, we introduced a fully rewritten SDK.
The new version uses separate sync and async clients, unified streaming, typed params and structured response objects, and resource-oriented methods:
Sync before/after:
- client = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
+ client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
# or, simply provide an ANTHROPIC_API_KEY environment variable:
+ client = anthropic.Anthropic()
- rsp = client.completion(**params)
- rsp["completion"]
+ rsp = client.completions.create(**params)
+ rsp.completionAsync before/after:
- client = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
+ client = anthropic.AsyncAnthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
- await client.acompletion(**params)
+ await client.completions.create(**params)The .completion_stream() and .acompletion_stream() methods have been removed;
simply pass stream=Trueto .completions.create().
Streaming responses are now incremental; the full text is not sent in each message,
as v0.3 sends the Anthropic-Version: 2023-06-01 header.
Example streaming diff
import anthropic
- client = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
+ client = anthropic.Anthropic()
# Streams are now incremental diffs of text
# rather than sending the whole message every time:
text = "
- stream = client.completion_stream(**params)
- for data in stream:
- diff = data["completion"].replace(text, "")
- text = data["completion"]
+ stream = client.completions.create(**params, stream=True)
+ for data in stream:
+ diff = data.completion # incremental text
+ text += data.completion
print(diff, end="")
print("Done. Final text is:")
print(text)The API documentation can be found here.
pip install anthropicfrom anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
anthropic = Anthropic(
# defaults to os.environ.get("ANTHROPIC_API_KEY")
api_key="my api key",
)
completion = anthropic.completions.create(
model="claude-2",
max_tokens_to_sample=300,
prompt=f"{HUMAN_PROMPT} how does a court case get to the Supreme Court?{AI_PROMPT}",
)
print(completion.completion)While you can provide an api_key keyword argument, we recommend using python-dotenv
and adding ANTHROPIC_API_KEY="my 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:
from anthropic import AsyncAnthropic, HUMAN_PROMPT, AI_PROMPT
anthropic = AsyncAnthropic(
# defaults to os.environ.get("ANTHROPIC_API_KEY")
api_key="my api key",
)
async def main():
completion = await anthropic.completions.create(
model="claude-2",
max_tokens_to_sample=300,
prompt=f"{HUMAN_PROMPT} how does a court case get to the Supreme Court?{AI_PROMPT}",
)
print(completion.completion)
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, HUMAN_PROMPT, AI_PROMPT
anthropic = Anthropic()
stream = anthropic.completions.create(
prompt=f"{HUMAN_PROMPT} Your prompt here{AI_PROMPT}",
max_tokens_to_sample=300,
model="claude-2",
stream=True,
)
for completion in stream:
print(completion.completion, end="", flush=True)The async client uses the exact same interface.
from anthropic import AsyncAnthropic, HUMAN_PROMPT, AI_PROMPT
anthropic = AsyncAnthropic()
stream = await anthropic.completions.create(
prompt=f"{HUMAN_PROMPT} Your prompt here{AI_PROMPT}",
max_tokens_to_sample=300,
model="claude-2",
stream=True,
)
async for completion in stream:
print(completion.completion, end="", flush=True)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".
You can estimate billing for a given request with the client.count_tokens() method, eg:
client = Anthropic()
client.count_tokens('Hello world!') # 3When the library is unable to connect to the API (e.g., due to network connection problems or a timeout), a subclass of anthropic.APIConnectionError is raised.
When the API returns a non-success status code (i.e., 4xx or 5xx
response), a subclass of anthropic.APIStatusError will be raised, containing status_code and response properties.
All errors inherit from anthropic.APIError.
import anthropic
client = anthropic.Anthropic()
try:
client.completions.create(
prompt=f"{anthropic.HUMAN_PROMPT} Your prompt here{anthropic.AI_PROMPT}",
max_tokens_to_sample=300,
model="claude-2",
)
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 |
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 anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
# Configure the default for all requests:
anthropic = Anthropic(
# default is 2
max_retries=0,
)
# Or, configure per-request:
anthropic.with_options(max_retries=5).completions.create(
prompt=f"{HUMAN_PROMPT} Can you help me effectively ask for a raise at work?{AI_PROMPT}",
max_tokens_to_sample=300,
model="claude-2",
)Requests time out after 10 minutes by default. You can configure this with a timeout option,
which accepts a float or an httpx.Timeout:
from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
# Configure the default for all requests:
anthropic = Anthropic(
# default is 10 minutes
timeout=20.0,
)
# More granular control:
anthropic = Anthropic(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
anthropic.with_options(timeout=5 * 1000).completions.create(
prompt=f"{HUMAN_PROMPT} Where can I get a good coffee in my neighbourhood?{AI_PROMPT}",
max_tokens_to_sample=300,
model="claude-2",
)On timeout, an APITimeoutError is thrown.
Note that requests which time out will be 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"},
)You can configure the following keyword arguments when instantiating the client:
import httpx
from anthropic import Anthropic
client = Anthropic(
# 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.
By default we will close the underlying HTTP connections whenever the client is garbage collected is called but you can also manually close the client using the .close() method if desired, or with a context manager that closes when exiting.
This package generally attempts to follow 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.
Python 3.7 or higher.