Anthropic Python API library

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.

Documentation

The REST API documentation can be found on docs.anthropic.com. The full API of this library can be found in api.md.

Installation

# install from PyPI
pip install anthropic

Usage

The 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-3-5-sonnet-latest",
)
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.

Async usage

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-3-5-sonnet-latest",
    )
    print(message.content)


asyncio.run(main())

Functionality between the synchronous and asynchronous clients is otherwise identical.

Streaming responses

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-3-5-sonnet-latest",
    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-3-5-sonnet-latest",
    stream=True,
)
async for event in stream:
    print(event.type)

Streaming Helpers

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-3-5-sonnet-latest",
    ) 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).

Token counting

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  # 10

You 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)

Message Batches

This SDK provides beta support for the Message Batches API under the client.beta.messages.batches namespace.

Creating a batch

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-latest",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Hello, world"}],
            },
        },
        {
            "custom_id": "my-second-request",
            "params": {
                "model": "claude-3-5-sonnet-latest",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Hi again, friend"}],
            },
        },
    ]
)

Getting results from a batch

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)

Tool use

This SDK provides support for tool use, aka function calling. More details can be found in the documentation.

AWS Bedrock

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-3-5-sonnet-20241022-v2:0",
)
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.

Google Vertex

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-3-5-sonnet-v2@20241022",
    max_tokens=100,
    messages=[
        {
            "role": "user",
            "content": "Hello!",
        }
    ],
)
print(message)

For a more complete example see examples/vertex.py.

Using types

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.

Pagination

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.

Handling errors

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-3-5-sonnet-latest",
    )
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 follows:

Request IDs

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-3-5-sonnet-latest",
)
print(message._request_id)  # req_018EeWyXxfu5pfWkrYcMdjWG

Note 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.

Retries

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-3-5-sonnet-latest",
)

Timeouts

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:

```python 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( ma