Client SDKs

Python SDK

Install and configure the Anthropic Python SDK with sync and async client support

The Anthropic Python SDK provides convenient access to the Anthropic REST API from Python applications. It supports both synchronous and asynchronous operations, streaming, and integrations with AWS Bedrock and Google Vertex AI.

For API feature documentation with code examples, see the API reference. This page covers Python-specific SDK features and configuration.

Installation

pip install anthropic

For platform-specific integrations, install with extras:

# For AWS Bedrock support
pip install anthropic[bedrock]

# For Google Vertex AI support
pip install anthropic[vertex]

# For improved async performance with aiohttp
pip install anthropic[aiohttp]

Requirements

Python 3.9 or later is required.

Usage

import os
from anthropic import Anthropic

client = Anthropic(
    # This is the default and can be omitted
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
)

message = client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello, Claude",
        }
    ],
    model="claude-opus-4-6",
)
print(message.content)

Async usage

import os
import asyncio
from anthropic import AsyncAnthropic

client = AsyncAnthropic(
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
)

async def main() -> None:
    message = await client.messages.create(
        max_tokens=1024,
        messages=[
            {
                "role": "user",
                "content": "Hello, Claude",
            }
        ],
        model="claude-opus-4-6",
    )
    print(message.content)

asyncio.run(main())

Using aiohttp for better concurrency

For improved async performance, you can use the aiohttp HTTP backend instead of the default httpx:

import os
import asyncio
from anthropic import AsyncAnthropic, DefaultAioHttpClient

async def main() -> None:
    async with AsyncAnthropic(
        api_key=os.environ.get("ANTHROPIC_API_KEY"),
        http_client=DefaultAioHttpClient(),
    ) as client:
        message = await client.messages.create(
            max_tokens=1024,
            messages=[
                {
                    "role": "user",
                    "content": "Hello, Claude",
                }
            ],
            model="claude-opus-4-6",
        )
        print(message.content)

asyncio.run(main())

Streaming responses

We provide support for streaming responses using Server-Sent Events (SSE).

from anthropic import Anthropic

client = Anthropic()

stream = client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello, Claude",
        }
    ],
    model="claude-opus-4-6",
    stream=True,
)
for event in stream:
    print(event.type)

Streaming helpers

The SDK also provides streaming helpers that use context managers and provide access to the accumulated text and the final message:

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-opus-4-6",
    ) 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 including event handlers and accumulation.

Alternatively, you can use client.messages.create({ ..., stream=True }) which only returns an iterator of the events in the stream and uses less memory (it does not build up a final message object for you).

Token counting

You can see the exact usage for a given request through the usage response property:

message = client.messages.create(...)
print(message.usage)
# Usage(input_tokens=25, output_tokens=13)

You can also count tokens before making a request:

count = client.messages.count_tokens(
    model="claude-opus-4-6",
    messages=[
        {"role": "user", "content": "Hello, world"}
    ]
)
print(count.input_tokens)  # 10

Tool use

This SDK provides support for tool use, also known as function calling. More details can be found in the tool use overview.

Tool helpers

The SDK also provides helpers for easily defining and running tools as Python functions:

import json
from anthropic import Anthropic

client = Anthropic()

def get_weather(location: str) -> str:
    """Get the weather for a given location.

    Args:
        location: The city and state, e.g. San Francisco, CA
    """
    return json.dumps({
        "location": location,
        "temperature": "68°F",
        "condition": "Sunny",
    })

# Use the tool_runner to automatically handle tool calls
runner = client.beta.messages.tool_runner(
    max_tokens=1024,
    model="claude-opus-4-6",
    tools=[get_weather],
    messages=[
        {"role": "user", "content": "What is the weather in SF?"},
    ],
)
for message in runner:
    print(message)

Message Batches

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

Creating a batch

Message Batches takes an array of requests, where each object has a custom_id identifier and the same request params as the standard Messages API:

client.messages.batches.create(
    requests=[
        {
            "custom_id": "my-first-request",
            "params": {
                "model": "claude-opus-4-6",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Hello, world"}],
            },
        },
        {
            "custom_id": "my-second-request",
            "params": {
                "model": "claude-opus-4-6",
                "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 = client.messages.batches.results(batch_id)
for entry in result_stream:
    if entry.result.type == "succeeded":
        print(entry.result.message.content)

File uploads

Request parameters that correspond to file uploads can be passed in many different forms:

A PathLike object (e.g., pathlib.Path)
A tuple of (filename, content, content_type)
A BinaryIO file-like object
The return value of the toFile helper

from pathlib import Path
from anthropic import Anthropic

client = Anthropic()

# Upload using a file path
client.beta.files.upload(
    file=Path("/path/to/file"),
    betas=["files-api-2025-04-14"],
)

# Upload using bytes
client.beta.files.upload(
    file=("file.txt", b"my bytes", "text/plain"),
    betas=["files-api-2025-04-14"],
)

Handling errors

When the library is unable to connect to the API, or if the API returns a non-success status code (i.e., 4xx or 5xx response), a subclass of APIError will be raised:

import anthropic
from anthropic import Anthropic

client = Anthropic()

try:
    message = client.messages.create(
        max_tokens=1024,
        messages=[
            {
                "role": "user",
                "content": "Hello, Claude",
            }
        ],
        model="claude-opus-4-6",
    )
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:

Status Code	Error Type
400	`BadRequestError`
401	`AuthenticationError`
403	`PermissionDeniedError`
404	`NotFoundError`
422	`UnprocessableEntityError`
429	`RateLimitError`
>=500	`InternalServerError`
N/A	`APIConnectionError`

Request IDs

For more information on debugging requests, see the errors documentation.

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-opus-4-6",
)
print(message._request_id)  # e.g., req_018EeWyXxfu5pfWkrYcMdjWG

Retries

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), 408 Request Timeout, 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

# Configure the default for all requests:
client = Anthropic(
    max_retries=0,  # default is 2
)

# Or, configure per-request:
client.with_options(max_retries=5).messages.create(
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ],
    model="claude-opus-4-6",
)

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:

import httpx
from anthropic import Anthropic

# Configure the default for all requests:
client = Anthropic(
    timeout=20.0,  # 20 seconds (default is 10 minutes)
)

# 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-opus-4-6",
)

On timeout, an APITimeoutError is thrown.

Note that requests which time out will be retried twice by default.

Long requests

We highly encourage you to use the streaming Messages API for longer running requests.

We do not recommend setting a large max_tokens value without using streaming. Some networks may drop idle connections after a certain period of time, which can cause the request to fail or timeout without receiving a response from Anthropic.

The SDK will throw an error if a non-streaming request is expected to take longer than approximately 10 minutes. Passing stream=True or overriding the timeout option at the client or request level disables this error.

An expected request latency longer than the timeout for a non-streaming request will result in the client terminating the connection and retrying without receiving a response.

Auto-pagination

List methods in the Claude API are paginated. You can use the for syntax to iterate through items across all pages:

from anthropic import Anthropic

client = Anthropic()

all_batches = []
# Automatically fetches more pages as needed.
for batch in client.messages.batches.list(limit=20):
    all_batches.append(batch)
print(all_batches)

For async iteration:

import asyncio
from anthropic import AsyncAnthropic

client = AsyncAnthropic()

async def main() -> None:
    all_batches = []
    async for batch in client.messages.batches.list(limit=20):
        all_batches.append(batch)
    print(all_batches)

asyncio.run(main())

Alternatively, you can request a single page at a time:

first_page = await client.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)}")

Default Headers

We automatically send the anthropic-version header set to 2023-06-01.

If you need to, you can override it by setting default headers on a per-request basis.

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()

client.messages.with_raw_response.create(
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ],
    model="claude-opus-4-6",
    extra_headers={"anthropic-version": "My-Custom-Value"},
)

Type system

Request parameters

Nested request parameters are TypedDicts. Responses are Pydantic models which also have helper methods for things like serializing back into JSON (v1, v2).

Response models

To convert a Pydantic model to a dictionary, use the helper methods:

message = client.messages.create(...)

# Convert to JSON string
json_str = message.to_json()

# Convert to dictionary
data = message.to_dict()

Handling null vs missing fields

In responses, you can distinguish between fields that are explicitly null versus fields that were not returned (missing):

if response.my_field is None:
    if "my_field" not in response.model_fields_set:
        print("field was not in the response")
    else:
        print("field was null")

Advanced usage

Accessing raw response data (e.g., headers)

The "raw" Response returned by httpx can be accessed via the .with_raw_response property on the client. This is useful for accessing response headers or other metadata:

from anthropic import Anthropic

client = Anthropic()

response = client.messages.with_raw_response.create(
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ],
    model="claude-opus-4-6",
)

print(response.headers.get("x-request-id"))
message = response.parse()  # get the object that `messages.create()` would have returned
print(message.content)

These methods return an APIResponse object.

Logging

The SDK uses the standard library logging module.

You can enable logging by setting the environment variable ANTHROPIC_LOG to one of debug, info, warn, or off:

export ANTHROPIC_LOG=debug

Making custom/undocumented requests

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.

Undocumented endpoints

To make requests to undocumented endpoints, you can use client.get, client.post, and other HTTP verbs. Options on the client, such as retries, will be respected when making these requests.

import httpx

response = client.post(
    "/foo",
    cast_to=httpx.Response,
    body={"my_param": True},
)

print(response.json())

Undocumented request params

If you want to explicitly send an extra param, you can do so with the extra_query, extra_body, and extra_headers request options.

Undocumented response properties

To access undocumented response properties, you can access the extra fields like response.unknown_prop. You can also get all extra fields on the Pydantic model as a dict with response.model_extra.

Configuring the HTTP client

You can directly override the httpx client to customize it for your use case, including support for proxies and transports:

import httpx
from anthropic import Anthropic, DefaultHttpxClient

client = Anthropic(
    http_client=DefaultHttpxClient(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

Use DefaultHttpxClient and DefaultAsyncHttpxClient instead of raw httpx.Client and httpx.AsyncClient to ensure the SDK's default configuration (timeouts, connection limits, etc.) is preserved.

Managing HTTP resources

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.

from anthropic import Anthropic

with Anthropic() as client:
    message = client.messages.create(...)

# HTTP client is automatically closed

Beta features

We introduce beta features before they are generally available to get early feedback and test new functionality. You can check the availability of all of Claude's capabilities and tools in the build with Claude overview.

You can access most beta API features through the beta property of the client. To enable a particular beta feature, you need to add the appropriate beta header to the betas field when creating a message.

For example, to use code execution:

from anthropic import Anthropic

client = Anthropic()

response = client.beta.messages.create(
    max_tokens=1024,
    model="claude-opus-4-6",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's 4242424242 * 4242424242?",
                },
            ],
        },
    ],
    tools=[
        {
            "name": "code_execution",
            "type": "code_execution_20250522",
        },
    ],
    betas=["code-execution-2025-05-22"],
)

Platform integrations

For detailed platform setup guides, see:

Amazon Bedrock

from anthropic import AnthropicBedrock

client = AnthropicBedrock()

message = client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello!",
        }
    ],
    model="anthropic.claude-opus-4-6-v1",
)
print(message)

For a full list of available Bedrock models, see the Amazon Bedrock documentation.

You can also configure AWS credentials explicitly:

client = AnthropicBedrock(
    aws_region="us-east-1",
    aws_access_key="...",
    aws_secret_key="...",
    # Optional
    aws_session_token="...",
    aws_profile="my-profile",
)

Google Vertex AI

from anthropic import AnthropicVertex

client = AnthropicVertex()

message = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello!",
        }
    ],
)
print(message)

For a full list of available Vertex models, see the Google Vertex AI documentation.

Additional resources

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Client SDKs

Python SDK

Install and configure the Anthropic Python SDK with sync and async client support

For API feature documentation with code examples, see the API reference. This page covers Python-specific SDK features and configuration.

Installation

pip install anthropic

For platform-specific integrations, install with extras:

# For AWS Bedrock support
pip install anthropic[bedrock]

# For Google Vertex AI support
pip install anthropic[vertex]

# For improved async performance with aiohttp
pip install anthropic[aiohttp]

Requirements

Python 3.9 or later is required.

Usage

import os
from anthropic import Anthropic

client = Anthropic(
    # This is the default and can be omitted
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
)

message = client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello, Claude",
        }
    ],
    model="claude-opus-4-6",
)
print(message.content)

Async usage

import os
import asyncio
from anthropic import AsyncAnthropic

client = AsyncAnthropic(
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
)

async def main() -> None:
    message = await client.messages.create(
        max_tokens=1024,
        messages=[
            {
                "role": "user",
                "content": "Hello, Claude",
            }
        ],
        model="claude-opus-4-6",
    )
    print(message.content)

asyncio.run(main())

Using aiohttp for better concurrency

For improved async performance, you can use the aiohttp HTTP backend instead of the default httpx:

import os
import asyncio
from anthropic import AsyncAnthropic, DefaultAioHttpClient

async def main() -> None:
    async with AsyncAnthropic(
        api_key=os.environ.get("ANTHROPIC_API_KEY"),
        http_client=DefaultAioHttpClient(),
    ) as client:
        message = await client.messages.create(
            max_tokens=1024,
            messages=[
                {
                    "role": "user",
                    "content": "Hello, Claude",
                }
            ],
            model="claude-opus-4-6",
        )
        print(message.content)

asyncio.run(main())

Streaming responses

We provide support for streaming responses using Server-Sent Events (SSE).

from anthropic import Anthropic

client = Anthropic()

stream = client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello, Claude",
        }
    ],
    model="claude-opus-4-6",
    stream=True,
)
for event in stream:
    print(event.type)

Streaming helpers

The SDK also provides streaming helpers that use context managers and provide access to the accumulated text and the final message:

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-opus-4-6",
    ) 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 including event handlers and accumulation.

Token counting

You can see the exact usage for a given request through the usage response property:

message = client.messages.create(...)
print(message.usage)
# Usage(input_tokens=25, output_tokens=13)

You can also count tokens before making a request:

count = client.messages.count_tokens(
    model="claude-opus-4-6",
    messages=[
        {"role": "user", "content": "Hello, world"}
    ]
)
print(count.input_tokens)  # 10

Tool use

This SDK provides support for tool use, also known as function calling. More details can be found in the tool use overview.

Tool helpers

The SDK also provides helpers for easily defining and running tools as Python functions:

import json
from anthropic import Anthropic

client = Anthropic()

def get_weather(location: str) -> str:
    """Get the weather for a given location.

    Args:
        location: The city and state, e.g. San Francisco, CA
    """
    return json.dumps({
        "location": location,
        "temperature": "68°F",
        "condition": "Sunny",
    })

# Use the tool_runner to automatically handle tool calls
runner = client.beta.messages.tool_runner(
    max_tokens=1024,
    model="claude-opus-4-6",
    tools=[get_weather],
    messages=[
        {"role": "user", "content": "What is the weather in SF?"},
    ],
)
for message in runner:
    print(message)

Message Batches

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

Creating a batch

Message Batches takes an array of requests, where each object has a custom_id identifier and the same request params as the standard Messages API:

client.messages.batches.create(
    requests=[
        {
            "custom_id": "my-first-request",
            "params": {
                "model": "claude-opus-4-6",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Hello, world"}],
            },
        },
        {
            "custom_id": "my-second-request",
            "params": {
                "model": "claude-opus-4-6",
                "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 = client.messages.batches.results(batch_id)
for entry in result_stream:
    if entry.result.type == "succeeded":
        print(entry.result.message.content)

File uploads

Request parameters that correspond to file uploads can be passed in many different forms:

A PathLike object (e.g., pathlib.Path)
A tuple of (filename, content, content_type)
A BinaryIO file-like object
The return value of the toFile helper

from pathlib import Path
from anthropic import Anthropic

client = Anthropic()

# Upload using a file path
client.beta.files.upload(
    file=Path("/path/to/file"),
    betas=["files-api-2025-04-14"],
)

# Upload using bytes
client.beta.files.upload(
    file=("file.txt", b"my bytes", "text/plain"),
    betas=["files-api-2025-04-14"],
)

Handling errors

When the library is unable to connect to the API, or if the API returns a non-success status code (i.e., 4xx or 5xx response), a subclass of APIError will be raised:

import anthropic
from anthropic import Anthropic

client = Anthropic()

try:
    message = client.messages.create(
        max_tokens=1024,
        messages=[
            {
                "role": "user",
                "content": "Hello, Claude",
            }
        ],
        model="claude-opus-4-6",
    )
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:

Status Code	Error Type
400	`BadRequestError`
401	`AuthenticationError`
403	`PermissionDeniedError`
404	`NotFoundError`
422	`UnprocessableEntityError`
429	`RateLimitError`
>=500	`InternalServerError`
N/A	`APIConnectionError`

Request IDs

For more information on debugging requests, see the errors documentation.

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-opus-4-6",
)
print(message._request_id)  # e.g., req_018EeWyXxfu5pfWkrYcMdjWG

Retries

You can use the max_retries option to configure or disable this:

from anthropic import Anthropic

# Configure the default for all requests:
client = Anthropic(
    max_retries=0,  # default is 2
)

# Or, configure per-request:
client.with_options(max_retries=5).messages.create(
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ],
    model="claude-opus-4-6",
)

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:

import httpx
from anthropic import Anthropic

# Configure the default for all requests:
client = Anthropic(
    timeout=20.0,  # 20 seconds (default is 10 minutes)
)

# 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-opus-4-6",
)

On timeout, an APITimeoutError is thrown.

Note that requests which time out will be retried twice by default.

Long requests

We highly encourage you to use the streaming Messages API for longer running requests.

An expected request latency longer than the timeout for a non-streaming request will result in the client terminating the connection and retrying without receiving a response.

Auto-pagination

List methods in the Claude API are paginated. You can use the for syntax to iterate through items across all pages:

from anthropic import Anthropic

client = Anthropic()

all_batches = []
# Automatically fetches more pages as needed.
for batch in client.messages.batches.list(limit=20):
    all_batches.append(batch)
print(all_batches)

For async iteration:

import asyncio
from anthropic import AsyncAnthropic

client = AsyncAnthropic()

async def main() -> None:
    all_batches = []
    async for batch in client.messages.batches.list(limit=20):
        all_batches.append(batch)
    print(all_batches)

asyncio.run(main())

Alternatively, you can request a single page at a time:

first_page = await client.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)}")

Default Headers

We automatically send the anthropic-version header set to 2023-06-01.

If you need to, you can override it by setting default headers on a per-request basis.

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()

client.messages.with_raw_response.create(
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ],
    model="claude-opus-4-6",
    extra_headers={"anthropic-version": "My-Custom-Value"},
)

Type system

Request parameters

Nested request parameters are TypedDicts. Responses are Pydantic models which also have helper methods for things like serializing back into JSON (v1, v2).

Response models

To convert a Pydantic model to a dictionary, use the helper methods:

message = client.messages.create(...)

# Convert to JSON string
json_str = message.to_json()

# Convert to dictionary
data = message.to_dict()

Handling null vs missing fields

In responses, you can distinguish between fields that are explicitly null versus fields that were not returned (missing):

if response.my_field is None:
    if "my_field" not in response.model_fields_set:
        print("field was not in the response")
    else:
        print("field was null")

Advanced usage

Accessing raw response data (e.g., headers)

The "raw" Response returned by httpx can be accessed via the .with_raw_response property on the client. This is useful for accessing response headers or other metadata:

from anthropic import Anthropic

client = Anthropic()

response = client.messages.with_raw_response.create(
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ],
    model="claude-opus-4-6",
)

print(response.headers.get("x-request-id"))
message = response.parse()  # get the object that `messages.create()` would have returned
print(message.content)

These methods return an APIResponse object.

Logging

The SDK uses the standard library logging module.

You can enable logging by setting the environment variable ANTHROPIC_LOG to one of debug, info, warn, or off:

export ANTHROPIC_LOG=debug

Making custom/undocumented requests

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.

Undocumented endpoints

To make requests to undocumented endpoints, you can use client.get, client.post, and other HTTP verbs. Options on the client, such as retries, will be respected when making these requests.

import httpx

response = client.post(
    "/foo",
    cast_to=httpx.Response,
    body={"my_param": True},
)

print(response.json())

Undocumented request params

If you want to explicitly send an extra param, you can do so with the extra_query, extra_body, and extra_headers request options.

Undocumented response properties

Configuring the HTTP client

You can directly override the httpx client to customize it for your use case, including support for proxies and transports:

import httpx
from anthropic import Anthropic, DefaultHttpxClient

client = Anthropic(
    http_client=DefaultHttpxClient(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

Managing HTTP resources

from anthropic import Anthropic

with Anthropic() as client:
    message = client.messages.create(...)

# HTTP client is automatically closed

Beta features

For example, to use code execution:

from anthropic import Anthropic

client = Anthropic()

response = client.beta.messages.create(
    max_tokens=1024,
    model="claude-opus-4-6",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's 4242424242 * 4242424242?",
                },
            ],
        },
    ],
    tools=[
        {
            "name": "code_execution",
            "type": "code_execution_20250522",
        },
    ],
    betas=["code-execution-2025-05-22"],
)

Platform integrations

For detailed platform setup guides, see:

Amazon Bedrock

from anthropic import AnthropicBedrock

client = AnthropicBedrock()

message = client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello!",
        }
    ],
    model="anthropic.claude-opus-4-6-v1",
)
print(message)

For a full list of available Bedrock models, see the Amazon Bedrock documentation.

You can also configure AWS credentials explicitly:

client = AnthropicBedrock(
    aws_region="us-east-1",
    aws_access_key="...",
    aws_secret_key="...",
    # Optional
    aws_session_token="...",
    aws_profile="my-profile",
)

Google Vertex AI

from anthropic import AnthropicVertex

client = AnthropicVertex()

message = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello!",
        }
    ],
)
print(message)

For a full list of available Vertex models, see the Google Vertex AI documentation.

Additional resources

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