For Amazon Bedrock support

cli-sdks-libraries/sdks/python Changed · +20 / -18 lines

 
 ## Requirements
 
-Python 3.9 or later is required.
+Python 3.10 or later is required. If you are upgrading from a 0.x release of the SDK, see the [v1 migration guide](https://github.com/anthropics/anthropic-sdk-python/blob/main/MIGRATION.md) for the list of breaking changes.
 
 ## Usage
 

 
 ### Using aiohttp for better concurrency
 
-For improved async performance, you can use the `aiohttp` HTTP backend instead of the default `httpx`:
+For improved async performance, you can use the `aiohttp` HTTP backend instead of the default `httpx2`:
 
 ```python
 import os

     )
 except anthropic.APIConnectionError as e:
     print("The server could not be reached")
-    print(e.__cause__)  # an underlying Exception, likely raised within httpx
+    print(e.__cause__)  # an underlying Exception, likely raised within httpx2
 except anthropic.RateLimitError as e:
     print("A 429 status code was received; we should back off a bit.")
 except anthropic.APIStatusError as e:

 
 ## 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:
+By default requests time out after 10 minutes. You can configure this with a `timeout` option, which accepts a float or an `httpx2.Timeout` object:
 
 ```python
-import httpx
+import httpx2
 from anthropic import Anthropic
 
 # Configure the default for all requests:

 
 # More granular control:
 client = Anthropic(
-    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
+    timeout=httpx2.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
 )
 
 # Override per-request:

 
 ### Accessing raw response data (for example, headers)
 
-The "raw" `Response` returned by `httpx` can be accessed through the `.with_raw_response` property on the client. This is useful for accessing response headers or other metadata:
+The "raw" `Response` returned by `httpx2` can be accessed through the `.with_raw_response` property on the client. This is useful for accessing response headers or other metadata:
 
 ```python
 client = Anthropic()

 print(message.content)
 ```
 
-These methods return an `APIResponse` object.
+These methods return an `APIResponse` object. On the async client they return an `AsyncAPIResponse`, and `.parse()`, `.read()`, `.text()`, and `.json()` must be awaited.
 
 ### Streaming response body
 

 To make requests to undocumented endpoints, you can use `client.get`, `client.post`, and other HTTP verbs. Options on the client, such as retries, are respected when making these requests.
 
 ```python
-import httpx
+import httpx2
 
 response = client.post(
     "/foo",
-    cast_to=httpx.Response,
+    cast_to=httpx2.Response,
     body={"my_param": True},
 )
 

 
 ### Configuring the HTTP client
 
-You can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including support for proxies and transports:
+The SDK sends requests with [httpx2](https://httpx2.pydantic.dev), an API-compatible fork of `httpx`. To customize the HTTP client, including proxies and transports, pass your own [httpx2 client](https://httpx2.pydantic.dev/api/#client) as `http_client`:
 
 ```python
-import httpx
+import httpx2
 from anthropic import Anthropic, DefaultHttpxClient
 
 client = Anthropic(

     base_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"),
+        transport=httpx2.HTTPTransport(local_address="0.0.0.0"),
     ),
 )
 ```

 ```
 
 <Note>
-  Use `DefaultHttpxClient` and `DefaultAsyncHttpxClient` instead of raw `httpx.Client` and `httpx.AsyncClient` to ensure the SDK's default configuration (such as timeouts and connection limits) is preserved.
+  Use `DefaultHttpxClient` and `DefaultAsyncHttpxClient` instead of raw `httpx2.Client` and `httpx2.AsyncClient` to ensure the SDK's default configuration (such as timeouts and connection limits) is preserved. The `http_client` argument must be an `httpx2` client. Passing a client from the separate `httpx` package raises a `TypeError`.
 </Note>
+
+Tracing and mocking tools that patch `httpx` itself, such as OpenTelemetry's `HTTPXClientInstrumentor`, Sentry's `httpx` integration, `respx`, or `pytest-httpx`, do not see the SDK's requests by default. To use them, call `httpx2.alias_httpx()` once at startup, before anything imports `httpx`. This makes `import httpx` resolve to `httpx2` for the whole process.
 
 ### Managing HTTP resources
 

cli-sdks-libraries/sdks/python Changed · +5 / -19 lines

 client = Anthropic()
 
 # Upload using a file path
-client.beta.files.upload(
+client.files.upload(
     file=Path("/path/to/file"),
 )
 
 # Upload using bytes
-client.beta.files.upload(
+client.files.upload(
     file=("file.txt", b"my bytes", "text/plain"),
 )
 ```

 
 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](https://platform.claude.com/docs/en/api/beta-headers) to the `betas` field when creating a message.
 
-For example, to use the [Files API](https://platform.claude.com/docs/en/build-with-claude/files):
+For example, to enable [context editing](https://platform.claude.com/docs/en/build-with-claude/context-editing):
 
 ```python
 client = Anthropic()

 response = client.beta.messages.create(
     model="claude-opus-5",
     max_tokens=1024,
-    messages=[
-        {
-            "role": "user",
-            "content": [
-                {"type": "text", "text": "Please summarize this document for me."},
-                {
-                    "type": "document",
-                    "source": {
-                        "type": "file",
-                        "file_id": "file_abc123",
-                    },
-                },
-            ],
-        },
-    ],
-    betas=["files-api-2025-04-14"],
+    messages=[{"role": "user", "content": "Hello, Claude"}],
+    betas=["context-management-2025-06-27"],
 )
 ```
 

cli-sdks-libraries/sdks/python First recorded · 790 lines, first recorded

## Installation ## Requirements ## Usage ## Async usage ### Using aiohttp for better concurrency ## Streaming responses ### Streaming helpers ## Token counting ## Tool use ### Tool helpers ## Message batches ### Creating a batch ### Getting results from a batch ## File uploads ## Handling errors ## Request IDs ## Retries ## Timeouts ## Long requests ## Auto-pagination ## Default headers ## Type system ### Request parameters ### Response models ### Handling null vs missing fields ## Advanced usage ### Accessing raw response data (for example, headers) ### Streaming response body ### Logging ### Making custom/undocumented requests #### Undocumented endpoints #### Undocumented request params #### Undocumented response properties ### Configuring the HTTP client ### Managing HTTP resources ## Beta features ## Platform integrations ## Semantic versioning ### Determining the installed version ## Additional resources

The first capture of this source. The page was already there, and this is what it said.

---
title: Python SDK
url: https://platform.claude.com/docs/en/cli-sdks-libraries/sdks/python
description: Install and configure the Anthropic Python SDK with sync and async client support
---

The Anthropic Python SDK provides convenient access to the Claude API from Python applications. It supports both synchronous and asynchronous operations, streaming, and integrations with Amazon Bedrock, Claude Platform on AWS, Google Cloud, and Microsoft Foundry.

<Info>
  For API feature documentation with code examples, see the [API reference](https://platform.claude.com/docs/en/api/overview). This page covers Python-specific SDK features and configuration.
</Info>

## Installation

```bash
pip install anthropic
```

For platform-specific integrations or improved async performance, install with extras:

```bash
# For Amazon Bedrock support
pip install "anthropic[bedrock]"

# For Google Cloud support
pip install "anthropic[vertex]"

# For Claude Platform on AWS support
pip install "anthropic[aws]"

# Microsoft Foundry support is included in the base package

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

## Requirements

Python 3.9 or later is required.

## Usage

```python
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-5",
)

for block in message.content:
    if block.type == "text":
        print(block.text)
```

<Tip>
  Consider using [python-dotenv](https://pypi.org/project/python-dotenv/) to add `ANTHROPIC_API_KEY="my-anthropic-api-key"` to your `.env` file so that your API key isn't stored in source control.
</Tip>

For authentication options including Workload Identity Federation, see [Authentication](https://platform.claude.com/docs/en/manage-claude/authentication).

## Async usage

```python
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-5",
    )
    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`:

```python
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-5",
        )
        print(message.content)


asyncio.run(main())
```

## Streaming responses

The SDK provides support for streaming responses using Server-Sent Events (SSE).

```python
client = Anthropic()

stream = client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello, Claude",
        }
    ],
    model="claude-opus-5",
    stream=True,
)
for event in stream:
    print(event.type)
```

The async client uses the exact same interface:

```python
client = AsyncAnthropic()

stream = await client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Hello, Claude",
        }
    ],
    model="claude-opus-5",
    stream=True,
)
async 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:

```python
async def main() -> None:
    async with client.messages.stream(
        max_tokens=1024,
        messages=[
            {
                "role": "user",
                "content": "Say hello there!",
            }
        ],
        model="claude-opus-5",
    ) 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 accumulation and SDK-specific events.

Alternatively, you can use `client.messages.create(..., stream=True)` which only returns an iterable of the events in the stream and uses less memory (it doesn't 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:

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

You can also count tokens before making a request:

```python
count = client.messages.count_tokens(
    model="claude-opus-5", 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. For more details, see [Tool use with Claude](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview).

### Tool helpers

The SDK provides helpers for defining and running tools as pure Python functions. The `@beta_tool` decorator generates the tool schema from the function signature and docstring:

```python
import json
from anthropic import Anthropic, beta_tool

client = Anthropic()


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

    Args:
        location: The city and state, for example, San Francisco, CA
    Returns:
        A JSON-encoded string with the location, temperature, and weather condition.
    """
    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-5",
    tools=[get_weather],
    messages=[
        {"role": "user", "content": "What is the weather in SF?"},
    ],
)
for message in runner:
    print(message)
```

On every iteration, an API request is made. If the response includes a call to one of the given tools, the tool is automatically called, and the result is returned directly to the model in the next iteration.

## Message batches

This SDK provides support for [Batch processing](https://platform.claude.com/docs/en/build-with-claude/batch-processing) 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:

```python
client.messages.batches.create(
    requests=[
        {
            "custom_id": "my-first-request",
            "params": {
                "model": "claude-opus-5",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Hello, world"}],
            },
        },
        {
            "custom_id": "my-second-request",
            "params": {
                "model": "claude-opus-5",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Hi again, friend"}],
            },
        },

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