> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentbasis.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Anthropic Integration

> Automatically track Anthropic Claude API calls

The SDK allows you to monitor Anthropic's Claude models with zero code changes to your logic.

## Setup

Enable instrumentation with a single function call. This automatically tracks all subsequent calls to both `Anthropic` and `AsyncAnthropic` clients.

```python theme={null}
import agentbasis
from agentbasis.llms.anthropic import instrument

# Initialize AgentBasis first
agentbasis.init(api_key="your-api-key", agent_id="your-agent-id")

# Enable Anthropic instrumentation (covers sync and async)
instrument()
```

<Note>
  A single `instrument()` call instruments both synchronous and asynchronous clients. You don't need to call it twice.
</Note>

## Usage

Once instrumented, use the Anthropic client as you normally would. All `messages.create` calls are automatically traced.

### Synchronous

```python theme={null}
from anthropic import Anthropic

client = Anthropic()

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello there"}]
)

print(response.content[0].text)
```

### Asynchronous

```python theme={null}
from anthropic import AsyncAnthropic
import asyncio

async def main():
    client = AsyncAnthropic()
    
    response = await client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello there"}]
    )
    
    print(response.content[0].text)

asyncio.run(main())
```

## Streaming

Streaming responses are supported for both sync and async. The trace is recorded once the stream completes.

### Sync Streaming

```python theme={null}
with client.messages.stream(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Tell me a story"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="")
```

### Async Streaming

```python theme={null}
async def stream_response():
    client = AsyncAnthropic()
    
    async with client.messages.stream(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Tell me a story"}]
    ) as stream:
        async for text in stream.text_stream:
            print(text, end="")
```

## Captured Data

The integration automatically records:

| Field                        | Description                                   |
| ---------------------------- | --------------------------------------------- |
| `gen_ai.system`              | `anthropic`                                   |
| `gen_ai.request.model`       | Model ID (e.g., `claude-3-5-sonnet-20241022`) |
| `gen_ai.prompt`              | System and user messages                      |
| `gen_ai.completion`          | Response text                                 |
| `gen_ai.usage.input_tokens`  | Input token count                             |
| `gen_ai.usage.output_tokens` | Output token count                            |
| `duration`                   | Request latency                               |