Anthropic

Introduction

Logfire supports instrumenting calls to Anthropic with one extra line of code.

import anthropic
import logfire

client = anthropic.Anthropic()

logfire.configure()
logfire.instrument_anthropic(client)  # (1)!

response = client.messages.create(
    max_tokens=1000,
    model='claude-3-haiku-20240307',
    system='You are a helpful assistant.',
    messages=[{'role': 'user', 'content': 'Please write me a limerick about Python logging.'}],
)
print(response.content[0].text)

1. If you don't have access to the client instance, you can pass a class (e.g. logfire.instrument_anthropic(anthropic.Anthropic)), or just pass no arguments (i.e. logfire.instrument_anthropic()) to instrument both the anthropic.Anthropic and anthropic.AsyncAnthropic classes.

For more information, see the instrument_anthropic() API reference.

With that you get:

• a span around the call to Anthropic which records duration and captures any exceptions that might occur
• Human-readable display of the conversation with the agent
• details of the response, including the number of tokens used

Anthropic span and conversation

Span arguments including response details

Methods covered

The following Anthropic methods are covered:

• client.messages.create
• client.messages.stream
• client.beta.tools.messages.create

All methods are covered with both anthropic.Anthropic and anthropic.AsyncAnthropic.

Streaming Responses

When instrumenting streaming responses, Logfire creates two spans — one around the initial request and one around the streamed response.

Here we also use Rich's Live and Markdown types to render the response in the terminal in real-time.

import anthropic
import logfire
from rich.console import Console
from rich.live import Live
from rich.markdown import Markdown

client = anthropic.AsyncAnthropic()
logfire.configure()
logfire.instrument_anthropic(client)


async def main():
    console = Console()
    with logfire.span('Asking Anthropic to write some code'):
        response = client.messages.stream(
            max_tokens=1000,
            model='claude-3-haiku-20240307',
            system='Reply in markdown one.',
            messages=[{'role': 'user', 'content': 'Write Python to show a tree of files 🤞.'}],
        )
        content = ''
        with Live('', refresh_per_second=15, console=console) as live:
            async with response as stream:
                async for chunk in stream:
                    if chunk.type == 'content_block_delta':
                        content += chunk.delta.text
                        live.update(Markdown(content))


if __name__ == '__main__':
    import asyncio

    asyncio.run(main())

Shows up like this in Logfire:

Anthropic streaming response