This cookbook shows how to integrate Anthropic’s Claude models with Maxim for full observability and tracing. You’ll learn how to log both standard and streaming completions, making it easy to monitor and debug your LLM-powered applications.

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Prerequisites

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1. Set Up Environment Variables

import os
import dotenv

dotenv.load_dotenv()
MODEL_NAME = "claude-3-5-sonnet-20241022"
ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")

if not ANTHROPIC_API_KEY:
    raise RuntimeError("Missing ANTHROPIC_API_KEY environment variable")

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2. Initialize Maxim SDK

Maxim will automatically pick up MAXIM_API_KEY and MAXIM_LOG_REPO_ID from your environment variables. 
from maxim import Maxim
logger = Maxim().logger()

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3. Wrap Anthropic Client with Maxim

from uuid import uuid4
from anthropic import Anthropic
from maxim.logger.anthropic import MaximAnthropicClient

client = MaximAnthropicClient(Anthropic(api_key=ANTHROPIC_API_KEY), logger)

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4. Basic Usage: Log a Claude Completion

user_input = "What was the capital of France in 1800s?"

response = client.messages.create(
    model=MODEL_NAME,
    max_tokens=1024,
    messages=[{"role": "user", "content": user_input}],
    extra_headers={"x-maxim-trace-id": str(uuid4())}
)

print(response)

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5. Streaming Usage: Log a Claude Streaming Completion

user_input = "What was the capital of France in 1800s?"
final_response = ""
response_chunks = []

with client.messages.stream(
        max_tokens=1024,
        messages=[{"role": "user", "content": user_input}],
        model=MODEL_NAME,
    ) as stream:
    for text_chunk in stream.text_stream:
        # Collect streamed chunks
        response_chunks.append(text_chunk)
        # Print the streamed text chunk
        print(text_chunk, end="", flush=True)
    final_response = "".join(response_chunks)

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6. Visualize in Maxim

All requests, responses, and streaming events are automatically traced and can be viewed in your Maxim dashboard.
For more details, see the Anthropic Python SDK documentation and the Maxim Python SDK documentation.