Tracing Anthropic

MLflow Tracing provides automatic tracing capability for Anthropic LLMs. By enabling auto tracing for Anthropic by calling the mlflow.anthropic.autolog function, MLflow will capture nested traces and log them to the active MLflow Experiment upon invocation of Anthropic Python SDK.

Python

import mlflow

mlflow.anthropic.autolog()

MLflow trace automatically captures the following information about Anthropic calls:

• Prompts and completion responses
• Latencies
• Model name
• Additional metadata such as temperature, max_tokens, if specified.
• Function calling if returned in the response
• Any exception if raised

note

Currently, MLflow Anthropic integration only support tracing for synchronous calls for text interactions. Async APIs are not traced, and full inputs cannot be recorded for multi-modal inputs.

Prerequisites

To use MLflow Tracing with Anthropic, you need to install MLflow and the Anthropic SDK.

Development

For development environments, install the full MLflow package with Databricks extras and anthropic:

Bash

pip install --upgrade "mlflow[databricks]>=3.1" anthropic

The full mlflow[databricks] package includes all features for local development and experimentation on Databricks.

Production

For production deployments, install mlflow-tracing and anthropic:

Bash

pip install --upgrade mlflow-tracing anthropic

The mlflow-tracing package is optimized for production use.

note

MLflow 3 is highly recommended for the best tracing experience with Anthropic.

Before running the examples below, you'll need to configure your environment:

For users outside Databricks notebooks: Set your Databricks environment variables:

Bash

export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_TOKEN="your-personal-access-token"

For users inside Databricks notebooks: These credentials are automatically set for you.

API Keys: Ensure your Anthropic API key is configured:

Bash

export ANTHROPIC_API_KEY="your-anthropic-api-key"

Supported APIs

MLflow supports automatic tracing for the following Anthropic APIs:

Chat Completion	Function Calling	Streaming	Async	Image	Batch
✅	✅		✅ (*1)

(*1) Async support was added in MLflow 2.21.0.

To request support for additional APIs, please open a feature request on GitHub.

Basic Example

Python

import anthropic
import mlflow
import os

# Ensure your ANTHROPIC_API_KEY is set in your environment
# os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key" # Uncomment and set if not globally configured

# Enable auto-tracing for Anthropic
mlflow.anthropic.autolog()

# Set up MLflow tracking to Databricks
mlflow.set_tracking_uri("databricks")
mlflow.set_experiment("/Shared/anthropic-tracing-demo")

# Configure your API key.
client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

# Use the create method to create new message.
message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"},
    ],
)

Async

Python

import anthropic
import mlflow
import os

# Ensure your ANTHROPIC_API_KEY is set in your environment
# os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key" # Uncomment and set if not globally configured

# Enable trace logging
mlflow.anthropic.autolog()

# Set up MLflow tracking to Databricks if not already configured
# mlflow.set_tracking_uri("databricks")
# mlflow.set_experiment("/Shared/anthropic-async-demo")

client = anthropic.AsyncAnthropic()

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

Advanced Example: Tool Calling Agent

MLflow Tracing automatically captures tool calling response from Anthropic models. The function instruction in the response will be highlighted in the trace UI. Moreover, you can annotate the tool function with the @mlflow.trace decorator to create a span for the tool execution.

The following example implements a simple function calling agent using Anthropic Tool Calling and MLflow Tracing for Anthropic. The example further uses the asynchronous Anthropic SDK so that the agent can handle concurrent invocations without blocking.

Python

import json
import anthropic
import mlflow
import asyncio
from mlflow.entities import SpanType
import os

# Ensure your ANTHROPIC_API_KEY is set in your environment
# os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key" # Uncomment and set if not globally configured

# Set up MLflow tracking to Databricks if not already configured
# mlflow.set_tracking_uri("databricks")
# mlflow.set_experiment("/Shared/anthropic-tool-agent-demo")

# Assuming autolog is enabled globally or called earlier
# mlflow.anthropic.autolog()

client = anthropic.AsyncAnthropic()
model_name = "claude-3-5-sonnet-20241022"


# Define the tool function. Decorate it with `@mlflow.trace` to create a span for its execution.
@mlflow.trace(span_type=SpanType.TOOL)
async def get_weather(city: str) -> str:
    if city == "Tokyo":
        return "sunny"
    elif city == "Paris":
        return "rainy"
    return "unknown"


tools = [
    {
        "name": "get_weather",
        "description": "Returns the weather condition of a given city.",
        "input_schema": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    }
]

_tool_functions = {"get_weather": get_weather}


# Define a simple tool calling agent
@mlflow.trace(span_type=SpanType.AGENT)
async def run_tool_agent(question: str):
    messages = [{"role": "user", "content": question}]

    # Invoke the model with the given question and available tools
    ai_msg = await client.messages.create(
        model=model_name,
        messages=messages,
        tools=tools,
        max_tokens=2048,
    )
    messages.append({"role": "assistant", "content": ai_msg.content})

    # If the model requests tool call(s), invoke the function with the specified arguments
    tool_calls = [c for c in ai_msg.content if c.type == "tool_use"]
    for tool_call in tool_calls:
        if tool_func := _tool_functions.get(tool_call.name):
            tool_result = await tool_func(**tool_call.input)
        else:
            raise RuntimeError("An invalid tool is returned from the assistant!")

        messages.append(
            {
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "tool_use_id": tool_call.id,
                        "content": tool_result,
                    }
                ],
            }
        )

    # Send the tool results to the model and get a new response
    response = await client.messages.create(
        model=model_name,
        messages=messages,
        max_tokens=2048,
    )

    return response.content[-1].text


# Run the tool calling agent
cities = ["Tokyo", "Paris", "Sydney"]
questions = [f"What's the weather like in {city} today?" for city in cities]
answers = await asyncio.gather(*(run_tool_agent(q) for q in questions))

for city, answer in zip(cities, answers):
    print(f"{city}: {answer}")

Disable auto-tracing

Auto tracing for Anthropic can be disabled globally by calling mlflow.anthropic.autolog(disable=True) or mlflow.autolog(disable=True).