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. In Typescript, you can instead use the tracedAnthropic function to wrap the Anthropic client.

Python

python

import mlflow

mlflow.anthropic.autolog()

JS / TS

typescript

import Anthropic from "@anthropic-ai/sdk";
import { tracedAnthropic } from "mlflow-anthropic";

const client = tracedAnthropic(new Anthropic());

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
• Token usage information
• Any exception if raised

Supported APIs

MLflow supports automatic tracing for the following Anthropic APIs:

Python

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

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

TypeScript / JavaScript

Chat Completion	Function Calling	Streaming	Async
✅	✅ (*2)	-	✅

(*2) Function calls in responses are captured and can be rendered in the MLflow UI. The TypeScript SDK is natively async.

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

Basic Example

Python

python

import anthropic
import mlflow

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

# Optional: Set a tracking URI and an experiment
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("Anthropic")

# 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"},
    ],
)

JS / TS

typescript

import Anthropic from "@anthropic-ai/sdk";
import { tracedAnthropic } from "mlflow-anthropic";

// Wrap the Anthropic client with the tracedAnthropic function
const client = tracedAnthropic(new Anthropic());

// Invoke the client as usual
const message = await client.messages.create({
    model: "claude-3-7-sonnet-20250219",
    max_tokens: 1024,
    messages: [
        {"role": "user", "content": "Hello, Claude"},
    ],
});

Async

MLflow Tracing has supported the asynchronous API of the Anthropic SDK since MLflow 2.21.0. Its usage is the same as the synchronous API.

Python

python

import anthropic

# Enable trace logging
mlflow.anthropic.autolog()

client = anthropic.AsyncAnthropic()

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

JS / TS

Anthropic Typescript / Javascript SDK is natively async. See the basic example above.

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

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}")

Token usage

MLflow >= 3.2.0 supports token usage tracking for Anthropic. The token usage for each LLM call will be logged in the mlflow.chat.tokenUsage attribute. The total token usage throughout the trace will be available in the token_usage field of the trace info object.

Python

python

import json
import mlflow

mlflow.anthropic.autolog()

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

# Get the trace object just created
last_trace_id = mlflow.get_last_active_trace_id()
trace = mlflow.get_trace(trace_id=last_trace_id)

# Print the token usage
total_usage = trace.info.token_usage
print("== Total token usage: ==")
print(f"  Input tokens: {total_usage['input_tokens']}")
print(f"  Output tokens: {total_usage['output_tokens']}")
print(f"  Total tokens: {total_usage['total_tokens']}")

# Print the token usage for each LLM call
print("\n== Detailed usage for each LLM call: ==")
for span in trace.data.spans:
    if usage := span.get_attribute("mlflow.chat.tokenUsage"):
        print(f"{span.name}:")
        print(f"  Input tokens: {usage['input_tokens']}")
        print(f"  Output tokens: {usage['output_tokens']}")
        print(f"  Total tokens: {usage['total_tokens']}")

JS / TS

typescript

import * as mlflow from "mlflow-tracing";

// After your Anthropic call completes, flush and fetch the trace
await mlflow.flushTraces();
const lastTraceId = mlflow.getLastActiveTraceId();

if (lastTraceId) {
  const client = new mlflow.MlflowClient({ trackingUri: "http://localhost:5000" });
  const trace = await client.getTrace(lastTraceId);

  // Total token usage on the trace
  console.log("== Total token usage: ==");
  console.log(trace.info.tokenUsage); // { input_tokens, output_tokens, total_tokens }

  // Per-span usage (if provided by the provider)
  console.log("\n== Detailed usage for each LLM call: ==");
  for (const span of trace.data.spans) {
    const usage = span.attributes?.["mlflow.chat.tokenUsage"];
    if (usage) {
      console.log(`${span.name}:`, usage);
    }
  }
}

bash

== Total token usage: ==
  Input tokens: 8
  Output tokens: 12
  Total tokens: 20

== Detailed usage for each LLM call: ==
Messages.create:
  Input tokens: 8
  Output tokens: 12
  Total tokens: 20

Supported APIs:

Token usage tracking is supported for the following Anthropic APIs:

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

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

Disable auto-tracing

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