Anthropic Integration

Updated on Aug 14, 2026

Use this integration when your app is implemented in Python, TypeScript, or .NET.

Setup

PythonTypeScript.NET

1. Install SDK

bash

pip install progress-observability

2. Instrument your app

python

Observability.instrument(
    app_name=os.getenv("OBSERVABILITY_APP_NAME"),
    api_key=os.getenv("OBSERVABILITY_API_KEY")
)

3. Complete example

python

import os

from anthropic import Anthropic
from dotenv import load_dotenv

from progress.observability import Observability

load_dotenv()

# LLM INSTRUMENTATION
Observability.instrument(
    app_name=os.getenv("OBSERVABILITY_APP_NAME"),
    api_key=os.getenv("OBSERVABILITY_API_KEY"),
)


def main() -> None:
    client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
    model = os.getenv("ANTHROPIC_MODEL", "claude-haiku-4-5-20251001")

    response = client.messages.create(
        model=model,
        max_tokens=256,
        system="You are a helpful AI assistant.",
        messages=[{"role": "user", "content": "What is the capital of France?"}],
    )



try:
    main()
finally:
    Observability.shutdown()

To get a richer trace tree when building agents on top of this provider, use the @agent, @tool, @workflow, or @task decorators. See the Python SDK for details.

1. Install SDK

bash

npm install @progress/observability

2. Instrument your app

TS

import '@progress/observability/register/hooks';
import { Observability } from '@progress/observability';
await Observability.instrument({
 appName: process.env.OBSERVABILITY_APP_NAME,
 apiKey: process.env.OBSERVABILITY_API_KEY
});

3. Complete example

TS

import '@progress/observability/register/hooks';
import 'dotenv/config';

import Anthropic from '@anthropic-ai/sdk';
import { Observability } from '@progress/observability';

const anthropicModel =
  process.env.ANTHROPIC_MODEL || 'claude-haiku-4-5-20251001';

async function main() {
  await Observability.instrument({
    appName: process.env.OBSERVABILITY_APP_NAME ?? 'weather-agent-anthropic-sdk',
    apiKey: process.env.OBSERVABILITY_API_KEY,
  });

  try {
    const client = new Anthropic({
      apiKey: process.env.ANTHROPIC_API_KEY,
    });

    const result = await client.messages.create({
      model: anthropicModel,
      max_tokens: 256,
      temperature: 0.7,
      system: 'You are a helpful AI assistant.',
      messages: [{ role: 'user', content: 'What is the capital of France?' }],
    });

    const textResponse = result.content
      .filter((block) => block.type === 'text')
      .map((block) => block.text)
      .join('\n');

  } finally {
    await Observability.shutdown();
  }
}

main().catch((error) => {
  console.error(error);
  process.exitCode = 1;
});

To get a richer trace tree when building agents on top of this provider, use the @agent, @tool, @workflow, or @task decorators. See the TypeScript SDK for details.

1. Install SDK

bash

dotnet add package Progress.Observability.Instrumentation

2. Instrument your app

cs

try
{
 chatClient = chatClient.AddObservability((options) =>
 {
 options.AppName = Environment.GetEnvironmentVariable("OBSERVABILITY_APP_NAME")!;
 options.ApiKey = Environment.GetEnvironmentVariable("OBSERVABILITY_API_KEY")!;
 });
 
 // Your code here.
}
finally
{
 // Call the Shutdown() method before exiting your agent to flush any remaining data.
 ObservabilityTracer.Shutdown();
}

3. Complete example

cs

using Anthropic.SDK;
using Anthropic.SDK.Constants;
using Microsoft.Extensions.AI;
using System.ComponentModel;
using Progress.Observability.Extensions.AI;
using DotNetEnv;

namespace Examples.Anthropic;

/// <summary>
/// Example demonstrating Microsoft.Extensions.AI support for Anthropic.
/// </summary>
public class Program
{
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
     => $"The weather in {location} is cloudy with a high of 15°C.";

 public static async Task Main(string[] args)
 {
     try
     {
         Env.Load(".env");

         // Create Anthropic client using the provider SDK
         var anthropicClient = new AnthropicClient(
             Environment.GetEnvironmentVariable("ANTHROPIC_API_KEY")!);

         // Convert to IChatClient using Anthropic's Messages API
         IChatClient chatClient = anthropicClient.Messages
             // Add observability instrumentation
             .AddObservability((options) =>
             {
                 options.AppName = "Anthropic Example";
                 options.ApiKey = Environment.GetEnvironmentVariable("OBSERVABILITY_API_KEY")!;
             })
             // Enable automatic function invocation
             .AsBuilder()
             .UseFunctionInvocation()
             .Build();

         // Configure model and tools
         var options = new ChatOptions
         {
             ModelId = AnthropicModels.Claude45Haiku,
             MaxOutputTokens = 512,
             Tools = [AIFunctionFactory.Create(GetWeather)]
         };

         // Add tool observability
         options.AddToolObservability();

         var response = await chatClient.GetResponseAsync(
             "What is the capital of France?",
             options);

         Console.WriteLine(response.Text ?? "No assistant text returned.");
     }
     finally
     {
         ObservabilityTracer.Shutdown();
     }
 }
}

To get a richer trace tree when building agents on top of this provider, use custom spans. See the .NET SDK for details.