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Foundry Toolkit 中的跟踪 (Tracing)

Foundry Toolkit 提供跟踪功能，可帮助您监视和分析 AI 应用的性能。您可以跟踪 AI 应用的执行情况（包括与生成式 AI 模型的交互），从而深入了解其行为和性能。

Foundry Toolkit 托管一个本地 HTTP 和 gRPC 服务器来收集跟踪数据。该收集器服务器与 OTLP（OpenTelemetry 协议）兼容，大多数语言模型 SDK 要么直接支持 OTLP，要么具有非微软的插桩库来支持它。使用 Foundry Toolkit 可以可视化收集到的插桩数据。

支持 OTLP 且遵循生成式 AI 系统语义约定的所有框架或 SDK 都受支持。下表包含经过兼容性测试的常用 AI SDK。

Azure AI Inference Foundry Agent Service Anthropic Gemini LangChain OpenAI SDK 3 OpenAI Agents SDK
Python ✅ ✅ ✅ (traceloop, monocle)1,2 ✅ (monocle) ✅ (LangSmith, monocle)1,2 ✅ (opentelemetry-python-contrib, monocle)1 ✅ (Logfire, monocle)1,2
TS/JS ✅ ✅ ✅ (traceloop)1,2 ❌ ✅ (traceloop)1,2 ✅ (traceloop)1,2 ❌
  1. 括号中的 SDK 是非微软工具，因为官方 SDK 不支持 OTLP，所以它们用于添加 OTLP 支持。
  2. 这些工具并未完全遵循生成式 AI 系统的 OpenTelemetry 规则。
  3. 对于 OpenAI SDK，仅支持 Chat Completions API。尚不支持 Responses API。

如何开始使用跟踪

  1. 在树状视图中选择 Tracing 以打开跟踪网页视图。

  2. 选择 Start Collector 按钮以启动本地 OTLP 跟踪收集器服务器。

    Screenshot showing the Start Collector button in the Tracing webview.

  3. 使用代码片段启用插桩。有关不同语言和 SDK 的代码片段，请参阅设置插桩部分。

  4. 通过运行应用来生成跟踪数据。

  5. 在跟踪网页视图中，选择 Refresh 按钮以查看新的跟踪数据。

    Screenshot showing the Trace List in the Tracing webview.

设置插桩 (Instrumentation)

在 AI 应用中设置跟踪以收集跟踪数据。以下代码片段展示了如何为不同的 SDK 和语言设置跟踪

所有 SDK 的流程都类似

  • 将跟踪添加至您的大语言模型 (LLM) 或智能体应用。
  • 设置 OTLP 跟踪导出器以使用 AITK 本地收集器。
Azure AI Inference SDK - Python

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http azure-ai-inference[opentelemetry]

设置

import os
os.environ["AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED"] = "true"
os.environ["AZURE_SDK_TRACING_IMPLEMENTATION"] = "opentelemetry"

from opentelemetry import trace, _events
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk._logs import LoggerProvider
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk._events import EventLoggerProvider
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter

resource = Resource(attributes={
    "service.name": "opentelemetry-instrumentation-azure-ai-agents"
})
provider = TracerProvider(resource=resource)
otlp_exporter = OTLPSpanExporter(
    endpoint="https://:4318/v1/traces",
)
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

logger_provider = LoggerProvider(resource=resource)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(OTLPLogExporter(endpoint="https://:4318/v1/logs"))
)
_events.set_event_logger_provider(EventLoggerProvider(logger_provider))

from azure.ai.inference.tracing import AIInferenceInstrumentor
AIInferenceInstrumentor().instrument(True)
Azure AI Inference SDK - TypeScript/JavaScript

安装

npm install @azure/opentelemetry-instrumentation-azure-sdk @opentelemetry/api @opentelemetry/exporter-trace-otlp-proto @opentelemetry/instrumentation @opentelemetry/resources @opentelemetry/sdk-trace-node

设置

const { context } = require('@opentelemetry/api');
const { resourceFromAttributes } = require('@opentelemetry/resources');
const {
  NodeTracerProvider,
  SimpleSpanProcessor
} = require('@opentelemetry/sdk-trace-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-proto');

const exporter = new OTLPTraceExporter({
  url: 'https://:4318/v1/traces'
});
const provider = new NodeTracerProvider({
  resource: resourceFromAttributes({
    'service.name': 'opentelemetry-instrumentation-azure-ai-inference'
  }),
  spanProcessors: [new SimpleSpanProcessor(exporter)]
});
provider.register();

const { registerInstrumentations } = require('@opentelemetry/instrumentation');
const {
  createAzureSdkInstrumentation
} = require('@azure/opentelemetry-instrumentation-azure-sdk');

registerInstrumentations({
  instrumentations: [createAzureSdkInstrumentation()]
});
Foundry Agent Service - Python

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http azure-ai-inference[opentelemetry]

设置

import os
os.environ["AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED"] = "true"
os.environ["AZURE_SDK_TRACING_IMPLEMENTATION"] = "opentelemetry"

from opentelemetry import trace, _events
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk._logs import LoggerProvider
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk._events import EventLoggerProvider
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter

resource = Resource(attributes={
    "service.name": "opentelemetry-instrumentation-azure-ai-agents"
})
provider = TracerProvider(resource=resource)
otlp_exporter = OTLPSpanExporter(
    endpoint="https://:4318/v1/traces",
)
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

logger_provider = LoggerProvider(resource=resource)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(OTLPLogExporter(endpoint="https://:4318/v1/logs"))
)
_events.set_event_logger_provider(EventLoggerProvider(logger_provider))

from azure.ai.agents.telemetry import AIAgentsInstrumentor
AIAgentsInstrumentor().instrument(True)
Foundry Agent Service - TypeScript/JavaScript

安装

npm install @azure/opentelemetry-instrumentation-azure-sdk @opentelemetry/api @opentelemetry/exporter-trace-otlp-proto @opentelemetry/instrumentation @opentelemetry/resources @opentelemetry/sdk-trace-node

设置

const { context } = require('@opentelemetry/api');
const { resourceFromAttributes } = require('@opentelemetry/resources');
const {
  NodeTracerProvider,
  SimpleSpanProcessor
} = require('@opentelemetry/sdk-trace-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-proto');

const exporter = new OTLPTraceExporter({
  url: 'https://:4318/v1/traces'
});
const provider = new NodeTracerProvider({
  resource: resourceFromAttributes({
    'service.name': 'opentelemetry-instrumentation-azure-ai-inference'
  }),
  spanProcessors: [new SimpleSpanProcessor(exporter)]
});
provider.register();

const { registerInstrumentations } = require('@opentelemetry/instrumentation');
const {
  createAzureSdkInstrumentation
} = require('@azure/opentelemetry-instrumentation-azure-sdk');

registerInstrumentations({
  instrumentations: [createAzureSdkInstrumentation()]
});
Anthropic - Python

OpenTelemetry

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-instrumentation-anthropic

设置

from opentelemetry import trace, _events
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk._logs import LoggerProvider
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk._events import EventLoggerProvider
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter

resource = Resource(attributes={
    "service.name": "opentelemetry-instrumentation-anthropic-traceloop"
})
provider = TracerProvider(resource=resource)
otlp_exporter = OTLPSpanExporter(
    endpoint="https://:4318/v1/traces",
)
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

logger_provider = LoggerProvider(resource=resource)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(OTLPLogExporter(endpoint="https://:4318/v1/logs"))
)
_events.set_event_logger_provider(EventLoggerProvider(logger_provider))

from opentelemetry.instrumentation.anthropic import AnthropicInstrumentor
AnthropicInstrumentor().instrument()

Monocle

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http monocle_apptrace

设置

from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Import monocle_apptrace
from monocle_apptrace import setup_monocle_telemetry

# Setup Monocle telemetry with OTLP span exporter for traces
setup_monocle_telemetry(
    workflow_name="opentelemetry-instrumentation-anthropic",
    span_processors=[
        BatchSpanProcessor(
            OTLPSpanExporter(endpoint="https://:4318/v1/traces")
        )
    ]
)
Anthropic - TypeScript/JavaScript

安装

npm install @traceloop/node-server-sdk

设置

const { initialize } = require('@traceloop/node-server-sdk');
const { trace } = require('@opentelemetry/api');

initialize({
  appName: 'opentelemetry-instrumentation-anthropic-traceloop',
  baseUrl: 'https://:4318',
  disableBatch: true
});
Google Gemini - Python

OpenTelemetry

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-instrumentation-google-genai

设置

from opentelemetry import trace, _events
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk._logs import LoggerProvider
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk._events import EventLoggerProvider
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter

resource = Resource(attributes={
    "service.name": "opentelemetry-instrumentation-google-genai"
})
provider = TracerProvider(resource=resource)
otlp_exporter = OTLPSpanExporter(
    endpoint="https://:4318/v1/traces",
)
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

logger_provider = LoggerProvider(resource=resource)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(OTLPLogExporter(endpoint="https://:4318/v1/logs"))
)
_events.set_event_logger_provider(EventLoggerProvider(logger_provider))

from opentelemetry.instrumentation.google_genai import GoogleGenAiSdkInstrumentor
GoogleGenAiSdkInstrumentor().instrument(enable_content_recording=True)

Monocle

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http monocle_apptrace

设置

from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Import monocle_apptrace
from monocle_apptrace import setup_monocle_telemetry

# Setup Monocle telemetry with OTLP span exporter for traces
setup_monocle_telemetry(
    workflow_name="opentelemetry-instrumentation-google-genai",
    span_processors=[
        BatchSpanProcessor(
            OTLPSpanExporter(endpoint="https://:4318/v1/traces")
        )
    ]
)
LangChain - Python

LangSmith

安装

pip install langsmith[otel]

设置

import os
os.environ["LANGSMITH_OTEL_ENABLED"] = "true"
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://:4318"

Monocle

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http monocle_apptrace

设置

from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Import monocle_apptrace
from monocle_apptrace import setup_monocle_telemetry

# Setup Monocle telemetry with OTLP span exporter for traces
setup_monocle_telemetry(
    workflow_name="opentelemetry-instrumentation-langchain",
    span_processors=[
        BatchSpanProcessor(
            OTLPSpanExporter(endpoint="https://:4318/v1/traces")
        )
    ]
)
LangChain - TypeScript/JavaScript

安装

npm install @traceloop/node-server-sdk

设置

const { initialize } = require('@traceloop/node-server-sdk');
initialize({
  appName: 'opentelemetry-instrumentation-langchain-traceloop',
  baseUrl: 'https://:4318',
  disableBatch: true
});
OpenAI - Python

OpenTelemetry

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-instrumentation-openai-v2

设置

from opentelemetry import trace, _events
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk._logs import LoggerProvider
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk._events import EventLoggerProvider
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
import os

os.environ["OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT"] = "true"

# Set up resource
resource = Resource(attributes={
    "service.name": "opentelemetry-instrumentation-openai"
})

# Create tracer provider
trace.set_tracer_provider(TracerProvider(resource=resource))

# Configure OTLP exporter
otlp_exporter = OTLPSpanExporter(
    endpoint="https://:4318/v1/traces"
)

# Add span processor
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(otlp_exporter)
)

# Set up logger provider
logger_provider = LoggerProvider(resource=resource)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(OTLPLogExporter(endpoint="https://:4318/v1/logs"))
)
_events.set_event_logger_provider(EventLoggerProvider(logger_provider))

# Enable OpenAI instrumentation
OpenAIInstrumentor().instrument()

Monocle

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http monocle_apptrace

设置

from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Import monocle_apptrace
from monocle_apptrace import setup_monocle_telemetry

# Setup Monocle telemetry with OTLP span exporter for traces
setup_monocle_telemetry(
    workflow_name="opentelemetry-instrumentation-openai",
    span_processors=[
        BatchSpanProcessor(
            OTLPSpanExporter(endpoint="https://:4318/v1/traces")
        )
    ]
)
OpenAI - TypeScript/JavaScript

安装

npm install @traceloop/instrumentation-openai @traceloop/node-server-sdk

设置

const { initialize } = require('@traceloop/node-server-sdk');
initialize({
  appName: 'opentelemetry-instrumentation-openai-traceloop',
  baseUrl: 'https://:4318',
  disableBatch: true
});
OpenAI Agents SDK - Python

Logfire

安装

pip install logfire

设置

import logfire
import os

os.environ["OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"] = "https://:4318/v1/traces"

logfire.configure(
    service_name="opentelemetry-instrumentation-openai-agents-logfire",
    send_to_logfire=False,
)
logfire.instrument_openai_agents()

Monocle

安装

pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http monocle_apptrace

设置

from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Import monocle_apptrace
from monocle_apptrace import setup_monocle_telemetry

# Setup Monocle telemetry with OTLP span exporter for traces
setup_monocle_telemetry(
    workflow_name="opentelemetry-instrumentation-openai-agents",
    span_processors=[
        BatchSpanProcessor(
            OTLPSpanExporter(endpoint="https://:4318/v1/traces")
        )
    ]
)

示例 1：使用 OpenTelemetry 通过 Azure AI Inference SDK 设置跟踪

以下端到端示例在 Python 中使用 Azure AI Inference SDK，并展示了如何设置跟踪提供程序和插桩。

前提条件

要运行此示例，您需要满足以下先决条件

设置开发环境

请遵循以下说明部署包含运行此示例所需的所有依赖项的预配置开发环境。

  1. 设置 GitHub 个人访问令牌

    使用免费的 GitHub Models 作为示例模型。

    打开 GitHub 开发者设置，然后选择 Generate new token。

    重要事项

    令牌需要具有 models:read 权限，否则会返回未授权错误。该令牌将发送到微软服务。

  2. 创建环境变量

    使用以下代码片段之一创建一个环境变量，将您的令牌设置为客户端代码的密钥。将 <your-github-token-goes-here> 替换为您实际的 GitHub 令牌。

    bash

    export GITHUB_TOKEN="<your-github-token-goes-here>"

    powershell

    $Env:GITHUB_TOKEN="<your-github-token-goes-here>"

    Windows 命令提示符

    set GITHUB_TOKEN=<your-github-token-goes-here>

  3. 安装 Python 包

    以下命令用于安装使用 Azure AI Inference SDK 进行跟踪所需的 Python 包

    pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http azure-ai-inference[opentelemetry]

  4. 设置跟踪

    1. 在计算机上为该项目创建一个新的本地目录。

      mkdir my-tracing-app

    2. 导航到您创建的目录。

      cd my-tracing-app

    3. 在该目录中打开 Visual Studio Code

      code .

  5. 创建 Python 文件

    1. 在 my-tracing-app 目录中，创建一个名为 main.py 的 Python 文件。

      您将添加用于设置跟踪并与 Azure AI Inference SDK 进行交互的代码。

    2. 将以下代码添加到 main.py 并保存文件

      import os

### Set up for OpenTelemetry tracing ###
os.environ["AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED"] = "true"
os.environ["AZURE_SDK_TRACING_IMPLEMENTATION"] = "opentelemetry"

from opentelemetry import trace, _events
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk._logs import LoggerProvider
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk._events import EventLoggerProvider
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter

github_token = os.environ["GITHUB_TOKEN"]

resource = Resource(attributes={
    "service.name": "opentelemetry-instrumentation-azure-ai-inference"
})
provider = TracerProvider(resource=resource)
otlp_exporter = OTLPSpanExporter(
    endpoint="https://:4318/v1/traces",
)
processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

logger_provider = LoggerProvider(resource=resource)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(OTLPLogExporter(endpoint="https://:4318/v1/logs"))
)
_events.set_event_logger_provider(EventLoggerProvider(logger_provider))

from azure.ai.inference.tracing import AIInferenceInstrumentor
AIInferenceInstrumentor().instrument()
### Set up for OpenTelemetry tracing ###

from azure.ai.inference import ChatCompletionsClient
from azure.ai.inference.models import UserMessage
from azure.ai.inference.models import TextContentItem
from azure.core.credentials import AzureKeyCredential

client = ChatCompletionsClient(
    endpoint = "https://models.inference.ai.azure.com",
    credential = AzureKeyCredential(github_token),
    api_version = "2024-08-01-preview",
)

response = client.complete(
    messages = [
        UserMessage(content = [
            TextContentItem(text = "hi"),
        ]),
    ],
    model = "gpt-4.1",
    tools = [],
    response_format = "text",
    temperature = 1,
    top_p = 1,
)

print(response.choices[0].message.content)

  6. 运行代码

    1. 在 Visual Studio Code 中打开一个新终端。

    2. 在终端中，使用命令 python main.py 运行代码。

  7. 在 Foundry Toolkit 中检查跟踪数据

    运行代码并刷新跟踪网页视图后，列表中会出现一个新的跟踪记录。

    选择该跟踪记录以打开跟踪详情网页视图。

    Screenshot showing selecting a trace from the Trace List in the Tracing webview.

    在左侧 span 树状视图中检查应用的完整执行流程。

    在右侧 span 详情视图中选择一个 span，以在 Input + Output 选项卡中查看生成式 AI 消息。

    选择 Metadata 选项卡以查看原始元数据。

    Screenshot showing the Trace Details view in the Tracing webview.

示例 2：使用 Monocle 通过 OpenAI Agents SDK 设置跟踪

以下端到端示例在 Python 中结合使用 OpenAI Agents SDK 与 Monocle，并展示了如何为多智能体旅行预订系统设置跟踪。

前提条件

要运行此示例，您需要满足以下先决条件

设置开发环境

请遵循以下说明部署包含运行此示例所需的所有依赖项的预配置开发环境。

  1. 创建环境变量

    使用以下代码片段之一为您的 OpenAI API 密钥创建环境变量。将 <your-openai-api-key> 替换为您实际的 OpenAI API 密钥。

    bash

    export OPENAI_API_KEY="<your-openai-api-key>"

    powershell

    $Env:OPENAI_API_KEY="<your-openai-api-key>"

    Windows 命令提示符

    set OPENAI_API_KEY=<your-openai-api-key>

    或者，在您的项目目录中创建一个 .env 文件

    OPENAI_API_KEY=<your-openai-api-key>

  2. 安装 Python 包

    创建一个包含以下内容的 requirements.txt 文件

    opentelemetry-sdk
opentelemetry-exporter-otlp-proto-http
monocle_apptrace
openai-agents
python-dotenv

    使用以下命令安装包

    pip install -r requirements.txt

  3. 设置跟踪

    1. 在计算机上为该项目创建一个新的本地目录。

      mkdir my-agents-tracing-app

    2. 导航到您创建的目录。

      cd my-agents-tracing-app

    3. 在该目录中打开 Visual Studio Code

      code .

  4. 创建 Python 文件

    1. 在 my-agents-tracing-app 目录中，创建一个名为 main.py 的 Python 文件。

      您将添加用于通过 Monocle 设置跟踪并与 OpenAI Agents SDK 进行交互的代码。

    2. 将以下代码添加到 main.py 并保存文件

      import os

from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Import monocle_apptrace
from monocle_apptrace import setup_monocle_telemetry

# Setup Monocle telemetry with OTLP span exporter for traces
setup_monocle_telemetry(
    workflow_name="opentelemetry-instrumentation-openai-agents",
    span_processors=[
        BatchSpanProcessor(
            OTLPSpanExporter(endpoint="https://:4318/v1/traces")
        )
    ]
)

from agents import Agent, Runner, function_tool

# Define tool functions
@function_tool
def book_flight(from_airport: str, to_airport: str) -> str:
    """Book a flight between airports."""
    return f"Successfully booked a flight from {from_airport} to {to_airport} for 100 USD."

@function_tool
def book_hotel(hotel_name: str, city: str) -> str:
    """Book a hotel reservation."""
    return f"Successfully booked a stay at {hotel_name} in {city} for 50 USD."

@function_tool
def get_weather(city: str) -> str:
    """Get weather information for a city."""
    return f"The weather in {city} is sunny and 75°F."

# Create specialized agents
flight_agent = Agent(
    name="Flight Agent",
    instructions="You are a flight booking specialist. Use the book_flight tool to book flights.",
    tools=[book_flight],
)

hotel_agent = Agent(
    name="Hotel Agent",
    instructions="You are a hotel booking specialist. Use the book_hotel tool to book hotels.",
    tools=[book_hotel],
)

weather_agent = Agent(
    name="Weather Agent",
    instructions="You are a weather information specialist. Use the get_weather tool to provide weather information.",
    tools=[get_weather],
)

# Create a coordinator agent with tools
coordinator = Agent(
    name="Travel Coordinator",
    instructions="You are a travel coordinator. Delegate flight bookings to the Flight Agent, hotel bookings to the Hotel Agent, and weather queries to the Weather Agent.",
    tools=[
        flight_agent.as_tool(
            tool_name="flight_expert",
            tool_description="Handles flight booking questions and requests.",
        ),
        hotel_agent.as_tool(
            tool_name="hotel_expert",
            tool_description="Handles hotel booking questions and requests.",
        ),
        weather_agent.as_tool(
            tool_name="weather_expert",
            tool_description="Handles weather information questions and requests.",
        ),
    ],
)

# Run the multi-agent workflow
if __name__ == "__main__":
    import asyncio

    result = asyncio.run(
        Runner.run(
            coordinator,
            "Book me a flight today from SEA to SFO, then book the best hotel there and tell me the weather.",
        )
    )
    print(result.final_output)

  5. 运行代码

    1. 在 Visual Studio Code 中打开一个新终端。

    2. 在终端中，使用命令 python main.py 运行代码。

  6. 在 Foundry Toolkit 中检查跟踪数据

    运行代码并刷新跟踪网页视图后，列表中会出现一个新的跟踪记录。

    选择该跟踪记录以打开跟踪详情网页视图。

    Screenshot showing selecting a trace from the Trace List in the Tracing webview.

    在左侧 span 树状视图中检查应用的完整执行流程，包括智能体调用、工具调用和智能体委派。

    在右侧 span 详情视图中选择一个 span，以在 Input + Output 选项卡中查看生成式 AI 消息。

    选择 Metadata 选项卡以查看原始元数据。

    Screenshot showing the Trace Details view in the Tracing webview.

您学到了什么

在本文中，您学习了如何

  • 使用 Azure AI Inference SDK 和 OpenTelemetry 在 AI 应用中设置跟踪。
  • 配置 OTLP 跟踪导出器，以将跟踪数据发送到本地收集器服务器。
  • 运行您的应用以生成跟踪数据，并在 Foundry Toolkit 网页视图中查看跟踪。
  • 通过 OTLP 在多个 SDK 和语言（包括 Python 和 TypeScript/JavaScript）以及非微软工具中使用跟踪功能。
  • 使用提供的代码片段对各种 AI 框架（Anthropic、Gemini、LangChain、OpenAI 等）进行插桩。
  • 使用跟踪网页视图 UI（包括 Start Collector 和 Refresh 按钮）来管理跟踪数据。
  • 设置开发环境（包括环境变量和包安装）以启用跟踪。
  • 使用 span 树和详情视图分析应用的执行流程，包括生成式 AI 消息流和元数据。
10/03/2025
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