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Braintrust 集成

本指南演示了如何使用 OpenTelemetry 将 BraintrustCrewAI 集成,以实现全面的追踪和评估。读完本指南后,您将能够追踪您的 CrewAI 智能体,监控其性能,并使用 Braintrust 强大的可观测性平台评估其输出。
什么是 Braintrust? Braintrust 是一个 AI 评估和可观测性平台,为 AI 应用程序提供全面的追踪、评估和监控,并内置了实验跟踪和性能分析功能。

开始使用

我们将通过一个简单的示例,介绍如何使用 CrewAI 并通过 OpenTelemetry 将其与 Braintrust 集成,以实现全面的可观测性和评估。

步骤 1:安装依赖项

uv add braintrust[otel] crewai crewai-tools opentelemetry-instrumentation-openai opentelemetry-instrumentation-crewai python-dotenv

步骤 2:设置环境变量

设置 Braintrust API 密钥并配置 OpenTelemetry 以将追踪数据发送到 Braintrust。您需要一个 Braintrust API 密钥和您的 OpenAI API 密钥。
import os
from getpass import getpass

# Get your Braintrust credentials
BRAINTRUST_API_KEY = getpass("🔑 Enter your Braintrust API Key: ")

# Get API keys for services
OPENAI_API_KEY = getpass("🔑 Enter your OpenAI API key: ")

# Set environment variables
os.environ["BRAINTRUST_API_KEY"] = BRAINTRUST_API_KEY
os.environ["BRAINTRUST_PARENT"] = "project_name:crewai-demo"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

第 3 步:使用 Braintrust 初始化 OpenTelemetry

初始化 Braintrust OpenTelemetry 检测工具,以开始捕获追踪数据并将其发送到 Braintrust。
import os
from typing import Any, Dict

from braintrust.otel import BraintrustSpanProcessor
from crewai import Agent, Crew, Task
from crewai.llm import LLM
from opentelemetry import trace
from opentelemetry.instrumentation.crewai import CrewAIInstrumentor
from opentelemetry.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.sdk.trace import TracerProvider

def setup_tracing() -> None:
    """Setup OpenTelemetry tracing with Braintrust."""
    current_provider = trace.get_tracer_provider()
    if isinstance(current_provider, TracerProvider):
        provider = current_provider
    else:
        provider = TracerProvider()
        trace.set_tracer_provider(provider)

    provider.add_span_processor(BraintrustSpanProcessor())
    CrewAIInstrumentor().instrument(tracer_provider=provider)
    OpenAIInstrumentor().instrument(tracer_provider=provider)


setup_tracing()

第 4 步:创建 CrewAI 应用程序

我们将创建一个 CrewAI 应用程序,其中两个智能体协作研究并撰写一篇关于人工智能进展的博客文章,并启用全面的追踪功能。
from crewai import Agent, Crew, Process, Task
from crewai_tools import SerperDevTool

def create_crew() -> Crew:
    """Create a crew with multiple agents for comprehensive tracing."""
    llm = LLM(model="gpt-4o-mini")
    search_tool = SerperDevTool()

    # Define agents with specific roles
    researcher = Agent(
        role="Senior Research Analyst",
        goal="Uncover cutting-edge developments in AI and data science",
        backstory="""You work at a leading tech think tank.
        Your expertise lies in identifying emerging trends.
        You have a knack for dissecting complex data and presenting actionable insights.""",
        verbose=True,
        allow_delegation=False,
        llm=llm,
        tools=[search_tool],
    )

    writer = Agent(
        role="Tech Content Strategist",
        goal="Craft compelling content on tech advancements",
        backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
        You transform complex concepts into compelling narratives.""",
        verbose=True,
        allow_delegation=True,
        llm=llm,
    )

    # Create tasks for your agents
    research_task = Task(
        description="""Conduct a comprehensive analysis of the latest advancements in {topic}.
        Identify key trends, breakthrough technologies, and potential industry impacts.""",
        expected_output="Full analysis report in bullet points",
        agent=researcher,
    )

    writing_task = Task(
        description="""Using the insights provided, develop an engaging blog
        post that highlights the most significant {topic} advancements.
        Your post should be informative yet accessible, catering to a tech-savvy audience.
        Make it sound cool, avoid complex words so it doesn't sound like AI.""",
        expected_output="Full blog post of at least 4 paragraphs",
        agent=writer,
        context=[research_task],
    )

    # Instantiate your crew with a sequential process
    crew = Crew(
        agents=[researcher, writer], 
        tasks=[research_task, writing_task], 
        verbose=True, 
        process=Process.sequential
    )

    return crew

def run_crew():
    """Run the crew and return results."""
    crew = create_crew()
    result = crew.kickoff(inputs={"topic": "AI developments"})
    return result

# Run your crew
if __name__ == "__main__":
    # Instrumentation is already initialized above in this module
    result = run_crew()
    print(result)

第 5 步:在 Braintrust 中查看追踪数据

运行您的 crew 后,您可以通过不同的视角在 Braintrust 中查看全面的追踪数据。
  • 追踪 (Trace)
  • 时间线 (Timeline)
  • 线程 (Thread)
Braintrust Trace View

第 6 步:通过 SDK 进行评估(实验)

您还可以使用 Braintrust 的评估 SDK 运行评估。这对于比较版本或离线评分输出非常有用。下面是一个 Python 示例,使用 Eval 类和我们上面创建的 crew。
# eval_crew.py
from braintrust import Eval
from autoevals import Levenshtein

def evaluate_crew_task(input_data):
    """Task function that wraps our crew for evaluation."""
    crew = create_crew()
    result = crew.kickoff(inputs={"topic": input_data["topic"]})
    return str(result)

Eval(
    "AI Research Crew",  # Project name
    {
        "data": lambda: [
            {"topic": "artificial intelligence trends 2024"},
            {"topic": "machine learning breakthroughs"},
            {"topic": "AI ethics and governance"},
        ],
        "task": evaluate_crew_task,
        "scores": [Levenshtein],
    },
)
设置您的 API 密钥并运行
export BRAINTRUST_API_KEY="YOUR_API_KEY"
braintrust eval eval_crew.py
有关更多详细信息,请参阅 Braintrust 评估 SDK 指南

Braintrust 集成的主要特点

  • 全面追踪:跟踪所有智能体交互、工具使用和 LLM 调用
  • 性能监控:监控执行时间、令牌使用量和成功率
  • 实验跟踪:比较不同的 crew 配置和模型
  • 自动评估:为 crew 输出设置自定义评估指标
  • 错误跟踪:监控和调试 crew 执行过程中的故障
  • 成本分析:跟踪令牌使用量及相关成本

版本兼容性信息

  • Python 3.8+
  • CrewAI >= 0.86.0
  • Braintrust >= 0.1.0
  • OpenTelemetry SDK >= 1.31.0

参考资料