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Braintrust 集成
本指南演示了如何使用 OpenTelemetry 将 Braintrust 与 CrewAI 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,并利用 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 应用提供全面的追踪、评估和监控功能,并内置了实验跟踪和性能分析工具。
开始使用
我们将通过一个简单的示例来演示如何结合使用 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 以将追踪数据发送至 Braintrust。您需要准备一个 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:初始化 OpenTelemetry 与 Braintrust
初始化 Braintrust OpenTelemetry 检测功能,开始捕获追踪数据并将其发送至 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
我们将创建一个 CrewAI 应用,其中两个智能体协作研究并撰写一篇关于 AI 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 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)
时间轴 (Timeline)
线程 (Thread)
第 6 步:通过 SDK 进行评估(实验)
您还可以使用 Braintrust 的 Eval SDK 进行评估。这对于离线比较版本或对输出结果进行打分非常有用。以下是一个在上述创建的 crew 中使用 Eval 类的 Python 示例:
# 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 Eval SDK 指南。
Braintrust 集成的核心功能
- 全面追踪:跟踪所有智能体交互、工具使用情况和 LLM 调用
- 性能监控:监控执行时间、Token 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:监控并调试 crew 执行过程中的故障
- 成本分析:跟踪 Token 使用量及相关成本
- Python 3.8+
- CrewAI >= 0.86.0
- Braintrust >= 0.1.0
- OpenTelemetry SDK >= 1.31.0
参考资料