from phoenix.otel import registertracer_provider =register( project_name="my-llm-app", # Default is 'default')
By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See Persistence or use one of the other deployment options to retain traces.
Launch your local Phoenix instance:
python3-mphoenix.server.mainserve
For details on customizing a local terminal deployment, see Terminal Setup.
Install packages:
pipinstallarize-phoenix-otel
Connect your application to your instance using:
from phoenix.otel import registertracer_provider =register( project_name="my-llm-app", # Default is 'default' endpoint="http://localhost:6006",)
from phoenix.otel import registertracer_provider =register( project_name="my-llm-app", # Default is 'default' endpoint="http://localhost:6006",)
For more info on using Phoenix with Docker, see Docker
If you don't want to host an instance of Phoenix yourself or use a notebook instance, you can use a persistent instance provided on our site. Sign up for an Arize Phoenix account athttps://app.phoenix.arize.com/login
Install packages:
pipinstallarize-phoenix-otel
Connect your application to your cloud instance:
import osfrom phoenix.otel import register# Add Phoenix API Key for tracingos.environ["PHOENIX_CLIENT_HEADERS"]="api_key=...:..."# configure the Phoenix tracerregister( project_name="my-llm-app", # Default is 'default' endpoint="https://app.phoenix.arize.com/v1/traces",)
Your Phoenix API key can be found on the Keys section of your dashboard.
Initialize the CrewAIInstrumentor before your application code.
from openinference.instrumentation.crewai import CrewAIInstrumentorCrewAIInstrumentor().instrument(tracer_provider=tracer_provider)
CrewAI uses Langchain under the hood. You can optionally also set up the LangChainInstrumentor to get even deeper visibility into your Crew.
from openinference.instrumentation.langchain import LangChainInstrumentorLangChainInstrumentor().instrument(tracer_provider=tracer_provider)
Run CrewAI
From here, you can run CrewAI as normal
import osfrom crewai import Agent, Task, Crew, Processfrom crewai_tools import SerperDevToolos.environ["OPENAI_API_KEY"]="YOUR_OPENAI_API_KEY"os.environ["SERPER_API_KEY"]="YOUR_SERPER_API_KEY"search_tool =SerperDevTool()# Define your agents with roles and goalsresearcher =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,# You can pass an optional llm attribute specifying what model you wanna use.# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7), 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)# Create tasks for your agentstask1 =Task( description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024. Identify key trends, breakthrough technologies, and potential industry impacts.""", expected_output="Full analysis report in bullet points", agent=researcher)task2 =Task( description="""Using the insights provided, develop an engaging blog post that highlights the most significant AI 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)# Instantiate your crew with a sequential processcrew =Crew( agents=[researcher, writer], tasks=[task1, task2], verbose=2, # You can set it to 1 or 2 to different logging levels process = Process.sequential)# Get your crew to work!result = crew.kickoff()print("######################")print(result)
Observe
Now that you have tracing setup, all calls to your Crew will be streamed to your running Phoenix for observability and evaluation.