import os
# Add Phoenix API Key for tracing
PHOENIX_API_KEY = "ADD YOUR API KEY"
os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com"
Launch your local Phoenix instance:
pip install arize-phoenix
phoenix serve
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"
docker pull arizephoenix/phoenix:latest
Run your containerized instance:
docker run -p 6006:6006 arizephoenix/phoenix:latest
This will expose the Phoenix on localhost:6006
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"
CrewAI uses either Langchain or LiteLLM under the hood to call models, depending on the version.
If you're using CrewAI<0.63.0, we recommend installing our openinference-instrumentation-langchain library to get visibility of LLM calls.
If you're using CrewAI>= 0.63.0, we recommend instead adding our openinference-instrumentation-litellm library to get visibility of LLM calls.
Setup
Connect to your Phoenix instance using the register function.
from phoenix.otel import register
# configure the Phoenix tracer
tracer_provider = register(
project_name="my-llm-app", # Default is 'default'
auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)
Run CrewAI
From here, you can run CrewAI as normal
import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool
os.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 goals
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,
# 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 agents
task1 = 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 process
crew = 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.
Resources
Sign up for an Arize Phoenix account at
Your Phoenix API key can be found on the Keys section of your .
For details on customizing a local terminal deployment, see .
See for more details
Pull latest Phoenix image from:
For more info on using Phoenix with Docker, see .
By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See or use one of the other deployment options to retain traces.
