> ## Documentation Index
> Fetch the complete documentation index at: https://crewai-cursor-default-model-gpt-5-6-luna.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Arize Phoenix

> Arize Phoenix integration for CrewAI with OpenTelemetry and OpenInference

# Arize Phoenix Integration

This guide demonstrates how to integrate **Arize Phoenix** with **CrewAI** using OpenTelemetry via the [OpenInference](https://github.com/openinference/openinference) SDK. By the end of this guide, you will be able to trace your CrewAI agents and debug agent behavior.

> **What is Arize Phoenix?** [Arize Phoenix](https://arize.com/phoenix/) is the open-source observability and evaluation option from [Arize AI](https://arize.com/?utm_source=crewai-docs\&utm_medium=partner\&utm_campaign=partner-docs\&utm_content=observability-arize-phoenix). Use Phoenix when you want to run locally or self-host. Use [Arize AX](https://arize.com/products/ax/) for a managed cloud or enterprise self-hosted platform for production AI systems.

[![Watch a Video Demo of Our Integration with Phoenix](https://storage.googleapis.com/arize-assets/fixtures/setup_crewai.png)](https://www.youtube.com/watch?v=Yc5q3l6F7Ww)

## Get Started

We'll walk through a simple example of using CrewAI and integrating it with Arize Phoenix via OpenTelemetry using OpenInference.

You can also access this guide on [Google Colab](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/crewai_tracing_tutorial.ipynb).

### Step 1: Install Dependencies

```bash theme={null}
pip install openinference-instrumentation-crewai crewai crewai-tools arize-phoenix-otel
```

### Step 2: Set Up Environment Variables

Configure your Phoenix API key and OpenTelemetry endpoint to send traces to Phoenix. The same setup works with a local or self-hosted Phoenix endpoint by changing the collector URL.

You can get your free Serper API key [here](https://serper.dev/).

```python theme={null}
import os
from getpass import getpass

# Get your Phoenix API key
PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix API key: ")

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

# Set environment variables
os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Change this to your own endpoint if you are using a self-hosted instance
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
os.environ["SERPER_API_KEY"] = SERPER_API_KEY
```

### Step 3: Initialize OpenTelemetry with Phoenix

Initialize the OpenInference OpenTelemetry instrumentation SDK to start capturing traces and send them to Phoenix.

```python theme={null}
from phoenix.otel import register

tracer_provider = register(
    project_name="crewai-tracing-demo",
    auto_instrument=True,
)
```

### Step 4: Create a CrewAI Application

We'll create a CrewAI application where two agents collaborate to research and write a blog post about AI advancements.

```python theme={null}
from crewai import Agent, Crew, Process, Task
from crewai_tools import SerperDevTool
from openinference.instrumentation.crewai import CrewAIInstrumentor
from phoenix.otel import register

# setup monitoring for your crew
tracer_provider = register(
    endpoint="http://localhost:6006/v1/traces")
CrewAIInstrumentor().instrument(skip_dep_check=True, tracer_provider=tracer_provider)
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=1, process=Process.sequential
)

# Get your crew to work!
result = crew.kickoff()

print("######################")
print(result)
```

### Step 5: View Traces in Phoenix

After running the agent, you can view the traces generated by your CrewAI application in Phoenix. You should see detailed steps of the agent interactions and LLM calls, which can help you debug and optimize your AI agents.

Open your Phoenix project and navigate to the project you specified in the `project_name` parameter. You'll see a timeline view of your trace with all the agent interactions, tool usages, and LLM calls.

![Example trace in Phoenix showing agent interactions](https://storage.googleapis.com/arize-assets/fixtures/crewai_traces.png)

### Version Compatibility Information

* Python 3.8+
* CrewAI >= 0.86.0
* Arize Phoenix >= 7.0.1
* OpenTelemetry SDK >= 1.31.0

### References

* [Phoenix Documentation](https://docs.arize.com/phoenix/) - Overview of the Phoenix platform.
* [Arize AX](https://arize.com/products/ax/) - Managed cloud and enterprise self-hosted observability and evaluation.
* [Arize agent evaluation guide](https://arize.com/guides/ai-agent-handbook/agent-evaluation/) - Production workflow for evaluating agent behavior from traces.
* [Arize LLM evaluation guide](https://arize.com/resources/llm-evaluation/) - Methods and metrics for evaluating LLM applications.
* [CrewAI Documentation](https://docs.crewai.com/) - Overview of the CrewAI framework.
* [OpenTelemetry Docs](https://opentelemetry.io/docs/) - OpenTelemetry guide
* [OpenInference GitHub](https://github.com/openinference/openinference) - Source code for OpenInference SDK.