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Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
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Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights. 📦 Ships with claude-code-templates
</> SKILL.md
observability-phoenix.SKILL.md
---
name : phoenix-observability
description : Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
version : 1.0.0
author : Orchestra Research
license : MIT
tags : [Observability, Phoenix, Arize, Tracing, Evaluation, Monitoring, LLM Ops, OpenTelemetry]
dependencies : [arize-phoenix>=12.0.0]
---
# Phoenix - AI Observability Platform
Open-source AI observability and evaluation platform for LLM applications with tracing, evaluation, datasets, experiments, and real-time monitoring.
## When to use Phoenix
**Use Phoenix when:**
- Debugging LLM application issues with detailed traces
- Running systematic evaluations on datasets
- Monitoring production LLM systems in real-time
- Building experiment pipelines for prompt/model comparison
- Self-hosted observability without vendor lock-in
**Key features:**
- **Tracing**: OpenTelemetry-based trace collection for any LLM framework
- **Evaluation**: LLM-as-judge evaluators for quality assessment
- **Datasets**: Versioned test sets for regression testing
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
- **Experiments**: Compare prompts, models, and configurations
- **Playground**: Interactive prompt testing with multiple models
- **Open-source**: Self-hosted with PostgreSQL or SQLite
**Use alternatives instead:**
- **LangSmith**: Managed platform with LangChain-first integration
- **Weights & Biases**: Deep learning experiment tracking focus
- **Arize Cloud**: Managed Phoenix with enterprise features
- **MLflow**: General ML lifecycle, model registry focus
## Quick start
### Installation
```bash
pip install arize-phoenix
# With specific backends
pip install arize-phoenix[embeddings] # Embedding analysis
pip install arize-phoenix-otel # OpenTelemetry config
pip install arize-phoenix-evals # Evaluation framework
pip install arize-phoenix-client # Lightweight REST client
```
### Launch Phoenix server
```python
import phoenix as px
# Launch in notebook (ThreadServer mode)
session = px.launch_app()
# View UI
session.view() # Embedded iframe
print(session.url) # http://localhost:6006
```
### Command-line server (production)
```bash
# Start Phoenix server
phoenix serve
# With PostgreSQL
export PHOENIX_SQL_DATABASE_URL="postgresql://user:pass@host/db"
phoenix serve --port 6006
```
### Basic tracing
```python
from phoenix.otel import register
from openinference.instrumentation.openai import OpenAIInstrumentor
# Configure OpenTelemetry with Phoenix
tracer_provider = register(
project_name="my-llm-app",
endpoint="http://localhost:6006/v1/traces"
)
# Instrument OpenAI SDK
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
# All OpenAI calls are now traced
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)
```
## Core concepts
### Traces and spans
A **trace** represents a complete execution flow, while **spans** are individual operations within that trace.
```python
from phoenix.otel import register
from opentelemetry import trace
# Setup tracing
tracer_provider = register(project_name="my-app")
tracer = trace.get_tracer(__name__)
# Create custom spans
with tracer.start_as_current_span("process_query") as span:
span.set_attribute("input.value", query)
# Child spans are automatically nested
with tracer.start_as_current_span("retrieve_context"):
context = retriever.search(query)
with tracer.start_as_current_span("generate_response"):
response = llm.generate(query, context)
span.set_attribute("output.value", response)
```
### Projects
Projects organize related traces:
```python
import os
os.environ["PHOENIX_PROJECT_NAME"] = "production-chatbot"
# Or per-trace
from phoenix.otel import register
tracer_provider = register(project_name="experiment-v2")
```
## Framework instrumentation
### OpenAI
```python
from phoenix.otel import register
from openinference.instrumentation.openai import OpenAIInstrumentor
tracer_provider = register()
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
```
### LangChain
```python
from phoenix.otel import register
from openinference.instrumentation.langchain import LangChainInstrumentor
tracer_provider = register()
LangChainInstrumentor().instrument(tracer_provider=tracer_provider)
# All LangChain operations traced
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o")
response = llm.invoke("Hello!")
```
### LlamaIndex
```python
from phoenix.otel import register
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
tracer_provider = register()
LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)
```
### Anthropic
```python
from phoenix.otel import register
from openinference.instrumentation.anthropic import AnthropicInstrumentor
tracer_provider = register()
AnthropicInstrumentor().instrument(tracer_provider=tracer_provider)
```
## Evaluation framework
### Built-in evaluators
```python
from phoenix.evals import (
OpenAIModel,
HallucinationEvaluator,
RelevanceEvaluator,
ToxicityEvaluator,
llm_classify
)
# Setup model for evaluation
eval_model = OpenAIModel(model="gpt-4o")
# Evaluate hallucination
hallucination_eval = HallucinationEvaluator(eval_model)
results = hallucination_eval.evaluate(
input="What is the capital of France?",
output="The capital of France is Paris.",
reference="Paris is the capital of France."
)
```
### Custom evaluators
```python
from phoenix.evals import llm_classify
# Define custom evaluation
def evaluate_helpfulness(input_text, output_text):
template = """
Evaluate if the response is helpful for the given question.
Question: {input}
Response: {output}
Is this response helpful? Answer 'helpful' or 'not_helpful'.
"""
result = llm_classify(
model=eval_model,
template=template,
input=input_text,
output=output_text,
rails=["helpful", "not_helpful"]
)
return result
```
### Run evaluations on dataset
```python
from phoenix import Client
from phoenix.evals import run_evals
client = Client()
# Get spans to evaluate
spans_df = client.get_spans_dataframe(
project_name="my-app",
filter_condition="span_kind == 'LLM'"
)
# Run evaluations
eval_results = run_evals(
dataframe=spans_df,
evaluators=[
HallucinationEvaluator(eval_model),
RelevanceEvaluator(eval_model)
],
provide_explanation=True
)
# Log results back to Phoenix
client.log_evaluations(eval_results)
```
## Datasets and experiments
### Create dataset
```python
from phoenix import Client
client = Client()
# Create dataset
dataset = client.create_dataset(
name="qa-test-set",
description="QA evaluation dataset"
)
# Add examples
client.add_examples_to_dataset(
dataset_name="qa-test-set",
examples=[
{
"input": {"question": "What is Python?"},
"output": {"answer": "A programming language"}
},
{
"input": {"question": "What is ML?"},
"output": {"answer": "Machine learning"}
}
]
)
```
### Run experiment
```python
from phoenix import Client
from phoenix.experiments import run_experiment
client = Client()
def my_model(input_data):
"""Your model function."""
question = input_data["question"]
return {"answer": generate_answer(question)}
def accuracy_evaluator(input_data, output, expected):
"""Custom evaluator."""
return {
"score": 1.0 if expected["answer"].lower() in output["answer"].lower() else 0.0,
"label": "correct" if expected["answer"].lower() in output["answer"].lower() else "incorrect"
}
# Run experiment
results = run_experiment(
dataset_name="qa-test-set",
task=my_model,
evaluators=[accuracy_evaluator],
experiment_name="baseline-v1"
)
print(f"Average accuracy: {results.aggregate_metrics['accuracy']}")
```
## Client API
### Query traces and spans
```python
from phoenix import Client
client = Client(endpoint="http://localhost:6006")
# Get spans as DataFrame
spans_df = client.get_spans_dataframe(
project_name="my-app",
filter_condition="span_kind == 'LLM'",
limit=1000
)
# Get specific span
span = client.get_span(span_id="abc123")
# Get trace
trace = client.get_trace(trace_id="xyz789")
```
### Log feedback
```python
from phoenix import Client
client = Client()
# Log user feedback
client.log_annotation(
span_id="abc123",
name="user_rating",
annotator_kind="HUMAN",
score=0.8,
label="helpful",
metadata={"comment": "Good response"}
)
```
### Export data
```python
# Export to pandas
df = client.get_spans_dataframe(project_name="my-app")
# Export traces
traces = client.list_traces(project_name="my-app")
```
## Production deployment
### Docker
```bash
docker run -p 6006:6006 arizephoenix/phoenix:latest
```
### With PostgreSQL
```bash
# Set database URL
export PHOENIX_SQL_DATABASE_URL="postgresql://user:pass@host:5432/phoenix"
# Start server
phoenix serve --host 0.0.0.0 --port 6006
```
### Environment variables
| Variable | Description | Default |
|----------|-------------|---------|
| `PHOENIX_PORT` | HTTP server port | `6006` |
| `PHOENIX_HOST` | Server bind address | `127.0.0.1` |
| `PHOENIX_GRPC_PORT` | gRPC/OTLP port | `4317` |
| `PHOENIX_SQL_DATABASE_URL` | Database connection | SQLite temp |
| `PHOENIX_WORKING_DIR` | Data storage directory | OS temp |
| `PHOENIX_ENABLE_AUTH` | Enable authentication | `false` |
| `PHOENIX_SECRET` | JWT signing secret | Required if auth enabled |
### With authentication
```bash
export PHOENIX_ENABLE_AUTH=true
export PHOENIX_SECRET="your-secret-key-min-32-chars"
export PHOENIX_ADMIN_SECRET="admin-bootstrap-token"
phoenix serve
```
## Best practices
1. **Use projects**: Separate traces by environment (dev/staging/prod)
2. **Add metadata**: Include user IDs, session IDs for debugging
3. **Evaluate regularly**: Run automated evaluations in CI/CD
4. **Version datasets**: Track test set changes over time
5. **Monitor costs**: Track token usage via Phoenix dashboards
6. **Self-host**: Use PostgreSQL for production deployments
## Common issues
**Traces not appearing:**
```python
from phoenix.otel import register
# Verify endpoint
tracer_provider = register(
project_name="my-app",
endpoint="http://localhost:6006/v1/traces" # Correct endpoint
)
# Force flush
from opentelemetry import trace
trace.get_tracer_provider().force_flush()
```
**High memory in notebook:**
```python
# Close session when done
session = px.launch_app()
# ... do work ...
session.close()
px.close_app()
```
**Database connection issues:**
```bash
# Verify PostgreSQL connection
psql $PHOENIX_SQL_DATABASE_URL -c "SELECT 1"
# Check Phoenix logs
phoenix serve --log-level debug
```
## References
- **[Advanced Usage](references/advanced-usage.md)** - Custom evaluators, experiments, production setup
- **[Troubleshooting](references/troubleshooting.md)** - Common issues, debugging, performance
## Resources
- **Documentation**: https://docs.arize.com/phoenix
- **Repository**: https://github.com/Arize-ai/phoenix
- **Docker Hub**: https://hub.docker.com/r/arizephoenix/phoenix
- **Version**: 12.0.0+
- **License**: Apache 2.0
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