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Phoenix provides OpenTelemetry-based distributed tracing to give you complete visibility into your LLM application’s execution. Tracing captures every step of your application—from user queries to LLM calls to retrieval operations—organized in a structured span hierarchy.

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What is Tracing?

Tracing records the execution path of your LLM application by creating spans—individual units of work that represent operations like:
  • LLM generation calls
  • Retrieval operations
  • Tool/function invocations
  • Chain executions
  • Embeddings generation
Spans are organized hierarchically to show parent-child relationships, making it easy to understand how different parts of your application interact.

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How It Works

Phoenix implements the OpenTelemetry standard with semantic conventions specifically designed for LLM applications through the OpenInference specification.

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Automatic Instrumentation

Phoenix provides automatic instrumentation for popular LLM frameworks with zero code changes:

OpenAI

Automatically trace all OpenAI API calls

LangChain

Capture chain execution and component interactions

LlamaIndex

Trace queries, retrievals, and index operations

Anthropic

Monitor Claude API interactions
1

Install instrumentation

Install the instrumentation package for your framework:
pip install openinference-instrumentation-openai
2

Configure instrumentation

Add a few lines to instrument your application:
from openinference.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

# Configure the tracer provider
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(
    SimpleSpanProcessor(OTLPSpanExporter("http://localhost:6006/v1/traces"))
)

# Instrument OpenAI
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
3

Run your application

Execute your application normally—traces will be automatically captured and sent to Phoenix.

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Span Hierarchy

Spans are organized in a tree structure based on the trace_id and parent_id attributes defined in /home/daytona/workspace/source/src/phoenix/trace/attributes.py. Each span contains:
  • Context: span_id, trace_id for linking spans together
  • Timing: start_time, end_time for performance analysis
  • Metadata: name, span_kind, status_code for categorization
  • Attributes: Nested key-value pairs with LLM-specific data
# Example span structure
{
  "context.trace_id": "abc123...",
  "context.span_id": "def456...",
  "parent_id": "parent789...",
  "name": "ChatCompletion",
  "span_kind": "LLM",
  "attributes": {
    "llm.model_name": "gpt-4",
    "llm.token_count.prompt": 150,
    "llm.token_count.completion": 75,
    "input.value": "What is Phoenix?",
    "output.value": "Phoenix is an observability platform..."
  }
}

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Span Attributes

Phoenix uses a sophisticated attribute system (implemented in src/phoenix/trace/attributes.py) that supports: Flattened Keys: Dot-separated paths like llm.token_count.completion are automatically unflattened into nested structures: 
# Flattened format (as received from OpenTelemetry)
{"llm.token_count.completion": 123}

# Unflattened format (used internally)
{"llm": {"token_count": {"completion": 123}}}
Array Support: Numeric indices create arrays for structured data: 
# Flattened
{
  "retrieval.documents.0.content": "First doc",
  "retrieval.documents.1.content": "Second doc"
}

# Unflattened
{
  "retrieval": {
    "documents": [
      {"content": "First doc"},
      {"content": "Second doc"}
    ]
  }
}

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Projects and Sessions

Phoenix organizes traces using projects and sessions:

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Projects

Projects group related traces together. Set the project name via the ResourceAttributes.PROJECT_NAME attribute: 
from openinference.semconv.resource import ResourceAttributes
from opentelemetry.sdk.resources import Resource

resource = Resource({
    ResourceAttributes.PROJECT_NAME: "chatbot-production"
})
You can also dynamically switch projects using the using_project context manager (from src/phoenix/trace/projects.py): 
from phoenix.trace import using_project

with using_project('experiment-1'):
    # All spans here are tagged with 'experiment-1'
    client.chat.completions.create(...)
The using_project context manager is deprecated and has been moved to openinference-instrumentation. Use it only in notebook environments for quick experimentation.

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Sessions

Sessions group traces within a project, typically representing a single user interaction or conversation thread. Sessions are identified by metadata attributes added to spans.

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Key Features

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Rich Metadata

Capture comprehensive details about each operation:
  • LLM calls: Model name, token counts, prompts, completions, parameters
  • Retrievals: Documents, scores, queries
  • Tools: Function names, parameters, results
  • Errors: Stack traces and error messages

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Performance Insights

Analyze latency at every level:
  • Total trace duration
  • Individual span timings
  • Time spent in LLM calls vs. retrieval vs. processing

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Evaluation Integration

Traces can be evaluated using the evaluation system (see Evaluation). Attach evaluations directly to spans: 
from phoenix.trace import SpanEvaluations
import pandas as pd

evaluations = SpanEvaluations(
    eval_name="relevance",
    dataframe=pd.DataFrame({
        "span_id": ["span1", "span2"],
        "score": [1.0, 0.8],
        "label": ["relevant", "relevant"]
    })
)

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Working with Traces

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Export Traces

Export traces to datasets for offline analysis using the TraceDataset class (from src/phoenix/trace/trace_dataset.py): 
from phoenix.trace import TraceDataset
import pandas as pd

# Create dataset from dataframe
trace_ds = TraceDataset(
    dataframe=spans_df,
    name="production-traces-2024-03"
)

# Save to disk
trace_ds.save()

# Load later
loaded_ds = TraceDataset.load(trace_ds._id)

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Create Datasets from Traces

Convert production traces into evaluation datasets (see Datasets): 
# Filter interesting traces
filtered_spans = trace_ds.dataframe[
    trace_ds.dataframe['attributes.llm.model_name'] == 'gpt-4'
]

# Create new dataset
new_dataset = TraceDataset(filtered_spans)

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Query Traces

Access trace data programmatically via the Phoenix client: 
import phoenix as px

client = px.Client()
traces = client.get_traces(
    project_name="my-project",
    start_time="2024-01-01",
    end_time="2024-01-31"
)

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Suppressing Tracing

Temporarily disable tracing for specific code sections using suppress_tracing (from openinference.instrumentation): 
from openinference.instrumentation import suppress_tracing

with suppress_tracing():
    # This LLM call won't be traced
    response = client.chat.completions.create(...)

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Next Steps

Evaluation

Learn how to evaluate traced spans with LLM judges

Datasets

Create versioned datasets from your traces

Experiments

Run systematic experiments on your LLM application

Instrumentation Guide

Detailed instrumentation setup for all frameworks

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