How to: Tracing

Guides on how to use traces

Setup Tracing

• Setup Tracing in Python or Typescript
• Add Integrations via Auto Instrumentation
• Manually Instrument your application

Customize Traces & Spans

How to set custom attributes and semantic attributes to child spans and spans created by auto-instrumentors.

How to track sessions

How to create custom spans

Setting metadata

Setting tags

Setting a user

Setting prompt template attributes

How to read attributes from context

Masking attributes on spans

Auto Instrumentation

Phoenix natively works with a variety of frameworks and SDKs across Python and JavaScript via OpenTelemetry auto-instrumentation. Phoenix can also be natively integrated with AI platforms such as LangFlow and LiteLLM proxy.

Manual Instrumentation

Create and customize spans for your use-case

Instrument: Python using OpenInference Helpers

Instrument: Python using Base OTEL

How to acquire a Tracer

How to create spans

How to create nested spans

How to create spans with decorators

How to get the current span

How to add attributes to a span

How to add semantic attributes

How to add events

How to set a span's status

How to record exceptions

Setup Tracing (TS)

Querying Spans

How to query spans to construct DataFrames to use for evaluation

How to run a query

How to specify a project

How to query for documents

How to apply filters

How to extract attributes

How to use data for evaluation

How to use pre-defined queries

Annotate Traces

Annotating in the UI

Annotating via the Client

Log Evaluation Results

How to log evaluation results to annotate traces with evals

How to log span evaluations

How to log document evaluations

How to specify a project for logging evaluations

Save and Load Traces

Saving Traces

Loading Traces

Cost Tracking

How to track token-based costs for your LLM applications

Setting up cost tracking

Model pricing configuration

Viewing cost data

Session and experiment costs