Phoenix
TypeScript APIPython APICommunityGitHubPhoenix Cloud
English
  • Documentation
  • Self-Hosting
  • Cookbooks
  • Learn
  • Integrations
  • SDK and API Reference
  • Release Notes
English
  • Arize Phoenix
  • Quickstarts
  • User Guide
  • Environments
  • Phoenix Demo
  • 🔭Tracing
    • Overview: Tracing
    • Quickstart: Tracing
      • Quickstart: Tracing (Python)
      • Quickstart: Tracing (TS)
    • Features: Tracing
      • Projects
      • Annotations
      • Sessions
    • Integrations: Tracing
    • How-to: Tracing
      • Setup Tracing
        • Setup using Phoenix OTEL
        • Setup using base OTEL
        • Using Phoenix Decorators
        • Setup Tracing (TS)
        • Setup Projects
        • Setup Sessions
      • Add Metadata
        • Add Attributes, Metadata, Users
        • Instrument Prompt Templates and Prompt Variables
      • Annotate Traces
        • Annotating in the UI
        • Annotating via the Client
        • Running Evals on Traces
        • Log Evaluation Results
      • Importing & Exporting Traces
        • Import Existing Traces
        • Export Data & Query Spans
        • Exporting Annotated Spans
      • Advanced
        • Mask Span Attributes
        • Suppress Tracing
        • Filter Spans to Export
        • Capture Multimodal Traces
    • Concepts: Tracing
      • How Tracing Works
      • What are Traces
      • Concepts: Annotations
      • FAQs: Tracing
  • 📃Prompt Engineering
    • Overview: Prompts
      • Prompt Management
      • Prompt Playground
      • Span Replay
      • Prompts in Code
    • Quickstart: Prompts
      • Quickstart: Prompts (UI)
      • Quickstart: Prompts (Python)
      • Quickstart: Prompts (TS)
    • How to: Prompts
      • Configure AI Providers
      • Using the Playground
      • Create a prompt
      • Test a prompt
      • Tag a prompt
      • Using a prompt
    • Concepts: Prompts
  • 🗄️Datasets & Experiments
    • Overview: Datasets & Experiments
    • Quickstart: Datasets & Experiments
    • How-to: Datasets
      • Creating Datasets
      • Exporting Datasets
    • Concepts: Datasets
    • How-to: Experiments
      • Run Experiments
      • Using Evaluators
  • 🧠Evaluation
    • Overview: Evals
      • Agent Evaluation
    • Quickstart: Evals
    • How to: Evals
      • Pre-Built Evals
        • Hallucinations
        • Q&A on Retrieved Data
        • Retrieval (RAG) Relevance
        • Summarization
        • Code Generation
        • Toxicity
        • AI vs Human (Groundtruth)
        • Reference (citation) Link
        • User Frustration
        • SQL Generation Eval
        • Agent Function Calling Eval
        • Agent Path Convergence
        • Agent Planning
        • Agent Reflection
        • Audio Emotion Detection
      • Eval Models
      • Build an Eval
      • Build a Multimodal Eval
      • Online Evals
      • Evals API Reference
    • Concepts: Evals
      • LLM as a Judge
      • Eval Data Types
      • Evals With Explanations
      • Evaluators
      • Custom Task Evaluation
  • 🔍Retrieval
    • Overview: Retrieval
    • Quickstart: Retrieval
    • Concepts: Retrieval
      • Retrieval with Embeddings
      • Benchmarking Retrieval
      • Retrieval Evals on Document Chunks
  • 🌌inferences
    • Quickstart: Inferences
    • How-to: Inferences
      • Import Your Data
        • Prompt and Response (LLM)
        • Retrieval (RAG)
        • Corpus Data
      • Export Data
      • Generate Embeddings
      • Manage the App
      • Use Example Inferences
    • Concepts: Inferences
    • API: Inferences
    • Use-Cases: Inferences
      • Embeddings Analysis
  • ⚙️Settings
    • Access Control (RBAC)
    • API Keys
    • Data Retention
Powered by GitBook

Platform

  • Tracing
  • Prompts
  • Datasets and Experiments
  • Evals

Software

  • Python Client
  • TypeScript Client
  • Phoenix Evals
  • Phoenix Otel

Resources

  • Container Images
  • X
  • Blue Sky
  • Blog

Integrations

  • OpenTelemetry
  • AI Providers

© 2025 Arize AI

On this page
  • Instrumentation
  • Exporter
  • Collector
  • OpenTelememetry Protocol

Was this helpful?

Edit on GitHub
  1. Tracing
  2. Concepts: Tracing

How Tracing Works

The components behind tracing

PreviousConcepts: TracingNextWhat are Traces

Last updated 25 days ago

Was this helpful?

The phoenix server is collector of traces over OTLP

Instrumentation

In order for an application to emit traces for analysis, the application must be instrumented. Your application can be manually instrumented or be automatically instrumented. With phoenix, there a set of plugins (instrumentors) that can be added to your application's startup process that perform auto-instrumentation. These plugins collect spans for your application and export them for collection and visualization. For phoenix, all the instrumentors are managed via a single repository called . The comprehensive list of instrumentors can be found in the

Exporter

An exporter takes the spans created via instrumentation and exports them to a collector. In simple terms, it just sends the data to the Phoenix. When using Phoenix, most of this is completely done under the hood when you call instrument on an instrumentor.

Collector

The Phoenix server is a collector and a UI that helps you troubleshoot your application in real time. When you run or run phoenix, Phoenix starts receiving spans from any application(s) that is exporting spans to it.

OpenTelememetry Protocol

OpenTelemetetry Protocol (or OTLP for short) is the means by which traces arrive from your application to the Phoenix collector. Phoenix currently supports OTLP over HTTP.

🔭
OpenInference
how-to guide.