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  • Arize Phoenix
  • Quickstarts
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  • 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
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  • Prompts
  • Datasets and Experiments
  • Evals

Software

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

Resources

  • Container Images
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  • Blue Sky
  • Blog

Integrations

  • OpenTelemetry
  • AI Providers

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  1. Tracing

Features: Tracing

Tracing is a critical part of AI Observability and should be used both in production and development

PreviousQuickstart: Tracing (TS)NextProjects

Last updated 2 months ago

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Phoenix's tracing and span analysis capabilities are invaluable during the prototyping and debugging stages. By instrumenting application code with Phoenix, teams gain detailed insights into the execution flow, making it easier to identify and resolve issues. Developers can drill down into specific spans, analyze performance metrics, and access relevant logs and metadata to streamline debugging efforts.

View the inner workings for your LLM Application

This section contains details on Tracing features:

  • Projects

  • Annotations

  • Sessions

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