Skip to main content
Your app is instrumented and traces are flowing in. Every request your app handles is now captured, but you might have thousands of them. This page walks you through how to find the ones that matter, understand what happened inside them, and organize your workflow.
Project overview showing the Traces tab with trace list, metrics, and navigation tabs
Open your project and you’ll see several tabs, each giving you a different lens on your data:

​
Traces

This is where most investigations start. The Traces tab shows every request your app has handled. Click any trace to expand its span tree and see exactly what happened: which components were called, what inputs and outputs flowed through, how long each step took, and where things went wrong.
Expanded span tree showing parent-child hierarchy, inputs, outputs, and evaluations

​
Sessions

Your users don’t send one-off requests — they have conversations. The Sessions tab groups traces into conversation threads by session.id, so you can follow an entire interaction from start to finish and spot where things go off track.
Sessions tab showing conversation threads with session IDs, messages, duration, and token counts
  • Sort and filter sessions by duration, total traces, user ID, or token count
  • Click into a session to see the full conversation with inputs and outputs for each turn
  • Export session data to CSV or via the Python SDK
  • Run session-level evals to detect patterns like user frustration or unresolved issues
session.id must be a non-empty string. Spans with an empty session ID won’t appear in the Sessions tab.

​
Spans

Sometimes you need to look across all traces at a specific type of operation — all LLM calls, all tool invocations, or all retrievals. The Spans tab lets you do that, showing individual units of work across your entire project.
Spans tab showing different span kinds with status, inputs, outputs, latency, and evaluations

​
Token counts

Token usage directly impacts cost. Arize automatically computes token counts for LLM spans using the llm.token_count.prompt, llm.token_count.completion, and model name attributes, and aggregates them up to the trace level. In the span tree, each span shows its total token count. Hover over the number to see a breakdown of prompt tokens and completion tokens, along with how they were computed (user-provided, tiktoken, or character estimate).
Span tree showing token counts with tooltip breakdown of prompt and completion tokens
From here you can sort traces or sessions by token count to find expensive requests, or define custom metrics and monitors on token usage for cost control.

​
Explore with Skills or Alyx

Three steps to explore traces with your AI coding agent:Install skill
npx skills add Arize-ai/arize-skills --skill "arize-trace" --yes
Set up authentication
export ARIZE_API_KEY="YOUR_API_KEY"
export ARIZE_SPACE_ID="YOUR_SPACE_ID"
Ask your agent
# Ask your AI coding agent:
"Export the last day of traces with errors from my project 
and tell me what's going wrong"
Works with Cursor, Claude Code, Codex, and more. The skill runs ax traces export under the hood, reads the output, and summarizes patterns for you.
Claude Code using the arize-trace skill to export traces and summarize errors in the terminal

​
Querying and filters

Once you know what you’re looking for, use filters to narrow down. Click the filter bar to start querying — you can type, select from suggestions, or use AI Search to generate filters from natural language.
Span Query filter bar showing query syntax, Filter With AI, and available dimensions
  • Format: "dimension name" = 'value'
  • Operators: =, !=, <, >, IN, contains
  • Null checks: "attributes.input.value" = null or is null
  • Combine with AND / OR
  • Example: "attributes.openinference.span.kind" = 'LLM' AND "status_code" = 'ERROR'

​
Multi-span filters

Need to find traces based on complex patterns — like traces where an LLM call follows a retrieval, or traces with errors but no tool calls? Create multiple span queries and define relationships between them.
Multi-span filter with two queries A and B combined with Find Traces Matching A AND B
  1. Define your first Span Query
  2. Click Add Span Query (up to 5)
  3. Use Find Traces Matching to combine them with operators: AND, OR, NOT, -> (temporal), => (parent-child)
  • AND — traces with spans matching both A and B
  • OR — traces with spans matching A or B
  • NOT — traces without spans matching the query
  • -> (Indirectly Calls) — A starts before B
  • => (Directly Calls) — A is the direct parent of B
  • Combine with parentheses: (A AND B) OR C, A AND NOT(B -> C)

​
Time filters

Use the Time Selector to focus on the period that matters. Choose presets, use the calendar, or type a custom range directly.
Time selector dropdown showing presets, custom range, and timezone options
  • Presets: Last 15 Min, Last Hour, Last 12 Hours, Last 7 Days, Last Month, Last 3 Months, Last 6 Months
  • Custom range: Select “Custom” to pick a specific start and end date/time
  • Time zone: Set your preferred timezone; it persists across sessions

​
Saved Views

Found a useful filter + column setup? Save it so you don’t have to rebuild it every time. Saved Views preserve your exact Tracing table configuration — filters, columns, sort order, and time range — so you and your team always come back to the same starting point.
Saved Views dropdown showing built-in and custom views with actions menu
Arize provides two built-in views: Arize Default (no filters) and Recent Errors (15m) (pre-filtered to status_code = 'ERROR'). You can’t overwrite them, but you can fork them with Save as new view. To create a view: set up the table how you want it (filters, columns, sort, time range). A Save button (disk icon) appears in the toolbar when you have unsaved changes. Click it and choose Save as new view. Views are shared per project. Each user can set their own personal default via the view’s actions menu.
SettingStored
FilterFull query including subqueries
ColumnsVisible columns and order
SortColumn and direction
Time rangePreset or custom window and timezone

​
Columns

Not every column is relevant to every investigation. Customize which columns are visible using the Columns button in the table toolbar. Columns are organized into groups:
Column selector showing Span columns, Span Evaluations, Session Evaluations, and Annotations groups
  • Span columns — TraceID, SpanID, Input, Output, Start/End Timestamp, Total Tokens, Status
  • Span Evaluations — scores and labels from your span-level evals
  • Session Evaluations — scores and labels from your session-level evals
  • Annotations — human labels and scores from your team
Toggle individual columns or select/deselect entire groups. Your column configuration is saved when you create a Saved View.

​
Export traces

Need the data outside Arize? Export trace data for evaluations, fine-tuning, or further analysis.
Export dropdown showing Export to Notebook, Export as CSV, Configure Export via Agent, and Configure Export via Code
  • Export to Notebook — generates a prefilled Python snippet using the Arize SDK to export your data to a DataFrame. Copy it into a Jupyter notebook to start analyzing.
  • Export as CSV — downloads the top 10,000 rows currently displayed. Customize columns before exporting.
  • Configure Export via Agent (Skills) — opens the Arize Skills setup for exporting via your coding agent.
  • Configure Export via Code — shows CLI, Python SDK, and REST API snippets for programmatic export.

​
Next step

You can explore individual traces and sessions. Now zoom out to see how your agents behave in aggregate:

Next: See Agent Insights