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AI Apps is the dedicated dashboard section for production apps that call OpenAI or Anthropic at runtime — your customer-facing copilots, your agentic features, your AI-powered endpoints. It lives at voight.xyz/dashboard/ai-apps and ships once you wrap your provider client with @voightxyz/openai or @voightxyz/anthropic. The section is separate from the coding-agent dashboard on purpose. Your team’s Claude Code / Cursor / Codex telemetry and your production LLM app’s user-facing telemetry are different operational questions — different cadences, different SLOs, different people read them. Keeping them in their own surface lets each one stay opinionated about what it shows.

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What you get out of the box

Wrap your client once, deploy, and the dashboard populates within seconds. Five sub-tabs aggregate the same event stream from different angles:
Sub-tabThe question it answers
Overview”How is this app doing right now?” — cost, traffic, error rate, latency, all with prior-window deltas
Traces”What happened in this specific request?” — every withTrace block as a card, drillable to individual events
Models”Which model is eating my budget?” — cost & token mix by model, ranked
Tools”Which functions does the model call most?” — tool-use frequency, success rate, p50/p95 duration
Users”Which end-user costs me money?” — per-user spend, traces, tokens (powered by per-user tags)

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The data model behind the section

Every event in AI Apps comes from one of two sources:
  • Wrapped LLM calls — captured automatically by @voightxyz/openai or @voightxyz/anthropic when you wrap your client. One event per chat.completions.create, responses.create, or messages.create call. Carries model, tokens (input/output/cache reads/cache creations), tool calls, latency, finish reason, and (if you call withTrace) your tags map.
  • log() events inside withTrace — when you call log() from inside a withTrace block to mark a domain event ("user submitted form", "retrieval returned 0 results"). Stamped with the same tags as the surrounding LLM calls, but type: 'log' so they’re distinguishable.
Both flow through the same ingestion pipeline as the rest of Voight. The AI Apps section adds a server-side filter: only agents whose framework is openai or anthropic are included, so your coding-agent traffic never bleeds in.

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Filter pills

Three filters live in the top bar, all composable:
  • Agent — narrow to a single wrapped agent (e.g. production-chat-api vs internal-summarizer-bot). All agents using @voightxyz/openai or @voightxyz/anthropic appear here.
  • Provider — narrow to OpenAI or Anthropic only. Useful when one wrapper agent calls both providers (it happens — multi-provider routers, fallback chains) and you want to isolate one.
  • User — narrow to a single tags.userId value. Drives the Users sub-tab but is also available globally so you can see Models / Tools / Traces scoped to one customer.
The filter state is reflected in the URL — share a link to /dashboard/ai-apps?provider=anthropic&tag.plan=enterprise and the recipient lands on the same view.

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Provider filter: per-event, not per-agent

A subtle bug we hit early: a single agent that calls both OpenAI and Anthropic (failover, A/B routing) gets agent.framework pinned to whichever wrapper initialized first. If you filtered by agent.framework, choosing “OpenAI” would still show Claude models from that same agent. The fix shipped in May 2026: provider filtering uses metadata.source (per-event, set by each wrapper) instead of agent.framework (per-agent, first-seen). Multi-provider agents now filter cleanly — choose OpenAI and you see only events where the wrapper that emitted them was @voightxyz/openai.

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Overview sub-tab

The default landing tab. Three regions:
  1. KPI strip — cost USD, total calls, error rate, p50 latency, p95 latency. Each KPI carries a delta vs the prior identical window (28d vs prior 28d by default; toggleable to 7d / 1d).
  2. Activity pulse — stacked-area chart of traffic over time, coloured by provider. The pulse refreshes every 15s so you can watch live traffic during a deploy.
  3. Top models / Top tools / Top users — three compact leaderboards as entry points to the deeper sub-tabs.
Everything respects the active filters. Set the Provider pill to OpenAI and the KPIs, the pulse, and the leaderboards all narrow.

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Traces sub-tab

A withTrace block becomes one trace card. The card shows:
  • Route tag (POST /api/chat)
  • Total duration of the block
  • Cost USD
  • Number of LLM calls, tool calls, errors
  • User tag (if set) — clickable to filter the whole section to that user
Click a card and you get the full timeline: every chat.completions.create, every tool call, every log() event, in order, with prompts and responses revealed by an eye toggle (masked by default, same UX as the coding-agent traces). Derived child spans: if a model response includes tool_calls, the dashboard renders each one as a synthetic child span under the parent LLM call — even though they’re not separate events on the wire. Mirror what most engineers expect from APM tools.

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Models sub-tab

One row per (provider, model) pair active in the time window:
  • Cost USD (sum)
  • Calls (count)
  • Tokens (input + output + cache reads + cache creations, decomposed)
  • p50 / p95 latency
  • Share of overall cost as a sparkline
Sorted by cost descending. Click any row to filter Traces and Overview to that specific model.

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Tools sub-tab

One row per unique tool.name the model invoked:
  • Invocations (count)
  • Success rate (outcome === 'success' / total)
  • p50 / p95 duration of the tool execution itself
  • Last seen timestamp
  • Top users for this tool (mini-leaderboard inline)
Includes both function-calling (OpenAI / Anthropic tool_use) and any explicit log({ type: 'tool', toolExecuted }) calls inside withTrace.

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Users sub-tab

Powered by per-user tags. See that page for the 1-line code pattern that makes this tab populate. Key columns:
  • User (the tags.userId value, verbatim)
  • Spend (USD)
  • Traces (withTrace block count)
  • Tokens (total)
  • Last seen
  • Top model
Sorted by spend desc. Empty state if you haven’t wired tags.userId yet, with a deep link to the per-user-spend setup guide.

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Privacy in AI Apps

The wrapper SDKs ship with the same three privacy levels as the rest of Voight — Minimal, Standard, Full. Set on wrap: 
wrapOpenAI(new OpenAI(), { privacy: 'standard', agent: '...' })
LevelPrompts & responsesTool argumentsTokens & costTags
Minimaldroppeddroppedkeptkept
Standard ★scrubbed via scrubPii()scrubbedkeptkept
Fullverbatimverbatimkeptkept
Tags are treated as operational identifiers (not user content) and pass through every level. Numbers always pass through. The AI Apps section works in all three modes — Minimal still gives you cost, model, latency, and user attribution; you lose only the prompt/response previews.

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Time window

The window selector in the top-right controls every panel in the section. Defaults to 28 days. Available windows: 1 day, 7 days, 14 days, 28 days, 90 days. All deltas (the green/red percentage chips on KPIs) compare to the prior identical window — 28d vs prior 28d, 7d vs prior 7d, etc. Retention cap is governed by your pricing tier — events older than your retention window are purged.

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How AI Apps relates to the rest of the dashboard

Dashboard surfaceWhat it showsWhen you reach for it
AI Apps (this page)Production LLM apps using the wrapper SDKsCustomer-facing copilots, agentic features, B2B AI products
Overview (/dashboard)Coding-agent sessions (Claude Code, Cursor, Codex) + library-mode botsYour team’s dev workflow telemetry
Audit logEvery event across every surface, filterable”Show me what happened at 3:42pm” forensics
SessionsPer-process timelinesLong-running autonomous agents, multi-hour Claude Code runs
TracesPer-prompt timelinesOne agent turn from prompt to final response
Explorer (/explore)Public Solana agent registryOn-chain identity, reputation, x402
Same backend, same event schema, same privacy model. The AI Apps section is the opinionated view for production LLM-app telemetry — built so a product engineer can answer “is this app healthy?” without learning the rest of the dashboard.

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API access

Every panel is backed by a public endpoint under /v1/me/ai-apps/*: 
curl -H "authorization: Bearer $PRIVY_JWT" \
  "https://api.voight.xyz/v1/me/ai-apps/overview?windowDays=28&provider=anthropic"

curl -H "authorization: Bearer $PRIVY_JWT" \
  "https://api.voight.xyz/v1/me/ai-apps/users?windowDays=7&tag.plan=enterprise"
Build your own internal admin UIs, weekly cost reports, customer-facing per-tenant dashboards — same data the AI Apps section consumes.

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