Analytics explorer

The Analytics Explorer page monitors event volume, LLM performance, token consumption, and cost in near real time. Use it to investigate production incidents, track the impact of model changes, or audit LLM spend during peak traffic. Navigation: Project -> Insights -> Analytics Explorer Date range: Set at the upper-right area of the page, for example Last 7 days. Every tab follows this range. Filters: Select All environments and All channels at the top of the page to narrow the data by deployment environment and channel. Filter selections are reflected in the page URL, so you can bookmark a filtered view or share it with a colleague.

The page organizes its detail into six tabs.

Tab	What it shows
Overview	Headline volume, error, token, and cost metrics for the selected period.
LLM Performance	Call volume, latency, token, and cost metrics, broken down by model.
Sessions Explorer	Session-level records with filters, columns, and export.
Traces Explorer	Individual trace events, with timeline and waterfall views.
Generations	Every recorded model call, with latency, token, cost, and credential detail.
Query	Ad-hoc SQL queries on project event data.

Overview

Six metric cards summarize the selected period at a glance.

Metric	Description
Sessions	Total sessions in the selected period. A session is a single end-to-end interaction between a user and the agent system.
Messages	Total messages across all sessions, including both user messages and agent responses.
LLM Calls	Total LLM API calls agents made during the period. Includes calls to all configured models, for example routing, generation, and evaluation.
Errors	Total errors during agent execution, including LLM failures, timeout errors, and tool invocation errors.
Tokens	Total LLM tokens, both input and output, across all calls.
Cost	Estimated cost based on token usage and per-model pricing. Use it to track spend relative to budgets or compare cost-efficiency across model configurations.

LLM performance

LLM Performance reports call volume, latency, tokens, and cost for the model calls in the selected period, broken down by model. Use it to benchmark models and to find the ones that drive latency or spend. Five metric cards summarize the period.

Metric	Description
LLM Calls	Total model calls in the selected period.
Avg Latency	Mean time a model call took to complete.
P95 Latency	Latency that 95% of calls finished within. Read it alongside the average, because a few slow calls can raise it while the average stays flat.
Tokens	Total input and output tokens across all calls.
Cost	Estimated cost based on token usage and per-model pricing.

Charts

Two charts show how latency and cost develop across the period.

Chart	Description
Latency Trend	Plots call latency across the selected period, so you can spot when calls slowed down.
Cost by Model	Shows each model’s share of total cost, with a legend that lists the models in the period.

Model performance

The Model Performance table lists one row per model, sorted by Cost in descending order, so the models that cost the most appear first.

Column	Description
Model	Model that served the calls, for example `gpt-realtime-1.5`.
Provider	Provider of the model, for example `openai`.
Calls	Number of calls the model served.
Tokens	Total tokens the model’s calls consumed.
Cost	Estimated cost of the model’s calls.

LLM Performance groups calls by model, not by credential. To see which credential or auth profile supplied provider access for a call, use the Credential Name column on the Generations tab.

Sessions explorer

Sessions Explorer lists every session in the selected period so you can isolate the ones worth investigating. Four metric cards summarize the set.

Metric	Description
Total Sessions	Sessions recorded in the selected period.
Active	Sessions currently in progress.
Avg Duration	Mean session duration across the set.
Error Rate	Percentage of sessions that recorded at least one error.

Narrow the list

Status chips filter the list to All, Active, Completed, Escalated, Failed, or Ended. Below the chips, five controls refine the results further.

Control	Description
Search sessions	Matches sessions by keyword.
Channel	Filters by the channel the session arrived on. Defaults to All channels.
Environment	Filters by deployment environment: `development`, `staging`, `production`, or `working-copy`. Defaults to All environments.
Source	Filters by session source. Defaults to All sources.
Filters	Opens additional filter options.

Select Columns to choose which columns the table displays. Select Export to download the session list.

Sessions started from a project’s working copy, the pre-deployment draft, are recorded under the working-copy environment. Filter it out to keep pre-deployment testing from skewing your production metrics, or select it to review draft behavior on its own.

Session list

Column	Description
Session ID	Unique session identifier, with a copy control.
Agent	Agent that handled the session, for example `MainSupervisor`.
Status	Current session state, for example `ended`.
Environment	Environment the session ran in.
Last Activity	Time since the last recorded activity. Sorts the table by default.
Traces	Number of trace events the session generated.
Duration	Total session duration.
Tokens	Total tokens the session consumed.
Cost	Estimated cost for the session. Values below the display threshold show as `<$0.001`.
Errors	Error count for the session. Non-zero values are highlighted.

Expand a row to see the full session ID, channel, trace event count, and last activity. Select View Traces to open that session in Traces Explorer.

Traces explorer

Traces Explorer inspects the individual events inside a session.

Filter traces

Type chips filter events to All, LLM Call, Tool Call, Decision, Handoff, Error, or Agent. Each applied filter appears as a chip below the search field, for example Type: LLM Call. Remove a single filter from its chip, or select Clear all to remove them all. Reset filter shows the number of active filters and restores the unfiltered list. Use Search traces to match events by keyword, Filters for additional filter options, and Export to download the trace list.

Sessions panel

The left panel lists the sessions in scope. Each card shows the agent name, status, span count, turn count, and duration. Select a session to load its events in the detail panel.

Timeline and waterfall

The detail panel shows the selected session’s events in one of two views. A counter reports how many events the current filter matches, for example 4 of 84 events.

View	Description
Timeline	Lists events in sequence as expandable cards. Use it to read an event’s full payload.
Waterfall	Plots spans on a shared time axis, with each span’s offset from the session start and its duration bar. Use it to find latency gaps and see where errors occurred.

Waterfall opens with a summary bar reporting the trace mode (for example, Historical), Spans, Duration, Tokens, Cost, and Errors. Each span row shows a status icon for success or failure, the agent and model, the offset from session start, and the duration bar. Chips on the row repeat the span duration, token count, and cost.

Event detail

Every event card carries the event type, agent name, and duration, plus phase, run, and cause badges. Expand a card for the full record.

Field	Description
Trace ID	Identifier for the trace the event belongs to.
Session ID	Session the event belongs to.
Span ID	Identifier for this span.
Event ID	Identifier for this event.

Each identifier has a copy control. An LLM Call event adds model execution detail: Model, Provider, Input Tokens, Output Tokens, Total Tokens, Cost, and Streaming. Expand Messages to read the prompt and user turns, and Response to read the model output. A Tool Call event shows the Tool name, an expandable Input, and Show raw data for the unformatted payload.

Generations

The Generations tab lists every model call the platform recorded, drawn from a unified LLM call ledger. The ledger captures calls from across the platform in one place, including agent turns, pipeline runs, and guardrail evaluations, so you can account for model spend and latency across the whole project instead of one surface at a time. Each call also records the credential that supplied provider access, so you can attribute token consumption and cost to a specific credential or auth profile. Use Search generations to match calls by keyword, Filters for additional filter options, Columns to choose which columns the table displays, and Export to download the list.

Generation list

Column	Description
Model	Model that served the call, for example `gpt-5.2`.
Name	Caller that issued the request: an agent name such as `MainSupervisor`, or an internal operation such as `pipeline_generate_text` or `guardrail_evaluation`.
Status	Outcome of the call, for example `Success`.
Channel	Channel the call originated on, for example `web_debug`. Shows `--` for calls not tied to a channel, such as pipeline runs.
Source	Part of the platform that issued the call. See Sources.
Latency	Time the call took end to end.
Credential Name	Name of the credential or auth profile that supplied provider access for the call. See Credential attribution.
Provider	Provider of the model that served the call, for example `openai`.
Credential Type	Kind of source that supplied provider access, for example `credential`. See Credential attribution.
Total Tokens	Input and output tokens combined.
Cost	Estimated cost for the call. Values below the display threshold show as `<$0.001`.
Start Time	Date and time the call started.
TTFT	Time to first token. Shows `NA` when the call didn’t report one.

Sources

The Source column shows a readable label rather than an internal identifier, so you can tell at a glance which part of the platform issued a call.

Source	What it covers
Runtime	Model calls agents make while handling conversations.
Runtime Internal	Supporting model calls the runtime makes outside the agent’s own turn.
Pipelines	Model calls made by analytics pipelines, such as LLM-as-judge evaluations.
Guardrails	Model calls made while evaluating content with guardrail policies.

Filter by Source to separate what your agents spend from what evaluation and safety checks spend. Pipeline and guardrail calls run on every qualifying conversation, so they can account for a large share of total calls without appearing in any single agent’s numbers.

Credential attribution

Credential Name and Credential Type identify the source that supplied provider access for each call. Use them to see which credential or auth profile consumed tokens and cost when a project and its workspace use several model configurations side by side.

Credential type	What it identifies	Credential name shown
`credential`	A named credential that a model connection uses.	The credential name. Credential names can’t change, so the name always matches the name at the time of the call.
`auth_profile`	An auth profile that a model connection uses.	The auth profile’s current name. When you rename an auth profile, every call it supplied, past and future, shows the new name.
`platform_credit`	Model access that the platform manages.	`platform-credit`
`direct_key`	A legacy model connection that stores a provider key directly, with no credential or auth profile.	`Direct-key`

Credentials and auth profiles that share a name stay separate. A project-level credential, a workspace-level credential, and a credential in another project never combine, even when their names match. The following rules decide which credential a call reports and how long it keeps it.

Situation	Behavior
Calls recorded before attribution	Don’t carry a credential. The platform doesn’t backfill earlier calls.
Replacing a provider key	Create a new credential for the new key. The new credential becomes a separate source, and its usage doesn’t merge with the old credential’s.
Failed calls	Carry a credential on the same terms as successful calls.
Pipeline and guardrail calls	Carry a credential when they write to the LLM call ledger.
Deleting a credential or auth profile	Calls keep their credential name for as long as the platform retains the call.
Secrets	Attribution stores only the credential identity and name. It never stores provider keys or secret values.

Query

The Query tab runs SQL on project event data for investigations the pre-built views don’t cover.

Select a table, for example abl_platform.platform_events.
Write your SQL, or select Example Queries to start from a template. Templates include a starter query for the feedback table, which returns ratings, comments, and the custom dimensions captured on each session.
Select Execute. Select Clear to empty the editor.

Queries use the same scoping tokens as dashboard widgets. Use {tenantId:String} and {projectId:String} for tenant and project isolation, and {from:DateTime64(3)} and {to:DateTime64(3)} to follow the selected time range. {sessionId:String} is optional. Results are capped at 1,000 rows. The following query counts events by type and flags errors:

SELECT
  event_type,
  count() AS cnt,
  countIf(has_error = 1) AS errors
FROM abl_platform.platform_events
WHERE tenant_id = {tenantId:String}
  AND project_id = {projectId:String}
  AND timestamp >= {from:DateTime64(3)}
  AND timestamp <= {to:DateTime64(3)}
GROUP BY event_type
ORDER BY cnt DESC
LIMIT 100

The results panel reports the row count and runtime, for example Results — 20 rows (11ms). Select Copy to copy the result set, or Add to dashboard to save the query as a widget on a custom dashboard.

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Analytics and insights

References

Overview

LLM performance

Charts

Model performance

Sessions explorer

Narrow the list

Session list

Traces explorer

Filter traces

Sessions panel

Timeline and waterfall

Event detail

Generations

Generation list

Sources

Credential attribution

Query

​Overview

​LLM performance

​Charts

​Model performance

​Sessions explorer

​Narrow the list

​Session list

​Traces explorer

​Filter traces

​Sessions panel

​Timeline and waterfall

​Event detail

​Generations

​Generation list

​Sources

​Credential attribution

​Query

Overview

LLM performance

Charts

Model performance

Sessions explorer

Narrow the list

Session list

Traces explorer

Filter traces

Sessions panel

Timeline and waterfall

Event detail

Generations

Generation list

Sources

Credential attribution

Query