> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runagain.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Run baselines

> Automatically flag when an agent run's runtime, cost, or span count is abnormal for that agent.

Run baselines are **online evals** that score every agent run against the recent
history of the *same agent*. Instead of comparing two runs by hand, RunAgain learns
what "normal" looks like for each agent and flags the runs that stand out.

Three metrics are scored per run:

* **Runtime** — wall-clock duration of the trace.
* **Cost** — total cost of the run.
* **Span count** — number of spans (a proxy for tool-loop / retry behavior).

## How it works

* Runs are grouped by `agent_name`. Each new run is compared against that agent's
  most recent runs (the **baseline window**).
* A run is only scored once the agent has enough history — the **learning phase**.
  Until then the trace shows `baseline learning (n/5)` instead of a verdict.
* Each metric gets an **ok / warning / drift** verdict, shown as a badge on the
  trace detail (`vs baseline: Runtime … Cost … Spans …`).
* Results are persisted as trace-level eval scores (`baseline.duration_ms`,
  `baseline.total_cost`, `baseline.span_count`), so they show up in the Scores table
  and can be queried like any other score.

## Baseline methods

All five express the tolerance `k` in the same units (standard-deviation
equivalents), so one threshold behaves consistently:

* **Robust z-score (median / MAD)** — *default.* Median ± k·MAD. Outlier-resistant,
  best for skewed, spiky latency/cost.
* **Z-score** — mean ± k·σ. Classic, but sensitive to the outliers you're hunting.
* **IQR / Tukey fences** — nonparametric, based on the interquartile range.
* **Percentile rank** — flags runs beyond the normal-equivalent tail of the baseline.
* **EWMA control chart** — trend-aware; the center tracks a moving average.

## Configure

<Steps>
  <Step title="Open project Settings">
    Go to **Settings → Run baselines**.
  </Step>

  <Step title="Enable and tune">
    Turn baselines on, pick a method, and set the learning-phase minimum, the baseline
    window (how many prior runs to compare against), and the threshold `k`.
  </Step>
</Steps>

<Tip>
  "Same agent" is matched by `agent_name`. Set it on your spans so runs group correctly
  (the Claude Code integration sets `claude-code` automatically).
</Tip>

<Warning>
  A perfectly constant metric produces no verdict (zero spread → no false drift), and
  runs below the learning-phase minimum are never scored.
</Warning>

## Run health

The same eval pass also checks each run's **health** and records a `run.health` score
when a run is incomplete — it made model calls but produced no final response. The
status names the likely cause: `error`, `truncated` (hit the token limit),
`dangling_tool_call` (ended asking for a tool), or `no_output`. Healthy runs get no
score, so the presence of a `run.health` score means the run had a problem. This surfaces
on the traces list (a **no output** badge) and the trace detail (a banner).