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

# Trajectory View

> Visualize agent behavior patterns as a bubble chart across execution steps

The Trajectory view plots each span as a bubble on a category-by-step grid, providing a visual fingerprint of how an agent session unfolded.

<Frame caption="Trajectory view showing agent behavior as a bubble chart grouped by event type">
  <img src="https://mintcdn.com/honeyhiveai/sFOpWw98R-jnkhpC/images/trajectory-view.png?fit=max&auto=format&n=sFOpWw98R-jnkhpC&q=85&s=562a952c38c289a2ac3d789e74d6f168" alt="Trajectory bubble chart with Model, Tool, and Chain groups plotted across agent steps" width="3024" height="1556" data-path="images/trajectory-view.png" />
</Frame>

## Reading the chart

* **Y-axis** groups spans by event type and name (e.g., Model: `customer_support`, Tool: `search_knowledge_base`)
* **X-axis** shows chronological step order (Agent Steps 1, 2, 3...)
* **Bubble size** represents the selected metric value (duration by default). Larger bubbles indicate higher values, making outliers easy to spot
* **Bubble color** uses a green-to-red gradient based on the metric. Green indicates good performance, red indicates poor performance, relative to whether higher or lower values are better for that metric

## Switching metrics

Each event-type group (Model, Tool, Chain) has a dropdown to switch the displayed metric:

* **Duration** - how long each span took
* **Cost** - estimated cost of the operation
* **Evaluator scores** - any evaluator metrics attached to the spans

The "lower is better" / "higher is better" indicator next to each dropdown shows the direction of the color scale.

## Interacting with bubbles

Click any bubble to jump to that span in the [Tree view](/v2/tracing/tree-view), where you can inspect its full inputs, outputs, and metadata.

## When to use

Use the Trajectory view to:

* Spot behavioral patterns across agent steps (e.g., repeated tool calls, escalating latency)
* Compare how different agent runs unfold structurally
* Identify anomalous steps where a metric spikes unexpectedly
* Understand the rhythm of model-tool-chain interactions in agentic workflows
