Run & observe
Analytics
Two views built on the same telemetry: workspace-level cost analytics at /analytics, and per-run swarm observability that replays a swarm execution node by node.
Cost analytics
The analytics page leads with four numbers — month-to-date spend, total tokens, average latency, and active agents — followed by:
- Spend over time — your daily spend charted over the selected window. Spikes are worth chasing the same day; they are usually one experiment, one loop, or one oversized context.
- Cost by provider — where the money goes across AgentSwarms AI, OpenAI, Anthropic, and any bring-your-own-key providers you've connected.
- Cost by agent — almost always the chart with the surprise in it: one agent on one expensive model tends to dominate.
Spend caps and alerts live at /budgets; per-run detail lives in traces.
Swarm observability
Swarm runs get their own deep-inspection view at Analytics → Swarm Observability. Opening a run shows three tabs:
- Canvas
- The swarm graph as it was at run time, so you can see the shape of what executed — including for runs of swarms you've since edited.
- Timeline
- The execution order, node by node, with each step's kind and model. Clicking a step opens its detail: input, output, thinking, and tool calls.
- Data flow
- Every message that crossed an edge — which node produced it, which node consumed it. This is where context-window problems become visible: you can see exactly how much text was handed to each node.
The workflow that pays off: after any interesting swarm run, open its timeline and find the slowest and the most expensive step. Those two nodes are nearly always the next thing worth optimizing — a cheaper model, a tighter prompt, or a parallel branch.