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Every vendor row on the Agents page’s Coding Agents tab opens to an Overview tab: a seeded, read-only dashboard scoped to that vendor and honoring the global time-range picker. The same dashboard is reused, scoped to a single user, when you click into a row on the Users tab.

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Widgets

  • Stat cards: Total sessions, Active users, Total cost, Total tool calls, Lines of code accepted, Acceptance %, Commits, Pull requests.
  • Trends: Sessions over time, Lines over time (accepted vs. rejected).
  • Breakdowns: Sessions by vendor, Top tools, Top models, Top repositories, Top users, Top users by accepted lines, Acceptance breakdown, Session outcomes (merged, committed, abandoned, cancelled), Edit decisions (auto-applied vs. user-reviewed), MCP servers in use.
Clicking Top users or Top repositories drills into that user’s or repository’s own scoped view of the same dashboard.

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How the numbers are computed

  • Acceptance % is computed from edit-decision counts - accept and auto_accepted decisions against reject decisions - not from a raw lines-changed ratio.
  • Commits and Pull requests are detected heuristically from shell/Bash tool calls (git commit, gh pr ...), since this isn’t a native VCS integration. Treat them as a strong signal rather than an exact count.
  • Every widget reads the same {{filter.timeLimit.start}} / {{filter.timeLimit.end}} bindings as the rest of OpenLIT’s dashboards, so changing the time range picker updates the whole board consistently.

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Query the same data yourself

The Overview dashboard reads from the same otel_traces table as everything else in OpenLIT, so you can build your own custom dashboard widgets against it:
The metrics pipeline emits the same rollups as counters, if you’d rather point a Prometheus/Mimir/Datadog backend at the OpenLIT collector directly: coding_agent.session.count, coding_agent.session.duration, coding_agent.tool.call.count, coding_agent.lines_of_code.count, coding_agent.edit.decision.count, coding_agent.commit.count, coding_agent.pull_request.count.

Sessions

Drill from an aggregate metric down to the individual sessions behind it

Users

The same dashboard, scoped to one developer