UiPath — Engineering Performance
28 engineers now deliver what 36 would have in Q1 2025.
Performance snapshot
Today's rolling 90-day reading for UiPath, compared with the start of the series. Pick a window to move that comparison point.
Avg. perf / dev / mo
−58.8%
2.90 → 1.19 ETV
Active engineers
+2700.0%
1.0 → 28.0
Features
−34.9pp
64.5% → 29.6%
vs. 500 OSS index
0.31x
3.2x → 0.31x · −69% below
Engineering capacity
Effective engineers behind UiPath, against its pre-AI baseline. Each subject has its own: UiPath's is 0.92 ETV / dev / mo, its first reading in Q1 2025. Per-engineer ETV divided by that gives a capacity multiple, and that multiple applied to the engineers active in the trailing 90 days turns it into engineer-equivalents. The line is the real headcount, so the gap between line and area is what the leverage is worth. Because each baseline is its own, every subject opens at 1.0x on its first day: multiples measure improvement and are not comparable between subjects.
UiPath vs. 500 OSS Performance Index
Per-engineer ETV for UiPath against the pooled 500 OSS Performance Index. Both lines are 90-day rolling averages scaled to a 30-day month, so they share one axis and can be read against each other at any point. Pick a window to zoom the chart to it. Latest reading: UiPath is 69% below the index (1.19 vs 3.85 ETV/dev/mo). At the start of the tracked period the gap was 216% above.
Behind the numbers
Written summary of the work completed each month.
During August 2026, the team recorded 214 commits across design system stabilization, accessibility enhancements, and test runner upgrades. Output remained steady and in line with the recent run rate, with a Features score of 13 compared to the 10-point 5-month average (+27%). Key efforts focused on refining apollo-wind form controls and updating testing infrastructure via litellm integration.
Highlights
- Standardized UI panel controls and accessibility states across apollo-wind and apollo-react via e48d6d9b (David), introducing inline validation and ARIA attributes.
- Integrated DAP variable picker and workflow layout patterns into Storybook templates in 3fae0cf0 (David) and 6f0f7bed (David).
- Upgraded coder-eval test framework to 0.11.5, routing checker LLMs through litellm with
azure/gpt-5.6-lunain 636db813 (Akshaya).
Observations
- Storybook documentation and DAP layout patterns underwent repeated iterative adjustments across commits bf72991b (David), 7d5b6c8f (David), adeb0302 (David), and 53dbe864 (David) to resolve build and component rendering issues.
- Field validation styling and ARIA error semantics required multiple sequential updates across apollo-wind in 1d369f4e (David), 52785f4e (David), aaf2daf3 (David), d29216a2 (David), and c0cd386a (David).
- Maestro SDK skills preview files and CLI catalogs underwent frequent synchronization routines, including preview syncs in fad54955 (uipreliga), 770ed0d6 (uipreliga), and 47ebbe24 (uipreliga), alongside CLI catalog refreshes in 798e0eb0 (uipreliga) and 44d72c5e (uipreliga).
Based on 214 commits19.2 ETVUpdated Sep 30, 2026, 4:01 PM
Repositories
Where each repository stands: average performance per engineer per month over the last 90 days, with the rolling 90-day curve behind it. The range picks the window (past 90d): it sets how much of the curve you see, the Δ across it, and the span the work mix is measured over. Ranked highest first.
uipath-integrations-python
Avg performance
1.26 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
+1974.2%
Work mix
Features
33%
Maint
1%
Tests
61%
Docs
5%
Fixes
0%
apollo-ui
Avg performance
1.12 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-9.6%
Work mix
Features
42%
Maint
9%
Tests
29%
Docs
8%
Fixes
12%
uipath-langchain-python
Avg performance
0.58 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
+111.7%
Work mix
Features
22%
Maint
3%
Tests
59%
Docs
3%
Fixes
14%
skills
Avg performance
0.55 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-40.0%
Work mix
Features
15%
Maint
24%
Tests
27%
Docs
26%
Fixes
9%
uipath-python
Avg performance
0.25 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-25.3%
Work mix
Features
16%
Maint
2%
Tests
59%
Docs
2%
Fixes
21%
Performance Composition
Each month's output split by type of work: Features (new value), Maintenance (sustaining systems), Tests, Docs, and Fixes (rework). The yellow line is output per engineer, so when it rises each engineer is delivering more, whatever the team size did. Unit: Engineering Throughput Value (ETV).
Cost per Performance Unit
+143%
If performance per engineer dropped 59%, each unit of engineering performance now costs approximately 143% more than at the baseline 90-day window (ending 2025-04-01). Treat this as a direction, not a price: the exact figure depends on fully-loaded engineer cost, but which way it moved is not in doubt.
Effective Capacity Lost
−16 engineers
At today's productivity, the current 28-person team delivers the performance equivalent of 12 engineers at the baseline 90-day rolling window (ending 2025-04-01). Per-engineer productivity has dipped below the baseline, leaving the team roughly 16 engineers behind that pace.
CapEx vs OpEx
Each month's work split two ways. CapEx (capitalizable investment) is the work that builds the asset: Features, plus the Tests that prove it works and the Docs that explain it. OpEx (operating expense) is keeping it running: Maintenance and Fixes. The yellow line is the CapEx share, so a rising line means more of the month went into building new rather than sustaining what exists. Unit: Engineering Throughput Value (ETV).
Hours per Repository
Trailing 90-day window (64 working days). Org-level capacity is allocated to each repo by its share of org performance, then split CapEx / OpEx by that repo's own Features + Tests + Docs vs Maintenance + Fixes mix.
- CapEx %
- 99.2%
- OpEx %
- 0.8%
- CapEx %
- 83.1%
- OpEx %
- 16.9%
- CapEx %
- 79.2%
- OpEx %
- 20.8%
- CapEx %
- 76.6%
- OpEx %
- 23.4%
- CapEx %
- 67.1%
- OpEx %
- 32.9%
- CapEx %
- 80.3%
- OpEx %
- 19.7%
Feature Contribution
Who shipped each month's new feature work, as a share of that month's total. Bands of similar width mean new value is coming from across the team; one band that stays wide means most of it rests on the same person. Named engineers shipped the most Features over the period. Everyone else is grouped as Others.
Quarterly Summary
Engineers and the Features / Maintenance / Tests / Docs / Fixes mix for each quarter. Cost / Perf Unit is what one unit of performance costs against Q2'25. Engineers vs headcount is measured against this org's own pre-AI baseline instead, its first reading in the index, so it agrees with the capacity tile and chart above.
- Engineers
- 7
- Cost / Perf Unit
- 0%
- Engineers vs headcount
- +3 engineers
- Work mix
- 41% Features
- Engineers
- 15
- Cost / Perf Unit
- +33%
- Engineers vs headcount
- 0 engineers
- Work mix
- 57% Features
- Engineers
- 27
- Cost / Perf Unit
- −3%
- Engineers vs headcount
- +11 engineers
- Work mix
- 41% Features
- Engineers
- 35
- Cost / Perf Unit
- +18%
- Engineers vs headcount
- +6 engineers
- Work mix
- 33% Features
- Engineers
- 34
- Cost / Perf Unit
- +17%
- Engineers vs headcount
- +6 engineers
- Work mix
- 32% Features