Chatwoot Engineering Benchmarks
Chatwoot delivery benchmarks are based on 128 merged pull requests in the last 30 days (617 all-time), with 16.5 hours median cycle time, 0.2 hours median review time, and 13 active contributors. AI-related signals appear on 2.3% of recent work. PR volume is up 39% compared to the prior period.
Repo vs Chatwoot Average
Delivery Trend and AI Signals
PR Backlog (Humans)
Excludes AI agents and automation accounts.
PR Backlog (AI Agents)
AI coding agents only.
Cycle Time by AI-Related Signals
PR Breakdown
Top AI Tools
PR Type Breakdown
Recent Pull Requests
View PR Explorer| Title | Author | AI |
|---|---|---|
| fix(whatsapp): send messages to business scoped user ids (B… | marcoazcabral | — |
| fix(whatsapp): surface Meta's real error when an outbound c… | tds-1 | — |
| fix: harden conversation participant api | sony-mathew | — |
| fix: prevent page overflow from hidden tooltips | iamsivin | — |
| feat: Add account suspension metadata in Super Admin | sony-mathew | — |
Methodology
Metrics are computed from merged pull request data synced daily from GitHub. AI-related metrics are directional signals inferred from pull request activity patterns and heuristics. Summary metrics use a 30-day rolling window; trends use a 90-day window. Last updated 29 minutes ago.
Updated daily from public GitHub pull requests.
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