Roll out LGTM across many repos
Install LGTM org-wide once, then enroll specific repos progressively. Per-repo config inherits from org defaults. Multi-repo rollout in days, not months.
Org has 50 repos. Manual setup per repo is a multi-day project nobody wants to own. LGTM's install-then-enroll model scales without per-repo config overhead.
The install model
GitHub App installs at the ORG level (or personal-account level). A single install grants LGTM access to the repositories the installer selects — could be 1, could be 'all repos in org'.
Once installed, LGTM picks up webhook deliveries for all enrolled repos. The first PR opened on each enrolled repo triggers index + first review. No per-repo manual setup required.
Adding repos later: github.com/organizations/{org}/settings/installations → LGTM → Configure → add repos. New repos start getting reviews on the next PR opened against them.
Org-level defaults, repo-level overrides
Default review behavior is configured once at the org level: which agents enabled, which models, what verdict threshold, what severity blocks merge. New repos inherit these defaults.
Override per repo when needed: Settings → Repos → {repo} → Config. Override specific values; unset values inherit from org. Visual diff in the UI shows what's inherited vs overridden, so config drift is easy to detect.
Common pattern: org defaults are 'flagship model + strict severity + all agents'. Marketing/internal repos override to cheap model + relaxed severity. Production-critical repos override to flagship model + auto-approve disabled (human always reviews).
Progressive rollout strategy
Don't enroll all 50 repos on day one. Phased rollout reduces noise + lets the team adjust thresholds before scale.
Week 1: enroll 1-3 'pilot' repos. Active monitoring of review quality, false-positive rate, team feedback. Adjust verdict threshold + agent selection based on what fires.
Week 2-3: expand to 10 repos covering different stacks (front-end / back-end / infra / etc.). Confirm cross-stack quality. Per-repo config tweaks for any with unusual patterns.
Week 4+: open enrollment. New repos opt in via a CODEOWNERS file or simple PR adding the repo to LGTM's selected list. Engineering team's responsibility, not central platform.
Billing on multi-repo
LGTM's pricing is per-account, not per-repo. ₹399/mo Pro covers unlimited reviews across all repos in your install. No per-repo tax, no per-PR tax (just BYOK token cost which is a separate provider bill).
Cost scales with REVIEW VOLUME, not repo count. 100 repos with low PR activity might cost less than 10 repos with constant PR churn — because the BYOK token bill scales with token usage.
Org-level usage dashboard: Settings → Usage. Shows reviews per repo, tokens per agent, average review latency, per-provider spend. Useful for spotting expensive repos that could move to cheaper models.
See LGTM pricing — unlimited repos, no per-seat
₹399/mo Pro · BYOK · org-wide install · per-repo overrides
Go to the product pageFAQs
Can I roll back a repo enrollment if reviews are noisy?
Yes — github.com/{org}/settings/installations → LGTM → Configure → remove the repo. LGTM stops reviewing immediately. Existing reviews + data are retained until you uninstall completely.
What about repos with their own custom review bot?
Multiple review bots can coexist on a repo. LGTM doesn't interfere with other Apps. If you have an existing AI review bot (CodeRabbit, Greptile, etc.), LGTM runs in parallel. Compare findings; deprecate the redundant one if LGTM's quality is higher.
How do I roll out without spamming reviewers?
On enrollment, LGTM reviews only NEW PRs by default. Historical PRs don't get retroactively reviewed unless you explicitly trigger via `lgtm review --pr <n>` from the CLI. Spam-free rollout.
Can I have different teams use different LGTM installs?
Yes — install LGTM separately on each org you own. Each install has its own settings, its own billing, its own BYOK keys. Useful for separate billing entities (parent + subsidiary, or multiple clients).
What about archived repos?
Archived repos don't get PR webhooks, so LGTM doesn't see them. No reviews fire. If you un-archive later, the next PR triggers index + review as normal.
Related across LGTM
Related use cases
Use LGTM as a CI gate
Configure LGTM's review verdict as a required Check Run in branch protection. A failed review blocks the merge until findings are resolved — automated quality gate, no human bottleneck.
Onboard a monorepo to AI code review
Connect a 1k–100k-file monorepo to LGTM in under 10 minutes. Tree-sitter indexes all 12 languages, PageRank ranks cross-package context, BYOK keeps per-PR cost manageable.
Generate compliance audit evidence
Every LGTM review is a timestamped, immutable audit log entry. Export per-repo or per-quarter as JSON or CSV for SOC2, ISO 27001, or DPDP evidence.