Discover. Fix. Prevent.
The complete CI reliability platform.
Drape detects flaky tests, suppresses false negatives, enforces coverage, catches CVEs, and surfaces code quality issues — giving your team and AI agents the context to decide what matters and ship with confidence.
The full CI reliability lifecycle — one platform.
Discover
Stop chasing phantom failures
A test that passes 9 times out of 10 wastes more engineering time than one that consistently fails. Drape tracks pass/fail patterns and flags flaky tests before they erode trust in CI.
- Flakiness scoring based on historical pass/fail rates
- Branch-aware detection — distinguish environment flakes from code bugs
- Group flaky tests by detection commit for batch triage
- Track CI time wasted on retries caused by flakes
Prevent
Coverage that only goes forward
Percentage targets are a blunt instrument. Teams game the number, and a 0.1% drop is noise. Drape enforces coverage at the line level: if a PR drops coverage on changed files, it gets flagged before it merges.
- Line-level tracking, not just percentages
- Commit-to-commit comparison — see exactly what changed and when
- Detect regressions on changed files, not the whole codebase
- Supports Cobertura, LCOV, and Go coverage — no proprietary format required
- Cross-repo visibility for monorepos and multi-service architectures
Fix
Suppress flaky tests without losing track
Suppression isn't an excuse to ignore problems. Drape enforces suppression hygiene: expiration dates, linked fix tickets, and automatic staleness alerts ensure flaky tests get fixed, not forgotten.
- Branch-pattern matching — suppress on
mainonly,feature/*, or everywhere - Expiration dates — suppressions auto-deactivate if not renewed
- Ticket URL linking — every suppression points to a fix ticket
- Staleness tracking — surface tests suppressed for 30+ days with no progress
Discover
Diagnose failures across your entire test suite in seconds
Google built TestGrid. Meta built Probabilistic Flakiness Scores. Spotify built Odeneye. Dropbox built Athena. Every engineering org at scale ends up building an internal test health dashboard. Drape gives you one out of the box.
- Grid view — tests as rows, commits as columns, status at every intersection
- Spot patterns instantly: scattered red means flaky, solid columns mean infrastructure
- Duration heatmaps — see which tests are getting slower over time
- Filter by branch, status, or test name — drill into what matters
- Retry detection — multiple runs on the same commit grouped automatically
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Fix
Context that AI agents can act on
AI coding agents are powerful, but they can't fix test failures they don't understand. Drape feeds agents structured failure context, flakiness history, and root-cause data so they can help diagnose issues and recommend fixes.
- Structured API — failure context, flakiness scores, and affected lines in machine-readable format
- MCP integration — connect Drape directly to Claude Code, Cursor, and other agentic tools
- Historical context — agents see whether a failure is new, intermittent, or environment-specific
- Coverage gaps — agents know exactly which lines need tests before a PR can merge
- Works with any agent — open API, no vendor lock-in to a specific AI tool
Claude Code + Drape
Prevent
Catch container vulnerabilities before they ship
Drape ingests SARIF scan results from grype, trivy, and other container scanners. Track CVEs per image, diff against baselines on every PR, and enforce SLA-based fix timelines by severity — so critical vulnerabilities never slip into production.
- SARIF ingestion from any container scanner — no vendor lock-in
- PR diff against baseline: new, fixed, and unchanged CVEs at a glance
- SLA tracking by severity — critical, high, medium, low
- Org-level image dashboard across all repositories
- Suppression with disposition types: suppressed, not affected, risk accepted
Prevent
Surface code quality and security issues across every PR
Ingest lint and SAST results via SARIF from ESLint, Pylint, Semgrep, Bandit, golangci-lint, and more. Track violations per file, diff against baselines on PRs, and suppress rules that don't apply — all alongside your test and coverage data.
- SARIF ingestion from any linter or SAST tool
- Per-file violation tracking with tool attribution
- PR diff: new violations, fixed violations, per-tool filtering
- Rule-level suppressions with justification and audit trail
- Unified view: tests, coverage, security, and code quality in one platform
The infrastructure every scaling team builds
Google, Meta, Spotify, and Dropbox each invested years building internal CI reliability infrastructure. Your team should be shipping features, not rebuilding their tooling.
Google — TestGrid
Open-source dashboard for visualizing test results across thousands of builds. Maintained by a dedicated team.
Meta — Probabilistic Flakiness
Bayesian scoring system monitoring millions of tests. Built to create accountability and drive reliability improvements.
Spotify — Odeneye
Grid visualization that reduced flaky test rates from 6% to 4% — just by making the data visible to teams.
Dropbox — Athena
Automated build health management system. Detects, suppresses, and tracks flaky tests across all repositories.
Drape gives you what these teams spent years building — deployed in 5 minutes.
Your CI data. One platform.
No proprietary SDK. No uploader binary. No framework lock-in. Drape ingests JUnit XML for test results, SARIF for security scans and lint results, and Cobertura, LCOV, or Go coverage for code coverage — the formats your tools already produce. Unified visibility across repos, monorepos, and multi-language stacks.
Discover
AI that understands your test failures
Is it a real bug, a flaky test, or a CI environment issue like a Docker image pull timeout? Stop digging through CI logs. Drape's AI classifies failures and explains them in plain language — across all frameworks, not just Playwright.
- Failure classification: Bug / Flaky / CI environment issue
- Root cause patterns from historical data across branches
- Plain-language failure explanations with relevant stack traces
- Works across any framework (unlike Playwright-only competitors)
Prevent
Insights where you already work
Drape comments on PRs with information your CI can't provide: new flaky tests introduced by this change, coverage regressions on specific lines, and tests running significantly slower than their baseline.
- New flakes detected on this PR
- Line-level coverage regressions (not percentage drops)
- Duration regressions vs. 30-day baseline
- Suppressed test failures flagged but not blocking
Drape Analysis for PR #142
⚠️ 3 previously-covered lines lost coverage in src/auth/middleware.py
🆕 New flake: test_user_login_timeout (failed 2/5 runs)
🐌 test_export_large_dataset is 3.2x slower than baseline