Engineering teams waste 6-8 hours per week on flaky tests. FlakyGuard automatically detects, quarantines, and diagnoses test flakiness so your builds stay green and your team stays productive.
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These are real problems every engineering team faces. FlakyGuard was built to solve them.
From detection to diagnosis to resolution, FlakyGuard handles the full lifecycle of test flakiness management.
Analyzes your CI test results in real-time. Identifies tests that flip between pass and fail without code changes using statistical flip-rate analysis.
Automatically quarantines flaky tests so they stop blocking your team. Tests run in a separate lane and are tracked until stable.
Pattern-matching engine classifies failures into timing issues, race conditions, resource contention, environment problems, and more.
Each flaky test gets a concrete, copy-pasteable fix suggestion tailored to the diagnosed root cause. No more guessing.
Track flakiness trends across your organization. See which repos, test suites, and patterns cause the most CI waste.
Get alerts in Slack when new flaky tests are detected. Weekly digest summaries. GitHub Check annotations on PRs.
Set up in under 2 minutes. No config files. No test code changes.
One-click install on your GitHub org. FlakyGuard subscribes to workflow run events automatically.
Every CI run is ingested. FlakyGuard tracks pass/fail history per test and computes a flakiness score using flip-rate analysis.
Tests crossing the flakiness threshold are auto-quarantined. Your main builds stay green while the flaky tests are investigated.
The classification engine analyzes error messages, stack traces, and patterns to identify timing issues, race conditions, environment problems, and more.
Watch FlakyGuard scan a CI pipeline, detect flaky tests, diagnose root causes, and quarantine them -- all automatically.
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For open source projects and small teams
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For large engineering organizations
Practical guides to help your team understand, detect, and fix flaky tests.
Proven techniques from retry-based detection to statistical flip-rate analysis.
Read more →StrategyThe quarantine pattern separates flaky failures from real regressions.
Read more →Deep DiveA systematic guide to diagnosing the 6 root cause categories of test flakiness.
Read more →Deep DiveHow AI agents automatically detect, quarantine, and recommend fixes for flaky tests in your CI pipeline.
Read more →GuideStep-by-step guide to identifying and fixing timing issues, resource limits, and environment drift in CI.
Read more →ComparisonManual scripts vs CI retry plugins vs dedicated platforms. Feature comparison and cost analysis.
Read more →DataData from 1,000+ teams: developer time, CI costs, delayed releases, and eroded trust. Includes ROI calculator.
Read more →GuideStop retrying and start fixing. 7 battle-tested patterns with code examples for Jest and Pytest.
Read more →Everything you need to know about flaky tests and how FlakyGuard handles them.
A flaky test is a test that passes and fails intermittently without any code changes. Common causes include race conditions, timing dependencies, shared mutable state, and environment differences between local and CI.
FlakyGuard monitors every CI run and tracks the pass/fail history of each test. It uses statistical flip-rate analysis to identify tests that switch between pass and fail at abnormal rates, flagging them as flaky before they erode your team's trust in CI.
No. FlakyGuard works by analyzing CI run results through the GitHub App integration. There are no code changes, no config files, and no test framework plugins required. Install the GitHub App and you're done.
FlakyGuard currently supports GitHub Actions with plans to expand to GitLab CI, CircleCI, and Jenkins. It ingests JUnit XML and JSON test reports from any test framework.
When a test crosses the flakiness threshold, FlakyGuard automatically quarantines it. Quarantined tests still run but their failures no longer block your main pipeline. The test is monitored and automatically un-quarantined once it stabilizes.
The AI engine analyzes error messages, stack traces, and failure patterns to classify flaky tests into root cause categories: timing/race conditions, resource contention, environment differences, test ordering dependencies, and external service flakiness. Each diagnosis comes with a concrete fix suggestion.
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