evo
Local-first structural drift detector for teams using AI coding tools.
Evolution Engine fills a real gap: a drift detector that correlates multiple signals rather than flagging surface-level code issues. It's best for teams that rely heavily on AI coding tools and want a local, evidence-based way to catch structural regressions. The free tier is generous, but Pro's $19/dev/month is reasonable for the cross-signal value.
- Engineering teams using AI coding tools that want to catch structural drift early
- Developers who prefer local-first analysis with no code uploads
- Teams needing cross-signal correlation (e.g., CI failures after dependency updates)
- Proactive codebase maintenance with evidence-based deviation metrics
- Real-time alerting or dashboard-heavy monitoring (no always-on service)
- Code quality linting or static analysis (different problem space)
- Large-scale enterprise deployment without dedicated support beyond email
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In short
evo — Local-first structural drift detector for teams using AI coding tools. Best for Engineering teams using AI coding tools that want to catch structural drift early, Developers who prefer local-first analysis with no code uploads, Teams needing cross-signal correlation (e.g., CI failures after dependency updates). Free to start; paid plans from $19/mo.
What's new in evo
Checked 12 days agoAcross the latest 1 update: 1 launch.
Viability Score
How likely is evo to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Local-first analysis (no code uploads)
- Zero-config auto-detection from configs/lockfiles
- Cross-signal correlation (git + CI + deps + deploys)
- Modified z-score deviation metrics
- Generate AI investigation prompts
- Interactive HTML reports
- Verification reporting after fixes
- Git analysis (commits, file changes, co-change patterns)
- Dependency analysis (pip, npm, go, cargo, bundler)
- CI pipeline monitoring (GitHub Actions, GitLab CI, CircleCI)
- Deployment monitoring (GitHub Releases, GitLab Releases)
- Error tracking (Sentry)
- Testing analysis (JUnit XML)
- Coverage analysis (Cobertura XML)
- Security alerts (Dependabot)
About evo
Evolution Engine by CodeQual is a CLI tool that detects structural drift in your codebase by cross-correlating signals from git, CI, dependencies, and deployments. Designed for engineering teams using AI coding assistants, it runs 100% locally with no code uploads, providing evidence-based deviation metrics (modified z-scores) calibrated across 48 open-source repos. Key features include zero-config auto-detection from configs and lockfiles, an open adapter ecosystem (11 built-in adapters in the free tier), and the ability to generate AI investigation prompts for course-correction. Unlike linters or scanners, Evolution Engine focuses on structural patterns that emerge across multiple signals—like CI failures after big dependency updates—making it a proactive maintenance tool for teams that want to catch regressions before they break builds. Its free tier covers git and dependency analysis forever, while the Pro tier ($19/dev/month) adds CI, deployment, security, testing, and error tracking signals.
Behind the Verdict
Evolution Engine is not another linter. It's a structural drift detector that ties together git, CI, dependency, and deployment signals to surface patterns no single tool catches. For teams using AI coding assistants that sometimes introduce subtle regressions, this is a practical safety net. The zero-config auto-detection works well—just run `evo analyze .`—and the local-first approach means no data leaves your machine. The free tier (git + dependency analysis) is genuinely useful for solo developers. Where it falls short: the Pro tier's $19/dev/month is a modest cost for CI and security signals, but the ecosystem of adapters is still young, and some planned integrations (Datadog, PagerDuty) aren't available yet. Compared to a tool like SonarQube, Evolution Engine targets a different problem—structural drift over time rather than code quality per commit. It complements rather than replaces existing tools. Best for teams that want proactive signals without managing a dashboard.
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Use Cases
- Detect structural drift in a Python monorepo after a large refactor to identify increased change locality.
- Correlate CI failures with recent dependency updates across multiple package managers.
- Generate an AI investigation prompt for an LLM to explain why codebase cohesion metrics degraded.
- Verify that a planned architecture fix resolved drift by re-running analysis in verification mode.
- Automate drift checks in pre-commit hooks to catch early signs of erosion.
- Track co-change novelty to identify when unrelated files are being modified together.
Limitations
- As of July 2026, many adapters (monitoring, incidents, work items) remain in 'Planned' status and are not available.
- The Pro tier pricing is not publicly disclosed, and some features like multi-repo or cloud dashboards are absent.
- The tool also requires Python 3.10+ and may not integrate with non-standard CI/CD pipelines.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
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