UnvibeCode reverse-engineers a software repository into linked views of business workflows, code relationships, risk findings, and exportable context for further AI analysis. It addresses a common failure mode in heavily LLM-generated codebases: teams can ship code faster than they can document its behavior, dependencies, and business effects.

The Python project has more than 1,500 GitHub stars, publishes its repository under Apache 2.0, and distributes its CLI through a PyPI package. After installation, a single review command accepts a local repository path and starts the analysis.

Four views, one repository

UnvibeCode traces execution paths across files, symbols, imports, decisions, and state changes. It organizes the results into four connected artifacts:

• Business Workflow Map: Reconstructs workflows from source code and links each step to its decisions, dependencies, state changes, and implementation.
• Connected Code Map: Displays an interactive graph of files, imports, and symbols. Reviewers can select nodes and export focused source context.
• Business Risk Findings: Reports implementation defects with source evidence, affected workflows, likely impact, and remediation guidance.
• Complete Repository Context: Packages normalized repository content into a ZIP file for analysis by another LLM.

Findings with a paper trail

Each risk finding connects a claimed defect to source evidence, affected workflows, expected impact, and remediation guidance. The analyzer can also record that available evidence is insufficient, and the documentation warns that some repositories produce no confirmed findings. Reviewers can inspect the cited code before accepting or rejecting each result.