Reverse engineer a complex codebase into business workflows, connected code, edge cases, and evidence-backed risks.
⭐ Hit Star to help increase UnvibeCode's visibility among developers.
Unvibe complex code. Trace what the business actually does.
Reading files one by one does not show the complete business workflow. Pasting a large repository into an LLM can also lose the connections between entry points, state changes, dependencies, edge cases, and business outcomes.
UnvibeCode reverse-engineers a complex codebase into business workflows, connected code, edge cases, and evidence-backed risks.
Requires Python 3.11 or newer.
py --version
py -m pip install --upgrade unvibecode
py -m unvibecode --help
py -m unvibecode review --repository "D:\path\to\repository"Example:
py -m unvibecode review --repository "D:\Projects\customer-support-agent"python3 --version
python3 -m pip install --upgrade unvibecode
python3 -m unvibecode --help
python3 -m unvibecode review --repository "/path/to/repository"Example:
python3 -m unvibecode review --repository "/Users/yourname/Projects/customer-support-agent"python3 --version
python3 -m pip install --upgrade unvibecode
python3 -m unvibecode --help
python3 -m unvibecode review --repository "/path/to/repository"Example:
python3 -m unvibecode review --repository "/home/yourname/projects/customer-support-agent"No activation key. No customer OpenAI API key. The repository path is the only required input.
smolagents · PocketFlow · mini-swe-agent · django-payments
“The business workflow getting lost across 500 files is the real issue. AI can keep generating code, but once nobody can trace why the product behaves the way it does, the speed advantage disappears fast.”
Kavitha Ranganathan — Senior PM, Better
“By tracing a customer recovery workflow from the screen to the code, I found that the system could mark a customer as having promised to pay based on the last digit of their ID, not an actual promise”
Fernanda Teixeira — QA Engineer, Vertigo
UnvibeCode helps developers understand what a codebase does, how its components connect, and what could break — without manually tracing every file.
Reconstruct business workflows from source code. Explore execution paths, decisions, dependencies, state changes, and the implementation behind each workflow.
Useful for: onboarding, reverse engineering, understanding legacy applications, and exploring unfamiliar repositories.
Explore interactive relationships between source files, imports, symbols, and dependencies. Select a file, inspect its connections, and download focused code context for further investigation.
Useful for: debugging, dependency tracing, architecture exploration, and preparing connected code for AI assistants.
Identify potential business-impacting implementation defects supported by source evidence. Explore affected workflows, implementation behavior, potential impact, remediation, and verification checks.
Useful for: investigating business logic defects, reviewing AI-generated applications, and identifying fragile workflows.
Not every repository produces confirmed risk findings. UnvibeCode distinguishes supported findings from cases where evidence is insufficient.
Download a normalized repository ZIP for code review, documentation, or further AI-assisted investigation. Need less code? Use the Connected Code Map to retrieve a focused context package around a selected file.
Useful for: engineering handoffs, repository exploration, and reusable source context.
More source code doesn't automatically mean better code understanding.
| Tool | Primary strength | UnvibeCode difference |
|---|---|---|
| Aider | AI-assisted code editing | Repository-wide workflow and risk investigation without an editing task |
| Repomix | Repository packaging for AI | Interactive code maps, business workflows, and evidence-backed risks |
| Qodo PR-Agent | Pull-request review | Understand existing application behavior beyond a proposed code change |
| CodeQL | Static security and correctness analysis | Business workflow explanations connected to implementation and operational consequences |
Understanding an unfamiliar repository
Discover business workflows, connected components, and implementation paths without manually opening every file.
Reviewing AI-generated applications
Understand what was actually implemented and investigate potential business logic defects.
Debugging across multiple files
Trace dependencies and surrounding code before changing a function in isolation.
Investigating application risks
Review source-backed findings and understand potential operational consequences.
Preparing context for AI coding assistants
Download complete repository context or focused connected code for further investigation.
Connected-code mapping and context preparation recognize:
Analysis depth varies by language and repository structure. See limitations for details.
UnvibeCode uses lightweight safeguards to reduce accidental credential sharing and keep AI requests focused on supported analysis tasks. Before the official UnvibeCode client sends code for AI analysis, it looks for recognizable credentials—such as API keys, passwords, access tokens and private keys—and replaces detected values with placeholders. Deeply encoded or custom secret format files could be skipped by the safeguards
- Quick Start
- Understanding the Four Outputs
- How UnvibeCode Works
- Limitations and Responsible Use
- Data Processing and Privacy
- Troubleshooting
- Public Repository Reviews
Contributions are welcome in documentation, testing, examples, and supported tooling.
Found a problem or have a feature request? Open a GitHub issue.
For product questions, repository reviews, or collaboration, contact divya.singaravelu@iiml.org.
If you discover a security vulnerability in UnvibeCode, please report it privately using the link below.
Report a vulnerability privately
Include the affected version, steps to reproduce, and potential impact. Please do not disclose security vulnerabilities in public GitHub issues.
See LICENSE for reuse and distribution terms.