FinanceFlash/unvibecode

▲ 36 stars today★ 223⑂ 70

Complex code hides connections, workflows, and business risks.Unvibe complex code. Trace business workflows.

About FinanceFlash/unvibecode

FinanceFlash/unvibecode is an open-source project on GitHub, mainly written in Jupyter Notebook. Complex code hides connections, workflows, and business risks.Unvibe complex code. Trace business workflows. It currently holds 223 stars and 70 forks with 122 open issues, and was last pushed on 2026-10-09 (repository created 2026-08-04).

Project Overview

Git Homed tracks it on the Today's Trending board, currently at rank #87 with 36 new stars today.

GitHub Repository Details

Repository FinanceFlash/unvibecode · default branch main · size 10837 KB · watchers 2 · source: GitHub REST API and repository README

README

UnvibeCode

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.

UnvibeCode AI codebase analysis demo showing connected code, business workflows, and risk findings

Quality checks PyPI version Python 3.11+ License

Your LLM helped write 100,000 lines of code. Now how do you understand what it actually built?

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.

Try UnvibeCode

Requires Python 3.11 or newer.

Windows

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"

macOS

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"

Linux

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.

One repository review. Four connected outputs.

UnvibeCode helps developers understand what a codebase does, how its components connect, and what could break — without manually tracing every file.

1. Business Workflow Map — Understand what the system does

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.

Business Workflow Map

2. Connected Code Map — Trace where behavior lives

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.

Connected Code Map

3. Business Risk Findings — Discover what could break

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.

Business Risk Findings

4. Complete Repository Context — Take the analysis further

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.

---

Why not just dump your entire codebase into an LLM?

More source code doesn't automatically mean better code understanding.

Before: full-code dumps into an LLM. After: structured code relationships, graph-aware context, and evidence-gated findings with UnvibeCode.

What our users are saying

Understanding complex code

“The workflow and code maps helped me quickly trace relationships between routes, services, models, and database-related code.”

Subhankar Nath

Finding a real bug

“The Business Risk Findings report caught a real bug I didn't know about: my meditation-save endpoint always inserts a new row, but the model has a unique constraint on user + date, so a repeat save on the same day throws an unhandled error.”

Rohit Sanju Patil

---

How does UnvibeCode compare?

| 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 |

---

What's new in v0.3.8?

A clearer, easier-to-explore repository review experience.

View UnvibeCode on PyPI

---

When should you use UnvibeCode?

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.

---

Supported source languages

Connected-code mapping and context preparation recognize:

| Language | File types | |---|---| | Python | .py | | JavaScript | .js, .jsx, .mjs, .cjs | | TypeScript | .ts, .tsx | | Rust | .rs | | PHP | .php | | Ruby | .rb | | HTML and CSS | .html, .htm, .css |

Analysis depth varies by language and repository structure. See limitations for details.

---

Documentation

Contributing

Contributions are welcome in documentation, testing, examples, and supported tooling.

Support and feedback

Found a problem or have a feature request? Open a GitHub issue.

For product questions, repository reviews, or collaboration, contact [email protected].

License

See LICENSE for reuse and distribution terms.

GitHub Stars & Activity

223Stars
70Forks
122Open issues
Jupyter NotebookLanguage

GitHub Popularity

GitHub stars223
Forks70
Open issues122
Primary languageJupyter Notebook
LicenseApache-2.0
Stars gained today36
Created2026-08-04
Last pushed2026-10-09

Trending History

Daily boardrank #87 · ▲ 36 stars

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