Glupe

Use LLMs in your codebase without letting them rewrite your codebase.

LLMs are powerful, but unconstrained generation is dangerous. Glupe is a CLI tool that acts as a strict constraint layer (or firewall) for AI code generation. You write the load-bearing architecture; the AI is only allowed to fill in the explicit blanks.

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🎬 Glupe in Action

Refine	Build	Fix Errors (auto-retry)

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🧠 The problem

Using LLMs for code today looks like this:

• paste prompt → get code → paste into project
• or let an agent rewrite entire files

This creates real issues:

• ❌ no boundaries (AI can change anything)
• ❌ hard to review (what actually changed?)
• ❌ not reproducible
• ❌ easy to break working code

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✅ What Glupe does

Glupe adds explicit boundaries around AI-generated code.

int add(int a, int b) {
    $${
        // implement addition
    }$$
}

• AI can only write inside $$ { ... } $$
• Everything else is guaranteed unchanged
• You control structure, AI fills implementation

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⚡ TL;DR

• You write the structure
• AI fills small, isolated regions
• Your codebase stays stable

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🔧 The workflow

1. Write normal code + intent

#include <vector>

void process_data(std::vector<int>& data) {
    $${
        // 1. Remove negative numbers
        // 2. Sort descending
        // 3. Remove duplicates
    }$$
}

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2. Run Glupe

glupe main.cpp -fill -local

Glupe will:

• extract the container
• send only that part to the LLM
• inject the result back into the file

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🤔 Why not just prompt?

You can — and for small tasks, you probably should.

Glupe is useful when:

• you care about not touching the rest of the file
• you want repeatable structure
• you want reviewable diffs
• you’re integrating AI into a real codebase

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⚖️ What this is (and isn’t)

✔️ This is

• a constraint layer for LLM code generation
• a way to keep AI edits local and predictable
• a tool for gradual adoption of AI in real projects

❌ This is NOT

• not a new programming language
• not a replacement for compilers
• not deterministic (LLMs are still LLMs)
• not magic — bad prompts still produce bad code

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⚡ Installation

Recommended: Pre-compiled Binaries

Download the standalone executable for your OS directly from the Releases page.

Alternative: Quick Install Scripts

Note: Piping scripts directly to your shell carries inherent security risks. We recommend using the pre-compiled binaries above, but provide these scripts for convenience. You can review install.ps1 and install.sh in the repository.

Windows

1. Press Win + R, type cmd, and press Enter.
2. In the command prompt, type powershell and press Enter.
3. Run:

irm https://raw.githubusercontent.com/alonsovm44/glupe/master/install.ps1 | iex

Linux/macOS

curl -fsSL https://raw.githubusercontent.com/alonsovm44/glupe/master/install.sh | bash

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🚀 Quick Start

glupe --init
glupe hello.glp -o hello.exe -cpp -local
./hello.exe

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⚙️ How it works

Glupe is a thin layer between your code and an LLM:

1. scans your file
2. finds $$ { ... } $$ blocks
3. sends only those blocks to the model
4. injects generated code
5. optionally compiles + retries on failure

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🔒 Determinism & Caching

A major issue with AI code generation is non-determinism: regenerating an app might introduce completely new bugs.

Glupe solves this via caching. Once a $$ block { ... } $$ block successfully compiles and passes your tests, its hash is locked in a .glupe.lock file.

Re-running the CLI will not regenerate that block unless you explicitly change the prompt inside the container. It behaves like incremental compilation: once a block works, it stays frozen safely.

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🔥 Features

Scoped AI generation

Only touch what you explicitly allow.

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Multi-file output

EXPORT: "mylib.h"
$$ myfunc { define a function that returns square }$$
EXPORT: END

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Auto-fix compile errors

[Pass 1] Missing include
[Pass 2] Type error
[Pass 3] BUILD SUCCESSFUL

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One-step run

glupe app.glp -o app.exe -cpp -local -run

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👤 Who this is for

• developers experimenting with LLM-assisted coding
• people who don’t want AI rewriting entire files
• teams that want controlled integration of AI

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⚠️ Tradeoffs

• LLM output is still non-deterministic
• requires writing inside containers
• adds an extra step vs raw prompting

This is intentional:

Glupe trades speed for control and safety

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⚙️ Configuration

Local model

glupe config model-local qwen2.5-coder:latest

Cloud model

glupe config api-key "YOUR_KEY"
glupe config model-cloud gemini-1.5-flash

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🧰 Utility Commands

fix

glupe fix project.c "fix segfault" -local

explain

glupe explain main.cpp -cloud english

diff

glupe diff v1.py v2.py -cloud

sos

glupe sos english -local "KeyError in pandas"

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🌍 Vision

LLMs are useful, but unsafe by default.

Glupe’s goal is simple:

make LLM-assisted programming controllable enough to use in real systems

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📄 White Paper

https://github.com/alonsovm44/glupe/blob/master/.DOCUMENTATION/paper.md

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🧩 Syntax Highlight Extension

https://github.com/alonsovm44/glupe-tutorial

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👥 Contributors

• Alonso Velazquez (Mexico)
• Krzysztof Dudek (Poland)

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📜 License

MIT License