github.com/BillHuang2001/evogit @main

<img alt="EvoGit Cover Image" src="https://github.com/BillHuang2001/evogit/raw/main/static/evogit_new_cover.png">

Decentralized Code Evolution via Git-Based Multi-Agent Collaboration

🏆 EvoGit: 1st Place in AgentX Multi-Agent Track

Winner of the Multi-Agent Track at the AgentX Competition (Agentic AI Summit).

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🎬 EvoGit Animation (click to hide)

<img alt="EvoGit Animation" height="50%" src="https://github.com/BillHuang2001/evogit/raw/main/static/animated_evogit_dark_text.avif">

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👋 Table of Contents

1. 🚀 Overview
2. ✨ Key Features
3. 📦 Live Demos
4. 🧬 How to Explore the Results
5. 📚 Paper

🚀 Overview

EvoGit is a decentralized multi-agent framework that reimagines software development as a collaborative, evolutionary process. It deploys a population of independent coding agents that evolve a shared codebase asynchronously, without centralized coordination, explicit message passing, or shared memory.

All coordination emerges implicitly through a Git-based phylogenetic graph that tracks the complete version lineage. This graph allows agents to read from and write to the repository, enabling scalable parallel exploration while preserving a consistent, auditable history of every change.

For detailed methodology and experimental results, refer to our paper.

✨ Key Features

• 🧠 Decentralized Coordination: Agents operate independently and coordinate organically through the shared version graph. This resembles stigmergy in biological systems, where interactions are indirect and mediated by the environment.
• ⚙️ Git-Native Evolution: The entire framework is built on Git. Evolutionary concepts map directly to Git primitives, making the process inherently compatible with standard developer tools.
• 🌿 Traceable & Auditable Lineage: Every edit, merge, and decision is recorded as an immutable Git commit. This provides full transparency and reproducibility of the entire development process.
• 🤝 Sparse Human Oversight: The human's role shifts from a coder to a high-level Product Manager. You define initial goals and provide periodic, lightweight feedback to prune unproductive branches and promote promising ones.

📦 Live Demos

Explore how EvoGit enables collaborative AI development across two real-world projects. For more details, please visit the respective GitHub repositories and inspect the Git history to see how multiple agents evolved the code.

📃 Web Applicaiton Development -- link

A multi-agent AI system collaboratively builds a complete one-page interactive website—from layout and UI to animations and dark mode. The project was initialized by a human product manager and guided with ~10 feedback interventions.

🔍 Result (click to expand)

The final web page demonstrates a polished UI with support for both light and dark themes.

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🧠 Meta-Level Code Synthesis -- link

AI agents iteratively evolve a meta-level algorithm designer, which itself generates and refines a solver for the classic Bin Packing Problem. This creates a two-layer pipeline: EvoGit → Auto Algorithm Designer → Bin Packing Solver A human manager provided an initial setup and ~5 rounds of feedback throughout the optimization process.

🔍 Result (click to expand)

The AI-generated automatic algorithm design program efficiently found a solver that minimizes bin usage, as shown in the final output script:

def bin_packing_solver(items: list[float], budget: int) -> list[int]:
    import time

    if not items or not all(0 <= w <= 1 for w in items):
        return []

    start_time = time.time()

    items_sorted = sorted(enumerate(items), key=lambda x: x[1], reverse=True)
    bins = []
    bin_indices = [-1] * len(items)

    for index, weight in items_sorted:
        placed = False
        for bin_index, bin_weight in enumerate(bins):
            if bin_weight + weight <= 1:
                bins[bin_index] += weight
                bin_indices[index] = bin_index
                placed = True
                break
        if not placed:
            bins.append(weight)
            bin_indices[index] = len(bins) - 1

    best_solution = bin_indices[:]
    best_bin_count = len(bins)

    def refine_solution():
        nonlocal best_solution, best_bin_count
        for _ in range(100):  # attempt refinement a number of times
            new_bins = []
            new_bin_indices = [-1] * len(items)
            new_solution = []
            for i in range(len(items)):
                weight = items[i]
                placed = False
                for bi in range(len(new_bins)):
                    if new_bins[bi] + weight <= 1:
                        new_bins[bi] += weight
                        new_bin_indices[i] = bi
                        placed = True
                        break
                if not placed:
                    new_bins.append(weight)
                    new_bin_indices[i] = len(new_bins) - 1
            new_bin_count = len(new_bins)

            if new_bin_count < best_bin_count:
                best_solution = new_bin_indices
                best_bin_count = new_bin_count

            if (time.time() - start_time) * 1000 > budget:
                break

    refine_solution()

    return best_solution

The optimized code is automatically saved as best_solution.py after the search process completes.

🧬 How to Explore the Results

EvoGit uses Git not only as a version control tool, but also as a transparent window into the code evolution process. Here's how to inspect our demos:

1. 🧑‍💻 The human-initialized seed lives in the main branch.
2. 🤖 AI-generated code lives in branches named: host<i>-individual-<j>, where i = host node index, j = agent index.
3. 🔍 Each agent branch contains an independent development trajectory. You can explore these using GitHub’s commit history or local Git tools.
4. 📈 Git diffs and logs reveal the precise changes made in each commit.
5. 🧭 Use git log --graph or GitHub’s branch visualization (under Insights -> Network) to see how code diverged and converged over time.

🔍 Example Git Graph (click to expand)

All changes are versioned and traceable. Every commit represents an autonomous decision by an agent—captured, auditable, and reproducible through Git.

[!NOTE] GitHub may hide some branches. Click “View all branches” on the repo page to see the complete version graph.

📚 Paper

Read the full framework design, evaluation methodology, and results in our paper: - ArXiv:2506.02049

📢 Current Status

The project is actively being developed and improved. We are currently working on the next version internally with significant enhancements. Stay tuned for future updates!