# Holon-Bench: A Benchmark Framework for Evaluating AI Programming Agents in Maintainer Workflows

> Holon-Bench is an open-source benchmark framework designed to evaluate the performance of AI programming agents in open-source software maintainer workflows, covering scenarios like fix loops, regression safety, scope control, and multi-language patches.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-04T11:15:25.000Z
- 最近活动: 2026-06-04T11:21:59.281Z
- 热度: 157.9
- 关键词: AI编程代理, 基准测试, 代码修复, 开源维护, 多语言, 回归测试, 评估框架
- 页面链接: https://www.zingnex.cn/en/forum/thread/holon-bench-ai
- Canonical: https://www.zingnex.cn/forum/thread/holon-bench-ai
- Markdown 来源: floors_fallback

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## Introduction / Main Post: Holon-Bench: A Benchmark Framework for Evaluating AI Programming Agents in Maintainer Workflows

Holon-Bench is an open-source benchmark framework designed to evaluate the performance of AI programming agents in open-source software maintainer workflows, covering scenarios like fix loops, regression safety, scope control, and multi-language patches.

## Original Author and Source

- **Original Author/Maintainer:** JohnYCChiang
- **Source Platform:** GitHub
- **Original Title:** holon-bench
- **Original Link:** https://github.com/JohnYCChiang/holon-bench
- **Publication Date:** June 4, 2026

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## Background: Why Do We Need a Specialized Benchmark for Programming Agents?

Current evaluations of AI programming agents mostly focus on single-shot code generation tasks, such as LeetCode-style algorithm problems. However, real-world software maintenance is far more complex—agents need to handle fix loops, understand validator feedback, control modification scope, and avoid regression issues.

Holon-Bench is designed to fill this evaluation gap. It focuses on whether AI agents can work like real maintainers, rather than whether they can write correct code snippets in one go.

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## Project Overview

Holon-Bench is an open-source benchmark framework specifically designed to evaluate the performance of AI programming agents in open-source software maintainer workflows. It measures core capabilities that matter in real maintenance scenarios:

- **First Pass**: Generate a correct patch on the first submission
- **Repaired Pass**: Fix their work after reading validator feedback
- **Scope Control**: Keep modifications within allowed file ranges
- **Hidden Verifier**: Pass hidden regression checks that the agent cannot see
- **Repair Tax Rate**: Converge without exhausting the repair budget

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## 1. Fix Loop Capability

Real-world bug fixes rarely succeed on the first try. Holon-Bench evaluates whether agents can:
- Understand test failure messages
- Diagnose the root cause of problems
- Iterate on fixes until passing
- Control the number of repair attempts and token costs

## 2. Scope Control

Does the agent only modify files that should be changed? Does it accidentally touch protected interfaces or contracts? Holon-Bench verifies this through protected reference implementations and scope checkers.

## 3. Regression Safety

Does fixing one bug introduce new issues? The framework includes hidden verifiers that the agent cannot see but are checked during the final evaluation.

## 4. Multi-Language Support

Supports evaluation tracks for multiple programming languages:
- Python (CLI tools, library APIs, test coverage)
- Rust (core library logic, ECS game architecture, semantic porting)
- Go (standard library patterns, authoritative server logic)
- Dart/Flutter (cross-platform widgets and state correctness)

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