# GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging

arXiv: https://arxiv.org/abs/2508.18993 · HTML: https://mlsanity.com/paper/2508.18993/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 33 of its 154 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 33)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation](https://mlsanity.com/paper/2211.11501/) (2022)
- [PaperBench: Evaluating AI's Ability to Replicate AI Research](https://mlsanity.com/paper/2504.01848/) (2025)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion](https://mlsanity.com/paper/2310.11248/) (2023)
- [ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation](https://mlsanity.com/paper/2308.01861/) (2023)
- [xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations](https://mlsanity.com/paper/2506.13651/) (2025)
- [Competitive Programming with Large Reasoning Models](https://mlsanity.com/paper/2502.06807/) (2025)
- [SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?](https://mlsanity.com/paper/2502.12115/) (2025)
- [Jigsaw: Large Language Models meet Program Synthesis](https://mlsanity.com/paper/2112.02969/) (2021)
- [Execution-Based Evaluation for Open-Domain Code Generation](https://mlsanity.com/paper/2212.10481/) (2022)
- [Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization](https://mlsanity.com/paper/2404.02183/) (2024)
- [ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code](https://mlsanity.com/paper/2311.09835/) (2023)
- [LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?](https://mlsanity.com/paper/2506.11928/) (2025)
- [MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs](https://mlsanity.com/paper/2408.09955/) (2024)
- [HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation](https://mlsanity.com/paper/2412.21199/) (2024)
- [ToolCoder: Teach Code Generation Models to use API search tools](https://mlsanity.com/paper/2305.04032/) (2023)
- [RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving](https://mlsanity.com/paper/2505.21577/) (2025)

## Built on by (most cited first, 25 of 25)

- [FeatureBench: Benchmarking Agentic Coding for Complex Feature Development](https://mlsanity.com/paper/2602.10975/) (2026)
- [LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces](https://mlsanity.com/paper/2602.14337/) (2026)
- [Self-Improvements in Modern Agentic Systems: A Survey](https://mlsanity.com/paper/2607.13104/) (2026)
- [A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks](https://mlsanity.com/paper/2512.04329/) (2025)
- [DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle](https://mlsanity.com/paper/2601.20882/) (2026)
- [How Well Does Agent Development Reflect Real-World Work?](https://mlsanity.com/paper/2603.01203/) (2026)
- [PDEAgent-Bench: A Multi-Metric, Multi-Library Benchmark for PDE Solver Generation](https://mlsanity.com/paper/2605.09636/) (2026)
- [AgentRewind: Recoverable Execution for Long-Horizon LLM Agents](https://mlsanity.com/paper/2608.14380/) (2026)
- [CATArena: Evaluating Evolutionary Capabilities of Code Agents via Iterative Tournaments](https://mlsanity.com/paper/2510.26852/) (2025)
- [Gistify! Codebase-Level Understanding via Runtime Execution](https://mlsanity.com/paper/2510.26790/) (2025)
- [SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering](https://mlsanity.com/paper/2605.17526/) (2026)
- [ATM: CID-Brokered Pre-Write Admission for Multi-Agent Code Co-Synthesis](https://mlsanity.com/paper/2607.00041/) (2026)
- [Independent Patch Verification for Coding Agents with a Bidirectional Reconstruct-and-Verify Framework](https://mlsanity.com/paper/2608.08950/) (2026)
- [EnvX: Agentize Everything with Agentic AI](https://mlsanity.com/paper/2509.08088/) (2025)
- [RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms](https://mlsanity.com/paper/2603.05026/) (2026)
- [Multilingual Prompt Localization for Agent-as-a-Judge: Language and Backbone Sensitivity in Requirement-Level Evaluation](https://mlsanity.com/paper/2604.04532/) (2026)
- [Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development](https://mlsanity.com/paper/2605.20456/) (2026)
- [FastCode: Fast and Cost-Efficient Code Understanding and Reasoning](https://mlsanity.com/paper/2603.01012/) (2026)
- [RAT: RunAnyThing via Fully Automated Environment Configuration](https://mlsanity.com/paper/2604.23190/) (2026)
- [To Add Is Machine, To Delete Is Human: Measuring and Mitigating Deletion Avoidance in LLM Code Editing](https://mlsanity.com/paper/2607.28887/) (2026)
- [Projecting the Emerging Mindset of SWE Agent by Launching a Wild Code Understanding Journey](https://mlsanity.com/paper/2606.08500/) (2026)
- [ExplainBench: Evaluating Code Explanations from Agents](https://mlsanity.com/paper/2607.26451/) (2026)
- [MADE: Belief-Driven Dual-Agent Coordination for Autonomous Model Deployment](https://mlsanity.com/paper/2608.01189/) (2026)
- [Update from Hell: Can Coding Agents Survive Hidden Breakage in Dependency Upgrades?](https://mlsanity.com/paper/2608.30300/) (2026)
- [VibeMemBench: Evaluating Memory Systems for Coding Agents on Real Repository Coding Tasks](https://mlsanity.com/paper/2609.23570/) (2026)

## Abstract (arXiv)

Beyond scratch coding, exploiting large-scale code repositories (e.g., GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evaluate code agents in such authentic, workflow-driven scenarios. To bridge this gap, we introduce GitTaskBench, a benchmark designed to systematically assess this capability via 54 realistic tasks across 7 modalities and 7 domains. Each task pairs a relevant repository with an automated, human-curated evaluation harness specifying practical success criteria. Beyond measuring execution and task success, we also propose the alpha-value metric to quantify the economic benefit of agent performance, which integrates task success rates, token cost, and average developer salaries. Experiments across three state-of-the-art agent frameworks with multiple advanced LLMs show that leveraging code repositories for complex task solving remains challenging: even the best-performing system, OpenHands+Claude 3.7, solves only 48.15% of tasks (recent progress has pushed the frontier further, with RepoMaster+Claude 3.5 achieving a new record of 62.96%). Error analysis attributes over half of failures to seemingly mundane yet critical steps like environment setup and dependency resolution, highlighting the need for more robust workflow management and increased timeout preparedness. By releasing GitTaskBench, we aim to drive progress and attention toward repository-aware code reasoning, execution, and deployment -- moving agents closer to solving complex, end-to-end real-world tasks. The benchmark and code are open-sourced at https://github.com/QuantaAlpha/GitTaskBench.