Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks
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Abstract (arXiv)
The software engineering capabilities of general-purpose agent harnesses remain underexplored, and existing benchmarks offer limited support for comparing these harnesses under consistent conditions. To address this gap, we introduce Claw-SWE-Bench, a unified benchmark that enables researchers to systematically assess the capabilities and efficiency of general-purpose harnesses on software engineering tasks. The benchmark contains 350 real-world GitHub issue-resolution instances across eight programming languages and 43 repositories and provides a shared adapter protocol to align task inputs, outputs, and execution environments across harnesses. Experiments show that general-purpose harnesses can effectively resolve real-world software issues and that their success rates and resource consumption vary substantially even when the underlying model is held fixed. To lower evaluation costs and support faster debugging and iteration, we also provide Claw-SWE-Bench Lite, an 80-instance subset designed to preserve the key evaluation properties of the full benchmark. We hope this benchmark will help researchers better evaluate and understand the performance of general-purpose harnesses on software engineering tasks and guide the development of more capable and efficient harnesses. The data is available at https://github.com/opensquilla/claw-swe-bench and https://huggingface.co/datasets/TokenRhythm/Claw-SWE-Bench.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 32 of its 62 references that are in the map. First posted on arXiv 10 Jun 2026.
Builds on
32 of 62 references.
References · 32
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2021Program Synthesis with Large Language Modelscited by 3,637
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
- 2023GAIA: a benchmark for General AI Assistantscited by 1,128
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 1,002
- 2021Measuring Coding Challenge Competence With APPScited by 902
- 2026GLM-5: from Vibe Coding to Agentic Engineeringcited by 587
- 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
- 2024Agent Workflow Memorycited by 383
20 more
- 2024WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?cited by 363
- 2024tinyBenchmarks: evaluating LLMs with fewer examplescited by 310
- 2026Meta-Harness: End-to-End Optimization of Model Harnessescited by 309
- 2025SWE-smith: Scaling Data for Software Engineering Agentscited by 285
- 2025SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?cited by 279
- 2024AutoCodeRover: Autonomous Program Improvementcited by 278
- 2024AI Agents That Mattercited by 219
- 2024OS-Copilot: Towards Generalist Computer Agents with Self-Improvementcited by 205
- 2025Survey on Evaluation of LLM-based Agentscited by 204
- 2023CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completioncited by 183
- 2024AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?cited by 158
- 2024AgentScope: A Flexible yet Robust Multi-Agent Platformcited by 121
- 2025Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluationcited by 92
- 2026Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agentscited by 64
- 2026WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluationcited by 37
- 2026ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspacescited by 33
- 2025SWE-Effi: Re-Evaluating Software AI Agent System Effectiveness Under Resource Constraintscited by 17
- 2026ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agentscited by 17
- 2025CodeClash: Benchmarking Goal-Oriented Software Engineeringcited by 10
- 2026GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)cited by 10
Built on by
Citing papers per year: 2026: 8.
8 papers.
- 2026From Question Answering to Task Completion: A Survey on Agent System and Harness Designcited by 12
- 2026Agentic Routing: The Harness-Native Data Flywheelcited by 4
- 2026HarnessRisk: A Lifecycle-Oriented Benchmark for Agent Harness Safetycited by 2
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environmentscited by 1
- 2026BIABench: Evaluating AI agents on real-world bioimage analysis taskscited by 0
- 2026K-Dense BYOK: An Open-Source AI Research Assistant That Runs Locally and Keeps a Hash-Chained Lab Notebookcited by 0
- 2026What Does a Harness Buy? Tokens, Mostlycited by 0
- 2026Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agentscited by 0
- 2026Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents14 Oct 2026
- 2026What Does a Harness Buy? Tokens, Mostly3 Oct 2026
- 2026BIABench: Evaluating AI agents on real-world bioimage analysis tasks28 Sep 2026
- 2026K-Dense BYOK: An Open-Source AI Research Assistant That Runs Locally and Keeps a Hash-Chained Lab Notebook4 Sep 2026
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environments20 Aug 2026
- 2026HarnessRisk: A Lifecycle-Oriented Benchmark for Agent Harness Safety18 Aug 2026
- 2026Agentic Routing: The Harness-Native Data Flywheel13 Jul 2026
- 2026From Question Answering to Task Completion: A Survey on Agent System and Harness Design14 Jun 2026