title: Agentless: Demystifying LLM-based Software Engineering Agents
summary: Recent advancements in large language models (LLMs) have significantly
  advanced the automation of software development tasks, including code
  synthesis, program repair, and test generation. More recently, researchers and
  industry practitioners have developed various autonomous LLM agents to perform
  end-to-end software development tasks. These agents are equipped with the
  ability to use tools, run commands, observe feedback from the environment, and
  plan for future actions. However, the complexity of these agent-based
  approaches, together with the limited abilities of current LLMs, raises the
  following question: Do we really have to employ complex autonomous software
  agents? To attempt to answer this question, we build Agentless -- an agentless
  approach to automatically solve software development problems. Compared to the
  verbose and complex setup of agent-based approaches, Agentless employs a
  simplistic two-phase process of localization followed by repair, without
  letting the LLM decide future actions or operate with complex tools. Our
  results on the popular SWE-bench Lite benchmark show that surprisingly the
  simplistic Agentless is able to achieve both the highest performance (27.33%)
  and lowest cost (\$0.34) compared with all existing open-source software
  agents! Furthermore, we manually classified the problems in SWE-bench Lite and
  found problems with exact ground truth patch or insufficient/misleading issue
  descriptions. As such, we construct SWE-bench Lite-S by excluding such
  problematic issues to perform more rigorous evaluation and comparison. Our work
  highlights the current overlooked potential of a simple, interpretable
  technique in autonomous software development. We hope Agentless will help reset
  the baseline, starting point, and horizon for autonomous software agents, and
  inspire future work along this crucial direction.
paper_authors._id: 6684e6b5c1cbe5e008dbf21a
paper_authors.name: Chunqiu Steven Xia
paper_authors.status: claimed_verified
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paper_authors.fullname: Chunqiu Steven Xia
paper_authors.type: user
paper_authors.user: nevetsaix
paper_authors._id: 6684e6b5c1cbe5e008dbf21b
paper_authors.name: Yinlin Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T07:54:02.536Z
paper_authors._id: 64596fa005b3d19242708528
paper_authors.avatarUrl: /avatars/42695a25ebdab0c82940ca5309fdbc18.svg
paper_authors.fullname: Deng
paper_authors.type: user
paper_authors.user: Yinlin
paper_authors._id: 6684e6b5c1cbe5e008dbf21c
paper_authors.name: Soren Dunn
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-04T08:04:05.848Z
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paper_authors.avatarUrl: /avatars/dd7002d0c08b5cc5a52a8c7aa47e5c99.svg
paper_authors.fullname: Soren Dunn
paper_authors.type: user
paper_authors.user: soren-dunn
paper_authors._id: 6684e6b5c1cbe5e008dbf21d
paper_authors.name: Lingming Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T07:57:02.292Z
paper_authors._id: 656f473c14fa8cfccd14559e
paper_authors.avatarUrl: /avatars/8f4fef3d835a7a11c2ab66dbf04f3424.svg
paper_authors.fullname: Lingming Zhang
paper_authors.type: user
paper_authors.user: lingming
paper_title: Agentless: Demystifying LLM-based Software Engineering Agents
paper_summary: Recent advancements in large language models (LLMs) have significantly
  advanced the automation of software development tasks, including code
  synthesis, program repair, and test generation. More recently, researchers and
  industry practitioners have developed various autonomous LLM agents to perform
  end-to-end software development tasks. These agents are equipped with the
  ability to use tools, run commands, observe feedback from the environment, and
  plan for future actions. However, the complexity of these agent-based
  approaches, together with the limited abilities of current LLMs, raises the
  following question: Do we really have to employ complex autonomous software
  agents? To attempt to answer this question, we build Agentless -- an agentless
  approach to automatically solve software development problems. Compared to the
  verbose and complex setup of agent-based approaches, Agentless employs a
  simplistic two-phase process of localization followed by repair, without
  letting the LLM decide future actions or operate with complex tools. Our
  results on the popular SWE-bench Lite benchmark show that surprisingly the
  simplistic Agentless is able to achieve both the highest performance (27.33%)
  and lowest cost (\$0.34) compared with all existing open-source software
  agents! Furthermore, we manually classified the problems in SWE-bench Lite and
  found problems with exact ground truth patch or insufficient/misleading issue
  descriptions. As such, we construct SWE-bench Lite-S by excluding such
  problematic issues to perform more rigorous evaluation and comparison. Our work
  highlights the current overlooked potential of a simple, interpretable
  technique in autonomous software development. We hope Agentless will help reset
  the baseline, starting point, and horizon for autonomous software agents, and
  inspire future work along this crucial direction.
paper_ai_summary: Agentless, a two-phase approach to software development automation, achieves high performance and low cost compared to complex autonomous agents on the SWE-bench Lite benchmark.
paper_ai_keywords: large language models code synthesis program repair test generation autonomous agents localization repair SWE-bench Lite SWE-bench Lite-S