SWE-agent

SWE-agent enables language models like GPT-4o and Claude Sonnet 4 to autonomously use tools for fixing GitHub repository issues, finding cybersecurity vulnerabilities, and performing custom software engineering tasks. While many AI coding assistants require developers to translate AI suggestions into action, SWE-agent gives models direct agency to read codebases, run commands, edit files, and verify fixes independently. Created by researchers from Princeton and Stanford universities, the platform achieves state-of-the-art performance on SWE-bench, the leading benchmark for autonomous software engineering. In February 2025, SWE-agent set new records on both SWE-bench full and verified benchmarks using Claude 3.7. What distinguishes SWE-agent from commercial alternatives is its commitment to research-grade transparency and customization: the entire system is configured through a single YAML file, and a minimal 100-line implementation demonstrates the core concepts. This philosophy of simplicity and hackability makes SWE-agent ideal for teams who need to understand and modify their autonomous coding systems rather than treating them as black boxes.

How It Works

SWE-agent operates by giving language models a specialized environment where they can interact with Git repositories through carefully designed tools and commands. The system begins by analyzing a GitHub issue, understanding the problem description and expected behavior through the language model's reasoning capabilities. Next, the agent explores the codebase using tools like file reading, directory traversal, and code search to locate relevant files and understand existing implementations. Once context is gathered, the agent formulates a fix strategy, considering factors like backwards compatibility, test coverage, and code style consistency. The agent then implements changes by editing files, running tests to verify correctness, and iterating based on test results or linting feedback. All configuration happens through a single YAML file where developers specify available commands, context limits, and behavioral constraints. This design provides maximum agency to the language model while maintaining transparency and customizability, and the system's free-flowing nature allows models to adapt strategies based on repository-specific patterns rather than following rigid workflows.

Core Features

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  State-of-the-Art SWE-bench Performance demonstrates real-world effectiveness through benchmark leadership. In February 2025, SWE-agent achieved the highest scores among open-source projects on both SWE-bench full and verified datasets using Claude 3.7, proving its capability to autonomously resolve genuine software engineering challenges from production repositories.

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  Free-Flowing Generalizable Approach gives language models maximum agency to explore repositories and formulate strategies without rigid procedural constraints. Unlike systems that enforce fixed workflows, SWE-agent allows models to adapt their approach based on codebase characteristics, problem complexity, and emerging insights, resulting in more natural problem-solving that mirrors human developer flexibility.

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  Single YAML Configuration provides complete system customization through one accessible file. Developers specify available commands, adjust context windows, tune behavioral parameters, and modify tool definitions without diving into source code, making it straightforward to adapt SWE-agent for specialized domains, security constraints, or company-specific development practices.

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  Research-Grade Transparency ensures teams can understand, modify, and extend every aspect of the system. The Mini-SWE-Agent implementation demonstrates core concepts in just 100 lines of code, achieving 65% on SWE-bench verified, proving the approach's fundamental simplicity and providing a learning resource for teams building their own autonomous coding systems.

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  Multi-Model Support enables experimentation with different language models including GPT-4o, Claude Sonnet 4, and other capable models. Teams can compare model performance on their specific codebases, optimize for cost versus capability trade-offs, and switch models as the landscape evolves without architectural changes.

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  Custom Task Definition extends beyond issue resolution to support cybersecurity vulnerability detection, codebase analysis, refactoring operations, and any task definable through natural language instructions. This flexibility makes SWE-agent a general-purpose autonomous software engineering platform rather than a narrow issue-fixing tool.

Who This Is For

SWE-agent serves research teams and engineering organizations prioritizing transparency and customization over turnkey convenience. It's ideal for companies with specialized development practices, security requirements, or domain-specific codebases that require tool customization rather than one-size-fits-all solutions. Academic researchers studying autonomous software engineering benefit from the system's hackable design and clear documentation, enabling experiments that extend capabilities or investigate agent decision-making patterns. Security-conscious teams appreciate the ability to audit every component and restrict agent actions through YAML configuration rather than trusting proprietary systems. The platform particularly suits teams comfortable with YAML configuration, command-line tools, and iterative refinement, who value understanding their tools deeply over immediate plug-and-play convenience. Organizations needing production-grade stability with vendor support should consider commercial alternatives, but teams wanting maximum control and transparency will find SWE-agent's research-oriented philosophy advantageous.