choose if you want a focused SWE-bench Verified harness rather than SWE-agent's broader research framework.
The #1 open-source SWE-bench Verified implementation
- 882
- Python
- NOASSERTION
autonomous coding agents / SWE-bench
SWE-agent is an LLM-powered autonomous agent that uses tools to fix GitHub issues and perform coding tasks, with configurable agent loops.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
choose if you want a focused SWE-bench Verified harness rather than SWE-agent's broader research framework.
The #1 open-source SWE-bench Verified implementation
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
choose if you want a TypeScript/TUI-based agent toolkit instead of a Python harness.
AI agent toolkit: unified LLM API, agent loop, TUI, coding agent CLI
choose if you want a Go terminal agent optimized for DeepSeek and prefix caching.
DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running.
choose if you prefer structure-aware program repair over SWE-agent's generic tool loop.
A project structure aware autonomous software engineer aiming for autonomous program improvement. Resolved 37.3% tasks (pass@1) in SWE-bench lite and 46.2% tasks (pass@1) in SWE-bench verified with each task costs less than $0.7.
choose if you are targeting Terminal-Bench instead of SWE-bench with an evolved harness.
Official AHE code — Agentic Harness Engineering: observability-driven automatic evolution of coding-agent harnesses (concurrent w/ meta-harness). NexAU-AHE reaches 84.7% ± 2.1 pass@1 on Terminal-Bench 2 (GPT-5.5). Lifts GPT-5.4 69.7→77.0% over 10 iters, beats Codex/ACE/Training-Free GRPO; frozen harness transfers to SWE-bench-Verified.
choose if you want a multi-agent orchestrator for terminal coding tasks rather than SWE-agent's single-agent loop.
Reached #13 on Stanford's Terminal Bench leaderboard. Orchestrator, explorer & coder agents working together with intelligent context sharing.
choose if you want a research harness targeting Terminal-Bench 2.0 instead of SWE-bench.
Meta-Harness: 76.4% on Terminal-Bench 2.0 (Claude Opus 4.6)
These projects were analysed and named SWE-agent among their alternatives. The relationship is not symmetric — how SWE-agent rates them is a separate judgement, made when SWE-agent is analysed in its own right.
calls SWE-agent “Choose when you want a proven GitHub-issue-fixing agent with pluggable LMs instead of building on LangGraph.”
An Open-Source Asynchronous Coding Agent
calls SWE-agent “Narrower issue-fixing agent; choose if your task is fixing GitHub issues rather than generating a full project from spec.”
🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming