YerbaPage/SWE-Debate
SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution
Integrates the Moatless framework for graph-driven code entity extraction and dependency traversal, then employs Monte Carlo Tree Search with multiple specialized LLM agents (ReAct-based reasoning, value estimation, discriminator voting) to collaboratively debate and refine fault localization. The architecture chains entity identification through code dependency graphs, uses MCTS for exploration with multi-agent consensus voting, and generates structured code modification plans guided by feedback loops.
Stars
25
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 11, 2025
Commits (30d)
0
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