AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation

2026-08-05

AI Summary

This work addresses the challenge of precisely adapting code snippets—either generated by large language models or retrieved from the web—into designated locations within an existing codebase. It proposes a novel multi-agent collaborative framework that formalizes code adaptation as a structured process, integrating intent understanding, domain-specific strategy derivation, contextual semantic mining, and compiler-driven iterative validation to produce minimal patches that are both functionally correct and stylistically consistent. Key innovations include a division of labor among specialized agents, typed intermediate representations for robust inter-agent communication, and a planning mechanism guided by domain-aware strategies. Experimental results on real-world datasets demonstrate substantial improvements over strong baselines, with generated patches closely mirroring developer behavior in both semantic correctness and coding style. Ablation studies further confirm the critical contribution of each component.

Abstract

Developers often need to adapt into their projects the code generated from LLMs or code snippets from online forums. However, integrating them into an existing repository remains challenging in a manual process. A successful integration typically requires more than copying code as a user must produce correct adapting changes at a designated location in the target repository. We formalize this as the code adaptation problem: given a snippet, functional intent, a target repository, and an adaptation location, generate a patch that adapts the snippet into the repository. We present AdaptAgent, a multi-agent, domain-guided reasoning framework for code adaptation. Rather than relying on single-shot prompting, AdaptAgent decomposes adaptation into specialized agents that communicate via typed artifacts: an Intent Summarizer extracts adaptation goals from Q&A text; a Policy Agent derives domain policies from six adaptation categories; a Domain Planner generates a self-ordered plan; a Context Miner distills sibling-method semantics from the target codebase; and a Code Adapter realizes the plan as a minimal unified diff, iteratively refined using a compiler-based Verifier. This division of labor enables robust, policy-aligned adaptations and supports adapting code snippets into a project. On a real-world dataset, AdaptAgent outperforms strong baselines in semantic correctness and produces patches that mirror developers' actual adaptation patterns. Our ablation study shows each agent's necessity, especially planning for code-hardening and exception-handling, and intent for logic customization.

Problem

• code adaptation
• code integration
• large language models
• software engineering
• code snippets

Innovation

• multi-agent
• code adaptation
• domain-guided reasoning
• compiler-based verification
• intent summarization