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

• URL: http://arxiv.org/abs/2608.04459v1
• Date: Wed, 05 Aug 2026 05:34:40 GMT
• Title: AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation
• Abstract summary: We present AdaptAgent, a multi-agent, domain-guided reasoning framework for code adaptation.<n>Rather than relying on single-shot prompting, AdaptAgent decomposes adaptation into specialized agents that communicate via typed artifacts.<n>On a real-world dataset, AdaptAgent outperforms strong baselines in semantic correctness and produces patches that mirror developers' actual adaptation patterns.
• Score: 14.734454356396157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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