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This paper introduces AdaptAgent, a multi-agent framework designed to tackle the code adaptation problem by integrating code snippets into existing repositories. By decomposing the adaptation process into specialized agents that handle intent extraction, policy derivation, planning, context mining, and code adaptation, the framework achieves higher semantic correctness and aligns with actual developer practices. The results demonstrate that AdaptAgent significantly outperforms existing baselines, highlighting the importance of agent specialization in achieving robust code integration.
AdaptAgent achieves superior code adaptation by leveraging a multi-agent approach that mirrors real developer practices, outperforming traditional methods in semantic correctness.
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.
