Legal AI tasks require large language models (LLMs) to demonstrate robust legal reasoning for complex, knowledge-intensive, and multi-task scenarios such as legal judgment prediction (LJP), statute article generation (SAG), and legal document generation (LDG). However, the field’s emphasis on logical consistency, coupled with persistent hallucination issues, limits the practicality of conventional retrieval-augmented generation (RAG), which struggles with lengthy legal texts, limited context windows, and high computational costs. To address these challenges, we propose Legal-AP, a framework that enhances multi-task legal performance and reasoning quality through knowledge Augmentation and adapter-wise Parametric fusion. Instead of appending retrieved documents to the input, Legal-AP injects external legal knowledge and reasoning-critical patterns directly into model parameters via Low-Rank Adaptation (LoRA). Multiple task-specific adapters are dynamically composed to enable efficient, domain-aware reasoning across diverse legal tasks. Furthermore, we construct Legal-CA, a multi-task dataset covering criminal, administrative, and civil domains, with expert annotations emphasizing both substantive legal and reasoning aspects. Experiments show that Legal-AP consistently surpasses conventional RAG approaches and achieves comparable or superior performance to GPT-4o on several tasks, underscoring the promise of parametric knowledge integration for advancing reasoning-driven Legal AI.