MAGneT is a synthetic counseling session dataset generated using a novel Multi-Agent framework including: specialized response agents (reflection, questioning, solutions, normalizing, psycho-education), a technique agent, a CBT agent, and a response generation agent. The generations are conditioned on client profiles taken from Lee et al., 2024). Unlike prior single-agent approaches, MAGneT better captures the structure and nuance of real counseling. Empirically, MAGneT substantially outperforms existing methods: experts prefer MAGneT-generated sessions in 77.2% of cases, and sessions generated by MAGneT yield 3.2% higher general counseling skills and 4.3% higher CBT-specific skills on cognitive therapy rating scale (CTRS). A open source Llama3-8B-Instruct model fine-tuned on MAGneT-generated data also outperforms models fine-tuned using baseline synthetic datasets by 6.9% on average on CTRS.