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A comprehensive guide to building AI agent swarms using Kimi K2.6, an open-weight 1-trillion-parameter MoE model from Moonshot AI. The guide covers swarm architecture, the MuonClip optimizer for training stability, and the orchestration pattern using Kimi for execution with Claude for planning.
AgentJet is a distributed swarm training framework for LLM agent reinforcement learning that decouples agent rollouts from model optimization, enabling heterogeneous multi-agent RL, multi-task training, fault tolerance, and live code iteration with 1.5-10x training speedup. It also introduces an automated research system capable of autonomously conducting multi-day RL studies on large-scale clusters.
A developer built 10 gamified, interactive slide decks within the AgentSwarms platform to teach Agentic AI concepts like ReAct loops, multi-agent swarms, and production RAG, using active recall instead of passive reading.