Kimi AI and kvcache-ai Open Sources ‘AgentENV’: A Distributed System that Powers Agentic Reinforcement Learning (RL) Training for Kimi K3
Moonshot AI’s Kimi team and kvcache-ai have open-sourced AgentENV (AENV), a distributed platform for running agent environments at scale. AgentENV powers agentic reinforcement learning (RL) training for Kimi K3, Moonshot’s 2.8-trillion-parameter Mixture-of-Experts model. The code ships under an MIT license. Why Environment Infra Holds Back Agentic RL Agentic RL does not just sample text. It requires the model to act inside a real computer. Every rollout needs an isolated Linux
AgentENV is a distributed system for running isolated computer environments at scale, designed to support reinforcement learning training where AI models take actions inside real operating systems rather than just generating text. The system uses Firecracker microVMs, which provide kernel-level isolation while remaining lightweight enough for training, and includes features like rapid snapshots, pausing, resuming, and forking that allow expensive setup steps to run once and then branch into parallel training runs. This addresses a fundamental trade-off in agent training: containers are fast but lack proper isolation, while full virtual machines isolate well but start slowly and consume memory when idle. The system is compatible with existing E2B APIs, meaning teams can run it without rewriting their agent code, and it has been open-sourced under an MIT license.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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Also reported by The Verge, SiliconANGLE and MarkTechPost
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Also reported by SiliconANGLE
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