Klavis AI

Klavis AI is a strong pick for AI labs and startups that need realistic, scalable environments for training agents on multi-step tasks. Its live sandboxes with 300+ services and full environment fidelity go beyond toy benchmarks, making it ideal for RL and eval pipelines. The page highlights integrations with MCP, CrewAI, and LlamaIndex, suggesting it works well in modern agent stacks. However, if your needs are simple single-step tasks or you're on a tight budget, the value proposition may not justify the cost. Its closest alternative might be manual cloud setup, but Klavis AI wins on automation and parallel isolation. Real-world caveat: you'll likely need to invest time in integrating with your existing training loops, though the API docs and community support help. The Y Combinator backing adds trust, but verify pricing and feature depth against your specific use case.

Klavis AI provides live, managed sandboxes for training and evaluating AI agents on complex, long-horizon tasks. Designed for frontier AI labs and developers building autonomous agents, it offers 300+ services, full environment fidelity, seeded state, state export, and parallel isolation. Key features include streamlined account management, instant state initialization, state export for verification, one-click reset and teardown, and isolated parallel runs. The platform supports training agents across browser sessions, computer actions, code repos, SaaS tools, and MCP servers. Backed by Y Combinator and SOC 2 Type II and GDPR compliant, Klavis AI positions itself as an enterprise-grade alternative to manual test environment management, removing infrastructure overhead for agentic RL and evaluation workflows.