KlavisAI

Klavis AI is a data infrastructure platform for AI teams that need verifiable, real-world training data for frontier agents. Instead of static Q&A pairs or demo-only tool calls, it delivers long-horizon coding tasks and agentic tool-use workflows executed in live, Dockerized environments. The platform is built for RL and SFT pipelines, where programmatic verification and granular rewards matter more than raw volume. It targets developers and AI teams at companies building production agents, from post-training labs to enterprise R&D groups. The core offering is two-fold. First, coding-agent data: tasks that require code editing, test writing, and debugging, with deterministic checks and Docker-packaged environments so you can run and verify agent behavior. Second, agentic tool-use data: realistic, state-mutating workflows across 600+ real tools, live SaaS apps, and production MCP servers. Every task comes with verifiable rewards—deterministic, rubric, or LLM-judge—so you can train reward models and fine-tune with confidence. Recent additions sharpen the enterprise angle. The Progressive Discovery MCP Server fetches tool definitions on demand to manage context windows, and Sandbox-as-a-Service offers deterministic MCP environments for benchmarking and RL training without touching production data. For security-sensitive teams, on-premises MCP deployment with RBAC is documented, and the company now holds SOC 2 Type 2 certification and full GDPR compliance, including EU infrastructure migration. Klavis is not a generic data labeler. It positions itself as the serious option for agent post-training, especially where deterministic, rubric, and LLM-judge rewards are needed. Backed by Y Combinator and with an open-source repo at 5.8k stars, it's a credible alternative to building your own environment infrastructure or relying on static datasets that don't capture the noise of real agentic workflows.