Klavis is a platform for managing Model Context Protocol (MCP) servers and providing sandboxed environments where AI agents can safely interact with external tools and services. It functions as an integration framework that orchestrates MCP server instances, exposes tools and resources for AI agents, and isolates agent interactions from production data through horizontally scalable sandbox environments.
The platform distinguishes itself through its ability to generate long-horizon agentic tasks that simulate realistic tool-use workflows with live SaaS applications and production MCP servers. It includes an agentic task generator that creates multi-step coding and tool-use challenges for training and evaluating AI agents, with deterministic world state seeding and programmatic outcome verification using tests, rubrics, and LLM-based judges. Klavis also optimizes agent context windows by structuring step-by-step interactions into efficient execution paths, and supports intent-driven action discovery that maps natural language queries to server capabilities without semantic search.
The platform provides capabilities for deploying over 100 prebuilt MCP server integrations with OAuth support via Docker or CLI commands, managing server instances through REST APIs, and authenticating external services through OAuth flows and API key management. It supports creating, resetting, and seeding isolated sandbox environments with predefined datasets, and allows agents to use MCP tools in sandboxed environments as if they were live services.
