Pyramis is a library for quantitative evaluation of hierarchical UML statecharts with non-Markovian stochastic timing and probabilistic choices. It implements an efficient numerical approach for transient analysis until absorption and steady-state analysis, separately evaluating the Semi-Markov Process (SMP) of each model component. As Pyramis facilitates code reusability, maintainability, and extensibility, it has been easily integrated with the FaultFlow library for dependability analysis of component-based systems, supporting efficient quantitative evaluation of stochastic static fault trees without repeated events. In this paper, we use Pyramis for quantitative evaluation of workflows where activities have non-Markovian stochastic duration and where precedence constraints define a Directed Acyclic Graph (DAG). Workflows have a Service Level Objective (SLO) on their end-to-end (E2E) response time distribution at low workloads of requests. Pyramis efficiently derives the workflow E2E response time distribution, yielding a stochastic upper bound for topologies with non-well-nested precedence DAGs. We report experiments for a workflow with topology derived from a real benchmark and execution times obtained from a dataset of the literature. Results are promising in terms of tradeoff between accuracy and complexity.