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In this final year project it is presented Triggerflow, a new extensible and serverless in design platform for the orchestration of serverless workflows in the cloud. Triggerflow follows an Event-Condition-Action model with stateful and dynamic triggers to be able to filter, aggregate, process, and route events from different sources. Thanks to the extensibility offered by the trigger’s fully programmable condition and action with a consistent and persistent state, we can orchestrate different serverless workflow abstractions such as Acyclic Directed Graphs, State Machines, and Code Workflows. Triggerflow has been implemented on open source systems of the Cloud Native Computing Foundation, such as CloudEvents, Kubernetes and KEDA (Kubernetes Event-dirven autoscaling). Triggerflow has been proven to be able to process large numbers of events per second, aggregating and synchronizing massively parallel tasks on serverless functions efficiently and with fault tolerance. A real scientific workflow on Triggerflow has been implemented in order to study the feasibility of event-based orchestration for this type of works. This work has been developed as part of the elaboration of the article "Triggerflow: Event-based orchestration for Serverless Workflows", presented and accepted to the ACM Distributed Event-Based Systems conference in 2020.