High performance computing systems are today composed of tens of thousands of processors and deep memory hierarchies. The next generation of machines will further increase the unbalance between I/O capabilities and processing power. To reduce the pressure on I/Os, the in situ analytics paradigm proposes to process the data as closely as possible to where and when the data are produced. Processing can be embedded in the simulation code, executed asynchronously on helper cores on the same nodes, or performed in transit on staging nodes dedicated to analytics. Today, software environ- nements as well as usage scenarios still need to be investigated before in situ analytics become a standard practice. In this paper we introduce a framework for designing, deploying and executing in situ scenarios. Based on a com- ponent model, the scientist designs analytics workflows by first developing processing components that are next assembled in a dataflow graph through a Python script. At runtime the graph is instantiated according to the execution context, the framework taking care of deploying the application on the target architecture and coordinating the analytics workflows with the simulation execution. Component coordination, zero- copy intra-node communications or inter-nodes data transfers rely on per-node distributed daemons. We evaluate various scenarios performing in situ and in transit analytics on large molecular dynamics systems sim- ulated with Gromacs using up to 1664 cores. We show in particular that analytics processing can be performed on the fraction of resources the simulation does not use well, resulting in a limited impact on the simulation performance (less than 6%). Our more advanced scenario combines in situ and in transit processing to compute a molecular surface based on the Quicksurf algorithm.