Flow graph models have recently become increasingly popular as a way to express parallel computations. However, most of these models either require specialized languages and compilers or are library-based solutions requiring coarse-grained applications to achieve acceptable performance. Yet, graph algorithms and other irregular applications are increasingly important to modern high-performance computing, and these applications are not amenable to coarsening without complicating algorithm structure. One effective existing approach for these applications relies on active messages. However, the separation of control flow between the main program and active message handlers introduces programming difficulties. To ameliorate this problem, we present Avalanche, a flow graph model for fine-grained applications that automatically generates active-message handlers. Avalanche is built as a C++ library on top of our previously-developed Active Pebbles model; a set of combinators builds graphs at compile-time, allowing several optimizations to be applied by the library and a standard C++ compiler. In particular, consecutive flow graph nodes can be fused; experimental results show that flow graphs built from small components can still efficiently operate on fine-grained data.