Competition and cooperation can boost the performance of search. Both can be implemented with a portfolio of algorithms which run in parallel, give hints to each other and compete for being the rst to nish and deliver the solution. In this paper we present a new generic framework for the application of algorithms for distributed constraint satisfaction which makes use of both cooperation and competition. This framework improves the performance of two di erent standard algorithms by one order of magnitude and can reduce the risk of poor performance by up to three orders of magnitude. We expect our new methods to be similarly bene cial for any tree-based distributed search and describe ways on how to incorporate them.