Developers heavily rely on Local Code Search (LCS)---the execution of a text-based search on a single code base---to find starting points in software maintenance tasks. While LCS approaches commonly used by developers are based on lexical matching and often result in failed searches or irrelevant results, developers have not yet migrated to the various research approaches that have made significant advancements in LCS. We hypothesize that two of the major reasons for this lack of migration are as follows. First, developers do not know which approach is the best, due to a lack of comparative field studies and the discrepancies in the underlying LCS process that these research approaches address. Second, developers lack access to a stable implementation of most of the research approaches. To address these issues, we studied a number of LCS approaches, distilled the general component structure underlying these approaches and, based on this structure, developed a LCS tool and framework, called Sando. Currently used by developers at ABB, Inc. and elsewhere, Sando also supports the flexible extension of its components to rapidly disseminate research advancements, and allows for user-based evaluation of competing approaches.