Architecture that automatically detects historical search contexts as well as behaviors related to a search query. Machine learning and hand-authored rules are employed to automatically identify search contexts. Historical information likely to be useful in the current context is surfaced. When a user enters a search query or executes another search behavior, past behaviors are exposed which are contextually related to the current behavior. The architecture also provides automatic discovery of historical contexts, features related to the contexts, and training or authoring of a system for classifying behavior into contexts, using some combination of the machine learning and/or hand-authored rules. A runtime system classifies the current user behavior into a context and surfaces contextual information to the user.