: Growing importance of distributed data mining techniques has recently attracted attention of researchers in multiagent domain. Several agent-based application have been already created to solve the data mining tasks. Most of these applications are based only on agentification of classic distributed data mining techniques. In this article we present a novel framework MALEF (MultiAgent Learning Framework) designed for both the agent-based distributed machine learning as well as data mining. Proposed framework is based on (i) the exchange of metalevel descriptions of individual learning process among agents and (ii) online reasoning about learning success and learning progress by learnts. Abstract architecture enables agents to exchange models of their local learning processes. We introduce also a full range of methods to integrate these processes. This allows us to apply existing agent interaction mechanisms to distributed data mining tasks thus leveraging the powerful coordination met...