Multipath TCP (MPTCP) is a burgeoning transport protocol which enables the server to transmit the traffic across multiple network interfaces in parallel. Classic MPTCPs have good friendliness and practicality such as relatively low overhead, but are hard to achieve consistent high-throughput and adaptability, especially for the ability to flexibly balance the congestion among different subpaths. In contrast, learning-based MPTCPs can essentially achieve consistent high-throughput and adaptability, but have poor friendliness and practicality. In this paper, we proposed MPLibra, a combined multipath congestion control framework that can complement the advantages of classic MPTCPs and learning-based MPTCPs together. MPLibra periodically leverages both classic MPTCPs and learning-based MPTCPs to make decisions and select the better one based on real-time network feedbacks. Extensive simulations on NS3 show that MPLibra can achieve good performance and outperform state-of-the-art MPTCPs under different network conditions. MPLibra improves the throughput by 40.5% and reduces the file download time by 29.94% compared with LIA, achieves good friendliness and balances congestion timely. What’s more, on the basis of MPLibra, we propose MPLibra+ which adds a safety module and an optimized packet scheduler and is the upgrade version of MPLibra. MPLibra+ has better ability to cope with untrained network environment and achieve better performance on heterogeneous scenarios compared with MPLibra.