The adoption of libraries provides developers with pre-existing functionality that is both robust and easy to use. As a code base grows over time, it is natural that the libraries become stale and require updates in order to sustain innovation. Nonetheless, updating a library comes at a cost; it can potentially introduce breaking changes in the code. Thus it may require a large portion of developer time for maintenance. To alleviate this, we propose to use artificial intelligence to parse release notes documentation and automatically recommend code updates to become compatible with new versions. The solution comes in the form of an IDE plugin that can automatically detect deprecated library usages in live code bases and suggest the recommended fixes in a user-friendly way. The system architecture is comprised of three components: a web crawler that sources and pre-processes library deprecation texts, a deprecations parser that transforms those texts into a structured form, and an IDE plugin that finds and updates the parsed deprecations in a code base. In order to validate our approach, we collect and annotate a dataset of 426 API deprecations from 7 popular Python libraries and obtain an overall average weighted subtree overlap of 31.3 and 45.9 on method deprecations. Finally, we demo our tool in the form of a plugin for the industry leading Python IDE PyCharm and test it on 33 internal repositories at J.P Morgan Chase.