We present challenges faced in making a domain-specific language (DSL) for graph algorithms adapt to varying requirements of generating a spectrum of efficient parallel codes. Graph algorithms are at the heart of several applications, and achieving high performance on graph applications has become critical due to the tremendous growth of irregular data. However, irregular algorithms are quite challenging to auto-parallelize, due to access patterns influenced by the input graph - which is unavailable until execution. Former research has addressed this issue by designing DSLs for graph algorithms, which restrict generality but allow efficient codegeneration for various backends. Such DSLs are, however, too rigid, and do not adapt to changes. For instance, these DSLs are incapable of changing the way of processing if the underlying graph changes. As another instance, most of the DSLs do not support more than one backends. We narrate our experiences in making an existing DSL, named Falcon, adaptive. The biggest challenge in the process is to retain the DSL code for specifying the underlying algorithm, and still generate different backend codes. We illustrate the effectiveness of our proposal by auto-generating codes for vertex-based versus edge-based graph processing, synchronous versus asynchronous execution, and CPU versus GPU backends from the same specification. © 2020 Association for Computing Machinery.