Background, Leucoptera coffeella (Guerin-Meneville, 1842) is a moth species (Lyonetiidae, Lepidoptera) pest that causes severe losses to coffee crops. Further information about its genomic data is required to allow molecular strategies for the development of sustainable pesticides and to gain in-depth knowledge on phylogenetics. However, the closest complete genome available is within the superfamily level (Yponomeutoidea). Here we report the generation of the first long-read genome, transcriptome and proteome results of L. coffeella and the in silico analysis performed in these molecular levels to investigate genes involved in the siRNA processing. Results, PACBio and paired-end Illumina combined DNA sequencing from pupae samples resulted in more than 436 Gb subreads and 31Mb reads with N50 read length of 15,512 nt, mean read length 13.8 Kb and max read length 420.7 Kb. Additionally, 20Gb data of short DNA sequencing was combined to produce 1,984 contigs comprising 397 Mb in total. The longest and shortest scaffold sizes are 10,809,567 nt and 15,247 nt, respectively (mean size 200,178 nt). The N50 scaffold was 275,598 nt and the GC content was 36.10%. Predicted coding DNA sequences counted 39.930 gene models. Searching of 5286 BUSCO groups revealed 91.7 percent of completeness (single and duplicated genes combined) compared to lepidoptera genomes (lepidoptera_odb10). Flow cytometry showed the 1C DNA content is approximately 295 Mb. RNA-Seq from seven development stages resulted in 28294 identified transcripts. Additionally, proteomics from immature stages resulted in 2045 proteins matching the gene models. Conclusions, This first nuclear genome of the Lyonetiidae family brings valuable molecular resources to study Lepidoptera genomes. Genome, transcriptome and proteome sequencing to raise genome annotation precision may resolve uncovered taxonomic issues. In addition, these combined approaches provide insights into plant-insect interaction players, as horizontally transferred genes (HGT) and endosymbionts. Put together, the generated data enables the development of molecular tools towards sustainable biotechnology solutions for lepidopteran pest control.