High-throughput DNA sequencing has enabled us to look beyond consensus reference sequences to the variation observed in sequences within organisms; their haplotypes. Recovery, or assembly of haplotypes has proved computationally difficult and there exist many probabilistic heuristics that attempt to recover the original haplotypes for a single organism of known ploidy. However, existing approaches make simplifications or assumptions that are easily violated when investigating sequence variation within a metagenome. We propose the "metahaplome" as the set of haplotypes for any particular genomic region of interest within a metagenomic data set and present Hansel and Gretel, a data structure and algorithm that together provide a proof of concept framework for the recovery of true haplotypes from a metagenomic data set. The algorithm performs incremental haplotype recovery, using smoothed Naive Bayes - a simple, efficient and effective method. Hansel and Gretel pose several advantages over existing solutions: the framework is capable of recovering haplotypes from metagenomes, does not require a priori knowledge about the input data, makes no assumptions regarding the distribution of alleles at variant sites, is robust to error, and uses all available evidence from aligned reads, without altering or discarding observed variation. We evaluate our approach using synthetic metahaplomes constructed from sets of real genes and show that up to 99% of SNPs on a haplotype can be correctly recovered from short reads that originate from a metagenomic data set.