I’ve been thinking a lot about what actually makes AI useful for DNA data.

A raw DNA file or VCF can contain thousands to millions of lines, and the hard part is rarely one isolated lookup. The real work involves navigating the file, finding the relevant variant or region, checking whether it was measured, connecting that result to sources like ClinVar, pharmacogenomics databases, GWAS/PRS resources, population references, and gene/disease evidence, then explaining what the evidence actually supports for each finding.

That feels like a good use case for an AI agent, because the agent can translate a human question into a sequence of technical lookups. It can search, compare sources, keep track of context, and explain the result in normal language. The model is good at navigation and explanation. The genome file and evidence databases are where the facts should come from.

That is the idea behind Genomi, an open-source local genomics harness for AI agents.

Genomi parses a VCF/gVCF or supported consumer DNA export, such as 23andMe or AncestryDNA, into a local SQLite index called the Active Genome Index. An agent with Genomi installed can work through that local index and call source-specific tools when useful. To start, I equipped Genomi with 80+ evidence-focused tools across variant lookup, gene/disease evidence, pharmacogenomics, GWAS/PRS context, population context, sequence/region utilities, and an evidence journal.

The technical goal is to make the agent auditable through architecture. The agent has structured tools for exact local lookup, source-specific evidence retrieval, provenance, evidence categories, and uncertainty. A good answer should make clear whether a variant was present in the file, whether the relevant region was covered, which source supports a claim, what kind of evidence is being used, and where the evidence runs out.

I see this as a data usability problem as much as an AI problem. Many people can download their DNA data, but the useful scientific context is scattered across changing databases and specialist tools. AI agents may be a good interface for that kind of continuously updating, source-heavy data when the factual layer comes from local lookup and external evidence, and the model handles routing, comparison, and explanation.

Genomi is early, experimental, and fully open source. It is meant for research and informational exploration. Clinical interpretation and medical decisions belong with qualified professionals.

I’m an open-source engineer, and I care a lot about building this with the right community around it. I can build quickly on the software side, but DNA tooling has many sharp edges: file formats, coverage, reference builds, variant normalization, evidence quality, privacy assumptions, and interpretation boundaries. This kind of project gets better when people who know the data actually try it, break it, challenge the assumptions, and point out where the tooling falls short.

So I’m sharing Genomi here because I want collaborators, testers, bug reports, edge cases, and technical criticism. If you work with raw DNA files, VCFs, annotation pipelines, ClinVar, pharmacogenomics, GWAS, PRS, population genetics, or consumer DNA exports, I’d be grateful for issues, pull requests, test cases, and feedback on what breaks.

Repo: https://github.com/exon-research/genomi

I built a local AI agent tool for exploring raw DNA files and genomics databases