Enrichr API

Enrichr REST API

Use the MCP tool first

For "run enrichment on these genes", call the enrichr_analysis MCP tool. It filters to significant terms, formats for reading, handles background correction, and manages token budget. Reach for the raw API only when you need something it does not expose:

The script

scripts/enrichr.py — no dependencies beyond the standard library.

python scripts/enrichr.py libraries                    # every live library + term count
python scripts/enrichr.py genemap TP53                 # TP53 appears in 214 libraries
python scripts/enrichr.py download KEGG_2026 kegg.gmt  # 352 terms as GMT
python scripts/enrichr.py enrich genes.txt KEGG_2026   # unfiltered JSON table
python scripts/enrichr.py enrich genes.txt KEGG_2026 --background bg.txt

Import it for batch work:

from enrichr import enrich_batch, enrich, genemap, download_library

clusters = {"cluster_0": [...], "cluster_1": [...]}   # e.g. Seurat/scanpy markers
results = enrich_batch(clusters, "KEGG_2026")          # {name: [term, ...] | None}

Endpoints

Base: https://maayanlab.cloud/Enrichr. Background correction lives on a different service: https://maayanlab.cloud/speedrichr/api.

A shortId from addList is a shareable web page — good for collaborators and methods sections: https://maayanlab.cloud/Enrichr/enrich?dataset=<shortId>

Traps

These are the reason this skill exists. Each one has bitten this repo.

A retired library returns {}, not an error. Enrichr retires libraries constantly and the API does not tell you — /enrich just answers with an empty object, which reads as "no enrichment" rather than "that library is gone". Never trust a hardcoded or remembered library name. Check datasetStatistics. As of 2026-07 there are 228 live libraries, and names drift in ways that look like typos (BioPlanet_2019 is live, Bioplanet_2019 is dead).

Prefer the current vintage. Many libraries ship a dozen years of snapshots (GO_Biological_Process_2013 … _2026). Use the newest unless the user is reproducing an old analysis. Current generation: GO_Biological_Process_2026, GO_Cellular_Component_2026, GO_Molecular_Function_2026, KEGG_2026, Reactome_Pathways_2024, GWAS_Catalog_2025.

Enrichr emits bare Infinity, which is not valid JSON. The odds ratio is infinite whenever every one of a term's background genes is in your gene list — with a custom background that is routine, not rare (36 of 82 terms in a real run). Consequences by language:

• JavaScript: JSON.parse throws outright. You must sanitize the text first.
• Python: json.loads accepts it silently — so you will not notice — and then json.dump writes Infinity straight back out, producing an invalid JSON file that breaks whatever reads it next. allow_nan=False does not save you; it raises instead of converting. Replace non-finite floats with None before encoding. scripts/enrichr.py does this in finite().

Background correction is a different service, and its 500s are ambiguous. Custom backgrounds are not available on /Enrichr/enrich at all — you must go through speedrichr (addList → addbackground → backgroundenrich). An HTTP 500 from backgroundenrich means one of two very different things:

1. You used the wrong userListId. The two services keep separate ID namespaces, and submitting your genes to /Enrichr/addList and then passing that id to backgroundenrich fails with a 500 every time — not a 400, not a helpful message. Submit to speedrichr/addList instead. (Verified: a classic id 500s on all retries where a speedrichr id returns 82 terms. The ranges even look different — classic ids are ~1.3e8, speedrichr ~2.0e9.)
2. The service is having a moment. It also throws genuinely transient 500s that clear on retry, sometimes across a whole library family at once, which can look convincingly like "this library doesn't support backgrounds". It does. Retry.

So: check your id namespace first, then retry. Do not fall through to the uncorrected endpoint without saying so loudly.

Background correction changes the answer enormously. It is not a refinement. For a 16-gene DNA-damage list against KEGG_2026, whole-genome gives 50 significant terms with a top adjusted p of 8.5e-19; a 40-gene background gives 14 significant terms with a top adjusted p of 1.6e-4. If the gene list came from a restricted universe — expressed genes, a targeted panel, detected proteins — the whole-genome default is simply the wrong test and will overstate significance. The background must be the universe the list was drawn from, not the list itself.

Enrichr rate-limits (HTTP 429). You will hit this within a handful of requests once you start looping — which is exactly what batch work does. Pace the loop and back off on 429. scripts/enrichr.py sleeps between lists and retries 429s.

/enrich and /export return every term with any overlap, not just significant ones. A 16-gene list against KEGG_2026 returns 82 terms, of which 50 are significant. That is a feature when you want to compute your own FDR or plot the full p-value distribution, and a token disaster if you paste it into context unfiltered. Filter before showing results to anyone.

Interpreting results

Each term is a 7-tuple, mapped to names in scripts/enrichr.py:

rank, term, p_value, odds_ratio, combined_score, genes, adj_p_value

Use adj_p_value (Benjamini-Hochberg), not p_value, for significance; < 0.05 is the usual cut. combined_score is Enrichr's own ranking heuristic (ln(p) × z) and is fine for ordering but is not a statistic anyone will accept in a paper. Report the adjusted p-value and the overlap.