leap-laboratories/discovery-engine

**Find novel, statistically validated patterns in tabular data** — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.
Made by Leap Laboratories.
---
What it actually does
Most data analysis starts with a question. Disco starts with the data.
Without biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like "patients aged 45–65 with low HDL *and* high CRP have 3× the readmission rate" — without you needing to hypothesise that interaction first.
Each pattern is:
- **Validated on a hold-out set** — increases the chance of generalisation
- **FDR-corrected** — p-values included, adjusted for multiple testing
- **Checked against academic literature** — to help you understand what you've found, and identify if it is novel.
The output is structured: conditions, effect sizes, p-values, citations, and a novelty classification for every pattern found.
**Use it when:** "which variables are most important with respect to X", "are there patterns we're missing?", "I don't know where to start with this data", "I need to understand how A and B affect C".
**Not for:** summary statistics, visualisation, filtering, SQL queries — use pandas for those
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Quickstart
Get an API key:
Or create a key at disco.leap-labs.com/developers.
Run your first analysis:
python
await engine.discover(
file="data.csv", # path, Path, or pd.DataFrame
target_column="outcome", # column to predict/explain
analysis_depth=2, # 2=default, higher=deeper analysis, lower = faster and cheaper
visibility="public", # "public" (always free, data and report is published) or "private" (costs credits)
column_descriptions={ # improves pattern explanations and literature context
"bmi": "Body mass index",
"hdl": "HDL cholesterol in mg/dL",
},
excluded_columns=["id", "timestamp"], # see "Preparing your data" above
use_llms=False, # Defaults to False. If True, runs are slower and more expensive, but you get smarter pre-processing, summary page, literature context and novelty assessment. Public runs always use LLMs.
title="My dataset",
description="...", # improves pattern explanations and literature context
)
python
Submit without waiting
run = await engine.run_async(file="data.csv", target_column="outcome", wait=False)
print(f"Submitted {run.run_id}, continuing...")
... do other things ...
result = await engine.wait_for_completion(run.run_id, timeout=1800)
python
result = engine.run(file="data.csv", target_column="outcome", wait=True)
or: pip install discovery-engine-api[jupyter] for notebook compatibility
json
{
"mcpServers": {
"discovery-engine": {
"url": "https://disco.leap-labs.com/mcp",
"env": { "DISCOVERY_API_KEY": "disco_..." }
}
}
}
python
estimate = await engine.estimate(file_size_mb=10.5, num_columns=25, analysis_depth=2, visibility="private")
estimate["cost"]["credits"] → 55
estimate["account"]["sufficient"] → True/False
| patient_id | age | bmi | smoker | outcome |