Eigrel

A programming language for Data, Machine Learning and AI.

Write what you want. Let the compiler decide how to run it.

Eigrel is a declarative language for data engineering, machine learning and GenAI. You describe datasets, transformations, features and models in one language; the compiler decides whether each step becomes Python, SQL, Spark or something else.

dataset customers from csv("data/customers.csv")

transform customers {
    filter age >= 18
    select age, income, purchases, churned
}

features customers {
    age
    income
    purchases
}

model churn = random_forest {
    trees = 100
}

train churn {
    target = churned
}

evaluate churn {
    metrics = [accuracy, precision, recall, f1]
}

Status

Eigrel is at v0.5 — ML: programs read CSV, Parquet, JSON, SQL databases and BigQuery, clean missing values, train scikit-learn, XGBoost or Spark MLlib models and register them in MLflow. They are checked for meaning before anything runs and compiled to Python, Apache Spark or SQL, so the same program runs on your laptop or on a Spark cluster. See the roadmap.

Getting started

Install from PyPI (Python 3.13+). The python extra adds pandas and scikit-learn, which eigrel run needs; add sql to read databases or bigquery for BigQuery (pip install "eigrel[python,sql]"):

pip install "eigrel[python]"
eigrel init churn             # creates churn/main.eig and sample data
eigrel run churn/main.eig

churn: random_forest classification, trained on 315 rows, validated on 79
  accuracy   0.7975
  precision  0.7442
  recall     0.8649
  f1         0.8000

CLI

The compiler catches mistakes before anything runs, and points at the exact spot:

error: column 'income' does not exist here; available columns: age, purchases, churned
 --> churn.eig:9:5
  |
9 |     income
  |     ^

How it works

source → lexer → parser → AST → semantic analysis → IR → Python backend → pandas + scikit-learn

eigrel ir shows the graph the backends work from:

%0 = load csv("data/customers.csv")  # customers
%1 = filter %0 (age >= 18)  # customers
%2 = select %1 [age, income, purchases, churned]  # customers
%3 = train %2 random_forest(trees=100, max_depth=8) classification features=[age, income, purchases] target=churned validation=0.2 seed=42  # churn
%4 = evaluate %3 [accuracy, precision, recall, f1]  # churn

Data sources

dataset customers from csv("data/customers.csv")
dataset events    from json("data/events.jsonl")
dataset orders    from sql(env("DATABASE_URL"), "shop.orders")
dataset users     from bigquery("my-project.analytics.users")

eigrel compile --target sql turns the data part of a program into a query for the engine each source lives in:

-- dataset users (bigquery, bigquery dialect)
SELECT `age`, `income`, `country` FROM `project.dataset.users` WHERE ((`age` > 18) AND (`income` <> 0));

Cleaning data, XGBoost and MLflow

transform customers {
    fill income = 0
    drop_missing age, purchases
}

model churn = xgboost {
    trees = 200
    learning_rate = 0.05
}

register churn {
    name = "customer-churn"
}

register logs the parameters, metrics and model to MLflow and registers a new version; the registered model takes raw rows, because encoding is part of its pipeline. It needs the xgboost and mlflow extras (pip install "eigrel[python,xgboost,mlflow]"; on macOS XGBoost also needs OpenMP: brew install libomp). See examples/mlflow.eig.

Spark

The same program runs on Spark with --target spark, which needs the spark extra and Java 17 or newer:

pip install "eigrel[python,spark]"
eigrel run --target spark churn/main.eig
eigrel compile --target spark churn/main.eig -o churn_spark.py   # e.g. for spark-submit

Files are read natively, sql() through JDBC and bigquery() through the spark-bigquery connector, and models train with Spark MLlib. See the Spark backend for how it differs from the Python backend.

Docker

The image on GitHub Container Registry (linux/amd64 and linux/arm64) includes the python and sql extras. Mount your project at /work and run as your own user, so the container can read your files and anything it writes stays yours:

alias eigrel='docker run --rm --user "$(id -u):$(id -g)" -v "$PWD:/work" ghcr.io/thentsation/eigrel'
eigrel init churn
eigrel run churn/main.eig

Paths must be inside the current directory, since only it is mounted. To build the image locally, use make docker-build and make docker-run.

AI agents

Eigrel is designed to be written by AI agents and verified by the compiler. Point your agent at llms.txt — the complete grammar in one file — and have it loop eigrel check --json FILE.eig until "ok": true; every error comes back with an exact line and column, so the fix is mechanical. What passes check is guaranteed to compile, and the same program runs on a laptop or a Spark cluster.

eigrel check --json churn.eig

Language

The full syntax is in docs/LANGUAGE.md. Examples live in examples/.

Contributing

Bug reports, language proposals and pull requests are welcome. Read the contributing guide to get set up, and note that this project follows a Code of Conduct. Security issues go through SECURITY.md.

License

Licensed under the Apache License 2.0.