ETLai 0.5.2

Local, folder-driven Dagster CSV transformation engine with hot-folder sensors, AI-assisted pipeline creation, and Tkinter configuration UI.

ETLai

A local, AI-assisted data transformation engine built on Dagster. Install it, scaffold a project, and let Claude Code create new pipelines — from CSV transformations to API ingestion.

Install

pip install ETLai

Requires Python 3.10+ and Tkinter (ships with most Python installations).

Quick start

etlai init ~/my-etl
cd ~/my-etl

# Option A: Create a pipeline with the 5-agent system
etlai create "join sales data with product catalog and compute revenue"

# Option B: Manual pipeline creation (see ARCHITECTURE.md)
etlai sync
etlai run

This starts a Dagster dev server at http://localhost:3000. Enable sensors in the UI, then drop CSV files into pipelines/<name>/inbox/.

Commands

Command	Purpose
etlai init <dir>	Scaffold project (templates, workflow, agents, example pipelines)
etlai create "request"	Create pipeline via 5-agent system with gate validation
etlai sync	Validate manifests, create folders, prompt path: ask, check env files
etlai run	Start Dagster dev server
etlai list	Show registered pipelines with atoms and min_files

How it works

Each pipeline is defined by a manifest (pipelines/<name>/manifest.yaml) that wires together:

• Atoms — reusable, single-unit-of-work transformations (domain-agnostic)
• Forms — first-run config UI or passthrough (no-UI, reads config.json)
• Triggers — what causes the pipeline to run (file sensor, cron, or both)
• Config — business logic (column names, thresholds, expressions) in config.json

Trigger fires → load files → configure (form/config.json) → execute steps
                               ├→ processed/ + output/     (success)
                               └→ rejected/ + *.error.txt  (failure)

Pipeline types

Single atom (file-based)

Drop files into inbox/. Sensor detects stable files and triggers the pipeline.

name: vlookup_rollnumber
atom: vlookup
form: vlookup_column_picker
min_files: 2

API-based (data ingestion)

Fetch data from REST APIs on a schedule. No inbox files needed.

name: fetch_hr_data
atom: api_fetch
form: passthrough
min_files: 0
env_file: ~/.etlai/secrets.env
requires_env: [HR_API_TOKEN]
trigger:
  rules:
    - type: schedule
      cron: "0 8 * * *"

Composite (multi-step chains)

Chain multiple atoms with reference file injection and branching.

name: sales_reconciliation
min_files: 1
inputs:
  - name: sales_data
    role: transient
    description: "Weekly sales CSV"
  - name: product_catalog
    role: reference
    pattern: "catalog.csv"
    inject_as:
      step: 0
      param: right_file
steps:
  - atom: vlookup                # step 0: join sales + catalog
  - atom: computed_column        # step 1: compute revenue
  - atom: group_aggregate        # step 2: aggregate by category
    input_from: 1                # reads step 1, not step 2 (branching)
  - atom: rename_columns         # final step: rehydrate column names
trigger:
  rules:
    - type: inbox_files
      min_files: 1

Shipped atoms (10)

Atom	Operation
vlookup	Left join two CSVs on a key column
computed_column	New column from expression (e.g. price * qty)
group_aggregate	Group + sum/mean/min/max/count aggregations
filter_rows	Keep rows matching a condition
flag_rows	Add boolean column from condition (keeps all rows)
rename_columns	Rename columns via mapping
sort_rows	Sort by one or more columns
groupby	Group by column with count
api_fetch	HTTP fetch → CSV (JSON/XML/CSV response parsing)
mock_generate	Generate synthetic data from CSV headers

Shipped forms

Form	Description
passthrough	No UI — reads config.json and passes to atom
vlookup_column_picker	Join column multi-select (Tkinter)
groupby_picker	Single column selection (Tkinter)

Creating pipelines with AI

The 5-agent system (etlai create) orchestrates pipeline creation through 7 phases with deterministic gate validators between each:

Agent	Role
Business Analyst	Loops with user to capture pipeline requirements
Separator	Strips domain terms, produces generic operation graph
Atom Smith	Matches/creates atoms (firewalled from business data)
Assembler	Wires manifest + config with real business values

The firewall physically hides business_mapping.json from Atom Smith, ensuring atoms remain domain-agnostic by construction.

See ARCHITECTURE.md for full details on the agent system.

Key concepts

inject_as (reference file injection)

Reference files in reference/ are injected into specific atom params at runtime:

inputs:
  - name: catalog
    role: reference
    pattern: "catalog.csv"
    inject_as: {step: 0, param: right_file}

input_from (non-linear step routing)

Steps read the previous step's output by default. Use input_from to branch:

steps:
  - atom: computed_column     # step 0
  - atom: rename_columns      # step 1 (reads step 0)
  - atom: group_aggregate     # step 2 reads step 0, NOT step 1
    input_from: 0

config.json

Step 0 reads flat top-level config. Steps 1+ read step_N keys:

{
  "left_column": "sku",
  "right_column": "sku",
  "step_1": {"expression": "price * qty", "output_column": "revenue"},
  "step_2": {"mapping": {"revenue": "Total Revenue"}}
}

Credentials

API credentials live in env files outside the project (e.g. ~/.etlai/secrets.env). etlai sync validates required vars are present without printing values.

Resolution order

• Atoms: ./atoms/<name>.py → etlai.atoms.<name> (package)
• Forms: ./forms/<name>.py → etlai.forms.<name> (package)

User files take precedence over shipped ones.

Development

git clone <repo>
pip install -e ".[dev]"
pytest --cov=etlai

See CONTRIBUTING.md for workflow, ARCHITECTURE.md for internals, and TESTS.md for testing guide.