Aegis DQ
The open-source agentic data quality framework. Point it at your policy docs and warehouse — it generates rules, validates your data, diagnoses every failure with LLM root-cause analysis, and proposes SQL fixes. Run from the CLI, Airflow, GitHub Actions, or conversationally via Hermes.
Real-world result: 12 AML policy docs → 55 rules generated → 11 BSA/OFAC violations detected → all diagnosed → $0.01 total LLM cost.
- 31 rule types — completeness, uniqueness, validity, referential integrity, statistical, ML anomaly detection
- 6 warehouse adapters — DuckDB, Postgres/Redshift, BigQuery, Databricks, AWS Athena, Snowflake
- Pluggable LLMs — Anthropic Claude, OpenAI, Ollama (local), AWS Bedrock
- Agentic pipeline — plan → parallel validation → LLM diagnose → RCA → SQL remediate → report
- Hermes + MCP — run full pipelines conversationally; listed on Glama.ai
GitHub Actions — Quick Start
Add a data quality gate to any workflow in under 2 minutes:
# .github/workflows/data-quality.yml
name: Data Quality
on: [push, pull_request]
jobs:
data-quality:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Validate data quality
uses: aegis-dq/aegis-dq@v0.7.0
with:
rules-file: rules.yaml
db: data/warehouse.duckdb
anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
