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 }}

The step fails the job automatically when any rules fail, blocking broken data from reaching production. Set fail-on-failure: 'false' to report without blocking.

Offline mode (no API key required):

      - name: Validate data quality (offline)
        uses: aegis-dq/aegis-dq@v0.7.0
        with:
          rules-file: rules.yaml
          db: data/warehouse.duckdb
          no-llm: 'true'

Action inputs

Input	Default	Description
rules-file	rules.yaml	Path to rules YAML
db	:memory:	DuckDB file path
warehouse	duckdb	duckdb · postgres · redshift
pg-dsn	—	PostgreSQL / Redshift connection DSN
no-llm	false	Skip LLM — free offline validation
llm	anthropic	anthropic · openai · ollama
llm-model	(provider default)	Override the default model
fail-on-failure	true	Fail the step when rules fail
version	(latest)	Pin a specific aegis-dq version
anthropic-api-key	—	Required when llm: anthropic
openai-api-key	—	Required when llm: openai

Action outputs

Output	Description
rules-checked	Total rules evaluated
passed	Rules that passed
failed	Rules that failed
pass-rate	Pass rate as a decimal (e.g. "91.67")
report-json	Absolute path to the full JSON report

Using outputs in downstream steps:

      - name: Validate data quality
        id: dq
        uses: aegis-dq/aegis-dq@v0.7.0
        with:
          rules-file: rules.yaml

      - name: Post summary
        run: echo "Pass rate: ${{ steps.dq.outputs.pass-rate }}%"

---

Demo

╭──────────────────────────────────────────────────────╮
│ Aegis DQ  —  RetailCo E-commerce Demo                │
│ LLM: amazon.nova-pro-v1:0 via AWS Bedrock            │
╰──────────────────────────────────────────────────────╯

✓ Pipeline complete in 7.1s · 12 rules · $0.0056 LLM cost

╭──────────────── Validation Summary ─────────────────╮
│  Rules checked  │  12                               │
│  Passed         │  1   │  Failed  │  11             │
│  Pass rate      │  8%  │  Cost    │  $0.005576      │
╰─────────────────────────────────────────────────────╯

LLM Diagnoses
  orders_customer_fk  →  Order placed with customer_id=99 that does not exist.
                         Likely cause: customer deleted or test record not cleaned up.

  products_sku_unique →  Duplicate SKU-001 — two products share the same identifier.
                         Likely cause: duplicate import from supplier feed.

Remediation SQL (LLM-generated)
  orders_status_valid          UPDATE orders SET status = 'SHIPPED' WHERE status = 'DISPATCHED';
  products_price_positive      UPDATE products SET price = ABS(price) WHERE price < 0;
  products_stock_non_negative  UPDATE products SET stock_quantity = 0 WHERE stock_quantity < 0;

---

Hermes integration — conversational data quality

Aegis DQ integrates with Hermes via MCP. Point Hermes at your context and your warehouse — it handles the rest.

You (Hermes chat)
     │
     ▼
Hermes — memory, scheduling, multi-channel delivery
     │  MCP
     ▼
Aegis DQ — rules engine, LLM diagnosis, audit trail
     │
     ▼
Your warehouse + Your LLM

Setup (2 steps):

pip install aegis-dq

Add to ~/.hermes/config.yaml:

mcp_servers:
  aegis:
    command: aegis
    args: [mcp]
    env:
      ANTHROPIC_API_KEY: "${ANTHROPIC_API_KEY}"

Define a pipeline manifest once:

# pipeline.yaml
name: orders-dq
rules: ./rules.yaml
database: ./warehouse.duckdb
kb:
  - ./policy.md     # business rules, SLAs, compliance docs
  - ./schema.md
goal: |
  Run all rules. For every failure explain the business impact,
  likely root cause, and a concrete remediation step.

Then just ask:

Load the pipeline at pipeline.yaml and run it.

Hermes calls load_pipeline → run_validation → returns a structured report. No flags, no re-explaining context on every run.

Full setup guide: aegis-dq.dev/integrations/hermes · MCP listing: glama.ai/mcp/servers/aegis-dq/aegis-dq

---

Why Aegis?

	Aegis DQ	Great Expectations / Soda	Monte Carlo / Anomalo
Open source	✅ Apache 2.0	✅	❌ Commercial
Agentic LLM diagnosis + RCA	✅	❌	✅ Proprietary
SQL auto-fix proposals	✅	❌	❌
Audit trail (per-decision log)	✅	Partial	✅ Proprietary
Pluggable LLM (Anthropic, OpenAI, Bedrock, Ollama)	✅	❌	❌
dbt integration	✅	✅	Partial
Portable open rule standard	✅	Partial	❌
ML anomaly detection	✅ built-in	❌	✅ Proprietary

---

Install

pip install aegis-dq

Extra	What it adds
aegis-dq[bigquery]	BigQuery adapter
aegis-dq[databricks]	Databricks adapter
aegis-dq[athena]	AWS Athena adapter
aegis-dq[postgres]	PostgreSQL / Redshift adapter
aegis-dq[snowflake]	Snowflake adapter
aegis-dq[rest]	REST API server (FastAPI + uvicorn)
aegis-dq[openai]	OpenAI LLM provider
aegis-dq[airflow]	Airflow AegisOperator
aegis-dq[mcp]	MCP server for Hermes, Claude Desktop, and any MCP-compatible agent
aegis-dq[ml]	scikit-learn anomaly detection

---

5-minute quickstart

Step 1 — Install

pip install aegis-dq

Step 2 — Seed a demo database

import duckdb

con = duckdb.connect("demo.db")
con.execute("""
    CREATE TABLE orders AS
    SELECT i AS order_id, 'placed' AS status, i * 9.99 AS revenue
    FROM range(1, 10001) t(i)
""")
# introduce some bad data
con.execute("UPDATE orders SET order_id = NULL WHERE order_id % 200 = 0")
con.execute("UPDATE orders SET revenue = -5.00 WHERE order_id % 500 = 0")
con.close()

Step 3 — Generate rules from your schema (no hand-writing)

export ANTHROPIC_API_KEY=sk-ant-...

# Generate rules from table schema alone
aegis generate orders --db demo.db --output rules.yaml

# Or point it at a policy doc and get business validation rules too
aegis generate orders --db demo.db --kb docs/orders_policy.md --output rules.yaml

The LLM introspects your schema and generates not_null, accepted_values, between, and custom_sql rules automatically. Generated rules are stamped status: draft — review and promote to active.

Step 4 — Run

aegis run rules.yaml --db demo.db

Run without an API key (pass/fail only, no LLM diagnosis):

aegis run rules.yaml --db demo.db --no-llm

---

Pipeline

Every aegis run passes your data through a LangGraph pipeline:

rules (Python / YAML)
    │
    ▼
  plan ──► parallel_table ──► reconcile ──► remediate ──► report
                 │
         ┌──────────────────┐
         │  per table:      │
         │  execute         │
         │  classify        │
         │  diagnose        │  ← concurrent across all tables
         │  rca             │
         └──────────────────┘

• plan — parse and validate rules, build an execution graph
• parallel_table — concurrently fans out per table: execute all rules, classify failures by severity, diagnose with LLM, and trace root causes
• reconcile — compare results against expected thresholds
• remediate — LLM proposes a targeted SQL fix for each diagnosed failure
• report — structured JSON + optional Slack notification

---

Rule types (31 total)

Category	Types
Completeness	not_null not_empty_string null_percentage_below
Uniqueness	unique composite_unique duplicate_percentage_below
Validity	sql_expression between min_value_check max_value_check regex_match accepted_values not_accepted_values no_future_dates column_exists
Referential	foreign_key conditional_not_null
Statistical	mean_between stddev_below column_sum_between
Timeliness	freshness date_order
Volume	row_count row_count_between custom_sql
Cross-table	reconcile_row_count reconcile_column_sum reconcile_key_match
ML / Anomaly	zscore_outlier isolation_forest learned_threshold

Example rule:

rules:
  - apiVersion: aegis.dev/v1
    kind: DataQualityRule
    metadata:
      id: orders_revenue_non_negative
      severity: critical
      owner: revenue-team
      tags: [revenue, validity]
    scope:
      warehouse: duckdb
      table: orders
    logic:
      type: sql_expression
      expression: "revenue >= 0"

---

Generate rules with the LLM

Instead of writing rules by hand, let Aegis introspect your table schema and generate a draft rules file:

# Schema-aware structural rules (not_null, between, unique, accepted_values...)
aegis generate orders --db warehouse.duckdb --output orders_rules.yaml

Add a --kb document — any plain text or markdown file describing your business logic — and the LLM generates business validation rules alongside structural ones:

aegis generate orders \
  --db warehouse.duckdb \
  --kb docs/orders_policy.md \
  --output orders_rules.yaml

What goes in a KB file? Anything your team knows about the data:

# orders_policy.md
- status must be one of: placed, confirmed, shipped, delivered, cancelled
- amount must be greater than 0; refunds are handled in a separate table
- customer_id must reference a valid customer (no test accounts: id > 1000)
- order_date must not be in the future
- discount_pct must be between 0 and 0.5 (max 50% discount)

The LLM turns these into accepted_values, sql_expression, between, and foreign_key rules automatically. Generated rules are stamped status: draft — review, promote to active, and commit.

All aegis generate options:

Flag	Default	Description
--db	—	DuckDB file for schema introspection
--kb	—	Business-context file (text/markdown)
--output	rules.yaml	Output YAML file
--max-rules	20	Cap on number of rules generated
--no-verify	false	Skip SQL verification of generated rules
--save-versions	false	Persist rules to version store
--provider	anthropic	LLM provider
--model	(default)	Override model

---

Warehouse adapters

Adapter	Install	Status
DuckDB	built-in	✅ GA
BigQuery	aegis-dq[bigquery]	✅ GA
Databricks	aegis-dq[databricks]	✅ GA
AWS Athena	aegis-dq[athena]	✅ GA
Postgres / Redshift	aegis-dq[postgres]	✅ GA
Snowflake	aegis-dq[snowflake]	✅ GA

---

LLM providers

Provider	Install	Default model
Anthropic (Claude)	built-in	claude-haiku-4-5
OpenAI	aegis-dq[openai]	gpt-4o-mini
Ollama (local)	aegis-dq[ollama]	llama3.2
AWS Bedrock	pip install boto3	amazon.nova-pro-v1:0

Switch providers at the CLI:

aegis run rules.yaml --llm openai --llm-model gpt-4o
aegis run rules.yaml --llm ollama --llm-model llama3.2
aegis run rules.yaml --llm bedrock --llm-model amazon.nova-pro-v1:0

---

Integrations

Integration	What it does
GitHub Action	CI/CD gate — fails the job when rules fail
aegis-dq[rest]	REST API server — aegis serve
aegis-dq[airflow]	AegisOperator — drop-in Airflow task
aegis-dq[mcp]	MCP server for Hermes, Claude Desktop, Cursor, and any MCP-compatible agent
aegis dbt generate	Convert dbt manifest.json to Aegis rules

---

CLI reference

Command	Description
aegis init	Generate a starter rules.yaml
aegis validate <config>	Check YAML syntax + schema (no warehouse needed)
aegis generate <table>	LLM-generate rules from table schema
aegis run <config>	Run validation, diagnose failures, produce a report
aegis rules list	Browse built-in rule templates
aegis audit trajectory <run-id>	Inspect the LLM decision trail for a past run
aegis audit search <query>	Full-text search across audit logs
aegis dbt generate <manifest>	Convert a dbt manifest to Aegis rules
aegis mcp	Start the MCP server for Hermes, Claude Desktop, or any MCP client

aegis run flags:

Flag	Default	Description
--db	:memory:	DuckDB file path
--llm	anthropic	LLM provider
--llm-model	(provider default)	Override model name
--no-llm	false	Skip LLM diagnosis entirely
--output-json	(none)	Write full JSON report to file
--notify	(none)	Slack webhook URL
--notify-on	failures	When to notify: all · failures · critical

---

Roadmap

Phase	Version	Items	Status
Foundation	v0.1	Core agent, DuckDB, CLI, audit trail	✅ Done
Differentiate	v0.5	BigQuery, Databricks, Athena, Airflow, Ollama, RCA, ShareGPT export, FTS5 search, dbt, MCP	✅ Done
Quality	v0.7	SQL verification pipeline, rule versioning, aegis generate (LLM + KB), GitHub Action, ML anomaly detection	✅ Done
Mature	v1.0	Postgres, REST API, parallel subagents, VS Code extension, eval suite, banking/healthcare packs	🚧 In progress

Full issue tracker: github.com/aegis-dq/aegis-dq/issues

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Contributing

Contributions are welcome. See CONTRIBUTING.md to get started.

Good first issues: label:good first issue

License

Apache 2.0