PyAirflowTester

Static analysis, dependency intelligence, and risk scoring for Airflow DAGs and dbt projects.

Not to be confused with PyDBTGuard or PyBeamGuard: PyAirflowTester validates Airflow DAGs (and dbt runs); PyDBTGuard analyzes dbt manifests; PyBeamGuard analyzes Beam/Dataflow pipelines.

Which project do I need?

Part of the Mullassery open-source family. These tools have similar names — here is the short version of who does what:

If you need to…	Use
Build a repository's dependency graph and match it against vulnerability advisories (Python, Rust, Node, containers)	PyDeepMesh
Understand why a Python dependency exists, who added it, and whether it is actually used	PyDependencyCheck
Statically analyze a dbt project (risky tests, test blast radius, cost)	PyDBTGuard
Statically analyze Apache Beam / Dataflow pipelines (cost, hot keys, reliability)	PyBeamGuard
Validate Airflow DAGs and dbt runs	PyAirflowTester
Simulate cascading-failure blast radius across infrastructure, data pipelines, and AI systems	PyBlastRadius
Check the quality of the data itself at runtime (schema, drift, anomalies)	StatGuardian
Discover devices, services, and vulnerabilities on a network	PyNetworkIntel
Discover and score API security risk (real traffic, agent/MCP, Cedar policy)	PyAPICheck
Trace and root-cause AI agent failures	PyAgentHound
Diagnose RAG retrieval failures	PyVectorHound
Orchestrate local, dependency-aware tasks (DAG scheduler + content-addressed cache)	PyTaskWireCheck

Browse all projects →

What it is

PyAirflowTester helps you catch reliability and security issues before deployment by statically analyzing Airflow DAGs, dbt manifests, and airflow.cfg. It builds a unified dependency graph to understand blast radius and impact, computes aggregate risk scores, and exports findings in formats suitable for CI (including SARIF for GitHub code scanning).

Features / Highlights

• Static analysis: 33 rules covering DAGs, dbt manifests, and Airflow configuration (secrets, SLAs, cycles, config issues, etc.).
• Unified dependency graph: Combines Airflow DAG edges with dbt lineage from manifest.json.
• Impact & blast radius: Understand what breaks downstream if a node changes.
• Risk scoring: Aggregate 0-100 risk score with filtering by severity.
• CI-friendly: JSON, HTML, and SARIF output for GitHub code scanning.
• Web dashboard: Optional FastAPI/uvicorn dashboard (serve) to visualize graph and findings.
• Library API: Dependency intelligence primitives for programmatic use (impact, blast radius, risk, observability helpers).

Who it's for / Use cases

• CI gating: Fail builds on critical findings or low risk scores.
• Pre-deployment checks: Assess blast radius before changing high-impact nodes.
• Security reviews: Catch hardcoded secrets, risky configs, and missing guardrails.
• dbt + Airflow alignment: See end-to-end dependencies across orchestration and transformation.
• Technical debt tracking: Identify orphans, cycles, and untested/high-importance nodes.

What it is not

• Not a live runtime correlator: The Analyzer/connect path for correlating against live Airflow metadata or dbt run history is explicitly stubbed (AnalyzerNotImplementedError).
• Not a test runner: It analyzes structure and metadata, not executes DAGs or dbt tests.
• Pure Python by default: A Rust core exists in src/ but is not wired into the CLI and not shipped; building it is optional.
• Alpha maturity: v0.5.2 - core scanning, graph analysis, and scoring are working; runtime correlation is planned.

Project status

Version 0.5.2. Core commands (scan, score, rules, dependency ..., serve) are functional. The runtime correlation feature is stubbed (not implemented). See ROADMAP_HONEST.md for details.

Installation

pip install pyairflowtester

With web dashboard:

pip install "pyairflowtester[web]"

From source:

git clone https://github.com/mullassery/pyairflowtester.git
cd pyairflowtester
pip install -e ".[dev]"

Requires Python 3.10+.

Quick start

# Scan current directory for DAGs, dbt, airflow.cfg
pyairflowtester scan . --dags dags/ --dbt dbt/ --airflow-cfg airflow.cfg

# Get aggregate risk score
pyairflowtester score . --dags dags/

# List all rules
pyairflowtester rules

# Build dependency graph and analyze blast radius
pyairflowtester dependency build --dags dags/ --dbt-manifest dbt/target/manifest.json
pyairflowtester dependency blast-radius -n dag_my_dag

# Launch web dashboard
pyairflowtester serve --port 8080

Programmatic usage

from pyairflowtester.dependency_intelligence import (
    UnifiedGraphBuilder,
    ImpactAnalysisEngine,
    BlastRadiusEngine,
)

graph = UnifiedGraphBuilder.build_unified_graph(
    dag_files=["dags/my_dag.py"],
    dbt_manifest="dbt/target/manifest.json",
)

impact = ImpactAnalysisEngine(graph).analyze("dag_my_dag")
print(f"Impact score: {impact.impact_score:.1%}, impacted: {len(impact.impacted_nodes)}")

blast = BlastRadiusEngine(graph).analyze(["dag_my_dag"])
print(f"Blast radius: {blast.blast_radius} nodes, deployable: {blast.deployable}")

Documentation

• ROADMAP_HONEST.md - Known limitations and planned work
• Contributing
• Security policy
• Code of conduct
• Changelog

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.