Astronomer Agents: AI Agent Toolkit for Airflow Data Engineering
AI agent tooling for data engineering workflows—unifying Airflow orchestration, warehouse analytics, and DAG development in one package.
Evidence: SECURITY.md provides vulnerability disclosure process; README mentions anonymous telemetry with opt-out; configuration uses environment variables and file permissions, but least privilege principle not explicit. Deductions: no explicit least privilege, user confirmation, data flow transparency, sensitive data handling details, dependency security audit, external effects control, rollback mechanism, source attribution.
Evidence: README and CI config consistent; tests disable telemetry; CI matrix covers Python and Airflow versions. Deductions: dependency availability not explicit, failure messages not detailed.
Evidence: README clearly identifies target users (data engineers) and scenarios (data discovery, DAG development); skill descriptions clear; supports multiple clients and environments. Deductions: capability boundaries not fully explicit, trigger precision relies on skill descriptions, environment fit not comprehensive.
Evidence: README well-structured; installation instructions detailed; examples and FAQ provided; Apache-2.0 license complete. Deductions: naming stability not explicit, known limitations not fully documented, versioning/changelog missing, maintenance responsibility not explicit.
Evidence: Output usability high (generates warehouse.md etc.); marginal value clear (automates data engineering workflows). Deductions: cost-benefit not quantified, relies on external services.
Evidence: README and CI config traceable, but no independent verification. Deductions: claim traceability limited, cross-source corroboration insufficient, fact-inference separation not explicit.
- Repository includes telemetry; verify data collection scope.
- Relies on external services (PyPI, GitHub); ensure supply chain security.
- No changelog provided; assess upgrade risks.
What does this agent do, and when should you use it?
Astronomer Agents is an Apache-2.0 licensed open-source project by Astronomer that equips AI coding agents with specialized capabilities for data engineering. The repository delivers three interlocking pieces: an MCP server (`astro-airflow-mcp`) exposing Airflow's REST API as Model Context Protocol tools for DAG management, triggering, and log retrieval; a CLI utility named `af` for terminal-based Airflow interaction; and over a dozen Skills—reusable prompt/instruction modules—that extend agents like Claude Code and Cursor with domain knowledge for warehouse discovery, lineage tracing, DAG authoring, testing, deployment, dbt integration, and Airflow upgrades. Installation is client-driven: a Claude Code plugin, a Cursor one-click MCP setup, or a generic `npx skills add` command for 25+ agents. The MCP server is compatible with any Airflow 2.x/3.x REST API endpoint, making it viable for both Astro and self-hosted deployments.
Concretely, the agent performs these operations: The Airflow MCP server (launched via uvx astro-airflow-mcp --transport stdio) converts Airflow REST API interactions into MCP tools, letting AI assistants list DAGs, trigger runs, fetch task logs, and check system health. The af CLI (invoked with uvx --from astro-airflow-mcp af) enables commands such as af health, af dags list, and af runs trigger <dag_id>. Skills extend agents with workflows: warehouse-init profiles configured databases (Snowflake, PostgreSQL, BigQuery, SQLAlchemy-compatible) to generate .astro/warehouse.md schema docs; analyzing-data runs SQL on a background Jupyter kernel to answer business questions; profiling-tables computes table statistics; lineage skills (tracing-downstream-lineage, tracing-upstream-lineage) analyze data flow; DAG skills (authoring-dags, testing-dags, debugging-dags) support code creation and validation; cosmos-dbt-core guides dbt Core integration; and migrating-airflow-2-to-3 assists version upgrades. Configuration lives in ~/.astro/agents/warehouse.yml and .env for warehouse credentials, with Airflow connection via environment variables or auto-discovery from a local dags/ folder.
- A data engineer using Claude Code inside an Airflow project wants to write a new DAG; they call the
authoring-dagsskill for best-practice suggestions and runtesting-dagsto validate locally. - An analyst who needs to understand warehouse schemas runs
warehouse-initto generate.astro/warehouse.md, then usesanalyzing-datato query customer tables via natural language. - A platform team debugging a failed DAG uses the
debugging-dagsskill for deep root-cause analysis and theafCLI to inspect task logs. - A data governance engineer traces the impact of a schema change using
tracing-downstream-lineageandtracing-upstream-lineageskills. - A team porting dbt Core projects into Airflow employs the
cosmos-dbt-coreskill to integrate with Astronomer Cosmos. - An organization upgrading from Airflow 2.x to 3.x follows the
migrating-airflow-2-to-3skill to refactor DAGs safely.
What are this agent's strengths and limitations?
- Deep Airflow integration: MCP server and
afCLI cover DAG listing, triggering, and log retrieval, reducing context switching. - Extensive Skills library spans the entire data engineering lifecycle—warehouse discovery, lineage, authoring, testing, debugging, deployment, dbt, and migration—and auto-triggers from natural language.
- Client-agnostic distribution: works with Claude Code via plugin, Cursor via MCP, and any MCP-compatible client; Skills installable to 25+ agents via npx.
- Open-source friendly: compatible with self-hosted Apache Airflow 2.x/3.x, not locked to Astronomer's managed platform.
- Warehouse connectors are built-in for only Snowflake, PostgreSQL, and BigQuery; other databases require SQLAlchemy configuration and manual driver installation.
- Runtime dependencies: requires
uvx(Python) andnpx(Node.js) availability, plus a network connection for package fetching and remote API calls. - The
afCLI collects anonymous telemetry unless disabled, which may raise data-governance concerns. - Advanced features like human-in-the-loop (HITL) to
airflow-hitlskill require Airflow 3.1+ and may not apply to older deployments.
How do you install or deploy this agent?
Prerequisites: Python with uv (or pip), Node.js for npx, and appropriate client. Installation options: 1. Claude Code: run claude plugin marketplace add astronomer/agents then claude plugin install astronomer-data@astronomer. 2. Cursor: use the one-click install button or manually add to ~/.cursor/mcp.json: {"mcpServers":{"airflow":{"command":"uvx","args":["astro-airflow-mcp","--transport","stdio"]}}}. 3. Generic MCP clients: execute uvx astro-airflow-mcp --transport stdio (optionally set AIRFLOW_API_URL, AIRFLOW_USERNAME, AIRFLOW_PASSWORD). 4. Skills: run npx skills add astronomer/agents --skill '*' (omit --skill to select individually). Configure warehouse connections in ~/.astro/agents/warehouse.yml and credentials in ~/.astro/agents/.env.
How do you use this agent?
- Trigger warehouse initialization: invoke
/astronomer-data:warehouse-initin the agent to generate.astro/warehouse.md. 2. Ask natural-language queries like "Show me revenue trends by product" or "Create a DAG that loads data from S3 to Snowflake daily"; the agent auto-invokes relevant skills. 3. Use theafCLI for direct terminal operations: aliasaf='uvx --from astro-airflow-mcp af', thenaf health,af dags list,af runs trigger <dag_id>. 4. For remote Airflow, setAIRFLOW_API_URL,AIRFLOW_USERNAME,AIRFLOW_PASSWORD, orAIRFLOW_AUTH_TOKENenvironment variables. Run from a directory containingairflow.cfgordags/to enable auto-discovery.
FAQ
Is this only for Astronomer's Astro platform?
AIRFLOW_API_URL to your self-hosted instance. Skills are tool-agnostic and work with any Airflow deployment.What credentials and permissions do I need?
warehouse.yml and secrets in .env. Schema discovery via warehouse-init only scans databases listed in the config; querying can access any database your user has permissions for.