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Introducing Flights: Agent-Native Data Pipelines in MotherDuck

2026/06/17

What is MotherDuck Flights?

Flights is MotherDuck's built-in Python runtime for data pipelines. You write Python code, it runs inside MotherDuck on a schedule, and the output lands directly in your databases. It works with AI agents through the MCP server — you describe the pipeline you want, and an agent like Claude or Cursor writes and deploys it. Any pip-installable package works in a Flight: DLT, pandas, API clients, whatever you need.

Three ways to create a Flight

MCP server — this is the primary interface. Point Claude, Cursor, or any MCP-compatible agent at the MotherDuck MCP server and describe what you want. The agent writes the Python, creates the Flight, and schedules it.

SQL table functions — call md_run_flight() from any SQL interface to trigger a Flight directly. Useful for chaining Flights together or triggering them from dbt and other SQL-based orchestrators.

MotherDuck UI — a lightweight interface for creating and monitoring Flights without touching code.

From prompt to scheduled pipeline

The webinar demo shows the full path in one session: Doo types a prompt asking for an ingest pipeline from a public API, the agent writes the Python using DLT, creates the Flight, and schedules it on a recurring cadence. The data lands in MotherDuck, and a Dive dashboard gets built on top of it — all without leaving the chat.

The cookbook has ready-to-use prompts for common ingest patterns you can replicate with your own sources.

What else can you do with Flights?

Beyond ingestion, Flights handles a few other categories of work:

  • Enrichment — call an LLM mid-pipeline to classify, tag, or summarize records
  • Orchestration — use md_run_flight() in SQL to build DAGs that chain multiple Flights
  • Automation — send Slack notifications, write to external systems, trigger any process that has a Python library
  • AI workloads — the demo included image processing via Modal, showing that Flights can coordinate with GPU-backed infrastructure through pip-installable SDKs

Availability

Flights is on Business and Enterprise plans and available to anyone on a trial. Static IP support for firewall whitelisting is not yet available. Private Python libraries can be included by referencing them in your requirements file or packaging them alongside your scripts.

FAQS

Flights is MotherDuck's built-in Python runtime for data pipelines. You write Python code, it runs inside MotherDuck on a schedule, and the output lands directly in your databases. It works with AI agents through the MCP server — you describe the pipeline you want, and an agent like Claude or Cursor writes and deploys it. Any pip-installable package works in a Flight: DLT, pandas, API clients, whatever you need.

There are three ways. Most people use the MCP server — connect an agent like Claude or Cursor to the MotherDuck MCP server, describe what you want, and the agent writes the Python and creates the Flight. You can also use SQL table functions. md_run_flight() triggers Flights from SQL, which works well if you're chaining pipelines or integrating with dbt. Or you can just use the MotherDuck UI to create and monitor Flights without writing code.

Yes. You can bring private Python libraries into a Flight by referencing them in your requirements file or packaging them alongside your scripts. They don't need to be on PyPI. The documentation on setting this up is thin, though — if you have a specific setup in mind, it's worth asking the MotherDuck team about your configuration. For publicly available packages, anything you can pip install works out of the box.

Flights handle ingestion and transformation — they get your data into MotherDuck. Dives are the visualization layer, where you build live dashboards on top of that data through the MCP server. The demo in the webinar shows both in sequence: a Flight ingests data from a public API on a schedule, then a Dive gets built on that database in the same chat session. You go from raw source to live dashboard without switching tools.

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