--- title: "MotherDuck Flights: Agent-Native Data Pipelines | MotherDuck | MotherDuck" description: "Learn how MotherDuck Flights lets you build and schedule Python data pipelines with an AI agent, going from a single prompt to a running pipeline in minutes." canonical: "https://motherduck.com/videos/motherduck-flights-agent-native-data-pipelines/" --- [BACK TO VIDEOS](/videos/) [Webinar](/videos/?category=Webinar#videos-and-webinars-library)[Data Pipelines](/videos/?category=Data%20Pipelines#videos-and-webinars-library)[AI, ML and LLMs](/videos/?category=AI,%20ML%20and%20LLMs#videos-and-webinars-library)[MotherDuck Features](/videos/?category=MotherDuck%20Features#videos-and-webinars-library)[Python](/videos/?category=Python#videos-and-webinars-library) # Introducing Flights: Agent-Native Data Pipelines in MotherDuck 2026/06/17Featuring: [Doo Shim](/authors/doo-shim/) , [Garrett O'Brien](/authors/garrett-obrien/) ## 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](/product/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](/docs/getting-started/) 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. TABLE OF CONTENTS - What is MotherDuck Flights? - Three ways to create a Flight - From prompt to scheduled pipeline - What else can you do with Flights? - Availability Start using MotherDuck now! [Try 7 Days Free](https://auth.motherduck.com/authorize?app_source=web&response_type=code&client_id=bza3KWQpxRAFlTlRFXUo29AOg9xD7zcp&redirect_uri=https%3A%2F%2Fapp.motherduck.com%2F&state=STATE&auth_flow=signup&screen_hint=signup&ext-ph_distinct_id=05ea9db6-d989-4f6d-a2df-501278d4bdca) ## FAQS ### What is MotherDuck Flights and what does it do? 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. ### How do I create a Flight in MotherDuck? 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. ### Can Flights run custom Python libraries or packages that aren't open source? 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. ### How do Flights and Dives work together? 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. ## Related Videos [60:29](/videos/ai-agents-semantic-layer/)[2026-08-26](/videos/ai-agents-semantic-layer/) ### [Do AI Agents Need a Semantic Layer?](/videos/ai-agents-semantic-layer/) Do AI agents need a semantic layer? Evals comparing plain-text context with a Malloy semantic layer show where each wins, and what it costs. Stream AI, ML and LLMs BI & Visualization SQL [63:17](/videos/agentic-data-engineering-stack/)[2026-08-18](/videos/agentic-data-engineering-stack/) ### [Beyond Copilots: We're Building a Data Stack Live with AI Agents](/videos/agentic-data-engineering-stack/) Agentic data engineering in practice: AI agents build dlt ingestion into MotherDuck and a governed Lightdash semantic layer, live in an hour. Stream AI, ML and LLMs Data Pipelines Ecosystem [52:41](/videos/guides-context-layer-ai-agents/)[2026-08-13](/videos/guides-context-layer-ai-agents/) ### [Introducing Guides: Warehouse-Native Context for AI Agents](/videos/guides-context-layer-ai-agents/) Guides store business context inside your warehouse so AI agents write correct SQL. See how warehouse-native context works in MotherDuck. Stream AI, ML and LLMs MotherDuck Features [View all](/videos/)