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15 starsTypeScript

Tracecast – open-source generative data apps built on top of Marimo

by malachyd·May 18, 2026·6 points·0 comments

AI Analysis

●●SolidShip ItNiche Gem

Cursor-style chat for Marimo apps, but hides edit mode which limits flexibility.

Strengths
  • LangGraph agent decouples exploratory analysis from final app generation cleanly.
  • AES-GCM encryption for data-source credentials at rest is a serious security touch.
  • Docker compose stack includes healthchecks ensuring services start in correct order.
Weaknesses
  • Hiding Marimo edit mode removes the ability to manually tweak generated code.
  • Only supports four data sources; Snowflake and BigQuery users are the primary target.
Target Audience

Data analysts and engineers who use Marimo notebooks

Similar To

Marimo Pair · Hex · Streamlit

Post Description

Hi HN, I'm Malachy, the founder of Tracecast. This project lets you generate interactive data apps on top of your data, using a Cursor-style AI chat. It stitches together Marimo, LangGraph agents, and data warehouse query tools. It has an Apache 2.0 license.

The initial use case that spurred this project was business analytics, specifically generating product usage dashboards.

This project's main inspiration is Marimo, an open source python notebook that can be "queried with SQL, run as a script, and deployed as an app" [1]. The recent release of Marimo Pair [2] demonstrated the power of connecting AI agents like Claude Code to Marimo notebooks directly. This project seeks to build on that work by incorporating a LangGraph agent with two key abilities: (1) the ability to execute queries against a connected data warehouse (such as Snowflake); (2) the ability to write Marimo notebooks.

When prompted, the LangGraph agent will run exploratory data analysis using database query tools. Then, it creates a polished Marimo notebook that's presented to the user in read-only mode. This project intentionally hides the Marimo edit mode. That means that the end user only ever sees a finished, read-only data app. Ease of use and trust in AI output were the main drivers behind this decision.

4 data sources are currently supported: Snowflake, BigQuery, Postgres, and Metabase. The code for the database query tools was derived from Google's open source MCP Toolbox for Databases.

There is currently no support for MCP. Instead, data query tools are hardcoded. This decision was made to ensure high quality AI queries and limit tool bloat.

This is an early stage project, and is configured to only run locally at this time.

[1] https://github.com/marimo-team/marimo [2] https://news.ycombinator.com/item?id=47678844

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