Hi r/datascience 👋

I spent most of my career working with databases, and one thing that keeps bugging me is how hard it is for AI agents to work with them.

Whenever I ask Claude or GPT about my data, it either invents schemas or hallucinates details. I then have to spend the next 10 messages re-explaining everything.

To fix that, I built Statespace. It's a free and open-source library to quickly build and share data apps that any AI agent on your team can discover and use.

So, how does it work?

Initialize a project, then ask your coding agent to help you build your data app:

$ claude "Help me document my schema and build tools to safely query it" 

Once ready, deploy and point any agent at it:

$ claude "Break down revenue by region for Q1 using https://demo.statespace.app" 

Works with everything

You can build and deploy data apps with:

• Any database - psql, duckdb, sqlite3, snowflake, bq. If it has a CLI or SDK, it works
• Any language - Python, TypeScript, or any script you already have
• Any file - CSVs, Parquets, JSONs, logs. Serve them as files that agents can read and query

Why you'll love it

• Safe by default - tool constraints ensure agents can never run DROP TABLE or DELETE
• Self-describing - context lives in the app itself, not in a system prompt you have to maintain
• Shareable - deploy to a URL, wire up as an MCP server, and share it with teammates

If you're tired of re-explaining your data to every agent, I really think Statespace could help. Would love your feedback!

TL;DR Streamlit for AI
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GitHub: https://github.com/statespace-tech/statespace

Docs: https://docs.statespace.com

A ⭐ on GitHub really helps with visibility!