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Give your AI agents database access without the risk.

1 starsPython

Give Your ORM Superpowers

by dipankarsarkar·Jun 18, 2026·2 points·0 comments

AI Analysis

●●●BangerBig BrainSolve My ProblemBold Bet

Policy enforcement at the ORM layer beats prompt-layer security for agent database access.

Strengths
  • Supports 8 ORMs across Python and TypeScript including SQLAlchemy, Prisma, and Drizzle
  • Field-level policies hide or mask PII automatically without prompt engineering
  • Built-in tenant scoping and query budgets prevent runaway agent costs
Weaknesses
  • AI agent database access market is still emerging and unproven
  • Competes with LangChain tools and MCP servers already solving agent-tool integration
Category
Target Audience

Developers building AI agents with database access

Similar To

LangChain Tools · MCP Servers · Vellum

Post Description

I am obsessed with ORMs and the simple reason was that I didn't want to keep using postgres or mysql on my local system.

Jk, The real reason has always been to enforce access policy, do easy CRUD interfaces and so on.

Now with all these agents being built, I figured they should not be making SQL calls directly (just like junior eng :P).

So i present ormAI, this is the official blurb.

OrmAI wraps your existing ORM models in a policy-enforced runtime. Your agents get typed tools for querying and writing data — while you keep control over what they can see and do. No raw SQL. No prompt injection into your database. Just safe, auditable, tenant-scoped database tools.

I did benchmark it with Text to SQL tools, and for obvious reasons this did not do badly at all.

Check it out, write more AI agents.

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