Compiling LLMs into a MegaKernel: A path to low-latency inference (zhihaojia.medium.com)

I've been working with the Featureform team on their new open-source project, [EnrichMCP][1], a Python ORM framework that helps AI agents understand and interact with your data in a structured, semantic way.

EnrichMCP is built on top of [MCP][2] and acts like an ORM, but for agents instead of humans. You define your data model using SQLAlchemy, APIs, or custom logic, and EnrichMCP turns it into a type-safe, introspectable interface that agents can discover, traverse, and invoke.

It auto-generates tools from your models, validates all I/O with Pydantic, handles relationships, and supports schema discovery. Agents can go from user → orders → product naturally, just like a developer navigating an ORM.

We use this internally to let agents query production systems, call APIs, apply business logic, and even integrate ML models. It works out of the box with SQLAlchemy and is easy to extend to any data source.

If you're building agentic systems or anything AI-native, I'd love your feedback. Code and docs are here: https://github.com/featureform/enrichmcp. Happy to answer any questions.

[1]: https://github.com/featureform/enrichmcp

[2]: https://modelcontextprotocol.io/introduction

Comments (10)

polskibus · 1h ago

This looks very interesting but I’m not sure how to use it well. Would you mind sharing some prompts that use it and solve a real problem that you encountered ?

simba-k · 1h ago

Imagine you're building a support agent for DoorDash. A user asks, "Why is my order an hour late?" Most teams today would build a RAG system that surfaces a help center article saying something like, "Here are common reasons orders might be delayed."

That doesn't actually solve the problem. What you really need is access to internal systems. The agent should be able to look up the order, check the courier status, pull the restaurant's delay history, and decide whether to issue a refund. None of that lives in documentation. It lives in your APIs and databases.

LLMs aren't limited by reasoning. They're limited by access.

EnrichMCP gives agents structured access to your real systems. You define your internal data model using Python, similar to how you'd define models in an ORM. EnrichMCP turns those definitions into typed, discoverable tools the LLM can use directly. Everything is schema-aware, validated with Pydantic, and connected by a semantic layer that describes what each piece of data actually means.

You can integrate with SQLAlchemy, REST APIs, or custom logic. Once defined, your agent can use tools like get_order, get_restaurant, or escalate_if_late with no additional prompt engineering.

It feels less like stitching prompts together and more like giving your agent a real interface to your business.

polskibus · 17m ago

are you saying that a current gen LLM can answer such queries with EnrichMCP directly? or does it need guidance via prompts (for example tell it which tables to look at, etc. ) ? I did expose a db schema to LLM before, and it was ok-ish, however often times the devil was in the details (one join wrong, etc.), causing the whole thing to deliver junk answers.

what is your experience with non trivial db schemas?

skuenzli · 1h ago

This is the motivating example I was looking for on the readme: a client making a request and an agent handling it using the MCP. Along with a log of the agent reasoning its way to the answer.

aolfat · 2h ago

Woah, it generates the SQLAlchemy automatically? How does this handle auth/security?

simba-k · 2h ago

Yep, we can essentially convert from SQLAlchemy into an MCP server.

Auth/Security is interesting in MCP. As of yesterday a new spec was released with MCP servers converted to OAuth resource servers. There's still a lot more work to do on the MCP upstream side, but we're keeping up with it and going to have a deeper integration to have AuthZ support once the upstream enables it.

knowsuchagency · 2h ago

Super interesting idea. How feasible would it be to integrate this with Django?

simba-k · 2h ago

Very! We had quite a few people do this at a hackathon we hosted this past weekend.

revskill · 1h ago

Do you provide prisma alternative ?

simba-k · 1h ago

Not sure exactly what you mean here. Prisma is an ORM for developers working with databases in TypeScript. EnrichMCP is more like an ORM for AI agents. It’s not focused on replacing Prisma in your backend stack, but it serves a similar role for agents that need to understand and use your data model.

It's also Python.