Hyperterse - The production data interface for AI agents

Hey PH! 👋

I’ve been heads-down building Hyperterse, and I’m excited to finally share it with you.

We are currently witnessing a massive shift from passive chatbots to active AI Agents. But for these agents to be useful, they need access to your production data, and right now, providing that access is a nightmare.

The "Data Access Gap" is real, and it’s expensive.

• The Reliability Trap: Relying on direct Text-to-SQL is dangerous; LLMs lack schema awareness and frequently hallucinate column names or relationships.
  

• The Security Risk: Giving agents raw database access opens the door to prompt injection, essentially handing a "blank check" to your sensitive data.
  

• The Developer Toil: To fix this, we usually hand-code API wrappers. But this creates a "maintenance nightmare" of rigid state machines, contributing to the 5+ hours per week developers already lose to unproductive "toil".
  

Hyperterse fixes this by treating data access as a declarative infrastructure.

It is an open-source, high-performance runtime that bridges your database and your AI agents using a "Define Once, Use Everywhere" philosophy.

How it works:

• Declarative Config: You define your queries once in a simple `.terse` file.

• Auto-Generation: Hyperterse automatically generates typed REST endpoints, OpenAPI specs, and LLM-friendly metadata.

• MCP Native: It instantly creates Model Context Protocol (MCP) tools that agents like Claude or Cursor can discover and call immediately.

• Security-by-Abstraction: The agent never sees your raw SQL or connection strings; it only interacts with secure, validated tools, effectively eliminating SQL injection risks.

Why you should care

Hyperterse supports PostgreSQL, MySQL, and Redis out of the box. It reduces the time-to-production for AI data tools by up to 90%, freeing you from writing CRUD boilerplate so you can focus on core AI logic.

If you are tired of building custom connectors for every new agent, give Hyperterse a spin.

🔗 Check it out on GitHub: https://github.com/hyperterse/hyperterse

🌏 Website: https://hyperterse.com

📖 Documentation: https://docs.hyperterse.com

If you like the project, a star ⭐ on GitHub would mean a lot!