🚀 Introducing Hyperterse: The Missing "Data Access Layer" for the Agentic AI Stack

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.

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.

If you like the project, a star ⭐ would mean a lot! Let me know what you think in the comments. 👇