SLayer, a semantic layer maintained by your agent
Hello HN! If you want to connect your agent to a database (say, to build a data analyst chatbot or any kind of agentic app) today you have 2 options: an SQL MCP server or a semantic layer. SQL MCP is the easiest path to setup, especially if you also have a .md knowledge base which the agent can update. It gets quite messy quickly though, especially if there's many interactions or DB is large. Generated SQL is hard to review if you want to understand where the numbers came from, and related queries can be hard to align and compare. The natural alternative is a semantic layer, which is an…
SLayer, a semantic layer maintained by your agent launched on May 11, 2026 with 12 votes, #1,101 of 20,538 launches that month and more than 90% of that year's launches.
In plain words
SLayer is a semantic layer that connects AI agents to databases for building applications like data analyst chatbots. Rather than generating raw SQL, it provides a structured interface with data models, measures, dimensions, and filters that handle database queries behind the scenes. This approach makes generated queries easier to review and compare, and reduces complexity when managing large databases or frequent interactions. It's designed for developers building agentic applications that need reliable, auditable database access.
written from the facts on this page · September 2026
From the sources
An embeddable, expressive semantic layer for AI agents and humans — by Motley - MotleyAI/slayer
An expressive, embeddable semantic layer for AI agents and humans. SLayer enables AI-powered data analytics on top of your warehouse. Agents get a governed, shared surface through which they access your data and metrics and give you reliable answers. SLayer handles database connectivity (read-only), SQL translation, common data transformations, and row-level security, so LLMs and humans don't have to. Adapt it to your workflows, not the other way around. Manage definitions easily with an agent or by yourself. SLayer can be used as a standalone tool or imported as a Python library, easily embeddable into any Python app. Use it for powering analytical MCP servers or APIs or simply to query…from github.com
In the maker’s words, at launch
Hello HN! If you want to connect your agent to a database (say, to build a data analyst chatbot or any kind of agentic app) today you have 2 options: an SQL MCP server or a semantic layer. SQL MCP is the easiest path to setup, especially if you also have a .md knowledge base which the agent can update. It gets quite…
Does a similar job
all alternatives →
A "Codebase" as an MCP Server2025 · github.com · ▲20We built a reference MCP server that lets your editor/agent learn a codebase directly from source (signatures, types, AST, comments). Docs are optional. The reference impl runs on our open-source project’s codebase. Why we built it Docs are important, but they add another abstraction layer between your code and your users. Keeping them at the right quality is hard (especially at a startup), and LLM-generated docs are often mediocre until you invest real polish. Exposing code to the model in a structured way keeps answers grounded and current, and it’s always available. You can even…
Representing Agents as MCP Servers2025 · github.com · ▲58Hey HN! A few months ago we shared mcp-agent (https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework that implements every agent pattern from Anthropic’s Building Effective Agents blog [3] and handles MCP server/client management seamlessly. Our core bet is that connecting LLMs to tools, resources, and external systems will soon be MCP-native by default. Today we're launching a significant update: Agents as MCP servers. Currently "agentic" behavior exists only on the MCP client side – clients like Claude or Cursor use MCP servers to solve tasks.…
MCP Memory – Fast Agent Memory Using Google's OKF and SQLite FTS5Aug 2026 · github.com · ▲70An OKF-backed Model Context Protocol (MCP) server delivering persistent long-term memory and SQLite FTS5 search for AI agents. - fellowgeek/mcp-memory
Gram FunctionsNov 2025 · speakeasy.com · ▲119Hi HN, Georges from the Gram team here. We're really excited to share a major update to Gram, our platform for curating tools for agents, in the form of Gram Functions. Gram initially started out by letting users upload OpenAPI documents and turning them into tools that can be curated into small MCP servers that power your agentic use cases. We've known for quite some time that many existing REST APIs are simply not suitable as LLM tools and we wanted to solve this problem by letting users simply write code and we would take care of deploying that code for them and hosting MCP servers. We…
Risk Analysis Database of Every MCP ServerFeb 2026 · mcp.armor1.ai · ▲22Hi HN, We’re building security tooling around agentic AI systems. Today, we're releasing our public MCP catalog with detailed risk analysis for every MCP server we've found on the internet: https://mcp.armor1.ai/mcp-directory We all love agents and the power that MCPs unlock: suddenly your AI assistant can query databases, manage files, call APIs, and interact with the real world. But when we started adopting MCPs ourselves, we kept running into the same nagging questions: Is this MCP safe? Where is my data actually going? Could it execute destructive actions? Is it…
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video
Source: Show HN post ↗ · launch text quoted in part
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
🌱 Private, quiet space for thinking. Simple app for .md files. - zakirullin/files.md
Dev tools · May 2026 · github.com