I built a database for AI agents
Hey HN, I just spent the last few weeks building a database for agents. Over the last year I built PostHog AI, the company's business analyst agent, where we experimented on giving raw SQL access to PostHog databases vs. exposing tools/MCPs. Needless to say, SQL wins. I left PostHog 3 weeks ago to work on side-projects. I wanted to experiment more with SQL+agents. I built an MVP exposing business data through DuckDB + annotated schemas, and ran a benchmark with 11 LLMs (from Kimi 2.5 to Claude Opus 4.6) answering business questions with either 1) per-source MCP access (e.g. one Stripe…
I built a database for AI agents launched on April 7, 2026 with 12 votes, #1,176 of 21,248 launches that month and more than 90% of that year's launches.
In plain words
This is a database built for AI agents that uses SQL and annotated schemas to help agents query business data from multiple sources. It's designed for developers building AI agents that need to answer business questions across platforms like Stripe and HubSpot. The creator benchmarked it against other approaches and found it achieved significantly higher accuracy while using far fewer tokens and running faster than traditional tool-based methods.
written from the facts on this page · September 2026
From the sources
The agent-first database. Contribute to DinobaseHQ/dinobase development by creating an account on GitHub.
Dinobase syncs 100+ sources — APIs, databases, files, MCP servers — annotates your data, and makes it SQL-ready forfrom github.com
In the maker’s words, at launch
Hey HN, I just spent the last few weeks building a database for agents. Over the last year I built PostHog AI, the company's business analyst agent, where we experimented on giving raw SQL access to PostHog databases vs. exposing tools/MCPs. Needless to say, SQL wins. I left PostHog 3 weeks ago to work on…
Does a similar job
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InsForgeMar 2026 · insforge.dev · ▲622We built InsForge because we wanted to use pure prompts to build a production-grade app faster. In practice, AI isn’t reliable enough when generating code or configuring backend pieces like schema, RLS, auth, or functions. Postgres MCP and Supabase MCP are a good starting point, but they felt more like API wrappers for agents than something designed to guide AI safely through backend tasks. So we built our own MCP server on top of Postgres and added a set of context-engineering tools to make prompt-driven workflows more predictable. It gradually turned into a Postgres-based BaaS. Key…
oqoqoAug 2026 · oqoqo.ai · ▲325Most benchmarks today exist in curated environments and do not translate well to the real world. We built Oqoqo to bridge this gap. Oqoqo makes it super simple to build realistic evals and custom benchmarks for tasks users actually care about. Oqoqo can: - Reliably measure how agent friendly your product surfaces are against Codex, Claude Code, OpenClaw, Hermes, Pi, Opencode, Cursor, GitHub Copilot - Regression test MCP, CLI, skills, SDK, and any agent facing interface (we are continuously using Oqoqo to dogfood and improve our own MCP/CLI) - Create and share custom benchmarks for how…
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