ai-agents
Browse all articles, tutorials, and guides about ai-agents
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One Key for Claude, GPT, and Gemini: the Gateway Pattern
Using three model providers usually means three API keys, three SDKs, and three billing relationships sprayed across your code. An AI gateway collapses that to one credential and one OpenAI-compatible endpoint. I proved it on a Neon Function: the same call answered by GPT, Claude, and Gemini.
A Postgres-Backed MCP Server in ~20 Lines
Most of what an MCP server does is run database queries on behalf of an AI agent. So I put one right next to the database. Here is a Postgres-backed MCP server built on Neon Functions, deployed onto a database branch, with the code, a live client test, and the repo.
Streaming an AI Agent Without a Function Timeout
Long agent loops and long token streams run into the same wall: a serverless function that hits its execution cap and cuts the connection. Neon Functions hold long-lived streaming connections by default. I deployed two endpoints to prove it: one streamed for 90 seconds, the other streamed an agent token by token starting at 466 ms.
I Gave an AI Agent a Database, Compute, Storage, and Models From One CLI
An AI agent usually needs four accounts: a database, somewhere to run, object storage, and a model provider. I wired all four from a single Neon credential and had a deployed image-generating agent in a few minutes. Here is the actual build log, the config that ties it together, and the honest caveats.