Stock News API vs Wolbarg

REST API for relevance-scored, ticker-linked financial news: a real-time stock news API for trading bots, AI agents, and dashboards. Free tier, no credit card.

Why most local multi-agent workflows don't need PostgreSQL for semantic memory, and when they actually do.

About Stock News API

REST API for relevance-scored, ticker-linked financial news: a real-time stock news API for trading bots, AI agents, and dashboards. Free tier, no credit card. Every article arrives scored for relevance and linked to tickers, so a trading bot or an agent backend can filter the wire at request time. Filter server-side Symbol, category, minimum relevance and delta polling are query params, so the wire is cut before it costs you a token. Enough to evaluate the feed and build a prototype.

About Wolbarg

Why most local multi-agent workflows don't need PostgreSQL for semantic memory, and when they actually do. When people build an AI application, the database choice looks obvious: PostgreSQL, maybe Redis for caching, maybe pgvector for semantic search. This post is about that decision, and why SQLite is often the better fit for local multi-agent workflows. Local agents usually run on one machine, one process, maybe a handful of agents sharing context. Strip away the marketing and the list is short: low latency, simplicity (no migrations, users, or roles to wrestle with), local execution (offline works, no cloud bill), reliability (transactions, not half-written memories), and persistence (a file you can copy, back up, or delete).