HeimWall vs Wolbarg

Signal, not surveillance. See how your engineers use Cursor, Claude Code, and Copilot: catch leaked secrets, PII, and confidential data, without blocking them or reading...

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

About HeimWall

Signal, not surveillance. See how your engineers use Cursor, Claude Code, and Copilot: catch leaked secrets, PII, and confidential data, without blocking them or reading their prompts. Nothing sits in your send path, your prompt reaches the tool untouched, and what you type into an AI tool is never sent to us. To read your prompt before it is sent, the app reads the composer through the macOS Accessibility API and watches the clipboard. Accessibility is a wide permission and macOS does not offer a narrower one for this.

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).