TensorSharp vs Wolbarg

TensorSharp is a native .NET GGUF inference engine with a CLI, Web UI, compatible APIs, Agent Skills, and optional sandboxed model-authored code.

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

About TensorSharp

TensorSharp is a native .NET GGUF inference engine with a CLI, Web UI, compatible APIs, Agent Skills, and optional sandboxed model-authored code. TensorSharp Wiki, Local GGUF inference and agentic work for .NET Skip to content. Everything runs on your own hardware : your laptop, workstation, or server. Inference and agent work stay local by default, there are no per-token fees, and the same engine powers a quick command-line test, a shared internal chatbot, and a production REST endpoint.

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).