Run Workplace Search and AI Agents on Postgres with Omni

Omni is a useful find for teams that want Glean-style workplace search without sending every knowledge source into a closed SaaS stack. The project connects to common work tools, builds a shared context layer, and gives agents search, citation, and tool-use paths over company data.

Best fit: technical teams that can self-host Docker services, already run Postgres, and want to test hybrid BM25 plus vector search before buying an enterprise search platform.

What to check before using it:

• Confirm the connectors you need are production-ready, especially Microsoft 365, Notion, GitHub, or custom internal systems.
• Review permission inheritance against your own identity model before indexing private content.
• Decide whether your LLM provider keeps data inside your boundary; self-hosting the index does not automatically make hosted model calls private.
• Test Postgres, ParadeDB, and pgvector performance on a realistic document count.
• Treat the project as beta unless your own deployment test proves it can handle your security and uptime requirements.

This is a tool to trial, not a blanket recommendation. Start with a small workspace, index non-sensitive sources first, and compare answer quality, latency, and review burden against your current internal search or RAG setup.