Powabase

Powabase. It's the Postgres backend for AI apps: per-project Postgres with pgvector, a built-in RAG pipeline, and an agent runtime behind a single REST API.

Powabase is the Postgres backend for AI apps. Every project gets its own Postgres, auth, storage, realtime, and dedicated compute, plus a RAG pipeline, an agent runtime, and workflows behind one REST API, so you never have to bolt a vector database and an agent framework onto your backend. Coding agents like Claude Code, Codex, and Cursor drive it through the Powabase Agent Skill or the Powabase MCP server.

Powabase builds on the Supabase stack, so you start with the same Postgres, auth, storage, realtime, and PostgREST. On top of that, we ship a RAG pipeline, an agent runtime, orchestrations, and workflows in the same project, with dedicated compute per project. If you only need a database and auth, Supabase covers it. If you'd otherwise run a vector database and an agent framework next to it, Powabase replaces that stack with one API.

Yes. Supabase ships pgvector, so you can store embeddings and run similarity search yourself. Extraction, chunking, embedding, indexing, reranking, and the agent layer are then yours to build. Powabase ships that pipeline on the same Postgres. Documents extract, embed, and index on upload, with vector, full-text, hybrid, and tree search built in.

No. Embeddings live in pgvector inside each project's own Postgres, next to your app data, so there's no separate vector database to run, pay for, or keep in sync.

Yes. Every Powabase project is a dedicated stack with its own Postgres, storage, keys, and compute. There are no shared logical databases, no cross-tenant traffic, and no noisy neighbours.

A production Postgres database with instant REST APIs (PostgREST), GoTrue authentication, object storage, realtime, and Row-Level Security. The AI layer sits in the same project: knowledge bases (RAG), agents, multi-agent orchestrations, and workflows, all on one consistent API.

Upload PDFs, Office files, images, or URLs and Powabase extracts (with OCR and vision models for scanned or image-heavy files), indexes, and retrieves them automatically. Embeddings live in your own Postgres (pgvector), with vector, keyword, and hybrid search plus reranking and multimodal retrieval. No separate vector database to run.

PDFs (including scanned, via OCR), Word, PowerPoint, and Excel, plain text, Markdown, and CSV, images (PNG, JPG, WebP, GIF, TIFF), and web pages crawled from URLs.

A model of your choice plus tools, optional knowledge bases, and session memory, running a reason-act-observe loop. Agents get eight built-in tools (database read/write, HTTP, code execution, storage, web search and scrape), plus your own HTTP tools and MCP servers, with human-in-the-loop approval for sensitive actions. They compose into orchestrations and workflows.