Powabase

The Postgres-native approach is interesting most RAG tooling treats the database as an afterthought rather than the foundation. Curious how you're handling vector indexing at scale: are you using pgvector under the hood, or did you build something custom? Would also love to know if the agent layer supports tool-calling across multiple data sources or just within a single Powabase instance.

Very cool @hunter_powabase ! Upvoted :)So the only thing I need to bring is API key for LLMs?

Postgres + RAG + agents is a useful bundle if it reduces the number of glue decisions teams have to make early.The question I’d have is where Powabase draws the line between prototype convenience and production control: evals for retrieval quality, prompt/version history, permissions, and audit logs. Those are usually the pieces teams discover they need right after the first demo works.

the 6-8 tools stitched together with glue code is painfully accurate. every AI project starts clean and ends up as a frankenstein of integrations within a month. unified backend makes sense if it actually reduces that sprawl

Hey Product Hunt 👋I'm Hunter, co-founder of Powabase. We've been running an AI dev shop since ChatGPT first came out, and after many client projects we noticed the same pattern repeating itself. Nearly every AI-native app ends up needing the same stack: Postgres, a vector store, RAG pipelines, an agent runtime, memory, auth, and file storage. Today you stitch that together from 6–8 tools, write a lot of glue code, and then watch your coding agent burn tokens navigating it. We've built ~100 production AI apps across regulated industries — finance, insurance, education, government — and the infra glue was always the slowest, most expensive part.So we abstracted it into a unified backend. Powabase is the backend we wished we'd had — and now every new AI project we take on ships in a fraction of the time.Powabase is that whole stack as one platform:Postgres + pgvector + file storage, provisioned per project in one clickStandard Supabase features like auth and realtimeA context engineering layer with multiple RAG algorithms that hits 98.7% on FinanceBenchSupports OpenAI, Anthropic, Google, or open-source LLMs via OpenRouterMultimodal embeddings, rerankers, OCR, web search, web scraping all included without separate third party API keys or integrationsReAct multi-agent orchestration with prebuilt tools (web search, database r/w, sandboxed code execution, etc.) and support for custom tool integrations via API and MCPN8n-like visual agent workflow builder for deterministic logic; built-in copilot can help you craft workflows using natural languageFull observability in agent reasoning, token usage, RAG context, tool calls, workflow executions, and system errorsOptimized for coding agents like Claude Code — clean primitives, predictable APIs, token-efficient by designAI apps deserve their own backend abstraction, not a Frankenstein of generic infra + LLM wrappers. Supabase made Postgres easy to use; we want to do that for the full AI-native stack.It's free to start, and our cookbook + example apps are open source on GitHub. We plan to open source a self-hosted version after early access period ends, likely around late June / July 2026.I'll be in the comments all day with @tonyzhangcy , @xin_chen17 , and @michael_t_chang . Tear it apart — what's missing, what's confusing, what would make you actually try it. 🙏Early access users get free lifetime benefits — try it at app.powabase.ai and tell us what you build 🚀