Powabase

Build AI apps with Postgres, RAG, and agents

Developer Tools

Artificial Intelligence

Database

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Have mixed feelings with these PostgreSQL wrappers. I loved PostgreSQL before it was fashionable and would much prefer to bring my own PostgreSQL, and have clear RAG and other enforcement in N8N, Flowise, or equivalent, and leverage existing agents which can be spread through different infrastructures while maintaining their own memory, skills, knowledge base, etc., in some nice centralized place.

Everything bundled, like Supabase's Edge functions feels like lock in to me. I know it can be run independently, but would prefer these workflows to flow independently of the data container.

Though, there are clear benefits to current agentic limitations having it all be bundled together as it allows better tool, skill, and MCP usage without the agent getting too confused jumping between skills and workspaces.

Looking forward to seeing how it all plays out. Good luck!!

the 'glue code between 6-8 tools' problem is real. spent way too many hours on that exact pattern before. curious how you handle the agent runtime side specifically — is there built-in support for tool calling and memory across conversation turns, or is that something you still wire up yourself?

Congrats on the launch, team!

Powabase looks like a massive time-saver for anyone building multi-agent systems without fighting glue code.

As a backend dev currently building infrastructure around the KYA (Know Your Agent) framework, I have a question regarding security boundaries. Since you unify Postgres and Agent Orchestration in one place, how do you manage the identity and dynamic access rights of autonomous agents? If an agent starts chaining tools or spawning sub-agents, how do you prevent context/prompt hijacking from executing malicious DB queries?

We are designing KYA to serve as a verifiable 'passport & guardrail' layer for AI entities. Are you planning to enforce standardized AI identities like KYA inside Powabase, or do you rely strictly on traditional Postgres Row-Level Security (RLS)?

I'm excited for this. I picked up your GPT Trainer back when you launched it. I preferred it over other agents I tried as your interface was intuitive, and the agent stuck to its designated materials and didn't have any "bleed" from general LLM knowledge impacting answers. You also kept users abreast of changes and updates, so I'm all in on what you've cooked up here because this (in theory) will be a better for for my anticipated workflow over Supabase.

I love working with @Powabase and @hunter_powabase for Ryden Solutions, the first & leading life science continuous quality and compliance assessment platform simulating FDA inspectors at all times while also becoming more efficient. Hunter's platform has simplified development and set us up for long term success as we scale.

Congrats team, loved this tool.

Are you also planning to add some usecase over the website?

I like that you’re focusing on token efficiency as a platform concern, not just a model concern. Hidden orchestration costs are becoming a massive issue.

Curious how opinionated Powabase is internally. Can teams swap components easily, or is the goal more of an integrated ecosystem experience?

The biggest value honestly might not be speed, it’s reducing architectural chaos. AI stacks become fragmented unbeliveably fast 😅

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