Launching today
Powabase
Build AI apps with Postgres, RAG, and agents
739 followers
Build AI apps with Postgres, RAG, and agents
739 followers
Powabase is a backend-as-a-service for AI-native applications, combining Postgres, RAG, agents, memory, workflows, and automation primitives in one platform. It helps agencies and in-house IT teams build new AI apps or add AI automation to existing products without stitching together fragmented infrastructure. Designed to work seamlessly with modern coding agents, Powabase helps teams ship faster while building more robust, token-efficient systems.
Powabase
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 click
Standard Supabase features like auth and realtime
A context engineering layer with multiple RAG algorithms that hits 98.7% on FinanceBench
Supports OpenAI, Anthropic, Google, or open-source LLMs via OpenRouter
Multimodal embeddings, rerankers, OCR, web search, web scraping all included without separate third party API keys or integrations
ReAct multi-agent orchestration with prebuilt tools (web search, database r/w, sandboxed code execution, etc.) and support for custom tool integrations via API and MCP
N8n-like visual agent workflow builder for deterministic logic; built-in copilot can help you craft workflows using natural language
Full observability in agent reasoning, token usage, RAG context, tool calls, workflow executions, and system errors
Optimized for coding agents like Claude Code — clean primitives, predictable APIs, token-efficient by design
AI 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 🚀
@tonyzhangcy @xin_chen17 @michael_t_chang @hunter_powabase The "infra glue was the slowest, most expensive part" framing is the honest reason this category exists, nobody's AI app died from a bad model, they died stitching six tools together. Since token-efficiency is the thread you keep pulling on, that's where I'd push: FinanceBench is clean and bounded, but a client's real knowledge base is messy, duplicated, and growing weekly. The hard part isn't retrieval quality on a benchmark, it's what the cost curve does at 10x the documents. Naive RAG either bloats context and quietly kills the token-efficiency claim, or over-prunes and misses the right chunk. So: as a project's corpus scales, does retrieval cost stay flat, and does precision hold on dirty data? That curve is the difference between a demo scoring 98% and a backend teams trust in production, and it's the one number that'd make "token-efficient by design" provable rather than aspirational.
Powabase
@tonyzhangcy @xin_chen17 @hunter_powabase @arturbrugeman You're naming the real tension — a benchmark isn't proof of production behavior. A few things we lean on: on retrieval cost, pgvector keeps vector search at ~O(log N) with HNSW, and rerankers cut through noise on the candidate set. On dirty data, the harder problem is upstream — one option is using custom metadata enrichment as a preprocessing step to extract structured signal before chunks ever hit the LLM. Precision still degrades with corpus noise, the question is how gracefully.
Production signal we have outside FinanceBench: client app corpora in the 10K–700K-doc range, growing weekly. Powabase is designed to be extensible, and a lot of what gets built per-client ends up as a reusable primitive — that's been our pattern for handling the messier-than-benchmark cases.
If you're working on something specific, would love to hear about it!
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.
Powabase
@studentzuo Great observation! Powabase has a full suite of Observability tools built in. It gives you a variety of information ranging from token usage breakdown by model to failed RAG indexings and LLM queries. Since it is a BaaS, every configuration and action is tracked in a Postgres DB table for your records. You can also build custom dashboards on top of auto-collected data to analyze it as you see fit.
Like Supabase, our Postgres db has row level permissions. For certain out-of-the-box agent tools (e.g., database_write), the user must grant explicit permission to the agent on a per-table basis before it can manipulate it. If you want human-in-the-loop workflows like Claude code, you can set up hooks in your agent orchestration so it waits for user's confirmation before continuing.
Would love to hear more about what projects you have in mind!
Powabase
@studentzuo Thanks for the support!
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)?
Powabase
@nurik_shurik Thanks for reaching out!
Really appreciate the depth of this question — it cuts to something we think about a lot.
Powabase's current model is layered defense, not a single identity primitive. The lower layers are designed to compose cleanly with attestation frameworks like KYA at the upper layers.
Where identity lives today. Agents have no ambient access. Every project is a sealed stack — its own Postgres, Kong gateway, GoTrue auth, storage, and AI worker, with no cross-project blast radius. Within a project, an agent's effective rights are the intersection of three things:
The schemas and tables explicitly granted when its database tools were assigned (schema-level scoping is configured at tool-assignment time, not at query time).
The Postgres role and RLS context of the API key the host app passes in — Service Role bypasses RLS for server-side calls, Anon respects RLS for anything client-reachable.
Any hook policies attached to the agent.
Preventing hijacked queries. Even if a prompt injection convinces the LLM to issue something malicious, several hard constraints sit between the model and the database:
database_query is locked to read-only SELECT, single-statement, results capped, and rolled back after read.
database_write requires a WHERE clause on every UPDATE/DELETE (no mass writes) and validates all identifiers against injection.
HTTP/custom tools have SSRF validation and 30-second timeouts.
The hook system runs at OnRunStart, PreToolUse, PostToolUse, and PreResponse. Hooks can be rule-based (CONTAINS, STARTS_WITH, regex MATCHES, IN), HTTP webhooks that allow/deny/rewrite tool arguments at runtime, or human-approval gates that pause the run on an SSE approval_requested event.
For chained tools and spawned sub-agents: doom-loop detection terminates the run on 3 identical tool calls, and orchestration recursion is capped at depth 3.
On KYA specifically. We rely on RLS plus the layers above today, not a standalone agent passport. But OnRunStart and PreToolUse HTTP hooks are the natural integration point — a KYA verifier could sit there and attest the agent (and any sub-agent before delegation) on every step, with the hook denying or rewriting the call based on what the passport authorizes. Standardized AI identities are absolutely on our radar as the space matures, and we'd love to do a reference integration. Drop into our Discord if you want to explore that — we'd be interested in trading notes with the KYA team.
Mesh
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!!
Powabase
@asherraph Genuinely appreciate this. The tradeoffs you're naming are real, and I'd rather engage them than try to talk you out of your preferences.
BYO Postgres is closer than it looks. Each project's Postgres is a normal Postgres — you get a direct connection string, your app tables live in the public (or your self-defined) schema (the AI features use a separate ai schema), and the self-hosted distribution runs as Docker Compose or Helm on your own infra. In self-hosted mode the "data container" literally is your Postgres. The lock-in surface is the ai schema and the service worker that maintains it — not the database underneath.
The architecture you described is actually a supported pattern. Agents are reachable via API and MCP, and they call tools over HTTP or MCP. So one common setup: keep your orchestration in n8n / Flowise / whatever, and use Powabase as the centralized brain — knowledge bases, sessions, agent configs, and memory live there, but they're invoked from your existing workflows. The inverse works too: Powabase agents can call your n8n flows as custom tools. We tried to keep the layers composable rather than mandatory, so you can adopt one piece without swallowing the rest.
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
Powabase
@tina_chhabra Thanks for confirming our experience! A lot of the custom gluing depends on the actual project specifications. If the business logic is complex and many third party integrations are needed, you might still need a good amount of glue code regardless of what backend framework you use.
Powabase's ReAct multi-agent orchestrations allow you to connect agents with custom tools and MCP servers though, so if you're building an AI app, hooking your agents up with tools is a lot easier than building everything from first principles.
Happy to hear more about what you might be working on!
Powabase
@tina_chhabra thanks for the support! We've already been dogfooding Powabase for other internal projects and it has definitely sped up development
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.
Powabase
@fberrez1 Yep, we are using pgvector under the hood. For multimodal content, we first convert the data into a text-based description using a VLM like Mistral OCR, then index the text. At retrieval time, the database searches the indexed text but pulls the original multimodal content (e.g., base 64 image) and feeds it as context to the LLM.
Agent layer does support tool-calling across multiple data sources. Out-of-the-box tools interface mostly with built-in capabilities of the BaaS itself (e.g., sandbox code execution, permissioned db read/write, web search via Exa, web crawl via Firecrawl, etc.). You can connect custom tools or MCP servers to any agent or orchestrations to supplement its set of tools.
Bababot
The biggest value honestly might not be speed, it’s reducing architectural chaos. AI stacks become fragmented unbeliveably fast 😅
Powabase
@aarav_pittman Indeed. A significant time sink during Powabase's design process was figuring out which available tools are best to include. There are so many out there and some optimize for performance while disregarding cost, some try to strike a good balance and aim at best value, and others make the pure open source free-to-use play.
Example: VLM-based text extraction include LightOnOCR-2, Mistral, or PaddleOCR. This is the default approach for our RAG ETL pipeline. But if you know the source is all native text, you can opt into faster and cheaper non-VLM parsers like OpenDataLoader, PyMuPDF (fitz), or pdfplumber.
We generally try to give developers a choice while setting default options to what we believe is the best value for most common use cases.
Powabase
@aarav_pittman Yes! I've been building apps using Powabase as a backend, and it simplifies the management of all the different tools/integrations needed for an AI app