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Field DispatchProduct Hunt№ 2 · 2026-05-27

Powabase

Build AI apps with Postgres, RAG, and agents

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.

Product HuntWebsite
日榜日期
2026-05-27
发布时间
2026/05/27
日榜排名
#2
票数
290
评论
45
评价
0
Powabase source image 1
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查看演示视频www.youtube.com/watch
Human Signal

创始人 / Maker 评论

优先展示 Product Hunt 上对理解条目有帮助的人类文本。

Topics
Developer ToolsArtificial IntelligenceDatabase

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 🚀

评论作者信息不可用25 votes查看原评论
痛点分析发布于 2026/05/27

痛点为 AI 基于上游原始证据的初步提炼；未包含额外中国市场检索。

痛点

开发AI原生应用时，团队需要将Postgres、向量数据库、RAG管道、代理运行时、记忆、认证和文件存储等6-8个独立工具拼接在一起，并编写大量胶水代码。这种碎片化基础设施导致开发速度缓慢，且编码代理在导航这些工具时会消耗大量token，增加成本。根据maker描述，在金融、保险等受监管行业中构建约100个生产级AI应用的经验表明，基础设施胶水代码始终是最慢、最昂贵的部分。这造成了明显的开发效率瓶颈和资源浪费。

Discussion

精选 Product Hunt 讨论

保留原始讨论语境，用来交叉验证上游条目的真实反馈。

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!!

评论作者信息不可用筛选：高信号评论0 votes查看原评论

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)?

评论作者信息不可用筛选：高信号评论0 votes查看原评论

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.

评论作者信息不可用筛选：高信号评论2 votes查看原评论
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      "body": "<p>Hey Product Hunt 👋</p><p></p><p>I'm Hunter, co-founder of <a href=\"https://powabase.ai/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"><strong>Powabase</strong></a>. 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. </p><p></p><p>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.</p><p></p><p>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.</p><p></p><p><strong>Powabase is that whole stack as one platform:</strong></p><ul><li><p>Postgres + pgvector + file storage, provisioned per project in one click</p></li><li><p>Standard Supabase features like auth and realtime</p></li><li><p>A context engineering layer with multiple RAG algorithms that hits <strong>98.7% on FinanceBench</strong></p></li><li><p>Supports OpenAI, Anthropic, Google, or open-source LLMs via OpenRouter</p></li><li><p>Multimodal embeddings, rerankers, OCR, web search, web scraping all included without separate third party API keys or integrations</p></li><li><p>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</p></li><li><p>N8n-like visual agent workflow builder for deterministic logic; built-in copilot can help you craft workflows using natural language</p></li><li><p>Full observability in agent reasoning, token usage, RAG context, tool calls, workflow executions, and system errors</p></li><li><p>Optimized for coding agents like Claude Code — clean primitives, predictable APIs, token-efficient by design</p></li></ul><p>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.</p><p></p><p>It's free to start, and our <a href=\"https://cookbook.powabase.ai/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">cookbook</a> + <a href=\"https://github.com/powabase-ai/cookbook\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">example apps</a> 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.</p><p></p><p>I'll be in the comments all day with <a href=\"https://www.producthunt.com/@tonyzhangcy\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@tonyzhangcy</a> , <a href=\"https://www.producthunt.com/@xin_chen17\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@xin_chen17</a> , and <a href=\"https://www.producthunt.com/@michael_t_chang\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@michael_t_chang</a> . Tear it apart — what's missing, what's confusing, what would make you actually try it. 🙏</p><p></p><p>Early access users get free lifetime benefits — try it at <a href=\"http://app.powabase.ai\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">app.powabase.ai</a> and tell us what you build 🚀</p>",
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      "body": "<p>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.</p><p></p><p>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.</p><p></p><p>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.</p><p></p><p>Looking forward to seeing how it all plays out. Good luck!! </p>",
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      "body": "<p>Congrats on the launch, team! <br><br>Powabase looks like a massive time-saver for anyone building multi-agent systems without fighting glue code.</p><p><br>As a backend dev currently building infrastructure around the <strong>KYA (Know Your Agent)</strong> 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?</p><p><br>We are designing KYA to serve as a verifiable 'passport &amp; 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)?</p>",
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      "body": "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.\n\nYou 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.",
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