Backprompter – create, test, and deploy agents without a back end

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Hi everyone, I am a professor in AI, and I often build AI apps as a hobby and for academic projects. I felt that I was wasting too much time setting up backends, DB, and secrets for each app, and testing the prompts, updating them, and redeploying seemed tedious. So I built Backprompter to do all this without any setup. You can create agents, track different versions, test them, create mock users, simulate conversations, and track your evaluations. It supports usual agent setups - model selection, prompts, RAG, and tools (currently, HTTP API calls). Agents can also be combined by simply tagging them and describing the orchestration in natural language. For production, there is a single-click deployment of a chatbot interface, no setup needed. Also, developers can integrate the agents with their own frontend (Backprompter handles authentication and data management), or with their own backend if they want more control over authentication and data management. With one click, you can deploy your edited agent to production. I think there are broad applications for hobbyists, small teams, and organizations who want to integrate AI but don't have the time or expertise to set up backend, secrets, databases, and evaluation workflows. I started building this about 9 months ago. This is a solo project, so I would love some feedback. It's free to start, but with some limits. If you want to test more, I am happy to provide more credits for free. Also, looking for advice on what features to add next: Would the ability to deploy your agent to Slack or ChatGPT (as a custom GPT) be an important feature? https://backprompter.com/ I also built a version that can be self-hosted for complete privacy and for deploying apps within the network.

Visit product View on Hacker NewsAug 27, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started, organizations · Missing: reddit linkedin, podcasting, created

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, month, users · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface, users · Missing: plus, platform, intuitive

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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