Build production-ready apps with everything you need to go live - authentication, database and monitoring included out of the box. Powered by an open-source framework designed for the AI era.
Aram and Eduard here - co-founders of Modelence. We previously built and scaled a startup where we kept solving the same infrastructure problems over and over: auth, database, APIs, cron jobs, deployments. So we built an open-source full-stack TypeScript + MongoDB framework to never do that again.
The problem: AI coding agents (Claude Code, Cursor, etc.) are great at writing application logic. But they constantly fail at backend setup from scratch - wiring up auth, database connection, infrastructure. That's because most platforms were designed for humans reading docs, not agents writing code.
We built Modelence from the ground up as an open-source framework for agentic development, unlike other app builders that simply use existing third party frameworks and platforms not designed for AI agents.
Built-in guardrails for agents to catch and auto-correct errors before you deploy.
Automated database schema and index management, so agents don't have to attempt to solve these tricky problems on their own every time. Agents focus on your product logic instead of burning tokens on boilerplate and failing at infrastructure setup.
Cloud that actually closes the loop - persistent containers, dev environments, one-click deploy, and built-in observability around every operation. Because building is the easy part - running in production is where things actually get hard.
And there is zero lock-in: you fully own all source code and data.
AI App Builder included - Type a prompt on modelence.com and get a working full-stack app. Then pull it locally, continue in your own IDE, and deploy back to Modelence Cloud.
What's coming next - A built-in DevOps agent that lives in your cloud environment, knows the framework end-to-end, and uses observability data to act on errors, alerts, and incidents automatically.
Our bet is that the real challenge in AI coding isn't the builder tool - it's the framework and platform underneath. If your agent has a solid foundation, it ships real apps. If it doesn't, it generates impressive demos that break in production.
Modelence is open-source - tell us what's missing, what you'd want to see, and how your workflow actually looks - we want to hear it.
@david_buniatyan we were looking into this very closely the last few days - Claude Agent SDK is doing a good job at compaction (and even after resetting context it is pretty good at recovering from the current source state), but ultimately we're going to implement our own context management layer to pick more carefully what goes into full context vs turn-level context that's cleared after a single turn.
So at the high level it's long term memory in Markdown files (maybe vector search on a larger set in the future) + selective context management optimized for agentic development.
@hovo_ghevondyan1 the biggest difference is that instead of using existing frameworks and platforms together we’ve built a framework + cloud specifically for agentic development, more opinionated and batteries included. This lets us generate apps with guaranteed working setup.
Report
Nice concept! How does Modelence handle more complex cases like custom middleware, background jobs, or multi-tenant setups? Is it easy to go beyond the happy path?
Thank you, @igorsorokinua. We have built-in support for cron jobs, but they are executed on the same instances as the main applications (a random instance picks up each task). This works for simple tasks, but for CPU-intensive work, we plan to use separate instances in the future.
We didn't have any requests for custom middleware, but since the framework uses Express under the hood, it will be fairly easy to add it.
Multi-tenant setups will work well if you sign in as a customer under the same domain (like Notion does), but we don't support subdomains yet.
Report
Everyone’s talking about AI building apps faster, but almost no one talks about what happens after the demo works.
Who do you see as your core user right now? Indie hackers experimenting with AI agents, or teams shipping production products?
@kristina__grits thank you for the question. We are focusing on teams building production products; the whole concept is to build applications the same way we would set them up manually.
Congrats, team! I’ve seen agents generate great logic but completely struggle with auth, DB wiring, and deployment setup. If Modelence truly abstracts that reliably (with guardrails + production-ready infra), that’s a big change!
Smooth experience with auth and DB pre-wired. On a first quick test, it feels similar to others. Real edge (production-readiness, observability) is invisible until later. Worth making that visible earlier :)
Modelence App Builder
Hey PH 👋
Aram and Eduard here - co-founders of Modelence. We previously built and scaled a startup where we kept solving the same infrastructure problems over and over: auth, database, APIs, cron jobs, deployments. So we built an open-source full-stack TypeScript + MongoDB framework to never do that again.
The problem: AI coding agents (Claude Code, Cursor, etc.) are great at writing application logic. But they constantly fail at backend setup from scratch - wiring up auth, database connection, infrastructure. That's because most platforms were designed for humans reading docs, not agents writing code.
We built Modelence from the ground up as an open-source framework for agentic development, unlike other app builders that simply use existing third party frameworks and platforms not designed for AI agents.
Built-in guardrails for agents to catch and auto-correct errors before you deploy.
Automated database schema and index management, so agents don't have to attempt to solve these tricky problems on their own every time. Agents focus on your product logic instead of burning tokens on boilerplate and failing at infrastructure setup.
Cloud that actually closes the loop - persistent containers, dev environments, one-click deploy, and built-in observability around every operation. Because building is the easy part - running in production is where things actually get hard.
And there is zero lock-in: you fully own all source code and data.
AI App Builder included - Type a prompt on modelence.com and get a working full-stack app. Then pull it locally, continue in your own IDE, and deploy back to Modelence Cloud.
What's coming next - A built-in DevOps agent that lives in your cloud environment, knows the framework end-to-end, and uses observability data to act on errors, alerts, and incidents automatically.
Our bet is that the real challenge in AI coding isn't the builder tool - it's the framework and platform underneath. If your agent has a solid foundation, it ships real apps. If it doesn't, it generates impressive demos that break in production.
Modelence is open-source - tell us what's missing, what you'd want to see, and how your workflow actually looks - we want to hear it.
Try it now: modelence.com 🚀
Trace
MultiDrive
@artahian Now product managers should be using apps like this. Thank you so much! Upvoted!
Modelence App Builder
@tetianai Exactly. We're seeing significant interest from Product Managers adopting Modelence.
Deep Lake - AI Knowledge Agent
Really nice! how do you handle context drift for web scale projects?
Modelence App Builder
@david_buniatyan we were looking into this very closely the last few days - Claude Agent SDK is doing a good job at compaction (and even after resetting context it is pretty good at recovering from the current source state), but ultimately we're going to implement our own context management layer to pick more carefully what goes into full context vs turn-level context that's cleared after a single turn.
So at the high level it's long term memory in Markdown files (maybe vector search on a larger set in the future) + selective context management optimized for agentic development.
Aikido Security
Modelence App Builder
@hovo_ghevondyan1 the biggest difference is that instead of using existing frameworks and platforms together we’ve built a framework + cloud specifically for agentic development, more opinionated and batteries included. This lets us generate apps with guaranteed working setup.
Nice concept! How does Modelence handle more complex cases like custom middleware, background jobs, or multi-tenant setups? Is it easy to go beyond the happy path?
Modelence App Builder
Thank you, @igorsorokinua. We have built-in support for cron jobs, but they are executed on the same instances as the main applications (a random instance picks up each task). This works for simple tasks, but for CPU-intensive work, we plan to use separate instances in the future.
We didn't have any requests for custom middleware, but since the framework uses Express under the hood, it will be fairly easy to add it.
Multi-tenant setups will work well if you sign in as a customer under the same domain (like Notion does), but we don't support subdomains yet.
Everyone’s talking about AI building apps faster, but almost no one talks about what happens after the demo works.
Who do you see as your core user right now? Indie hackers experimenting with AI agents, or teams shipping production products?
Modelence App Builder
@kristina__grits thank you for the question. We are focusing on teams building production products; the whole concept is to build applications the same way we would set them up manually.
FuseBase
Congrats, team! I’ve seen agents generate great logic but completely struggle with auth, DB wiring, and deployment setup. If Modelence truly abstracts that reliably (with guardrails + production-ready infra), that’s a big change!
Going to test it out 👀
Modelence App Builder
@kate_ramakaieva Thank you!
Smooth experience with auth and DB pre-wired. On a first quick test, it feels similar to others. Real edge (production-readiness, observability) is invisible until later. Worth making that visible earlier :)
Modelence App Builder
@chi_cindy_zhang Thank you for the feedback!