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.
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!
@nuseir_yassin1 One of the biggest differences is that instead of relying on existing framework and platform combinations, we built a custom open-source framework and cloud designed specifically for the AI era. This enables us to generate applications with a guaranteed working setup out of the box.
@roopreddy Modelence is built for both non-coders and developers. You can build an application from a prompt using the App Builder, or deploy a Modelence framework-based application using the Cloud Environment deployment flow. The latter is more for engineers who prefer to code and build in their local IDE.
Report
Congratulations. What is the DevOps agent and how will it work?
@zerotox DevOps Agent is in development and will run in your cloud environment, understand the framework end-to-end, and use observability data to automatically act on errors, alerts, and incidents. For example, if your app crashes, it will analyze logs, CPU/memory usage, cron jobs, and other resources, detect the cause of the issue, and even autofix it.
Report
Congrats on the launch, @artahian! How does the auto-fixing of the DevOps agent look like? Curious to know
@neilverma Great question! Imagine a scenario where your application crashes. The DevOps agent automates the same steps an engineer or DevOps specialist would take: it reads logs, analyzes CPU/memory profiling, reviews telemetry data, and examines the performance of methods, API endpoints, and cron jobs. In most cases, this data is enough to identify the root cause. From there, the DevOps agent will either suggest a fix (e.g., resolving a data inconsistency or adjusting a configuration variable) or attempt an autofix - such as updating a database index, disabling a specific cron job, tuning configuration parameters, or autoscaling servers or databases. In more complex cases DevOps agent can find the bug in the code based on the application logs and submit a PR with the fix :)
@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.
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
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!
Nas.io
How does it differ from something like @Solid?
Modelence App Builder
@nuseir_yassin1 One of the biggest differences is that instead of relying on existing framework and platform combinations, we built a custom open-source framework and cloud designed specifically for the AI era. This enables us to generate applications with a guaranteed working setup out of the box.
Documentation.AI
Congrats on the launch. Who is Modelence built for? Are you targeting devs and builders or largely non-coders?
Modelence App Builder
@roopreddy Modelence is built for both non-coders and developers. You can build an application from a prompt using the App Builder, or deploy a Modelence framework-based application using the Cloud Environment deployment flow. The latter is more for engineers who prefer to code and build in their local IDE.
Congratulations. What is the DevOps agent and how will it work?
Modelence App Builder
@zerotox DevOps Agent is in development and will run in your cloud environment, understand the framework end-to-end, and use observability data to automatically act on errors, alerts, and incidents. For example, if your app crashes, it will analyze logs, CPU/memory usage, cron jobs, and other resources, detect the cause of the issue, and even autofix it.
Congrats on the launch, @artahian! How does the auto-fixing of the DevOps agent look like? Curious to know
Modelence App Builder
@neilverma Great question! Imagine a scenario where your application crashes. The DevOps agent automates the same steps an engineer or DevOps specialist would take: it reads logs, analyzes CPU/memory profiling, reviews telemetry data, and examines the performance of methods, API endpoints, and cron jobs. In most cases, this data is enough to identify the root cause. From there, the DevOps agent will either suggest a fix (e.g., resolving a data inconsistency or adjusting a configuration variable) or attempt an autofix - such as updating a database index, disabling a specific cron job, tuning configuration parameters, or autoscaling servers or databases.
In more complex cases DevOps agent can find the bug in the code based on the application logs and submit a PR with the fix :)
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.