This is a way to give developers and semi-technical people a way to generate and run code-first workflows using a visual language, be it UML or otherwise. We will be expanding support for text prompts in the future

Very much so, and in next iterations we will be expanding the scope both in what the generated code does as well as the ways to prompt

Definitely fair feedback, we will look into it

1. A user can always go into the generated code and make changes to it. The code generated includes explanations for what each step in the workflow does

2. Dapr has 9 APIs, one of which is Workflows which indeed competes with Temporal. Dapr Workflows isn't DAG based, it's code-first. This tool allows you to start with an external representation

Great point. Our tool does UML/BPMN as well as regular drawn diagrams without a specific notation, and will soon expand into regular prompts

Dapr is a code-first workflow engine that doesn't require a DSL language and includes many other integration points like pub/sub, service discovery and more. It also runs and deploys natively on K8s

So for workflows it's like Airflow, Brigade or hatchet or ...? How do workflows integrate with k8s (ressources, ...)? Camunda can also deploy natively on k8s. However you still develop apps for Camunda and it seems like dapr is no different there? Why is it in CNCF if it doesn't provide a way to build a workflow out of k8s-native artifacts (PVs, Deployments, Jobs, ...)?

Thanks for the response! What you're saying about embedding and enriching existing code based is definitely next level and I'll bring it back to the team to explore

Great idea and a work in progress :)

Hi thanks for your question. You wrote "more or less" and that's very accurate - the generated code by just feeding it to any LLM provider usually doesn't work out of the box, includes many hallucinations and also doesn't provide a solution that can be immediately downloaded and run in your IDE

Dapr's state management API supports more than 20 different databases, with the ability to stream data at high scale right into the agent tasks. That's a major differentiator as you can plug in almost any database and message bus into Dapr Agents.

We wanted to create a vendor neutral framework that doesn't over pivot on features that are tied to the backing of a commercial product. The other and no less important point is the ecosystem that Dapr has around messaging and state integrations. A lot of the Agentic AI frameworks you see today will not withstand a restart of the process,let alone complete cluster shutdown. Dapr has durability built-in to handle these catastrophic failures

I'd lead with durability and streaming, back it with benchmarks, and suggest some use cases (e-commerce, etc).

So I might have titled this submission "A durable, streaming agent framework".

Good suggestion, thank you