u/PuzzleheadedMind874

Hey everyone,

We put Heym on GitHub 18 days ago, and the response has been really encouraging. The stars and feedback gave us a good push, so we started recording short videos that show real AI workflow patterns in action.

Heym is an AI-native workflow automation platform with a visual editor. You can wire together agents, LLM nodes, RAG, web scraping, HTTP calls, MCP, human review steps, and reusable templates on a canvas.

We would love feedback from AI developers on two things:

1. What workflow videos would actually be useful to watch?
2. What templates should we build next?

Links:

Platform: https://heym.run
GitHub: https://github.com/heymrun/heym
Templates: https://heym.run/templates

Any honest suggestions are welcome. We are especially interested in practical AI dev workflows, agent patterns, and templates that would save real setup time.

TL. DR: Created a tiered support automation system in n8n that routes, answers, and tags incoming user tickets automatically. It's like having a full-time support team that never sleeps. I was spending way too much time on repetitive support tasks. Dealing with the same questions about billing, login issues, and basic feature requests was killing my growth. I couldn't scale without hiring, but I didn't have the budget to bring on a full support desk yet, and training someone just to copy paste docs felt like a waste of time. The Solution: An AI Support Engine. I built an automated flow in n8n that monitors my inbox and Slack. It uses an LLM to categorize the intent and pulls relevant snippets from my internal knowledge base before drafting a response. I used n8nbuilder.dev helped sketch out the node layout for the routing logic quickly.

The Architecture: Ticket Router Agent. The core is a webhook that triggers whenever a new ticket lands. It runs a sentiment analysis check to filter for urgency, so anything marked high priority actually pings my phone, while everything else gets a polite response with links to the documentation using GPT-5.1-mini for fast inferences.

The gap we kept hitting: research-grade AI capabilities exist, but putting them into a reliable, inspectable, controllable production workflow requires gluing together too many tools.

We built Heym to address this. It's a self-hosted, source-available AI workflow automation platform. Visual canvas for building multi-agent pipelines, built-in vector store management for retrieval-augmented workflows, human-in-the-loop review checkpoints, full LLM execution traces, and an MCP Server to expose any workflow as a callable tool for AI assistants.

The execution engine builds a DAG from the workflow graph and runs independent nodes concurrently. Agent nodes have automatic context compression so long-running agents don't silently fail as context grows. Everything runs on your own infrastructure via Docker Compose.

PH : producthunt.com/products/heym/

GitHub: https://github.com/heymrun/heym

We kept running into the same problem: LangChain is powerful for building agent logic, but the moment you need a production-grade runtime with a visual canvas, human review checkpoints, scheduling, observability, and self-hosted deployment, you're assembling a lot of pieces yourself.

Heym is our answer to that. A self-hosted, source-available AI workflow automation platform. Visual canvas for building multi-agent pipelines, built-in knowledge retrieval, Human-in-the-Loop approval checkpoints that pause execution and generate a public review link, full LLM traces, and an MCP Server to expose any workflow as a callable tool for AI assistants.

The execution engine builds a DAG from the workflow graph and runs independent nodes concurrently. Agent nodes have automatic context compression so long-running agents don't silently fail as context grows.

Launching today. Source-available

GitHub: https://github.com/heymrun/heym