We built a self-hosted platform for production AI workflows — agents, retrieval, approval steps, and observability in one runtime

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.

Source available :)

https://github.com/heymrun/heym

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