heym
Eliminates vendor lock-in and data residency risk by running AI workflows on-premise while providing observability and human-in-the-loop control without comp…
Self-hosted AI workflow automation platform with visual canvas, agents, RAG, HITL, MCP, and observability in one runtime.
- Build multi-step AI workflows visually without writing code for document processing
- Create autonomous agents that retrieve company data and answer employee questions automatically
- Add human approval checkpoints to AI decisions before they execute in production systems
Eliminates vendor lock-in and data residency risk by running AI workflows on-premise while providing observability and human-in-the-loop control without complex multi-tool integration.
Enterprise ops teams building autonomous AI agents with compliance requirements and need for workflow visibility.
https://github.com/heymrun/heym
By heymrun
How to Get It
git clone https://github.com/heymrun/heym.git && cd heym && ./run.sh
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
After installing, paste this into Claude:
Help me build multi-step AI workflows visually without writing code for document processing
Trust Signals Auto-scanned
Community Pulse Active
Discussed on Hacker News, Reddit
- Heym — self-hosted AI workflow automation with agents, retrieval, approvals, and — Reddit · 10 pts
- Heym is a self-hosted and source-available AI workflow automation platform I hav — Reddit · 2 pts
- Hi AssetBuilders , We built Heym — Reddit · 1 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 947 GitHub stars; 18 contributors; last commit 1d ago; license MIT.
Things to check
- Self-hosted platform, not a Claude plugin — you run and operate it yourself. Licensed MIT with Commons Clause, which restricts commercial resale. Evaluated from public signals, not hands-on tested.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 947 GitHub stars; 18 contributors; last commit 1d ago; license MIT.