Heym

AI-Native Workflow Automation Platform

What Is Heym?

Heym is an AI-native automation platform built from the ground up around LLMs, agents, and intelligent tooling. Wire together AI agents, vector stores, web scrapers, HTTP calls, and message queues on a visual canvas — then deploy instantly via Docker.

Unlike platforms that started as classic trigger-action automation and layered AI on later, in Heym AI is the execution model.

Explore the product site at heym.run.

No Enterprise Gatekeeping

Many automation platforms turn essential production features into upgrade pressure: global variables, execution history and search, insights, AI Builder / Motherboard capabilities, observability, audit-style logs, team controls, scaling, or customer-facing portals.

Heym takes the opposite position. These are core workflow primitives, not enterprise bait. They ship in the free self-hostable product because serious AI automation should be inspectable, shareable, observable, and deployable from day one without any kind of weird production run limits.

Our enterprise offering is for commercial licensing, deployment help, dedicated support, and additional security layers. It is not a strategy for hiding core workflow and AI-native capabilities behind a sales call, now or later.

Product Demos

The demos below illustrate an agent–subagent layout instead of a purely step-by-step, single-thread agent chain. For a request like “How do I get from Berlin to Frankfurt?” and “What should I eat there?”, subagents can work on those parts in parallel. That tends to finish faster, keeps each model turn focused (less context bloat), and avoids pressuring one model to produce two large, unrelated answers in a single reply.

You can still answer with two separate LLM calls (one per question) or run several calls in sequence and merge the results in a final step—those patterns work—but for this kind of multi-part ask they are usually slower than parallel subagents behind an orchestrator.

Watch Heym Tutorials

Generate Workflows from Natural Language

Describe the agents, orchestration pattern, and user-facing result you want; Heym builds the workflow on the canvas.

Example prompt

Create a workflow for me that includes a Roadmap Agent and a Best Food Agent. When the Orchestrator Agent receives a request, it will invoke these subagents in parallel and return the result to the user.

Running Workflows

Execute the workflow directly from the canvas and inspect each step as results move through the graph.

Create Skills for Agents

Create agent skills from natural language, preview the generated SKILL.md, and attach them to the agent.

Example prompt

Create a skill for me and add it to the agent. The Orchestrator Agent will call this skill after receiving information from the subagents, and the skill will create a simple execution plan explaining what can actually be done in the destination city.

Call Workflows from Chat

Turn a workflow into a chat experience so users can invoke the orchestration with a natural request.

Example prompt

I live in Berlin and am planning to go to Frankfurt. How many kilometers is it on the Autobahn? Also, where can I find the best doner in Frankfurt?

📸 Screenshots

✨ Key Capabilities

• Visual Workflow Editor — Drag-and-drop canvas powered by Vue Flow with 30+ node types
• AI Assistant — Describe what you want in natural language (or voice) and the assistant generates and wires nodes on the canvas automatically
• Chat with Docs — Ask context-aware questions directly from the documentation header while the current article path is prioritized in the prompt
• AI Skill Builder — Create new Agent skills or revise existing ones from a modal chat with live SKILL.md and Python file previews
• LLM & Agent Nodes — First-class LLM node and a full Agent node with tool calling, canvas node tools, sandboxed Python tools, MCP connections, skills, optional persistent memory (per-node knowledge graph with background extraction), and LLM Batch API mode with live status branches for supported providers
• Multi-Agent Orchestration — One agent orchestrates named sub-agents and sub-workflows, all wired visually
• Human-in-the-Loop (HITL) — Pause agent execution to request user approval or input before proceeding
• Guardrails — Content filtering, NSFW protection, and multilingual safety checks on LLM and Agent nodes
• Built-In RAG — Insert documents and run semantic search against managed QDrant vector stores in two nodes
• MCP Support — Connect Agent nodes to any MCP server as a client; expose your workflows as an MCP server for Claude, Cursor, and other clients
• Portal — Turn any workflow into a public chat UI at /chat/{slug} with streaming responses and file uploads
• Webhook SSE Streaming — Generate ready-to-run cURL commands for /execute or /execute/stream, with per-node start messages and live node event output in the terminal
• Data Tables — Manage structured data directly in the dashboard and reference it from workflows
• Templates — Start from pre-built workflow templates to get up and running quickly
• Parallel Execution — Independent nodes run concurrently based on the graph structure, no configuration needed
• Auto Heal — Playwright selectors break? AI automatically detects and fixes them at runtime
• LLM Fallback — Automatic model fallback when the primary LLM fails or is unavailable
• Reasoning Support — Configure reasoning effort and temperature per Agent node for fine-grained control
• Command Palette — Ctrl+K for instant search, navigation, and workflow actions
• Evals — Define test suites and run them against any workflow with one click
• LLM Traces — Full observability for every agent call: requests, responses, tool calls, and timing
• LLM Cost Tracking — Per-trace token counts (input / output) with real-time USD cost calculation, historical analytics with time-range filtering, and a synced pricing table covering all major models
• Self-Hosted — Your data, your infrastructure

Full Feature Set

For a complete list of all features with short de