Explore Heym's self-hosted AI workflow platform, production use cases, dashboards, analyzer feedback, tech stack, and Docker or Kubernetes deployment.
See how Heym compares to other automation platforms. Built AI-first, not AI-added.
| Capability & details | Heym | n8n | Zapier | Make |
|---|---|---|---|---|
Built-in LLM Node Send prompts to language models for text generation, vision, image creation, and structured JSON output. | ✓ | ✓ | ✓ | ✓ |
LLM Batch API + Status Branches Send an array of prompts through the OpenAI Batch API, with a dedicated status branch for live progress from pending through completed—alongside your main result path on the canvas. | ✓ | ~ | ✗ | ~ |
Natural Language Workflow Builder Describe what you want in plain text or voice and the AI assistant generates the entire workflow on the canvas. | ✓ | ~ | ✓ | ✓ |
LLM Guardrails Block unsafe content categories like violence, hate speech, or harassment with configurable sensitivity levels per node. | ✓ | ✓ | ✓ | ~ |
Automatic Context Compression Agent conversations automatically compress when reaching context limits to prevent timeouts and enable long-running tasks. | ✓ | ✗ | ✗ | ✗ |
Heym product demo: a drag-and-drop canvas where agentic AI workflow nodes such as LLM, Agent, RAG / Vector Store, HTTP, and Slack are connected into a running automation pipeline.
Product tour
See the problems Heym removes, how each capability works, and why it matters in practice.
Overview
The problem
Teams keep stitching together separate tools for work that should feel simple.
The Heym answer
Heym brings sixteen once-difficult jobs onto one visual canvas.
Use the arrow keys to move through the tour
Why it matters
This tour starts with the problem, then shows the Heym answer. Each stop focuses on a real task teams already handle today, without burying the point under a long feature list.
Heym focuses on ten solution areas where controlled, observable AI operations matter most. Each one is backed by real workflows you can import and run.
Grounded employee and customer answers from corporate sources.
Sensitive workflows with human validation on key actions.
Specialized agents to collect, evaluate, and report data.
AI that auto-repairs broken browser selectors.
Collaborative reusable AI skills with execution history.
Real-time agent activity logs and actionable charts.
Trace every LLM token cost before scaling up.
Deploy logic flows as API endpoints or MCP servers.
Performance metrics before shipping code.
Trigger Slack, email, or webhook alerts on thresholds.
Every component is chosen for performance, developer experience, and reliability.
The frontend uses Vue.js 3 with TypeScript for a fast, type-safe development experience. Vite handles builds and Tailwind CSS takes care of styling.
A Python backend powered by FastAPI delivers async performance for concurrent workflow executions. Alembic handles database schema migrations.
PostgreSQL stores workflows and execution history, while pgvector powers built-in vector search for RAG pipelines. Docker keeps local development consistent.
JWT tokens in HttpOnly cookies with refresh token rotation secure user sessions. Passwords use bcrypt hashing, credentials are encrypted at rest with Fernet.
Connect to OpenAI, Ollama for local models, vLLM for high-throughput inference, or expose workflows through the Model Context Protocol.
First-party documentation lives inside Heym, alongside a native chat-with-docs experience. ESLint and Ruff keep the codebase healthy.
Answers to your common questions about Heym.
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