🟢 Beginner–Intermediate ⚙️ Type: AI Workflow Automation / Agent Canvas 💸 Free & Source-Available ⭐ Trending on GitHub
What is Heym?
Heym is a self-hosted, AI-native workflow automation platform built from the ground up around LLMs, agents, and intelligent tooling. While legacy automation platforms like Zapier or Make were built for simple, rule-based logic with AI bolted on later as an afterthought, Heym is purpose-built for the AI era.
It provides a beautiful visual canvas where you can wire together multi-agent orchestrations, Retrieval-Augmented Generation (RAG) pipelines, web scrapers, and HTTP calls. Instead of gluing together five different platforms to handle your agents, document retrieval, and monitoring, Heym integrates everything into a single, unified runtime.
Because it is entirely self-hosted via Docker, you maintain absolute control over your data with zero vendor lock-in. It even supports the modern Model Context Protocol (MCP) out of the box, allowing you to attach external skills to your agents instantly or expose your entire Heym workflow as a tool for another AI to call.
Who is it for?
- AI Engineers & Developers who are tired of fighting traditional automation platforms when trying to build complex, long-running agentic loops.
- No-Code Builders who want to create powerful multi-agent systems using a drag-and-drop visual canvas rather than writing massive Python scripts.
- Operations Teams needing Human-in-the-Loop (HITL) approvals. Heym natively pauses workflows for human sign-off before executing sensitive actions (like sending an email or spending money).
- Data-Driven Teams who require deep observability. You need to know exactly how much an agent costs per run and why it hallucinated—Heym provides this natively.
What makes it special?
- Generate Workflows by Prompt — You don’t even have to drag and drop. Using the built-in AI Assistant, you can type: “Create a workflow with a Research Agent and a Writer Agent running in parallel, orchestrated by a Manager,” and Heym will draw the complete graph for you.
- AI Convert (Migration Tool) — Have an existing automation stuck in another platform like n8n? Heym’s AI Convert tool can analyze your old workflow JSON and automatically translate the nodes and connections into a native Heym canvas.
- Granular LLM Tracing — Unlike basic workflow tools that just show a green checkmark, Heym traces every individual LLM call. You can click any node to see the exact request/response payload, latency, and exact token cost in real-time.
- Reusable “Skills” — You can generate agent skills from natural language, preview the generated
SKILL.md, and attach them across multiple different workflows without rewriting code.
Requirements before you start
Heym is designed to be self-hosted on your own infrastructure to protect your proprietary data:
- Docker & Docker Compose — The standard and easiest way to deploy the platform locally or on a cloud server (like DigitalOcean or AWS).
- LLM API Keys — You will need API credentials for the models you intend to use (e.g., OpenAI, Anthropic, Gemini, or a local provider).
- A Modern Web Browser — To access the visual canvas UI.
Step-by-step installation
Step 1 — Clone the Repository
Open your terminal and clone the official Heym GitHub repository to your local machine or server:
git clone https://github.com/heymrun/heym.git
cd heymStep 2 — Configure Environment Variables
Copy the example environment file to create your own configuration configuration:
cp .env.example .envOpen the .env file in your favorite text editor. Here, you can configure your database settings, default ports, and any required foundational API keys for the orchestrator.
Step 3 — Deploy via Docker
Once your environment is configured, use Docker Compose to pull the necessary images and spin up the platform (this includes the backend API, the frontend canvas, and the database):
docker compose up -dStep 4 — Access the Canvas
Once the containers are running, open your web browser and navigate to http://localhost:3000 (or the port you defined in your .env file).
You can instantly begin clicking the canvas to add nodes, or use the AI Assistant chat box to describe your desired automation and watch the nodes assemble themselves!
Common errors and fixes
| Error | What it means | How to fix it |
|---|---|---|
| Docker Port Conflicts (e.g., Port 3000 or 5432 already in use) | You are already running a web server, Next.js app, or PostgreSQL database on your machine that is blocking Heym from binding to its default ports. | Open your docker-compose.yml or .env file and map Heym’s external ports to something else (e.g., change 3000:3000 to 3005:3000). |
| Long-running agent workflows silently fail or timeout | Complex multi-agent tasks (like heavy web scraping and summarization) can exceed standard HTTP request timeouts or hit LLM API rate limits. | Open the Traces tab in Heym. It will show you the exact node where the failure occurred. If it is a timeout issue, you may need to adjust your container timeout settings; if it is a rate limit, the trace will display the exact 429 error from your LLM provider. |
| AI Convert fails to migrate an old workflow | You pasted JSON from a legacy platform (like Zapier or Make), but it contains highly proprietary, closed-source app integrations that Heym cannot perfectly map. | AI Convert is brilliant for mapping logical flow, HTTP requests, and data routing, but you may need to manually re-configure specific 3rd-party SaaS authentication nodes (like custom Salesforce or Slack OAuth connections) on the Heym canvas after the initial conversion. |
Free vs Paid comparison
| Feature | Heym (Self-Hosted) | Traditional Automation (e.g., Zapier) |
|---|---|---|
| Cost | 🟢 Free (Pay only for your own hosting & API keys) | 🔴 Expensive monthly subscriptions per task |
| Agentic Orchestration | ✅ Native (Designed for autonomous agents & RAG) | ⚠️ Rigid (Designed for static IF/THEN rules) |
| LLM Observability | 🟢 Deep (Node-level traces, token costs, latency) | 🔴 Shallow (Usually just pass/fail logs) |
| Model Context Protocol (MCP) | ✅ Yes (Built-in server/client capabilities) | ❌ No |
Bottom line: If you are trying to build complex AI workflows on platforms built five years ago, you are likely fighting the UI more than building your product. Heym is a breath of fresh air. By baking agents, RAG, Human-in-the-Loop, and deep observability directly into the visual canvas, it eliminates the need to bolt together three different platforms just to get a reliable AI automation running. The ability to self-host it via Docker seals the deal for privacy-conscious teams.
Alternatives — 3 similar tools
1. n8n
The titan of open-source workflow automation. n8n is incredibly mature, boasts hundreds of pre-built app integrations, and recently released massive updates to support AI agents and memory. While Heym is a newer platform built purely around AI from day one, n8n is often the better choice if your workflow relies heavily on connecting to dozens of legacy SaaS apps (like Salesforce, Shopify, or QuickBooks).
🔗 n8n.io
2. Flowise
An open-source, drag-and-drop UI explicitly designed for building customized LLM flows and LangChain applications. Flowise is fantastic for rapidly prototyping AI chatbots and RAG systems visually, though Heym offers a slightly more robust environment for broader workflow automation and multi-agent orchestration beyond just chat interfaces.
🔗 github.com/FlowiseAI/Flowise
3. Dify.ai
Dify is an open-source LLM application development platform that combines AI workflow orchestration with deep RAG capabilities and a beautiful prompt-engineering dashboard. It sits somewhere between Flowise (chatbot building) and Heym (workflow automation), making it a highly polished alternative for enterprise teams looking to deploy production AI apps.
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