Heym Changelog: 31 Releases, 456 Stars, and Month One

TL;DR: Heym launched on April 27, 2026. In the first 30 days the team shipped 31 releases, the repo reached 456 stars and 53 forks, and a YouTube tutorial playlist went live. Major additions: conversational AI Builder, nodes as agent tools, multi-MCP server clusters, the MCP Call Node, an AI Expression Helper, LLM token pricing in Traces, security hardening, and a Drive integration. This post is a complete record of what shipped and why.

Table of Contents

• By the Numbers
• How the Launch Day Looked
• Week 1: Foundation and First Iterations
• Week 2: MCP Depth and the Chat AI Builder
• Week 3: Agents as Tools, Security, and Drive
• Week 4: Observability, Glama, and Cost Tracking
• YouTube Tutorials
• Community Moments
• What Is Coming Next
• Thank You
• FAQ
• References

This is the Heym changelog for month one: a complete record of every feature, fix, and community milestone shipped between April 27 and May 26, 2026.

I am Ceren, one of the engineers building Heym. One month ago we pushed the button and made the repository public. I want to write down everything that happened, not as a press release, but as an honest record for the people who starred, forked, filed issues, and showed up in the community threads.

What is Heym? Heym (heym.run, github.com/heymrun/heym) is a self-hosted, open-source AI workflow automation platform. It provides a visual canvas for building workflows with LLM nodes, multi-agent orchestration, built-in RAG backed by Qdrant, MCP server and client support, human-in-the-loop checkpoints, and full LLM observability. It runs on your own infrastructure with a single Docker Compose command and is published under MIT with Commons Clause.

If you are new here: Heym is a self-hosted, open-source AI workflow automation platform. You get a visual canvas, LLM and agent nodes, built-in RAG, MCP server and client, human-in-the-loop checkpoints, and full LLM observability, all running on your own machine with a single Docker Compose command. The repository is at github.com/heymrun/heym.

By the Numbers

Before diving into the features, here is the first-month snapshot as of May 27, 2026.

Metric	Count
GitHub stars	456
Forks	53
Releases shipped	31
Days since launch	30
Pull requests merged	122
YouTube tutorial videos	growing playlist

31 releases in 30 days works out to one release per day on average. Some days saw two or three releases because a bug fix followed a feature the same afternoon. The pace was intentional: ship small, ship often, fix fast.

Key fact: Heym went from zero to 456 GitHub stars and 53 forks in its first 30 days as a public open-source project, shipping 31 releases between April 27 and May 26, 2026.

456 stars in the first month puts Heym well ahead of the trajectory we expected for a self-hosted AI workflow tool. The r/selfhosted, r/LocalLLaMA, and r/AI_Agents communities drove a lot of the early traffic, and we are grateful for every person who posted a genuine question or shared what they built.

How the Launch Day Looked

v0.0.1 shipped on April 27, 2026.

The initial release was not a minimal proof-of-concept. We had been building for several months before going public, so day one already included:

• Visual workflow canvas. Drag-and-drop node editor with concurrent execution for independent nodes and automatic dependency resolution for dependent ones.
• 38 node types. Triggers, AI nodes (LLM, Agent, RAG), logic nodes (branch, loop, merge), integration nodes (HTTP, Python, database), and control nodes (HITL, sub-workflow).
• AI Assistant. Describe a workflow in plain English and the assistant generates the full canvas with nodes, connections, and pre-filled properties. Voice input supported.
• Multi-agent orchestration. Multiple Agent nodes on the same canvas, each with its own model, toolset, and Python execution environment. Context compression keeps long-running agents stable. See our guide to multi-agent AI systems for how this pattern works.
• Built-in RAG. Vector store management backed by Qdrant. Upload documents, create stores, wire semantic search into workflows. The full retrieval pipeline is visible in one trace. For setup details, read how to build a RAG pipeline.
• MCP Server. Every Heym instance exposes a built-in MCP endpoint. Any workflow can be made callable from Claude Desktop, Cursor, or any MCP client. Agent nodes also connect to external MCP servers as tool sources. For background on what MCP is, we have a full explainer.
• Human-in-the-Loop (HITL). Pause execution at any node, generate a one-time public review URL, and resume from the exact stored snapshot. A single run can pause more than once.
• Portal. Toggle a workflow into a public-facing chat interface with a custom URL slug, streaming responses, file uploads, and per-user authentication.
• Traces. Every LLM call, agent step, token count, and latency is logged automatically. Filter by source, model, or credential.

That foundation took months to build. The 30 days after launch were about deepening each of those pillars and shipping the gaps that users surfaced immediately.

Week 1: Foundation and First Iterations

April 28 to May 4 (v0.0.2 through v0.0.10)

The first week after launch was mostly about stability, security foundations, and a first big UX improvement.

v0.0.2: Dependency Setup and CI

We configured Dependabot, set up automated dependency merges for patch-level updates, and added a frontend production build check to CI. Not glamorous, but it prevented a class of dependency drift bugs that would have compounded over the following weeks.

v0.0.3: Streamable MCP Client

The initial MCP client used HTTP+SSE transport. v0.0.3 added streamable HTTP transport support, which more recent MCP servers expect. If you tried to connect Heym as an MCP client to a server built after early 2025 and saw transport errors, this fixed them.

v0.0.5: Long-Lived Workflow Tokens

Before this, every Portal session and MCP call used short-lived JWTs. v0.0.5 added long-lived workflow authentication tokens so you can grant a client persistent access to a specific workflow without re-authenticating on every run. Useful for headless automation where rotating credentials is painful.

v0.0.6: Markdown on Canvas

Node output previews on the canvas now render markdown. This sounds minor but makes a real difference when you are debugging a chain: you can read formatted text and tables directly on the canvas without opening a separate panel.

v0.0.10: AI Expression Helper

This is the feature that generated the most discussion in week one.

Heym uses a $node.output, $vars.name DSL for wiring data between nodes. Writing these expressions by hand is precise but tedious, especially when you have a deeply nested JSON output from an HTTP node and want to extract a specific field.

The AI Expression Helper takes natural language and turns it into a valid expression, wired to your last run's actual output and your current variables. Wrong result? Hit Regenerate. It sees the error message and the output context and retries. It is one of those features that sounds like a nice-to-have until you try it once and then cannot imagine building without it.

Week 2: MCP Depth and the Chat AI Builder

May 2 to May 9 (v0.0.7 through v0.0.19)

Week two is where MCP support went from one endpoint to a full cluster architecture, and the AI Builder grew a conversation interface.

v0.0.7: Chat AI Builder and Chat History

The original AI Builder was a text input: type a description, get a canvas. That worked, but building complex workflows requires iteration. You say "add a human review step before the email sends," and you want the system to understand the existing canvas and modify it, not start over.

v0.0.7 shipped the Chat AI Builder. Same workflow generation engine, now inside a conversational interface. Describe what you want, refine in chat, preview the canvas, open the workflow when ready. Chat history persists so you can continue a building session across browser refreshes.

v0.0.9: Nodes as Agent Tools

Before v0.0.9, an Agent node could use tools from connected MCP servers or from Python code nodes. It could not use an arbitrary workflow node as a tool.

v0.0.9 changed that. Connect any node to an Agent's Tool input, mark which fields the agent should fill at runtime, and keep fixed values where you need control. This works for Set nodes, HTTP nodes, output nodes, and any custom Python node you have already built.

The practical result: you build a reusable node with all your business logic, then wire it as a tool for multiple agents on the same canvas. No duplication, no maintenance across copies. For the full picture of how agents use tools and memory, see how to build an AI agent. For persistent state across runs, the AI agent memory guide covers the entity-relationship graph Heym builds in the background.

v0.0.13: Multi-MCP Server Clusters

The initial release exposed one MCP endpoint per Heym instance. v0.0.13 let you create multiple named MCP endpoints and assign specific workflows to each.

Why does this matter? A single Heym instance can hold hundreds of workflows across many teams or projects. Before clusters, every MCP client that connected to your instance could see every published workflow. With named clusters you can expose a "customer support" toolset to your support team's Claude Desktop and a "data pipeline" toolset to your engineering team's Cursor, from the same Heym deployment.

We wrote a guide to building MCP servers if you want more context on how the server side works. If you want to connect Heym as an MCP client to public servers, the best MCP servers in 2026 is a good starting point.

v0.0.16: MCP Call Node

The Agent node connects to MCP servers and calls their tools automatically as part of its reasoning loop. That is the right behavior for autonomous agents.

But sometimes you want to call a specific MCP tool at a specific point in a workflow, deterministically, not via an agent's planning step. The MCP Call Node does that. Place it on the canvas, select the server and tool, wire in the inputs, and get the output directly. It is the difference between "agent, use whatever MCP tools you need" and "at step 3, call this specific tool with these parameters."

v0.0.17: Docker stdio MCP Support

Some MCP servers run as local Docker containers and communicate over stdio rather than HTTP. v0.0.17 added stdio transport support so Heym can connect to these servers as a client without any network configuration.

v0.0.19: Drive Integration Improvements

Workflows can now list Drive files with getAll and return metadata including name, size, MIME type, source, and download URL. Agent nodes that use Drive as a tool can fill File IDs from the listing rather than requiring them to be hardcoded. Drag and drop uploads on the Drive page also landed in this release.

Week 3: Agents as Tools, Security, and Drive

May 10 to May 16 (v0.0.21 through v0.0.26)

Week three had the most consequential single release of the month: the security hardening in v0.0.21.

v0.0.21: Security Hardening

Two CVEs were filed against Heym in the first two weeks: CVE-2026-45226 (incorrect authorization in subworkflow execution) and CVE-2026-45227 (protection mechanism failure in the Python tool sandbox). Both were reported responsibly and patched in a single release within two weeks of the public launch.

The three fixes were:

1. Python tool sandbox hardening. Custom Python tools run inside Agent nodes. The sandbox now prevents code from escaping the execution container or accessing resources outside its scope.
2. File upload path containment. Stored upload paths are now strictly contained to prevent directory traversal attacks where a crafted filename could write to or read from paths outside the upload directory.
3. Subworkflow access enforcement. A workflow's execute nodes and agent subWorkflowIds are now checked against the calling user's permission scope. Before this fix, an attacker could point a workflow node at a victim's workflow UUID and trigger it under attacker-controlled execution paths.

Thanks to @Hinotoi-agent for the responsible disclosure and the pull requests. All three fixes were merged in a single session, and the release was out the same day.

v0.0.22: Sticky Note Colors

Sticky notes on the canvas gained color support. You can now color-code your documentation nodes by theme, status, or team. Small quality-of-life improvement that makes large canvases much easier to navigate.

v0.0.23: Background Chat Notifications

When you run a long workflow through the Portal or the Chat tab and then close the browser tab, the workflow keeps running. Before v0.0.23, you had no way to know it finished unless you reopened the tab and refreshed.

v0.0.23 added a notification dot and a soft sound that appear the moment the workflow completes. Kick off a task, switch to another tab, come back when you hear the ding. The progress bar is still accurate, and the result is waiting.

v0.0.24: Workflow Search and Edit History

Workflow search lets you search by name across all your canvases. For instances with dozens or hundreds of workflows this was one of the most requested quality-of-life improvements. Edit history improvements landed in the same release, making undo behavior consistent across node types.

v0.0.25: Version Badge

A small version indicator was added to the UI so you always know which Heym build is running. Useful when you are running multiple instances or coordinating upgrades across a team.

v0.0.26: YouTube Tutorial Playlist and Cache Workers

The Heym YouTube tutorial playlist went live alongside v0.0.26. The playlist covers the full getting-started path: standing up Heym with Docker, building your first workflow, creating an AI agent with tools, setting up RAG, and using the Portal. More videos are being added weekly.

Cache workers landed in the same release. Background cache eviction and worker coordination reduce memory usage on long-running Heym instances without affecting workflow execution performance.

Week 4: Observability, Glama, and Cost Tracking

May 19 to May 26 (v0.0.27 through v0.0.31)

The final week of month one focused on observability and ecosystem visibility.

v0.0.27: Glama Listing, Cache Eviction, Multi-File AI Skill Builder

Heym was listed on Glama, the MCP server directory. If you are looking for MCP servers to connect to your AI clients, Heym's entry describes the full MCP server capability.

Cache eviction improvements landed alongside the Glama listing. The AI Skill Builder dialog now handles multiple file uploads with proper scroll behavior, which was causing workflow generation to fail silently on large template uploads.

v0.0.29: Security Dependency Updates and License Clarity

A security dependency update to idna (CVE-relevant transitive dependency) was applied, and the license structure was updated to separate the MIT body from the Commons Clause condition more clearly. The Commons Clause is now in its own COMMONS-CLAUSE.md file with an explicit explanation of what it does and does not restrict.

v0.0.31: LLM Token Pricing and Cost Observability

This is the release that finished a story we started on day one with basic Traces.

Traces always showed which models were called and how many tokens were used. What it lacked was a cost view: how much did this workflow run cost in USD? Which model is the most expensive in your stack? Which user or credential is driving the most spend?

What changed in v0.0.31: Heym's Traces view gained full LLM cost visibility, showing USD spend, token counts, latency, and error rate for over 1,100 models using Helicone pricing data, with per-user overrides and custom model support, filterable by time ranges from 1 hour to all time.

v0.0.31 added full LLM cost observability to the Traces dashboard:

• Tokens by model. Prompt and completion tokens broken out per model, per time range.
• USD spend. Dollar cost calculated from model pricing, shown per model and in aggregate.
• Latency and error rate. Side by side with cost, so you can see the cost-latency tradeoff across models.
• Helicone pricing sync. Pricing data for over 1,100 models is synced from the Helicone model registry. You do not need to configure pricing manually for any mainstream model.
• Per-user overrides. Set a different price per token for specific users or credentials, useful if you have negotiated pricing or want to track internal chargebacks.
• Custom model support. Add pricing for models not in the Helicone registry, including self-hosted models and fine-tuned variants.
• Time range filters. View cost data for the last 1 hour, 24 hours, 7 days, 30 days, or all time.

For teams running LLM orchestration pipelines in production, cost visibility is one of the first things that makes the difference between "we think this is affordable" and "we know this is affordable."

YouTube Tutorials

The tutorial playlist at youtube.com/playlist?list=PLPXd_ZbA4wgEHP5PXoaRqbsDJdat7OSd4 covers the full getting-started path. If you prefer to learn by watching rather than reading, the videos are the fastest way to go from zero to a running Heym workflow.

New videos are published regularly. Topics coming up include advanced agent patterns with persistent memory, building RAG workflows with custom Qdrant collections, and using the MCP server to connect Heym workflows to Claude Desktop.

Community Moments

The moment that stood out most in month one happened in week three. A developer posted to DEV Community: they had built a 4-agent adversarial code review system where one agent writes code, a second agent reviews it, a third agent stress-tests the review, and a fourth agent arbitrates. The whole thing runs in Heym and is exposed as an MCP server so any coding assistant can call it as a tool.

The post description put it well: the two Heym features the workflow leaned on were canvas node tools (any node on the canvas can be wired as an agent tool, with individual fields marked as agent-fillable at runtime) and native multi-agent orchestration (one agent calls named sub-agents and sub-workflows visually). Both of those shipped in the first two weeks.

If you have built something with Heym, share it in the GitHub Discussions or tag us on X. We read everything.

What Is Coming Next

Month two priorities, in rough order:

1. Workflow Alerts. A dedicated Alerts tab where you set thresholds on token spend, USD cost per run, and overall error rate. When a workflow crosses a threshold you get notified before a runaway cost or a silent failure reaches production. This fills the gap between the Traces view (which shows what happened) and proactive monitoring (which tells you when something is wrong before you go looking).
2. More node types. Database write nodes, richer webhook trigger options, and a dedicated Slack and Telegram output node.
3. Team workspaces. Role-based access control within a single Heym instance, so you can give different teams access to different workflow groups without running separate deployments.
4. Deeper Portal customization. Custom branding, domain support, and theme options for Portal chat interfaces.
5. Performance at scale. Workflow execution queue improvements for instances handling hundreds of concurrent runs.

These are our priorities, not a locked roadmap. If you have a feature you need, open a GitHub Discussion or submit a pull request. Every suggestion is read and considered. Several of the items above started as community requests filed in month one. If you have built something on top of Heym, found a rough edge, or have an idea for a node type that would save you hours, we want to hear it. Heym is built in public and that includes the roadmap.

The context engineering post covers one pattern that several of these roadmap items lean on, if you want to understand the underlying approach.

Thank You

Building in public means every star, every issue, every "I tried this and it broke" comment is visible and appreciated. Month one was better than we expected and harder than we expected at the same time.

Thank you to everyone who starred the repo. 456 stars in 30 days from a niche self-hosted AI workflow tool is not something we take for granted.

Thank you to the r/selfhosted, r/LocalLLaMA, r/AI_Agents, and r/automation communities for the honest feedback. The questions you asked shaped half the features in weeks two and three.

Thank you to @Hinotoi-agent for finding the security issues and writing clean patches instead of just filing tickets.

Thank you to everyone who watched the tutorial videos and sent feedback on what was confusing. The videos keep getting clearer because of you.

And thank you for reading this far. If something in this changelog made you curious enough to try Heym, the repository is at github.com/heymrun/heym and the Docker Compose setup takes about five minutes.

Month two starts now.

FAQ

References

• Heym GitHub repository: v0.0.1 through v0.0.31 release notes (2026)
• Helicone model pricing registry: 1,100+ model pricing data (2025)
• CVE-2026-45226: Incorrect Authorization in heymrun heym (2026)
• CVE-2026-45227: Protection Mechanism Failure in heymrun heym (2026)
• Glama MCP server directory: Heym listing (2026)
• Model Context Protocol specification, Anthropic (2025)