Claude Tag solved this at Anthropic — but it’s closed source, locked to Anthropic’s infra, and has zero audit trail. And I’ve been waiting for an open version since I first tried it.

Enter OpenTag — an MIT-licensed open-source implementation that landed on GitHub 48 hours ago and already has 259 stars. And it does exactly what I wanted: @agent in Slack or GitHub → routes to Claude Code, Codex, or a custom runner → result lands back in the thread with a full audit trail. No tab switching. No context loss.

What OpenTag Actually Does

But the architecture is clean. OpenTag runs a thin dispatcher between your work apps and your agent runners. When someone drops @agent in a Slack channel or a GitHub issue comment, here’s the chain:

1. First, the work app adapter normalizes the mention into a structured request
2. Then the dispatcher validates scope, persists the run ID, and manages leases
3. Then an approved runner (local daemon or VPS-hosted) claims the work
4. Then the executor — Claude Code CLI, OpenAI Codex, or a custom script — does the job
5. And callback adapters post the result back to the original thread

So the full loop stays inside your workflow. No IDE required. No extra chat window.

For a different approach to routing agent workloads — more of an OS-level take — I covered the ECC Agent Harness in my earlier review. It runs Claude Code and Codex as system services rather than embedded in chat apps.

Hands-On With OpenTag: My First Run

So I installed opentagd on my dev machine and ran the github-to-echo example. Honestly, this isn’t a five-minute setup — you need Node 22.x, pnpm, a SQLite or PostgreSQL backend, and you’re editing .env.example by hand. But once it’s configured, the moment of truth is satisfying.

Then I opened a test issue on a throwaway repo, posted @agent tell me what's in this repo's README, and waited. And about 8 seconds later, the daemon had picked up the mention, run the echo executor against my local checkout, and posted a comment back in the issue thread. That feedback loop — from mention to callback in under 10 seconds in the same browser tab — is the kind of thing that makes you see how teams will start using this daily.

Still, I’ll be honest: this is v0.1.0 from two days ago. The setup docs assume you already know how Slack app manifests and GitHub app permissions work. Still, that’s fine for early adopters but a hurdle for the broader audience.

What OpenTag Still Needs

Now, OpenTag is promising but raw. Here’s what I’d flag:

• Setup is multi-step. You configure a Slack app, a GitHub app, the dispatcher, and the daemon separately. No single npx opentag init yet.
• Production hardening needed. The dispatcher is intentionally thin. Multi-tenant hosting is a future concern, not a current feature.
• Docs assume familiarity. The .env.example file has placeholder values but no inline guides yet. You’ll be tab-juggling between the README and the Slack API docs.
• Community is just starting. 24 hours old, 259 stars, daily commits. The runway looks good, but there’s no plugin ecosystem or community runners yet.

But Amplify (the org behind it) is shipping daily — the commit history shows active development, not a side project that’s already abandoned.

Who Should Try OpenTag

• Developer teams on Slack/GitHub who are tired of the copy-paste dance into AI chat
• Self-hosting folks with a DigitalOcean or Vultr VPS who want their own agent mesh with audit trails
• DevOps engineers who need governance on AI-assisted code changes — the permission scopes and audit events are exactly right for this
• Anyone who looked at Claude Tag and wanted it open-source and under their own infrastructure

If you’re already running something like Pi Agent Harness for coding tasks, I wrote up my experience in my Pi Agent Harness review. Adding OpenTag for Slack/GitHub mentions fills the collaboration gap that standalone harnesses leave open.

OpenTag: The Bottom Line

So here’s my take: OpenTag solves a real problem — bringing agents into your existing workflow instead of forcing you into a separate AI workspace. It’s early, the setup is fiddly, and the docs need work. But the architecture is sound, the MIT license means nobody but you controls it, and the Claude Code + Codex support makes it genuinely multi-model from day one.

I’m keeping it installed. I think you should give it a spin too.