Automate Slack workflows with LangGraph

#langchain #aiagents #webdev #oauth

How to turn messy #product-support Slack messages into auditable tasks

We’ve been experimenting with AI workflows (like most of the dev world lately — and of course, using Scalekit for the auth layer. Full disclosure: I work for Scalekit. Our offering is always free for devs tinkering with stuff).

For our first one, we tried automating Slack workflows with LangGraph and Scalekit. Here’s what we built 👇

🎫 Auto-create GitHub issues or Zendesk tickets directly from Slack messages
🔐 Simplify OAuth, token management, and API calls using Scalekit
🔁 Use LangGraph to design modular, observable workflows

The idea

Slack is great for fast communication, but terrible as a system of record. Important messages about bugs or support requests get buried almost instantly.

So we built an agent that:

Watches specific Slack channels
Classifies each message (bug, support, ignore)
Creates a GitHub issue or Zendesk ticket based on that classification
Posts a confirmation reply in Slack

Why LangGraph + Scalekit

Scalekit takes care of all the auth plumbing, so no need to manually store Slack or GitHub tokens, refresh them, or juggle SDKs. You just call execute_tool with the right parameters, and Scalekit securely handles the rest.

LangGraph gives you a clean, node-based workflow where each step is independent and observable. Instead of a big procedural script, you have a graph that shows exactly how data moves, which is perfect when you want to debug or extend it later.

How it works

Here’s the basic flow:

# triage.py
from routing import classify_route
from actions import create_github_issue, post_slack_confirmation, handle_zendesk_ticket

def triage_message(slack_user, channel, ts, text):
    route = classify_route(text)

    if route == "github":
        issue = create_github_issue(slack_user, channel, ts, text)
        post_slack_confirmation(slack_user, channel, ts, f"Created GitHub issue → {issue['url']}")
    elif route == "zendesk":
        handle_zendesk_ticket(slack_user, channel, ts)
    else:
        # ignored message
        pass

Each message gets classified, turned into a structured task, and acknowledged back in Slack — no human in the loop.

Moving to LangGraph

Once we had the script working, we turned it into a LangGraph workflow:

from langgraph import StateGraph
graph = (
    StateGraph()
      .add("parse", parse_node)
      .add("classify", classify_node)
      .add("execute", execute_node)
      .add("confirm", confirm_node)
      .edge("parse", "classify")
      .edge("classify", "execute")
      .edge("execute", "confirm")
      .entry("parse")
)

Each node handles one job, and you can easily replace or expand them later, like plugging in an LLM classifier, adding retries, or branching into multiple integrations.