Hey everyone! I've been tasked with exploring some automation options for our support team. We're a 200-user SaaS company, and I wanted to share what we found comparing AgentGPT and CrewAI for automating some internal workflows.
We built a simple PoC with both: an agent that fetches recent high-severity alerts from our Prometheus/Grafana stack, summarizes them, and creates a Jira ticket draft. Here's the core difference we saw in setup.
**AgentGPT** was super quick to start via the web UI. Just typed a goal like "Check Prometheus for critical alerts in the last hour and draft a Jira ticket." But for our use case, we hit limits quickly—needed more control to integrate our internal APIs and alerting logic.
**CrewAI** required more code, but it was cleaner for our needs. We could define specific agents (like a "Monitor Analyst" and a "Jira Coordinator") and give them exact tools. Here's a tiny snippet of our `tasks` definition:
```python
fetch_alerts_task = Task(
description="Fetch critical alerts from our internal Prometheus API from the last 1 hour.",
agent=monitor_analyst,
expected_output="A list of alert names, severities, and instance counts."
)
```
The big finding: AgentGPT is great for one-off, exploratory tasks. CrewAI, while more setup, gave us the structure and control needed for a reliable, repeatable process that fits into our existing on-call workflow. For now, we're moving ahead with CrewAI for this project. Has anyone else tried something similar? Would love to see how others are connecting these tools to their monitoring systems!
Nice to see someone else going down this rabbit hole! That exact "quick UI vs. control" trade-off is why we eventually moved off AgentGPT for production workflows too. It's great for sketching an idea, but falls apart when you need to bake in auth, retries, or custom logic.
Your CrewAI snippet is the right path. One thing that helped us was wrapping those agent tools in our own internal libraries. For example, our "fetch alerts" tool handles PromQL, rate limiting, and formats the data consistently before the agent even sees it. Stops the LLM from getting creative with the query syntax.
How are you handling the hand-off between your agents? We found setting really clear expected outputs (like your snippet does) and using a small orchestrator script outside CrewAI made things more reliable.