Hey everyone — I've noticed a lot of initial hype and setup tutorials for SuperAGI, but I'm curious about longer-term, real-world use. The platform has evolved quite a bit over the past year, and I think the community would benefit from hearing from those who've moved past the proof-of-concept stage.
If you've been running SuperAGI in a production environment (or something close to it) for **six months or more**, I'd love to hear about your experience. To keep things constructive, maybe you could touch on a few key points:
* **Use case:** What are you actually automating or managing with it? (e.g., customer support triage, content generation pipelines, internal data analysis)
* **Stability & maintenance:** How has it held up? Any recurring issues, major downtime, or significant resource (time/money) spent on keeping it running?
* **Team adoption:** Was it just a developer tool, or did other roles (ops, marketing, support) end up using it? What was the learning curve like?
* **The "gotchas":** Were there any unexpected limitations, costs, or workflow adjustments you had to make that weren't obvious during the trial phase?
No need to write a novel — even a few bullet points from your experience would be incredibly valuable. This isn't about bashing or promoting, but about building a realistic picture of where the tool shines and where it might need more maturity.
Looking forward to some insightful discussion
Stay factual, stay helpful.
Good question. We ran it for about eight months, then ripped it out.
Use case was automated incident response triage in our K8s cluster - parsing alerts, checking logs, suggesting initial mitigation steps.
Stability was the main issue. The agent would "drift" during long-running tasks, requiring constant supervision. Downtime wasn't the problem, unpredictability was. We spent more engineering hours babysitting it than we saved.
The cost wasn't in the platform, it was in the GPT-4 API calls for complex reasoning. Bill was 3x our initial estimate.
Team adoption was zero outside the platform engineering team. The learning curve to build reliable workflows was steep, and the output wasn't trustworthy enough for ops to use without verification. It just became another fragile system to maintain.
Biggest gotcha? It's a framework, not a product. You're building the brains yourself. If your use case isn't rock solid, you'll waste time tuning prompts and debugging agent loops instead of solving the actual problem. We moved to a simpler, deterministic rules engine for triage and haven't looked back.
slow pipelines make me cranky