Used AutoGen for a few months. It's clever, I'll give it that. But clever doesn't mean reliable. Got tired of the random hangs, the silent failures where it just stops talking to itself. Debugging a chain of agents that went nowhere is a special kind of hell.
Switched to Agno last month. Night and day difference. It’s boring, in a good way. Does what you tell it, finishes the job, logs what actually happened. No more babysitting. The reliability isn't even a comparison. Agno feels like a tool. AutoGen still feels like a research project.
CRM is a necessary evil
I run AI orchestration for a 150-person logistics tech shop, and we've had both AutoGen and Agno handling our internal document processing flows in production for the last year.
1. **Production Reliability:** Agno. AutoGen would drop 5-10% of our multi-step workflows with opaque errors, requiring a full restart. Agno's error handling and state tracking meant we saw that drop to under 1%, and failures were traceable.
2. **Total Cost:** AutoGen. It's open-source, so your cost is engineering time. Agno starts around $25k/year for their core platform license, not including cloud inference costs. You're trading cash for developer sanity.
3. **Operational Overhead:** Agno. AutoGen needs constant monitoring and custom tooling for logging/observability. Agno ships with that, so our platform team spends maybe 2 hours a week on it versus 10+ with AutoGen.
4. **Integration Depth:** AutoGen. If you need to deeply customize agent logic or wire into a bizarre legacy system, AutoGen's code-first approach gives you the lever. Agno's cleaner abstraction can become a wall if your use case strays from their happy path.
I'd recommend Agno for any team that needs a dependable, set-and-forget workflow runner for business processes. Stick with AutoGen if you're prototyping novel agent patterns or have the engineering bandwidth to build your own platform on top of it.
trust but verify
Ah, the classic "developer sanity" line. I always wonder if that sanity check includes the quarterly invoice from Agno. You mention a $25k floor, but that's just the entry fee for the core platform. That doesn't include the multiplier once you scale teams or connectors, or the inevitable "enterprise feature" you'll need that's mysteriously in the next pricing tier.
The real lock-in starts when your "set-and-forget" workflows are so dependent on their abstraction that leaving becomes a migration horror story priced in months, not hours. Open source pain is upfront; vendor pain is a recurring subscription.
Buyer beware.
Totally feel this. I've seen that silent failure pattern in AutoGen, especially when you have a nested group chat that just... stops. The logs show nothing, and you're left replaying the whole conversation manually.
That "research project" vibe is spot on. It's fantastic for prototyping an agent idea quickly, but the moment you need it to run on a schedule without hand-holding, the cracks show. I had to wrap so much custom telemetry and timeouts around it just to get basic operational visibility, which kinda defeats the point of using a framework.
But have you hit any friction yet with Agno's more rigid structure? I sometimes miss being able to throw in a weird, custom agent loop like AutoGen allows, even if it was messy.
pipeline all the things
You've hit on a key tradeoff. The "rigid structure" is Agno's reliability mechanism. It enforces a clear state machine and defined communication patterns, which eliminates the weird deadlocks but does fence you in.
I've found the friction point isn't usually the core agent loop, but integrating a truly bespoke external tool or a non-standard feedback mechanism. For instance, I had a workflow that needed an agent to pause and wait for a manual human approval via a custom API - modeling that in Agno required mapping it to their existing "human-in-the-loop" primitive, which felt like a workaround. In AutoGen, I could have just hacked in a function call that waited.
The compromise is to treat Agno as your production orchestrator and keep a small AutoGen sandbox for rapid prototyping of those weird loops. Once a pattern is proven, you then formally model it for Agno. It's more steps, but it separates research chaos from operational stability.
That's a really smart way to frame it, treating AutoGen as a prototyping sandbox. I'm still figuring this stuff out, so it helps to see the workflow separated like that.
But doesn't that two-system approach add its own overhead? You're basically maintaining two different mental models and codebases for the same goal. How do you keep the transition from the AutoGen prototype to the Agno implementation smooth? Do you end up rewriting everything from scratch?
You're right to flag the overhead, it's the core problem with a dual-track approach. The smoothness of the transition depends entirely on how you prototype. If you treat the AutoGen prototype as a throwaway sketch, you're in for a rewrite.
My method is to prototype the *orchestration logic* in AutoGen, but strictly isolate all tool implementations and state schemas. Those become standalone Python modules or service calls. When you move to Agno, you're not porting agent chatter, you're just wiring those same, tested modules into Agno's task definitions. The mental model shift is real, but the actual business logic and tooling remain constant. You're essentially replacing the unreliable, chat-based "glue" with a structured orchestrator.
Measure twice, cut once.