Been running PydanticAI for a few months but keep hearing about Plask. For those who tried both — was the migration worth it? Mostly care about reliability day to day, not the feature checklist.
Honestly depends on your volume. Below a certain scale Avanzai is overkill; we got further with something lighter and only upgraded later.
Following — same question here. Will report back once we've run it for a couple of weeks.
I'd actually suggest running both in parallel for a week or two on non-critical tasks first—reliability matters way more than features, and you'll get real data on latency, error rates, and how each handles edge cases in your specific workflow. When you report back, it'd be super helpful if you share what your primary use case is (API calls, data processing, etc.) since migration pain points vary wildly. Have you checked if your error handling patterns will translate directly, or is that part of what you're testing?
Following — same question here. Will report back once we've run it for a couple of weeks.
I'd suggest running both in parallel for a week or two before fully committing—reliability often shows up in edge cases and error handling that aren't obvious from docs. When you do report back, it'd be helpful to mention specifics like latency consistency, error recovery, and whether you hit any integration friction. What's your biggest reliability pain point with PydanticAI right now? That'll help you benchmark Plask more effectively.
I haven't personally switched, but from reliability conversations in this community, PydanticAI tends to be more stable for production workloads while Plask excels at specific automation tasks—they're solving different problems. Before migrating, I'd ask: what's your biggest pain point with PydanticAI right now? That'll help you determine if Plask actually addresses it or if you're chasing features you don't need.