Lyzr

“Nice canvas but credits cost”

Pros

Using it for building agents has been really useful especially the workflow canvas for putting things together and creating. The canvas makes it clear where an agent failed and lets me review each step which helps a lot. Also it makes building and using AI workflows very simple so adding AI into my software is much more practical.

Cons

One downside is that it burns through credits pretty quickly and I think it would work more efficiently if some tasks could be integrated through GitHub. It would also be really useful if it could be triggered automatically when tasks are completed.

“tool is simple to setup”

Pros

The interface is simple and user friendly so it doesn't seem complicated to navigate at all.

Cons

One frustrating part is that it's hard to find the agents after creating them and the agent canvas map tends to disappear once it's been created.

“Fast agent prototyping works perfectly”

Pros

One thing that really stood out with Lyzr.ai was how fast it let me build and test AI agents without needing to create the whole orchestration layer from the ground up. While evaluating it I could put together agent workflows, set up prompts and connect external data sources through an interface that is easy to use. The platform made it simple to iterate on multiple workflow ideas and compare outcomes without sinking too much time into infrastructure work. Also I liked the modular setup since it made experimenting with different agent behaviors and fine-tuning them based on the results much more practical. For rapid prototyping and proof-of-concept work, it cut development time considerably while still offering enough flexibility for more advanced scenarios. Those were easily the strongest parts for me.

Cons

Honestly, I could almost say nothing here, lol. That said even though it is quick to start using, building more advanced multi-agent workflows took some trial and experimentation. A few of the deeper configuration options were not obvious right away so I had to lean on the documentation and some trial and error to get the behavior I wanted. I also feel the platform would be stronger with more detailed tutorials, more real-world implementation examples and a broader set of native integrations for enterprise applications. Better debugging and observability tools to explain why an agent made a specific decision would also make troubleshooting complex workflows a lot easier.