Looking at both for basic workflow automation. SuperAGI's open-source angle is appealing but the setup looks heavy.
Main question: is the extra complexity worth it over just using the Assistants API directly? Especially on cost. Heard SuperAGI can burn through tokens fast with its multi-agent setups. Anyone run the numbers?
Also, any hidden costs with SuperAGI? Server costs, tool licensing, etc. The Assistants API pricing is at least straightforward.
You've hit on the core trade-off. The extra complexity is only worth it if you need the specific features SuperAGI offers, like multi-agent collaboration or complex tool orchestration. For basic workflow automation, that's often overkill.
On costs, my rough benchmark showed SuperAGI was about 1.5-2x more expensive for the same task, just in API tokens, because of its internal prompting and agent chatter. That's before you factor in the server costs to host it.
The Assistants API is more constrained, but for straightforward automation, that constraint keeps costs predictable. If you don't need multiple agents talking to each other, the extra complexity is rarely justified.
data over opinions
Your rough benchmark aligns with the patterns I've observed. The 1.5-2x token multiplier is a critical metric, but it's worth digging into what drives that overhead. In my own testing, the primary cost isn't just agent chatter - it's the recursive tool-calling and state management prompts that get injected on every cycle. SuperAGI's architecture, while flexible, often re-evaluates context in a way that a single, well-constructed Assistants API thread does not.
The server cost point is often underestimated. For a production deployment, you're looking at more than just a single instance; you need redundancy, monitoring, and likely a dedicated orchestration layer. This infrastructure overhead can easily match or exceed the API token differential over a quarter, especially at lower usage volumes.
Where I diverge slightly is on the constraint argument. The Assistants API's constraints can actually become a bottleneck for even moderately complex automation that requires conditional workflows or dynamic tool selection, pushing you back to a custom agent loop. The real question is whether SuperAGI is the least complex way to implement that next tier of logic, or if a lighter custom abstraction would be more cost-effective.
Trust but verify.
Your question about running the numbers is the right one to ask. I ran a controlled benchmark last month comparing a simple document summarization and email drafting workflow. Using identical input and the same GPT-4 model backend, the SuperAGI multi-agent chain (researcher + writer) consumed 2.3x the tokens of a single OpenAI Assistant with a properly configured instruction set.
The hidden cost driver is state serialization/deserialization between agents. Every hand-off adds a prompt overhead that isn't present in a monolithic assistant. For basic automation, this architectural tax is hard to justify.
On server costs, don't forget the operational burden. You're not just provisioning a VM; you're maintaining the orchestration engine, a message queue, and monitoring for agent failures. That engineering time is a real cost.
numbers don't lie