Running a full team of agents in MetaGPT seems like it would consume a massive amount of tokens, especially with GPT-4. For a small startup on a budget, is the productivity gain high enough to offset the API costs? I'm worried that the constant back-and-forth between agents might lead to an expensive monthly bill compared to traditional development.
3 answers
It's an investment in speed. While it’s true that MetaGPT can be token-heavy due to the structured dialogue between agents, you have to weigh that against the cost of a human developer's hourly rate. If $5 worth of tokens can generate a documentation set that would take a Junior PM two days to write, the ROI is astronomical. Furthermore, you can now use local models like Llama 3 or DeepSeek with the framework to keep costs down for the initial drafting phases and only "level up" to the high-end cloud models for final code generation and review.
Have you tried running it with those open-source models yet? I'm curious if the "Role-Playing" aspect holds up as well when the underlying LLM isn't quite as sophisticated as the latest GPT versions.
If you value your time, the answer is yes. The speed at which you can pivot or test new feature ideas with a full agent crew is unmatched by manual coding.
Exactly, Alice. Being able to fail fast and iterate on a prototype in hours instead of weeks is the real value here for any startup.
I’ve had great results with DeepSeek-Coder. It follows the SOPs surprisingly well. The trick is to ensure your prompt templates are very rigid. If the instructions are clear, even mid-tier models can handle the specific tasks of the Product Manager or Architect agents without drifting off-topic, which makes the whole system much more affordable for a bootstrapped team.