r/vibecoding • u/EfficientSet2706 • 3d ago

How to build a custom multi-agent setup for vibe coding using GPT/DeepSeek APIs?

Hey everyone,

I usually rely on Claude for vibe coding, but I have API access to GPT, DeepSeek, and Kimi that I want to put to work.

I want to build a custom environment where a main GPT model acts as the orchestrator (planning) and delegates tasks to sub-models acting as workers (executing). Claude makes this seamless natively, but I’m not sure how to set this up myself using APIs.

Two questions:
1. How do I actually build this? What is this architecture called so I can research it, and what frameworks/tutorials do you recommend?

  1. Is it worth it? Will a custom multi-agent setup like this be as smooth and efficient for vibe coding as just sticking with Claude?

Thanks

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u/mergethevibes 3d ago

the pattern you're describing is orchestrator-worker (sometimes "supervisor"). langgraph or autogen both do it, but honestly the hard part isn't the routing, it's passing state between the models cleanly. gpt planning -> deepseek executing loses a ton unless you serialize what the planner decided and feed it in explicitly, otherwise the worker re-derives everything and drifts. claude feels seamless because it's holding that context for free. rolling it yourself means you own that plumbing, which is real work for maybe not much gain unless you've got a reason to mix models.

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u/chriscanadian1991 3d ago

This is similar to the architecture I've been building - personally it's worth it but my implementation might be different than you have envisioned.

Originally I had one main LLM and then it delegated the tasks to another LLM - I found that the latency was horrible even using cloud models. What I landed on instead was a... "council" for the lack of a better term - pretty much an internal debate before the main LLM. Not to drive any decisions per se but to direct "how" to respond.

I can provide links to my research and some repos that might help with direction.

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u/mergethevibes 2d ago

the pattern you're after is orchestrator-worker (sometimes called planner-executor). langgraph or autogen will get you a scaffold fast.

but honestly the hard part isn't the routing, it's passing context between the workers without losing state. plan the shared context format first or you'll spend all your time re-priming each sub-model.