Hi HN, We built a platform for orchestrating multi-agent debates (e.g., "Security" vs. "Refactoring" experts). The Challenge: Standard sequential agent chains (A -> B -> C) are too slow for real-time chat. The Fix (vLLM): We built a custom inference layer on top of vLLM to solve the bottleneck: Parallelism: We use continuous batching to generate multiple agent responses simultaneously rather than waiting for sequential turns. Memory: PagedAttention allows our agents to share the KV cache for the common context/system prompts, drastically reducing VRAM usage. We’d love feedback on the responsiveness. Create an expert, start a debate, and let us know if the parallel inference makes the conversation feel fluid enough.

You're burying the lede: SOTA 'Reasoning Models' (o1/GPT-4) are actually unusable for agent swarms because inference latency kills the recursion loop.

The real alpha here is Parallel Consensus. Running 5 Llama-3 instances via vLLM to critique each other at <200ms TTFT (Time To First Token) beats a single, slow GPT-4 wrapper every time.

Error correction belongs in the orchestration layer, not the model weights. Is the 'One Giant Model' era finally over for agents?