Hi HN,
I built AgentHub because I was frustrated by the trade-offs required to build multi-model agents in 2026. When you try to support GPT, Claude, and Gemini 3 simultaneously, you usually hit a wall: you either write thousands of lines of boilerplate code or use a "standardizing" wrapper that strips away what makes each model special.
While projects like Open Responses focus on creating vital standards for model transparency and evaluation, AgentHub provides a simple and light-weight interface to adopt those standards in production with zero code changes.
AgentHub takes a different approach: We don’t want to "standardize" the models; we want to provide an intuitive yet faithful interface that keeps you 100% consistent with official API specifications.
- Zero-Code Switching: You can transition your entire agent infrastructure from one provider to another via a simple configuration update. No refactoring, no logic changes—it’s a true zero-code conversion for your codebase.
- Faithful Validation: Unlike simple API forwarders, we perform comprehensive validation to ensure your payloads perfectly match SOTA specifications. This maintains 100% consistency with official API SDKs, eliminating the "intelligence loss" often caused by fragile manual schema mapping.
- Traceable Executions: We provide lightweight yet fine-grained tracing for debugging and auditing LLM executions, enabling deep post-mortem analysis of agent behavior.
I’m curious to hear from the HN community: In your production workflows, do you prefer a "Universal Standard" like Open Responses, or do you value 100% official SDK consistency more when switching between frontier models?