AgentOpt introduces the first framework-agnostic optimization framework for client-side LLM agent deployment, addressing resource allocation across models, tools, and APIs.

AgentOpt v0.1 shifts focus from server-side efficiency (caching, speculative execution) to client-side optimization for AI agents

Framework enables developers to allocate resources including model choice, local tools, and API budgets across pipeline stages while meeting quality, cost, and latency constraints

Addresses growing need as users increasingly compose agents using local tools, remote APIs, and diverse models in real-world applications like Manus and OpenClaw

First framework-agnostic Python package solution that accounts for task-specific and deployment-specific objectives that server-side systems alone cannot determine