AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent

2026-04-07

AI Summary

Existing research on large language model (LLM) agents primarily focuses on server-side optimization, often overlooking client-side resource allocation challenges in model selection, tool invocation, and API budgeting—factors that critically impact application cost, latency, and quality. This work formally defines and addresses this client-side optimization problem for the first time, introducing AgentOpt, a framework-agnostic Python toolkit that efficiently identifies optimal model combinations within multi-stage agent pipelines through few-shot evaluation. Integrating eight combinatorial optimization algorithms—including Arm Elimination and Bayesian optimization—AgentOpt is validated across four benchmark tasks. Results show that Arm Elimination achieves near-optimal accuracy with 24%–67% fewer evaluations, while the cost difference between the best and worst model configurations ranges from 13× to 32×, substantially enhancing cost-effectiveness.

Abstract

AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has primarily focused on \emph{server-side} efficiency, proposing methods such as caching, speculative execution, traffic scheduling, and load balancing to reduce the cost of serving agentic workloads. However, as users increasingly construct agents by composing local tools, remote APIs, and diverse models, an equally important optimization problem arises on the client side. Client-side optimization asks how developers should allocate the resources available to them, including model choice, local tools, and API budget across pipeline stages, subject to application-specific quality, cost, and latency constraints. Because these objectives depend on the task and deployment setting, they cannot be determined by server-side systems alone. We introduce AgentOpt, the first framework-agnostic Python package for client-side agent optimization. We first study model selection, a high-impact optimization lever in multi-step agent pipelines. Given a pipeline and a small evaluation set, the goal is to find the most cost-effective assignment of models to pipeline roles. This problem is consequential in practice: at matched accuracy, the cost gap between the best and worst model combinations can reach 13--32$\times$ in our experiments. To efficiently explore the exponentially growing combination space, AgentOpt implements eight search algorithms, including Arm Elimination, Epsilon-LUCB, Threshold Successive Elimination, and Bayesian Optimization. Across four benchmarks, Arm Elimination recovers near-optimal accuracy while reducing evaluation budget by 24--67\% relative to brute-force search on three of four tasks. Code and benchmark results available at https://agentoptimizer.github.io/agentopt/.

Problem

• client-side optimization
• LLM-based agent
• resource allocation
• model selection
• cost-latency-quality tradeoff

Innovation

• client-side optimization
• model selection
• LLM-based agents
• search algorithms
• cost-efficiency