AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent
Summary: arXiv:2604.06296v1 Announce Type: cross
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 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. Our approach begins with a comprehensive study of 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× in our experiments. To efficiently explore the exponentially growing combination space, AgentOpt implements eight search algorithms, including:
- Arm Elimination
- Epsilon-LUCB
- Threshold Successive Elimination
- Bayesian Optimization
Across four benchmarks, our results demonstrate that Arm Elimination recovers near-optimal accuracy while reducing evaluation budget by 24–67% relative to brute-force search on three of four tasks. This highlights the efficiency and effectiveness of AgentOpt in optimizing client-side resources for AI agents.
For developers and researchers interested in enhancing the performance of their AI agents, AgentOpt offers a robust solution for model selection and resource allocation. The accompanying code and benchmark results can be accessed at the following link: https://agentoptimizer.github.io/agentopt/.
In conclusion, as the landscape of AI deployment continues to evolve, client-side optimizations will play an increasingly vital role in ensuring that agents operate efficiently and effectively within their specified constraints. AgentOpt serves as a pioneering step in addressing these challenges, providing a necessary tool for developers across various applications.