AgentLens: Framework-Aware Characterization and Resource Control for Heterogeneous LLM Agents

Overview & Synopsis

LLM-based agents are increasingly deployed on shared hosts, yet their host-side resource demands remain poorly understood, limiting effective resource coordination. We present AgentLens, which combines a cross-framework workload characterization with a framework-aware resource controller for co-located agents. Across 10 frameworks and 11 matched coding tasks, we find that framework orchestration strongly shapes resource demand: framework identity explains 52.95% of peak RSS variance, compared with 4.22% for task identity, while execve events per tool call differ by 39x between MetaGPT and CAMEL. Resource dimensions are not interchangeable: peak RSS and subprocess count have a Spearman correlation of 0.13. Static budgets and framework-agnostic policies miss these structural differences and the alternation between API waiting and tool execution. AgentLens combines offline framework fingerprints with online execution-phase signals to coordinate memory through cgroup v2, without modifying framework internals. In trace-driven replay across three co-location pairs at a memory budget of 90% of aggregate peak demand, AgentLens keeps the protected worker's P95 allocation latency at 5.7-7.1 ms, improving on static allocation by 2.1-2.6x.