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Load testing LLM agents requires timing the full loop, not just API calls

By PulseAugur Editorial · [1 sources] ·

This article details how to effectively load test concurrent tool-calling requests for LLM agents, emphasizing the need to measure the entire agent loop rather than just individual API calls. It explains that a single user-visible request for an agent involves multiple model interactions and tool executions, and failing to time this loop accurately leads to misleading performance metrics. The author provides a Python Locustfile example demonstrating how to time each component of the loop, including model calls and tool executions, to gain a realistic understanding of latency and resource consumption. AI

IMPACT Provides a method for accurately measuring LLM agent performance under load, crucial for production deployments.

RANK_REASON The item describes a specific tool (Locust) and a method for load testing LLM agents, which is a practical application rather than a core AI release or research.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Load testing LLM agents requires timing the full loop, not just API calls

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The item describes a specific tool (Locust) and a method for load testing LLM agents, which is a practical application rather than a core AI release or research.
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High
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50 days old
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  1. dev.to — LLM tag TIER_1 English(EN) · Multigrid ·

    Load Testing Concurrent Tool-Calling Requests

    <p>A single chat completion is one request and one response. A tool-calling agent is a loop: the model returns tool calls, you execute them, you send the results back, and the model may ask again. Load-testing that as if it were one request produces numbers that are wrong by what…

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