Technical case study
See the system. Find the cause.
What Grafana and Langfuse revealed about a local multi-agent system.
Slow tasks did not reveal the source of the delay.
Grafana infrastructure metrics + Langfuse agent traces.
Model load/unload events: about 40+ → 7 per hour.
Approximate, author-reported observation · Not a latency benchmark.
The problem
Slow tasks alone did not explain whether the cause sat in the hardware, model loading or agent execution.
My contribution
I used Grafana for infrastructure metrics and Langfuse for execution traces, then investigated the different problems each view exposed.
Architecture choices
Grafana: infrastructure
GPU memory, loaded models and model load/unload activity.
Langfuse: execution
Agent calls, prompts, token counts, timing and responses.
Correlated investigation
Use the infrastructure view to investigate delays and traces to investigate behaviour.
What it showed
Model residency
A five-minute idle timeout repeatedly unloaded a 20 GB model. Keeping models resident reduced reported load/unload events from roughly 40+ to 7 per hour.
Trace coverage
Tracing exposed missing token counts and timing data, alongside uneven agent usage.
Status & next evaluation
Observation from a personal system, published 16 August 2026. The figures describe model load/unload events, not a measured reduction in latency.
A stronger follow-up would measure latency and task quality under the same workload before and after the change.
Project source
The project details and reported results come from my linked LinkedIn publication.
Read the original technical article