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Monitoring & Observability

skills/open-source/references/monitoring.md

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Monitoring & Observability

Table of Contents

Cost Tracking

python
agent = Agent(task="...", llm=llm, calculate_cost=True)
history = await agent.run()

# Access usage data
usage = history.usage
# Or via service
summary = await agent.token_cost_service.get_usage_summary()

Laminar

Native integration for AI agent monitoring with browser session video replay.

Setup

bash
pip install lmnr
python
from lmnr import Laminar

Laminar.initialize()  # Set LMNR_PROJECT_API_KEY env var

Features

  • Agent execution step capture with timeline
  • Browser session recordings (full video replay)
  • Cost and token tracking
  • Trace visualization

Authentication

Use browser-use auth for cloud sync (OAuth Device Flow), or self-host Laminar.

OpenLIT (OpenTelemetry)

Zero-code OpenTelemetry instrumentation:

Setup

bash
pip install openlit browser-use
python
import openlit

openlit.init()  # That's it — auto-instruments browser-use

Features

  • Execution flow visualization
  • Cost and token tracking
  • Debug failures with agent thought process
  • Performance optimization insights

Custom OTLP Endpoint

python
openlit.init(otlp_endpoint="http://your-collector:4318")

Integrations

Works with: Jaeger, Prometheus, Grafana, Datadog, New Relic, Elastic APM.

Self-Hosted

bash
docker run -d -p 3000:3000 -p 4318:4318 openlit/openlit

Telemetry

Browser Use collects anonymous usage data via PostHog.

Opt Out

bash
ANONYMIZED_TELEMETRY=false

Or in Python:

python
import os
os.environ["ANONYMIZED_TELEMETRY"] = "false"

Zero performance impact. Source: telemetry service.