App Observability
Grafana LabsOfficialPublished by Grafana Labs
- Skill
- Tool integration
- Cloud and DevOps
- Apache-2.0
- Low risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a unified approach to monitoring modern software stacks using Grafana Cloud. It covers three core pillars: Application Observability for backend APM via OTel spanmetrics, Frontend Observability using the Faro Web SDK for RUM and session replay, and AI Observability via OpenLIT for monitoring LLM performance, costs, and model evaluations. The guide details the necessary plumbing, including Grafana Alloy configuration, OTel resource attribute requirements, and correlation strategies across metrics, logs, and traces.
What it does
- Configures backend APM using OpenTelemetry and Grafana Alloy for RED metrics and service maps.
- Implements frontend RUM and session replay using the Faro Web SDK with React integration.
- Monitors LLM and GenAI applications for token usage, latency, costs, and hallucination evals via OpenLIT.
- Establishes cross-signal correlation between frontend errors, backend traces, and logs.
- Provides troubleshooting steps for missing service map edges and OTLP ingestion issues.
- Offers standardized environment variable and resource attribute configurations for consistent observability.
When to use it
- When setting up full-stack APM for a new or existing service.
- When you need to monitor browser performance and user sessions in a web application.
- When tracking LLM API costs, latency, and model quality in AI-powered applications.
- When debugging missing service map edges or correlating frontend errors to backend traces.
- When migrating to or configuring Grafana Cloud as your primary observability backend.
Advantages
- Unified observability across frontend, backend, and AI components.
- Leverages open standards like OpenTelemetry for vendor-agnostic instrumentation.
- Reduces manual configuration through pre-built dashboards and auto-instrumentation.
- Enables deep correlation between metrics, logs, traces, and profiles.
- Provides clear paths for monitoring modern GenAI workloads alongside traditional services.
Usage examples
- “How do I set up APM for my Node.js service to show up in Grafana Cloud?”
- The agent provides the OTel SDK installation steps, the required resource attributes (service.name, service.namespace), and the Grafana Alloy configuration to forward traces to your Cloud instance.
- “I need to monitor my OpenAI API usage and costs in Grafana.”
- The agent guides you through installing OpenLIT, initializing it in your Python app, and configuring the OTLP exporter to send GenAI metrics to your Grafana Cloud instance.
- “How can I link my React frontend errors to backend traces?”
- The agent explains how to initialize the Faro Web SDK with TracingInstrumentation, ensuring the traceparent header is injected to correlate browser events with backend Tempo traces.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/grafana/app-observability/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip app-observability.zip -d .agents/skills
Documentation by Grafana Labs
Grafana Cloud Application Observability
Docs: https://grafana.com/docs/grafana-cloud/monitor-applications/
Three products that share the same OTLP + Mimir / Loki / Tempo / Pyroscope plumbing:
Application Observability — APM from OTel spanmetrics
Frontend Observability — Faro Web SDK, RUM + session replay
AI Observability — LLM / vector-DB monitoring via OpenLIT
Prerequisites
Grafana Cloud stack + OTLP endpoint + numeric instance ID + API key with
MetricsPublisher+LogsPublisher+TracesPublisherFor APM: app instrumented with OTel SDK; for Frontend: a web app + Faro app key; for AI: Python ≥ 3.10
Grafana Alloy as the local OTLP receiver (recommended)
Common Workflows
1. Stand up APM — Alloy receiver → Grafana Cloud + verify
# 1. Set Cloud creds + start Alloy with config from references/apm.md
export GRAFANA_CLOUD_OTLP_ENDPOINT=https://otlp-gateway-prod-us-east-0.grafana.net/otlp
export GRAFANA_CLOUD_INSTANCE_ID=123456
export GRAFANA_CLOUD_API_KEY=glc_eyJ...
alloy fmt /etc/alloy/config.alloy # syntax check
alloy run /etc/alloy/config.alloy
# 2. Verify Alloy is receiving + forwarding
curl -s http://localhost:12345/api/v0/web/components \
| jq '.[] | select(.id|test("otelcol\\.exporter\\.otlphttp"))
| {id, health:.health.state}'
# Expect health.state == "healthy"
curl -s http://localhost:12345/metrics \
| grep -E 'otelcol_(receiver_accepted_spans|exporter_sent_spans)'
# 3. Point your app at Alloy (with required attributes!)
export OTEL_SERVICE_NAME="my-api"
export OTEL_RESOURCE_ATTRIBUTES="service.namespace=myteam,deployment.environment=production"
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
export OTEL_EXPORTER_OTLP_PROTOCOL=grpc
# 4. Verify spans landed in Tempo + spanmetrics generated
# Tempo (TraceQL): { resource.service.name = "my-api" }
# Mimir (PromQL): sum by (job) (rate(traces_spanmetrics_calls_total{service_name="my-api"}[5m]))
# Expect > 0 within ~1 minute.
# 5. Verify it's wired to App Observability
# Grafana → Application → Service Inventory: "my-api" should appear with RED metrics
# Click into it → Service Map edges visible (requires span.kind on outbound calls)Full Alloy block + required resource attributes + spanmetric names + correlation links: references/apm.md.
2. Instrument a React frontend with Faro
# 1. Install
npm install @grafana/faro-react @grafana/faro-web-tracing// 2. initializeFaro with TracingInstrumentation + ReactIntegration (see references/faro.md)
// Push a smoketest event so we have a known signal:
faro.api.pushEvent('faro_smoketest', { ts: Date.now().toString() });# 3. Verify in DevTools Network — POST to /collect returns 202
# (401 → wrong app key; 404 → wrong url region)
# 4. Verify in Grafana Cloud
# - Frontend Observability → your app → Sessions: your session appears
# - LogQL on Loki: {kind="event"} |= "faro_smoketest"
# - With TracingInstrumentation: open the session → the trace ID links to TempoFull React example, CDN setup, session config: references/faro.md.
3. Add AI / LLM observability
pip install openlit==1.42.0# At app startup
import openlit
openlit.init(application_name="my-ai-app", environment="production")
# Your existing OpenAI / Anthropic / Cohere calls now emit OTel spans + metrics.# Env (same OTLP endpoint as APM)
export OTEL_SERVICE_NAME="my-ai-app"
export OTEL_EXPORTER_OTLP_ENDPOINT="https://otlp-gateway-<region>.grafana.net/otlp"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Basic $(echo -n $ID:$KEY | base64)"
# Verify after a few LLM calls:
# PromQL: sum by (gen_ai_request_model) (rate(gen_ai_usage_input_tokens_total[5m]))
# Dashboard: Grafana → AI Observability → "GenAI Observability" auto-populatesFull OpenLIT install, evals/guards, GenAI metric list, dashboard names: references/ai-observability.md.
Full-stack correlation cheat sheet
| Signal | Product | Storage | Query |
|---|---|---|---|
| RED metrics | App Observability | Mimir | PromQL |
| Traces | Tempo | Tempo | TraceQL |
| Logs | Loki | Loki | LogQL |
| Profiles | Pyroscope | Pyroscope | ProfileQL |
| Browser RUM | Frontend Observability | Loki + Tempo | LogQL / TraceQL |
| LLM metrics | AI Observability | Mimir | PromQL |
Correlation keys: service.name joins all signals; trace exemplars embed trace IDs in metric points; traceID in logs and traceparent injected by Faro for FE → BE linking.
Troubleshooting
Service missing from Service Inventory → missing
service.namespace(job label) ordeployment.environmentresource attributeService Map edges missing →
span.kindnot set on outbound calls (must be CLIENT) or inbound (SERVER)Faro
/collectreturns 401 → wrong app key; 404 → region in URL doesn't match the Faro appNo GenAI metrics → confirm OpenLIT version matches OTel semantic-conv version expected by Cloud; verify auth with curl as in workflow #3
References
references/apm.md— APM essentials: how RED metrics are generated, required OTel resource attributes, Alloy config, correlation linksreferences/apm-setup.md— deep dive: full per-language OTel SDK setup (Node / Python / Java / Go), span-metrics options, complete Alloy configreferences/faro.md— Faro essentials: SDK init, instrumentations, session replayreferences/frontend-observability.md— deep dive: full Faro SDK reference, React/Vue/Angular integration, custom events, source mapsreferences/ai-observability.md— OpenLIT auto-instrumentation for OpenAI / Anthropic / Bedrock / Vertex AI
Resources
Files
- ATTRIBUTION.mdAdded by AutoGineer855 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md7.2 KiB
- references/ai-observability.md2.4 KiB
- references/apm-setup.md23.1 KiB
- references/apm.md3.2 KiB
- references/faro.md3.2 KiB
- references/frontend-observability.md14.7 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs network access, Needs an API key, Needs Python, Needs Node.js
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Attribution and licence
- Published by
- Grafana Labs
- Publisher
- Grafana Labs (Official)
- Source
- grafana/skills · skills/grafana-cloud/app-observability · commit 8fb72a5
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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