Grafana opentelemetry
Instrument any app with OpenTelemetry and ship metrics / logs / traces to Grafana Cloud or self-hosted Mimir / Loki / Tempo / Pyroscope. Covers SDK auto-instrumentation for Go, Java (Grafana JVM agent), Python (`opentelemetry-instrument`), Node.js, .NET (`Grafana.OpenTelemetry`), Beyla eBPF for zero-code; Grafana Cloud OTLP gateway + Basic-auth (instanceID + API key, base64); env-var config (`OTEL_EXPORTER_OTLP_*`, `OTEL_RESOURCE_ATTRIBUTES`); Alloy / OTel-Collector pipelines; Kubernetes Operator inject-annotations; and head + tail sampling. Use when instrumenting a service, pointing OTLP at Grafana Cloud, switching from Jaeger / Datadog / New Relic, choosing head- vs tail-sampling, or debugging "spans aren't showing in Explore" — even when the user says "auto-instrument my Java app", "send traces to Grafana", "what env vars do I set", "OTLP endpoint", or "Operator inject" without naming OpenTelemetry.From its SKILL.md
npx -y skills add Jylhis/skills --skill grafana-opentelemetryAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its file declares
Copied from the file, not written here
The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
6.9 KB, ~1.6k tokens by cl100k_base, as published. Nobody here has run it
OpenTelemetry with Grafana
Vendor-neutral instrumentation pipeline. Apps speak OTLP → Alloy (or direct) → Grafana Cloud (Mimir / Loki / Tempo / Pyroscope).
Backends
| Signal | Backend |
|---|---|
| Metrics | Grafana Mimir |
| Logs | Grafana Loki |
| Traces | Grafana Tempo |
| Profiles | Grafana Pyroscope |
Prerequisites
- Grafana Cloud stack OR self-hosted Mimir / Loki / Tempo
- Cloud OTLP endpoint:
https://otlp-gateway-<region>.grafana.net/otlp - Basic-auth credentials: numeric instance ID + API token with
MetricsPublisher+LogsPublisher+TracesPublisher - An app to instrument
Common Workflows
1. Authenticate to the Grafana Cloud OTLP endpoint
# 1. Build the auth header
INSTANCE_ID=123456
API_KEY="glc_eyJ..."
export OTEL_EXPORTER_OTLP_ENDPOINT=https://otlp-gateway-prod-us-east-0.grafana.net/otlp
export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Basic $(echo -n "${INSTANCE_ID}:${API_KEY}" | base64)"
export OTEL_RESOURCE_ATTRIBUTES="service.name=myapp,service.namespace=myteam,deployment.environment=prod"
# 2. Smoke-test creds with a curl POST against the OTLP traces endpoint (empty body)
curl -s -o /dev/null -w "%{http_code}\n" \
-X POST -H "Content-Type: application/x-protobuf" \
-H "Authorization: Basic $(echo -n "${INSTANCE_ID}:${API_KEY}" | base64)" \
"$OTEL_EXPORTER_OTLP_ENDPOINT/v1/traces" --data-binary '\n'
# Expect 400 (malformed payload) — NOT 401 (auth) or 404 (wrong endpoint).
2. Auto-instrument a Java app + verify
# 1. Download the Grafana JVM agent (single jar)
curl -sLO https://github.com/grafana/grafana-opentelemetry-java/releases/latest/download/grafana-opentelemetry-java.jar
# 2. Run with the agent + env from step 1
java -javaagent:./grafana-opentelemetry-java.jar -jar myapp.jar
# 3. Generate traffic, then verify in Grafana → Explore → Tempo:
# TraceQL: { resource.service.name = "myapp" }
# Expect spans within ~30s. Also verify metrics:
# PromQL: count by (service_name)({service_name="myapp"})
3. Auto-instrument a Python app
pip install "opentelemetry-distro[otlp]"
opentelemetry-bootstrap -a install
# Same env vars as step 1, then:
opentelemetry-instrument python app.py
# Verify the same way — Explore → Traces filter service.name=myapp.
4. Add Alloy as a buffering / sampling collector
# Application points at local Alloy (gRPC fastest)
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
export OTEL_EXPORTER_OTLP_PROTOCOL=grpc
# Alloy environment for forwarding to Cloud
export GRAFANA_CLOUD_OTLP_ENDPOINT=https://otlp-gateway-prod-us-east-0.grafana.net/otlp
export GRAFANA_CLOUD_INSTANCE_ID=$INSTANCE_ID
export GRAFANA_CLOUD_API_KEY=$API_KEY
alloy run /etc/alloy/config.alloy
# Verify Alloy received and forwarded
curl -s http://localhost:12345/metrics | grep otelcol_exporter_sent_spans
Full Alloy config + tail-sampling block + OTel Collector YAML + K8s Operator install: references/collector-config.md.
SDK-by-language details (Go full code, Node manual setup, .NET ASP.NET Core, all the env-var quirks): references/instrumentation.md.
5. Kubernetes — auto-inject via the Operator
apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata: { name: my-instrumentation }
spec:
exporter: { endpoint: http://otelcol:4317 }
propagators: [tracecontext, baggage]
java:
image: us-docker.pkg.dev/grafanalabs-global/docker-grafana-opentelemetry-java-prod/grafana-opentelemetry-java:2.3.0-beta.1
nodejs: {}
python: {}
Then annotate pods:
metadata:
annotations:
instrumentation.opentelemetry.io/inject-java: "true"
# or: inject-nodejs, inject-python, inject-dotnet
# Verify the operator injected the agent
kubectl describe pod <pod> | grep -A2 'opentelemetry-auto-instrumentation'
# Then run the same Grafana Explore checks.
Sampling — when to pick which
# Head sampling (cheap, decided at start; may lose rare errors)
export OTEL_TRACES_SAMPLER=parentbased_traceidratio
export OTEL_TRACES_SAMPLER_ARG=0.1 # 10%
Tail sampling (decides after seeing the whole trace — keep errors + sample the rest) requires an Alloy / OTel-Collector tail_sampling processor; full block in references/collector-config.md.
Key environment variables
| Variable | Example |
|---|---|
OTEL_EXPORTER_OTLP_ENDPOINT | https://otlp-gateway-prod-us-east-0.grafana.net/otlp |
OTEL_EXPORTER_OTLP_PROTOCOL | grpc or http/protobuf |
OTEL_EXPORTER_OTLP_HEADERS | Authorization=Basic <base64> |
OTEL_RESOURCE_ATTRIBUTES | service.name=app,service.namespace=team,deployment.environment=prod |
OTEL_SERVICE_NAME | shorthand for service.name |
OTEL_TRACES_SAMPLER / _ARG | parentbased_traceidratio / 0.1 |
Troubleshooting
- 401 from OTLP gateway → instance ID is not numeric, or API key missing publisher roles
- 404 → endpoint URL wrong (must end with
/otlp) - Spans missing → check
OTEL_EXPORTER_OTLP_PROTOCOLmatches transport (Cloud OTLP gateway =http/protobuf, Alloy local =grpc) - Node.js auto-instrumentation broken after bundling → bundlers like
@vercel/nccdefeat the require hooks - Python under Gunicorn / uWSGI shows no spans → reinit OTel providers in a post-fork hook
Resources
What ships with it: 2 files
38.7 KB alongside SKILL.md
references/
- collector-config.md18.1 KB
- instrumentation.md20.6 KB
Gives 0 of the 12 instructions most monitoring observability skills give in ~1.6k tokens
Counted across 530 of the 532 authors here whose files we hold, read 2026-09-06
- Use structured JSON loggingin 40 of 530, across 36 files
- Link every alert to a runbookin 29 of 530, across 27 files
- Attach correlation IDs to every log linein 19 of 530, across 16 files
- Alert on symptoms rather than causesin 19 of 530, across 17 files
- Use OpenTelemetry for distributed tracingin 15 of 530, across 14 files
- Alert on symptoms users feelin 15 of 530, across 13 files
- Implement health check endpointsin 14 of 530, across 10 files
- Inspect existing dashboards firstin 12 of 530, across 4 files
- Build the minimum useful boardin 12 of 530, across 4 files
- Start from operator questionsin 12 of 530, across 4 files
- Propagate trace context across boundariesin 11 of 530, across 10 files
- Include trace id in all log entriesin 10 of 530, across 9 files
Said here and by no other author read
- Authenticate to the Grafana Cloud OTLP endpoint
- Auto-instrument a Java app
- Add Alloy as a buffering collector
- Configure head sampling via environment variables
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.