Ag2 Telemetry
ag2ai/build-with-ag2
Add OpenTelemetry traces to an AG2 beta Agent via TelemetryMiddleware (autogen.beta.middleware.builtin).
Get RED metrics + service maps + frontend RUM + AI/LLM monitoring out of Grafana Cloud — Application Observability (tracesspanmetrics from OTel traces, p50/p95/p99 latency, exemplar-to-trace…
Install App Observability
$ npx skills add grafana/skills --skill app-observability -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install grafana/skills app-observability --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/grafana/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/grafana-cloud/app-observability .claude/skills/app-observability && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "app-observability" agent skill from https://github.com/grafana/skills/tree/main/skills/grafana-cloud/app-observability into .claude/skills/app-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "app-observability", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/grafana/skills/tree/main/skills/grafana-cloud/app-observabilityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add grafana/skills --skill app-observability -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install grafana/skills app-observability --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/grafana/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/grafana-cloud/app-observability .agents/skills/app-observability && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "app-observability" agent skill from https://github.com/grafana/skills/tree/main/skills/grafana-cloud/app-observability into .agents/skills/app-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "app-observability", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add grafana/skills --skill app-observability -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install grafana/skills app-observability --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/grafana/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/grafana-cloud/app-observability .cursor/skills/app-observability && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "app-observability" agent skill from https://github.com/grafana/skills/tree/main/skills/grafana-cloud/app-observability into .cursor/skills/app-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "app-observability", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/grafana/skills.git --path skills/grafana-cloud/app-observability--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add grafana/skills --skill app-observability -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install grafana/skills app-observability --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/grafana/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/grafana-cloud/app-observability .gemini/skills/app-observability && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "app-observability" agent skill from https://github.com/grafana/skills/tree/main/skills/grafana-cloud/app-observability into .gemini/skills/app-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "app-observability", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install grafana/skills app-observabilityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add grafana/skills --skill app-observability -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/grafana/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/grafana-cloud/app-observability .github/skills/app-observability && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "app-observability" agent skill from https://github.com/grafana/skills/tree/main/skills/grafana-cloud/app-observability into .github/skills/app-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "app-observability", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add grafana/skills --skill app-observability -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install grafana/skills app-observability --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/grafana/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/grafana-cloud/app-observability .opencode/skills/app-observability && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "app-observability" agent skill from https://github.com/grafana/skills/tree/main/skills/grafana-cloud/app-observability into .opencode/skills/app-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "app-observability", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
app-observability- GitHub stars
- 281
- Token cost
- ~1.8k tokens
- SKILL.md length
- 347 words
- Files
- 6 (incl. references)
- Skills in repo
- 51
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Get RED metrics + service maps + frontend RUM + AI/LLM monitoring out of Grafana Cloud — Application Observability (tracesspanmetrics from OTel traces, p50/p95/p99 latency, exemplar-to-trace…
- Works in 3 steps: Stand up APM — Alloy receiver → Grafana… → Instrument a React frontend with Faro → Add AI / LLM observability
- Standing up APM for a service
- SKILL.md covers Prerequisites, Common Workflows, Full-stack correlation cheat… and Troubleshooting, plus 2 more sections
- Calls curl, jq and npm; reaches otlp-gateway-prod-us-east-0.grafana.net; needs GRAFANA_CLOUD_API_KEY
What it does
App Observability is an agent skill from grafana/skills, published by the product's own GitHub organization. Get RED metrics + service maps + frontend RUM + AI/LLM monitoring out of Grafana Cloud — Application Observability (tracesspanmetrics from OTel traces, p50/p95/p99 latency, exemplar-to-trace, traces-to-logs / profiles), Frontend Observability with the Faro Web SDK (Core Web Vitals, session replay, pushError, React + router integration, TracingInstrumentation for browser → backend trace correlation), and AI Observability via OpenLIT (token / cost / latency, GPU, hallucination + toxicity evals). Use when standing…
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/ai-observability.md`, `references/apm-setup.md` and `references/apm.md`).
It sits in DevOps & Cloud, covering Monitoring and alerting, Observability and LLM cost and token optimization. It works with Grafana, OpenTelemetry, React and OpenAI. The licence is Apache-2.0.
When your agent uses it
- Standing up APM for a service
- Wiring an Alloy OTLP receiver + forwarding to Cloud
- Instrumenting a React frontend for RUM
- Debugging why service-map edges are missing
Example prompts
- “set up APM”
- “show service map”
- “monitor browser perf”
- “/app-observability”
Requirements
- Python 3
- Node.js
- A credential in GRAFANA_CLOUD_API_KEY
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 1ccacf2. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
curljqnpmpip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
otlp-gateway-prod-us-east-0.grafana.net
Also links to:
grafana.comgithub.comopenlit.io
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
GRAFANA_CLOUD_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
App Observability loads about 1.8k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 243 tokens; SKILL.md has 347 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from grafana/skills at commit 1ccacf2, republished under its Apache-2.0 licence (© grafana). 347 words, ~1,841 tokens.
Download SKILL.mdSave it as
.claude/skills/app-observability/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- app-observability
- description
- Get RED metrics + service maps + frontend RUM + AI/LLM monitoring out of Grafana Cloud — Application Observability (`traces_spanmetrics_*` from OTel traces, p50/p95/p99 latency, exemplar-to-trace, traces-to-logs / profiles), Frontend Observability with the Faro Web SDK (Core Web Vitals, session replay, `pushError`, React + router integration, `TracingInstrumentation` for browser → backend trace correlation), and AI Observability via OpenLIT (token / cost / latency, GPU, hallucination + toxicity evals). Use when standing up APM for a service, wiring an Alloy OTLP receiver + forwarding to Cloud, instrumenting a React frontend for RUM, debugging why service-map edges are missing, monitoring LLM cost drift, or correlating a frontend error to its backend trace — even when the user says "set up APM", "show service map", "monitor browser perf", "session replay", "RUM SDK", or "watch our OpenAI bill" without naming App / Frontend / AI Observability.
- license
- Apache-2.0
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+TracesPublisher - For 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 attribute - Service 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 app - No 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
© grafana, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files (references) in skills/grafana-cloud/app-observability of grafana/skills.
- SKILL.md
- references/ai-observability.md
- references/apm-setup.md
- references/apm.md
- references/faro.md
- references/frontend-observability.md
Open the folder on GitHubat commit 1ccacf2
Compare with similar skills
App Observability next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| App Observability this skillgrafana/skills | 281 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Ag2 Telemetryag2ai/build-with-ag2 | 252 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| OpenTelemetry Pipeline Metrics Speccomet-ml/opik | 22k | — | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| Archestra Dev Observabilityarchestra-ai/archestra | 4.4k | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| Frontmcp Observabilityagentfront/frontmcp | 146 | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Monitoring Observabilityahmedasmar/devops-claude-skills | 203 | — | ~3.9k | Automated safety check: Pass | None |
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Related
Categories
Questions about App Observability
What does App Observability do?
Get RED metrics + service maps + frontend RUM + AI/LLM monitoring out of Grafana Cloud — Application Observability (tracesspanmetrics from OTel traces, p50/p95/p99 latency, exemplar-to-trace…. App Observability is an agent skill from grafana/skills, published by the product's own GitHub organization. Get RED metrics + service maps + frontend RUM + AI/LLM monitoring out of Grafana Cloud — Application Observability (tracesspanmetrics from OTel traces, p50/p95/p99 latency, exemplar-to-trace, traces-to-logs / profiles), Frontend Observability with the Faro Web SDK (Core Web Vitals, session replay, pushError, React + router integration, TracingInstrumentation for browser → backend trace correlation), and AI Observability via OpenLIT (token / cost / latency, GPU, hallucination + toxicity evals).
When should I use App Observability?
App Observability fits situations like: standing up APM for a service; wiring an Alloy OTLP receiver + forwarding to Cloud; instrumenting a React frontend for RUM; debugging why service-map edges are missing.
How do I install App Observability in Claude Code?
Run `npx skills add grafana/skills --skill app-observability -a claude-code`. Or copy the skill folder (skills/grafana-cloud/app-observability in grafana/skills) into .claude/skills/app-observability in your project. Claude Code loads it when a task matches its description.
How do I install App Observability in Codex?
Run `npx skills add grafana/skills --skill app-observability -a codex`. Or copy the skill folder (skills/grafana-cloud/app-observability in grafana/skills) into .agents/skills/app-observability in your project. Codex loads it when a task matches its description.
Can I use App Observability in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add grafana/skills --skill app-observability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/app-observability, .gemini/skills/app-observability, .github/skills/app-observability and .opencode/skills/app-observability in your project.
What does App Observability need to run?
Going by SKILL.md and its folder, App Observability needs the command-line tools its instructions call (curl, jq, npm and pip) and credentials named GRAFANA_CLOUD_API_KEY. Our summary lists: Python 3; Node.js; A credential in GRAFANA_CLOUD_API_KEY.
Does App Observability access the network?
SKILL.md names 4 domains. In commands or code: otlp-gateway-prod-us-east-0.grafana.net; the agent is likely to contact it when it follows the instructions. As links in the text: grafana.com, github.com and openlit.io. This is read from the text; nothing was executed.
Is App Observability safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does App Observability use?
App Observability is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does App Observability use?
About 1.8k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 12k tokens, read only when the agent opens those files.
What are the alternatives to App Observability?
Skills that share tags, products or a category with App Observability: Ag2 Telemetry (ag2ai/build-with-ag2, 252 stars), OpenTelemetry Pipeline Metrics Spec (comet-ml/opik, 22k stars), Archestra Dev Observability (archestra-ai/archestra, 4.4k stars) and Frontmcp Observability (agentfront/frontmcp, 146 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains App Observability?
grafana (a GitHub organization, an official publisher) maintains it in grafana/skills, which has 281 GitHub stars. The repository holds 51 skills in this directory. The repository was last updated on October 8, 2026.
Source: grafana/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.