Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Observability Lgtm
Set up a full local LGTM observability stack (Loki + Grafana + Tempo + Prometheus + Alloy) for FastAPI apps. One Docker Compose, one Python import, unified d...
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Set up a full local LGTM observability stack (Loki + Grafana + Tempo + Prometheus + Alloy) for FastAPI apps. One Docker Compose, one Python import, unified d...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, assets/config/grafana/dashboards/fastapi-overview.json, assets/config/grafana/provisioning/dashboards/dashboards.yml, assets/config/grafana/provisioning/datasources/datasources.yml, assets/config/loki/loki.yml, assets/config/prometheus/prometheus.yml
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.2.0
Provenance
- Publisher
- nissan
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
observability-lgtm
Set up a full local observability stack (Loki + Grafana + Tempo + Prometheus + Alloy) for FastAPI apps on macOS (Apple Silicon) or Linux. One command to start, one import to instrument any app. Logs โ Loki, metrics โ Prometheus, traces โ Tempo, all unified in Grafana.
When to use
User is building a FastAPI web app and wants logs, metrics, and traces User wants a local Grafana dashboard without setting up ELK (too heavy) User wants to correlate logs โ traces โ metrics in one UI User has multiple local apps and wants universal observability
When NOT to use
Production cloud deployments (use managed Grafana Cloud or Datadog instead) Non-Python apps (the Python lib only works for FastAPI; the stack itself is language-agnostic) When Docker is not available
Prerequisites
Docker + Docker Compose v2 installed Python 3.10+ (for the instrumentation lib) FastAPI app to instrument
What gets installed
ServicePortPurposeGrafana3000Dashboards โ no login in dev modePrometheus9091Metrics scraping (avoids 9090 if MinIO running)Loki3300Log storage (avoids 3100 if Langfuse running)Tempo gRPC4317OTLP trace receiverTempo HTTP4318OTLP HTTP alternativeAlloy UI12345Agent status
Step 1 โ Check for port conflicts
lsof -iTCP -sTCP:LISTEN -n -P 2>/dev/null | grep -E ":(3000|3300|9091|4317|4318|12345)" | awk '{print $9, $1}' If any of the ports above are in use, update the relevant port in docker-compose.yml and the matching url: in config/grafana/provisioning/datasources/datasources.yml. Common conflicts: Langfuse on 3100, MinIO on 9090.
Step 2 โ Copy the stack
Copy these files from the skill directory into a projects/observability/ folder in the workspace: assets/docker-compose.yml assets/config/ (entire directory tree) assets/lib/observability.py assets/scripts/register_app.sh mkdir -p projects/observability cp -r SKILL_DIR/assets/* projects/observability/ mkdir -p projects/observability/logs touch projects/observability/logs/.gitkeep chmod +x projects/observability/scripts/register_app.sh
Step 3 โ Start the stack
cd projects/observability docker compose up -d Wait ~15 seconds for all services to start, then verify: curl -s -o /dev/null -w "Grafana: %{http_code}\n" http://localhost:3000/api/health curl -s -o /dev/null -w "Prometheus: %{http_code}\n" http://localhost:9091/-/healthy curl -s -o /dev/null -w "Loki: %{http_code}\n" http://localhost:3300/ready curl -s -o /dev/null -w "Tempo: %{http_code}\n" http://localhost:4318/ready All should return 200. If Loki or Tempo return 503, wait 10 more seconds and retry (they have a slower startup than Grafana/Prometheus).
Step 4 โ Install Python deps for the app
pip install \ "prometheus-fastapi-instrumentator>=7.0.0" \ "opentelemetry-sdk>=1.25.0" \ "opentelemetry-exporter-otlp-proto-grpc>=1.25.0" \ "opentelemetry-instrumentation-fastapi>=0.46b0" \ "python-json-logger>=2.0.7"
Step 5 โ Instrument the FastAPI app
Add to the app's app.py (or main.py), just after app = FastAPI(...): import sys sys.path.insert(0, "path/to/projects/observability/lib") from observability import setup_observability logger = setup_observability(app, service_name="my-service-name") That's it. The app now: Exposes /metrics for Prometheus Writes JSON logs to projects/observability/logs/my-service-name/app.log Sends traces to Tempo on localhost:4317
Step 6 โ Register with Prometheus
cd projects/observability ./scripts/register_app.sh my-service-name <port> # e.g.: ./scripts/register_app.sh image-gen-studio 7860 Prometheus hot-reloads the target within 30 seconds. Verify: curl -s "http://localhost:9091/api/v1/targets" | python3 -c " import json, sys data = json.load(sys.stdin) for t in data['data']['activeTargets']: svc = t['labels'].get('service', '') print(svc, '->', t['health']) "
Step 7 โ Open Grafana
Open http://localhost:3000 The FastAPI โ App Overview dashboard is pre-loaded. Select your service from the dropdown at the top. You'll see: Request rate (req/s) Error rate (%) Latency p50/p95/p99 Requests by endpoint HTTP status codes Live log panel (Loki) To jump from a log line to its trace: click the trace_id link in the log detail panel. It opens the full trace in Tempo automatically (datasource pre-wired).
Step 8 โ Import additional dashboards (optional)
In Grafana โ Dashboards โ Import: 16110 โ FastAPI Observability (richer alternative to the built-in) 13407 โ Loki Logs Overview 16112 โ Tempo Service Graph (service dependency map)
Useful commands
# Reload Prometheus config after registering a new app: curl -s -X POST http://localhost:9091/-/reload # Restart a single service without losing data: docker compose -f projects/observability/docker-compose.yml restart grafana # Stop everything (data volumes preserved): docker compose -f projects/observability/docker-compose.yml down # Nuclear reset (wipes all stored data): docker compose -f projects/observability/docker-compose.yml down -v # Check Alloy log shipping status: open http://localhost:12345
Manual tracing (optional)
from observability import get_tracer tracer = get_tracer(__name__) @app.get("/expensive-endpoint") async def handler(): with tracer.start_as_current_span("db-query") as span: span.set_attribute("db.table", "users") result = await db.query(...) return result
Log/trace correlation
The OTel instrumentation injects trace_id into every log record. Grafana Loki is pre-configured with a derived field that turns "trace_id":"abc123" into a clickable link to the Tempo trace. To manually include trace context in your own log calls: from opentelemetry import trace def trace_ctx() -> dict: ctx = trace.get_current_span().get_span_context() return {"trace_id": format(ctx.trace_id, "032x")} if ctx.is_valid else {} logger.info("Processing request", extra=trace_ctx())
Notes
Logs are written to projects/observability/logs/<service>/app.log as JSON. Alloy tails these files and ships to Loki โ no code changes needed beyond setup_observability(). All observability is local โ no data leaves the machine. data_classification: LOCAL_ONLY is the default for all traces/logs. The Alloy config drops DEBUG-level logs by default. Edit config/alloy/config.alloy to remove the stage.drop block if you need debug logs.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
5 Config1 Docs
- SKILL.md
- assets/config/grafana/dashboards/fastapi-overview.json
- assets/config/grafana/provisioning/dashboards/dashboards.yml
- assets/config/grafana/provisioning/datasources/datasources.yml
- assets/config/loki/loki.yml
- assets/config/prometheus/prometheus.yml