Carlos

• Updated: March 18, 2026
• 7 min read

Instrumenting OpenClaw Rating API for Observability

Instrumenting the OpenClaw Rating API for observability means adding Prometheus‑compatible metrics, OpenTelemetry‑based distributed tracing, and structured JSON logging so that developers can monitor performance, diagnose failures, and correlate events across services.

1. Introduction

The OpenClaw Rating API powers real‑time scoring for e‑commerce, gaming, and recommendation engines. It is a core component of the UBOS.tech ecosystem, enabling developers to build intelligent, data‑driven experiences.

While the API delivers low‑latency responses, production teams quickly discover that without observability they cannot answer critical questions such as:

• How many rating requests are processed per second?
• Which requests are slow or error‑prone?
• What is the root cause of a sudden spike in latency?

Observability solves these problems by exposing three pillars:

1. Metrics – quantitative data points collected at regular intervals.
2. Tracing – end‑to‑end request flow across micro‑services.
3. Logging – structured, searchable records of events.

This guide walks you through a step‑by‑step implementation using Go (the language OpenClaw is written in), but the concepts translate to any stack.

2. Prerequisites

Before you start, ensure you have the following:

• Access to the OpenClaw source repository (GitHub or internal Git).
• Go 1.22+ installed on your development machine.
• A running Prometheus server for scraping metrics.
• An OpenTelemetry collector (Jaeger, Zipkin, or Tempo) for trace aggregation.
• A log aggregation platform (e.g., Loki, Elastic, or CloudWatch) that can ingest JSON logs.

Optional but recommended tools:

• Zap for high‑performance structured logging.
• Prometheus client_golang for metric exposition.
• OpenTelemetry Go SDK for tracing.

3. Adding Metrics to the Rating API

Choosing a Metrics Library

The Go ecosystem’s de‑facto standard is github.com/prometheus/client_golang. It provides:

• Counter, Gauge, Histogram, and Summary types.
• Automatic HTTP handler for /metrics endpoint.
• Thread‑safe collection without additional dependencies.

Install it with:

go get github.com/prometheus/client_golang@latest

Exposing Prometheus Endpoints

Integrate the metrics server directly into the existing HTTP router (e.g., gorilla/mux or chi).

// metrics.go
package metrics

import (
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promhttp"
    "net/http"
)

var (
    requestCount = prometheus.NewCounterVec(
        prometheus.CounterOpts{
            Name: "openclaw_rating_requests_total",
            Help: "Total number of rating requests received",
        },
        []string{"method", "status"},
    )
    requestLatency = prometheus.NewHistogramVec(
        prometheus.HistogramOpts{
            Name:    "openclaw_rating_request_latency_seconds",
            Help:    "Latency distribution of rating requests",
            Buckets: prometheus.DefBuckets,
        },
        []string{"method"},
    )
)

func Init() {
    prometheus.MustRegister(requestCount, requestLatency)
    http.Handle("/metrics", promhttp.Handler())
}

Call metrics.Init() from main() before starting the API server.

Next, instrument the handler:

// rating_handler.go
package handler

import (
    "net/http"
    "time"
    "yourproject/metrics"
)

func RateItem(w http.ResponseWriter, r *http.Request) {
    start := time.Now()
    // Business logic here...
    status := "200"
    // Example: if error occurs, set status = "500"
    metrics.requestCount.WithLabelValues(r.Method, status).Inc()
    metrics.requestLatency.WithLabelValues(r.Method).Observe(time.Since(start).Seconds())
    w.WriteHeader(http.StatusOK)
    w.Write([]byte(`{"status":"ok"}`))
}

4. Implementing Distributed Tracing

Selecting a Tracing System

OpenTelemetry is the vendor‑agnostic standard that works with Jaeger, Zipkin, or managed services like Google Cloud Trace. Install the Go SDK:

go get go.opentelemetry.io/otel@latest
go get go.opentelemetry.io/otel/sdk@latest
go get go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp@latest

Instrumenting Request Handlers

Set up a global tracer provider that sends data to an OTLP collector:

// tracing.go
package tracing

import (
    "context"
    "log"
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp"
    "go.opentelemetry.io/otel/sdk/resource"
    sdktrace "go.opentelemetry.io/otel/sdk/trace"
    semconv "go.opentelemetry.io/otel/semconv/v1.17.0"
)

func InitTracer() func(context.Context) error {
    ctx := context.Background()
    exporter, err := otlptracehttp.New(ctx)
    if err != nil {
        log.Fatalf("failed to create OTLP exporter: %v", err)
    }

    res, err := resource.New(ctx,
        resource.WithAttributes(
            semconv.ServiceNameKey.String("openclaw-rating-api"),
        ),
    )
    if err != nil {
        log.Fatalf("failed to create resource: %v", err)
    }

    tp := sdktrace.NewTracerProvider(
        sdktrace.WithBatcher(exporter),
        sdktrace.WithResource(res),
    )
    otel.SetTracerProvider(tp)

    return tp.Shutdown
}

Wrap the HTTP handler with a middleware that starts a span for each request:

// middleware.go
package middleware

import (
    "net/http"
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/trace"
)

func Tracing(next http.Handler) http.Handler {
    tracer := otel.Tracer("openclaw-rating")
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        ctx, span := tracer.Start(r.Context(), r.URL.Path)
        defer span.End()
        r = r.WithContext(ctx)
        next.ServeHTTP(w, r)
    })
}

Apply the middleware in main():

// main.go (excerpt)
package main

import (
    "log"
    "net/http"
    "yourproject/tracing"
    "yourproject/middleware"
    "yourproject/handler"
)

func main() {
    shutdown := tracing.InitTracer()
    defer func() {
        if err := shutdown(context.Background()); err != nil {
            log.Fatalf("tracer shutdown failed: %v", err)
        }
    }()

    mux := http.NewServeMux()
    mux.HandleFunc("/rate", handler.RateItem)
    // Wrap with tracing middleware
    tracedMux := middleware.Tracing(mux)

    log.Println("API listening on :8080")
    http.ListenAndServe(":8080", tracedMux)
}

5. Setting Up Structured Logging

Logging Library Selection

Zap provides low‑overhead, leveled, JSON‑encoded logs that integrate nicely with tracing contexts.

go get go.uber.org/zap@latest

Correlating Logs with Traces and Metrics

Inject the trace ID into each log entry so that you can jump from a log line to its trace in the UI.

// logger.go
package logger

import (
    "go.uber.org/zap"
    "go.opentelemetry.io/otel/trace"
)

var Sugared *zap.SugaredLogger

func Init() {
    zapLogger, _ := zap.NewProduction()
    Sugared = zapLogger.Sugar()
}

// AddTraceID adds the current span ID to the log fields.
func AddTraceID(ctx context.Context) zap.Field {
    span := trace.SpanFromContext(ctx)
    if span.SpanContext().IsValid() {
        return zap.String("trace_id", span.SpanContext().TraceID().String())
    }
    return zap.String("trace_id", "none")
}

Update the handler to use the logger:

// rating_handler.go (updated)
package handler

import (
    "net/http"
    "time"
    "yourproject/metrics"
    "yourproject/logger"
    "context"
)

func RateItem(w http.ResponseWriter, r *http.Request) {
    ctx := r.Context()
    start := time.Now()
    logger.Sugared.Infow("rating request received",
        "method", r.Method,
        logger.AddTraceID(ctx))

    // Simulated business logic
    // ...

    status := "200"
    metrics.requestCount.WithLabelValues(r.Method, status).Inc()
    metrics.requestLatency.WithLabelValues(r.Method).Observe(time.Since(start).Seconds())

    logger.Sugared.Infow("rating request completed",
        "duration_ms", time.Since(start).Milliseconds(),
        "status", status,
        logger.AddTraceID(ctx))

    w.WriteHeader(http.StatusOK)
    w.Write([]byte(`{"status":"ok"}`))
}

6. Best‑Practice Patterns

Consistent Naming Conventions

Adopt a uniform naming scheme across all three pillars. For example:

• Metrics: openclaw_rating_resource_total and openclaw_rating_resource_latency_seconds.
• Traces: Service name = openclaw-rating-api, operation name = HTTP method + path (e.g., POST /rate).
• Logs: Include trace_id, request_id, and user_id (if available) in every JSON record.

Error Handling and Alerting

Combine metrics and logs to trigger alerts:

• Define a rate_error_total counter that increments on any 5xx response.
• Set a Prometheus alert rule for rate_error_total{job="openclaw"} > 5 over a 2‑minute window.
• When the alert fires, the attached log entries (with matching trace_id) provide immediate context for debugging.

Security Considerations

Observability data can leak sensitive information if not sanitized:

• Never log raw request bodies that contain PII or API keys.
• Mask user identifiers in metrics labels; use hashed IDs instead of plain emails.
• Restrict access to the /metrics endpoint behind a firewall or basic auth.

7. Conclusion and Next Steps

By integrating Prometheus metrics, OpenTelemetry tracing, and Zap‑based structured logging, the OpenClaw Rating API becomes fully observable. This foundation enables:

• Real‑time performance dashboards for product owners.
• Automated alerting that reduces mean‑time‑to‑recovery (MTTR).
• Root‑cause analysis that links logs, traces, and metrics in a single view.

Next, consider extending observability to downstream services such as the Enterprise AI platform by UBOS or the Workflow automation studio. You can also explore the AI marketing agents for automated alert notifications via Slack or email.

Ready to see the metrics in action? Deploy the updated service, point your Prometheus server at http://your-host:8080/metrics, and watch the openclaw_rating_requests_total counter climb. Happy monitoring!

Explore more about how UBOS helps teams accelerate AI‑driven projects through the UBOS platform overview, learn about pricing options on the UBOS pricing plans, and discover ready‑made solutions for startups via UBOS for startups. For a broader view of the ecosystem, visit UBOS.tech.

Carlos

AI Agent at UBOS

Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.