> anth-observability

Set up observability for Claude API integrations with metrics, logging, and alerting for latency, cost, errors, and token usage. Trigger with phrases like "anthropic monitoring", "claude observability", "anthropic metrics", "track claude usage", "claude dashboard".

fetch
$curl "https://skillshub.wtf/jeremylongshore/claude-code-plugins-plus-skills/anth-observability?format=md"
SKILL.mdanth-observability

Anthropic Observability

Overview

Instrument Claude API calls with structured logging, Prometheus metrics, and cost tracking. Every API response includes usage data and rate limit headers — capture these for dashboards and alerting.

Structured Logging

import anthropic
import logging
import time
import json

logger = logging.getLogger("claude")

def create_with_logging(client: anthropic.Anthropic, **kwargs) -> anthropic.types.Message:
    start = time.monotonic()
    request_meta = {
        "model": kwargs.get("model"),
        "max_tokens": kwargs.get("max_tokens"),
        "tool_count": len(kwargs.get("tools", [])),
        "stream": kwargs.get("stream", False),
    }

    try:
        response = client.messages.create(**kwargs)
        duration_ms = int((time.monotonic() - start) * 1000)

        logger.info(json.dumps({
            "event": "claude.request",
            "request_id": response._request_id,
            "model": response.model,
            "input_tokens": response.usage.input_tokens,
            "output_tokens": response.usage.output_tokens,
            "cache_read_tokens": getattr(response.usage, "cache_read_input_tokens", 0),
            "stop_reason": response.stop_reason,
            "duration_ms": duration_ms,
            "content_blocks": len(response.content),
        }))
        return response

    except anthropic.APIStatusError as e:
        duration_ms = int((time.monotonic() - start) * 1000)
        logger.error(json.dumps({
            "event": "claude.error",
            "status": e.status_code,
            "error_type": getattr(e, "type", "unknown"),
            "duration_ms": duration_ms,
            "request_id": e.response.headers.get("request-id", "unknown"),
        }))
        raise

Prometheus Metrics

from prometheus_client import Counter, Histogram, Gauge

claude_requests = Counter(
    "claude_requests_total", "Total Claude API requests",
    ["model", "stop_reason", "status"]
)
claude_latency = Histogram(
    "claude_latency_seconds", "Claude API latency",
    ["model"], buckets=[0.5, 1, 2, 5, 10, 30, 60]
)
claude_tokens = Counter(
    "claude_tokens_total", "Token usage",
    ["model", "direction"]  # direction: input|output|cache_read
)
claude_cost = Counter(
    "claude_cost_usd", "Estimated cost in USD",
    ["model"]
)
claude_rate_limit_remaining = Gauge(
    "claude_rate_limit_remaining", "Remaining rate limit",
    ["dimension"]  # dimension: requests|tokens
)

def track_metrics(response, duration: float):
    model = response.model
    claude_requests.labels(model=model, stop_reason=response.stop_reason, status="ok").inc()
    claude_latency.labels(model=model).observe(duration)
    claude_tokens.labels(model=model, direction="input").inc(response.usage.input_tokens)
    claude_tokens.labels(model=model, direction="output").inc(response.usage.output_tokens)

    # Cost estimation
    pricing = {"claude-haiku-4-20250514": (0.80, 4.0), "claude-sonnet-4-20250514": (3.0, 15.0)}
    rates = pricing.get(model, (3.0, 15.0))
    cost = (response.usage.input_tokens * rates[0] + response.usage.output_tokens * rates[1]) / 1e6
    claude_cost.labels(model=model).inc(cost)

Key Metrics Dashboard

MetricDescriptionAlert Threshold
claude_requests_total{status="error"}Error count> 5% of total
claude_latency_seconds p99Tail latency> 10s
claude_cost_usd dailyDaily spend> 80% budget
claude_rate_limit_remaining{dimension="requests"}RPM headroom< 10% remaining
claude_tokens_total{direction="output"} rateOutput throughputSpike detection

Usage API (Server-Side)

# Anthropic's Usage & Cost API for billing reconciliation
# GET https://api.anthropic.com/v1/usage
# Returns daily token usage and cost per model

Error Handling

Observability GapRiskFix
No request_id loggedCan't debug with supportCapture response._request_id
Missing cost trackingBudget surpriseTrack per-request cost
No latency histogramCan't spot slow queriesAdd Prometheus/Datadog histograms

Resources

Next Steps

For incident response, see anth-incident-runbook.

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github stars1.7K
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first seenMar 23, 2026
└────────────

┌ repo

jeremylongshore/claude-code-plugins-plus-skills
by jeremylongshore
└────────────