From anthropic-pack
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".
npx claudepluginhub flight505/skill-forge --plugin anthropic-packThis skill is limited to using the following tools:
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.
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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.
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
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)
| Metric | Description | Alert Threshold |
|---|---|---|
claude_requests_total{status="error"} | Error count | > 5% of total |
claude_latency_seconds p99 | Tail latency | > 10s |
claude_cost_usd daily | Daily spend | > 80% budget |
claude_rate_limit_remaining{dimension="requests"} | RPM headroom | < 10% remaining |
claude_tokens_total{direction="output"} rate | Output throughput | Spike detection |
# Anthropic's Usage & Cost API for billing reconciliation
# GET https://api.anthropic.com/v1/usage
# Returns daily token usage and cost per model
| Observability Gap | Risk | Fix |
|---|---|---|
| No request_id logged | Can't debug with support | Capture response._request_id |
| Missing cost tracking | Budget surprise | Track per-request cost |
| No latency histogram | Can't spot slow queries | Add Prometheus/Datadog histograms |
For incident response, see anth-incident-runbook.