---

name: clade-observability description: "Monitor Claude API calls \u2014 log tokens, latency, costs, errors,
\ and\nUse when working with observability patterns.\nset up alerts for production
\ Claude integrations.\nTrigger with "anthropic monitoring", "claude observability"
,\n"track claude usage", "anthropic logging".\n" allowed-tools: Read, Write, Edit version: 1.1.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• anthropic
• claude
• monitoring
• observability compatibility: Designed for Claude Code

---

Anthropic Observability

Overview

Every messages.create call should be instrumented. Track tokens, latency, cost, model, and errors.

Logging Wrapper

import Anthropic from '@claude-ai/sdk';

const client = new Anthropic();

async function trackedCreate(params: Anthropic.MessageCreateParams) {
  const start = performance.now();
  try {
    const message = await client.messages.create(params);
    const durationMs = Math.round(performance.now() - start);

    const log = {
      timestamp: new Date().toISOString(),
      model: message.model,
      input_tokens: message.usage.input_tokens,
      output_tokens: message.usage.output_tokens,
      cache_read_tokens: message.usage.cache_read_input_tokens || 0,
      duration_ms: durationMs,
      stop_reason: message.stop_reason,
      estimated_cost: estimateCost(message.model, message.usage),
    };
    console.log('anthropic_request', JSON.stringify(log));

    return message;
  } catch (err) {
    const durationMs = Math.round(performance.now() - start);
    console.error('anthropic_error', JSON.stringify({
      timestamp: new Date().toISOString(),
      model: params.model,
      error_type: err instanceof Anthropic.APIError ? err.error?.type : 'unknown',
      status: err instanceof Anthropic.APIError ? err.status : null,
      request_id: err instanceof Anthropic.APIError ? err.headers?.['request-id'] : null,
      duration_ms: durationMs,
    }));
    throw err;
  }
}

function estimateCost(model: string, usage: Anthropic.Usage): number {
  const rates: Record<string, [number, number]> = {
    'claude-opus-4-20250514': [15, 75],
    'claude-sonnet-4-20250514': [3, 15],
    'claude-haiku-4-5-20251001': [0.80, 4],
  };
  const [inputRate, outputRate] = rates[model] || [3, 15];
  return (usage.input_tokens * inputRate + usage.output_tokens * outputRate) / 1_000_000;
}

Key Metrics to Track

Metric	Source	Alert Threshold
Error rate	error logs	> 5% over 5 minutes
p95 latency	duration_ms	> 10s (Sonnet)
Daily cost	estimated_cost sum	> 2x daily average
429 rate	error_type = rate_limit	> 10/minute
529 rate	error_type = overloaded	> 5/minute
Token usage	input_tokens + output_tokens	> daily budget

Anthropic Console Monitoring

• Usage dashboard: console.anthropic.com → Usage
• Spending limits: console.anthropic.com → Settings → Limits
• API logs: Not available via API — use your own logging

Output

• Every Claude API call logged with tokens, latency, cost estimate, and model
• Error calls logged with request ID, status code, and error type
• Metrics dashboarded: error rate, p95 latency, daily cost, 429/529 rates
• Spending alerts configured in Anthropic console

Error Handling

Error	Cause	Solution
API Error	Check error type and status code	See clade-common-errors

Examples

See Logging Wrapper with trackedCreate(), estimateCost() function, Key Metrics table with alert thresholds, and Anthropic Console Monitoring section above.

Resources

• Usage Dashboard
• Rate Limits

Next Steps

See clade-incident-runbook for when things go wrong.

Prerequisites

• Completed clade-install-auth
• Logging infrastructure (console, structured logs, or observability platform)
• Production Claude integration to monitor

Instructions

Step 1: Review the patterns below

Each section contains production-ready code examples. Copy and adapt them to your use case.

Step 2: Apply to your codebase

Integrate the patterns that match your requirements. Test each change individually.

Step 3: Verify

Run your test suite to confirm the integration works correctly.