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
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.
1---2name: clade-observability3description: Monitor Claude API calls — log tokens, latency, costs, errors, and Use when working with observability patterns. set up alerts for production Claude integrations. Trigger with "anthropic monitoring", "claude observability", "track claude usage", "anthropic logging".4license: MIT5---6# Anthropic Observability
7
8## Overview
9
10Every `messages.create` call should be instrumented. Track tokens, latency, cost, model, and errors.
11
12## Logging Wrapper
13
14```typescript
15import Anthropic from '@claude-ai/sdk';
16
17const client = new Anthropic();
18
19async function trackedCreate(params: Anthropic.MessageCreateParams) {
20 const start = performance.now();
21 try {
22 const message = await client.messages.create(params);
23 const durationMs = Math.round(performance.now() - start);
24
25 const log = {
26 timestamp: new Date().toISOString(),
27 model: message.model,
28 input_tokens: message.usage.input_tokens,
29 output_tokens: message.usage.output_tokens,
30 cache_read_tokens: message.usage.cache_read_input_tokens || 0,
31 duration_ms: durationMs,
32 stop_reason: message.stop_reason,
33 estimated_cost: estimateCost(message.model, message.usage),
34 };
35 console.log('anthropic_request', JSON.stringify(log));
36
37 return message;
38 } catch (err) {
39 const durationMs = Math.round(performance.now() - start);
40 console.error('anthropic_error', JSON.stringify({
41 timestamp: new Date().toISOString(),
42 model: params.model,
43 error_type: err instanceof Anthropic.APIError ? err.error?.type : 'unknown',
44 status: err instanceof Anthropic.APIError ? err.status : null,
45 request_id: err instanceof Anthropic.APIError ? err.headers?.['request-id'] : null,
46 duration_ms: durationMs,
47 }));
48 throw err;
49 }
50}
51
52function estimateCost(model: string, usage: Anthropic.Usage): number {
53 const rates: Record<string, [number, number]> = {
54 'claude-opus-4-20250514': [15, 75],
55 'claude-sonnet-4-20250514': [3, 15],
56 'claude-haiku-4-5-20251001': [0.80, 4],
57 };
58 const [inputRate, outputRate] = rates[model] || [3, 15];
59 return (usage.input_tokens * inputRate + usage.output_tokens * outputRate) / 1_000_000;
60}
61```
62
63## Key Metrics to Track
64
65| Metric | Source | Alert Threshold |
66|--------|--------|----------------|
67| Error rate | error logs | > 5% over 5 minutes |
68| p95 latency | duration_ms | > 10s (Sonnet) |
69| Daily cost | estimated_cost sum | > 2x daily average |
70| 429 rate | error_type = rate_limit | > 10/minute |
71| 529 rate | error_type = overloaded | > 5/minute |
72| Token usage | input_tokens + output_tokens | > daily budget |
73
74## Anthropic Console Monitoring
75
76- **Usage dashboard**: console.anthropic.com → Usage
77- **Spending limits**: console.anthropic.com → Settings → Limits
78- **API logs**: Not available via API — use your own logging
79
80## Output
81
82- Every Claude API call logged with tokens, latency, cost estimate, and model
83- Error calls logged with request ID, status code, and error type
84- Metrics dashboarded: error rate, p95 latency, daily cost, 429/529 rates
85- Spending alerts configured in Anthropic console
86
87## Error Handling
88
89| Error | Cause | Solution |
90|-------|-------|----------|
91| API Error | Check error type and status code | See `clade-common-errors` |
92
93## Examples
94
95See Logging Wrapper with `trackedCreate()`, `estimateCost()` function, Key Metrics table with alert thresholds, and Anthropic Console Monitoring section above.
96
97## Resources
98
99- [Usage Dashboard](https://console.anthropic.com/settings/usage)
100- [Rate Limits](https://docs.anthropic.com/en/api/rate-limits)
101
102## Next Steps
103
104See `clade-incident-runbook` for when things go wrong.
105
106## Prerequisites
107
108- Completed `clade-install-auth`
109- Logging infrastructure (console, structured logs, or observability platform)
110- Production Claude integration to monitor
111
112## Instructions
113
114### Step 1: Review the patterns below
115
116Each section contains production-ready code examples. Copy and adapt them to your use case.
117
118### Step 2: Apply to your codebase
119
120Integrate the patterns that match your requirements. Test each change individually.
121
122### Step 3: Verify
123
124Run your test suite to confirm the integration works correctly.