Anthropic Rate Limits Skill

Anthropic Rate Limits

Overview

Anthropic enforces three types of limits: requests per minute (RPM), input tokens per minute (TPM), and output tokens per minute. Limits depend on your spend tier.

Rate Limit Tiers

| Tier | Qualification | RPM | Input TPM | Output TPM | |------|--------------|-----|-----------|------------| | Tier 1 | Free | 50 | 40,000 | 8,000 | | Tier 2 | $40+ spend | 1,000 | 80,000 | 16,000 | | Tier 3 | $200+ spend | 2,000 | 160,000 | 32,000 | | Tier 4 | $400+ spend | 4,000 | 400,000 | 80,000 | | Scale | Custom | Custom | Custom | Custom |

Check your tier: console.anthropic.com → Settings → Limits

Response Headers

Every API response includes rate limit headers:

claude-ratelimit-requests-limit: 1000
claude-ratelimit-requests-remaining: 998
claude-ratelimit-requests-reset: 2025-01-01T00:01:00Z
claude-ratelimit-tokens-limit: 80000
claude-ratelimit-tokens-remaining: 79500
claude-ratelimit-tokens-reset: 2025-01-01T00:01:00Z
retry-after: 5

Built-In SDK Retries

The SDK automatically retries 429 and 529 errors with exponential backoff:

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

const client = new Anthropic({
  maxRetries: 3, // default: 2. Set to 0 to disable.
});

Custom Backoff

async function callWithBackoff(params: Anthropic.MessageCreateParams, maxRetries = 5) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      return await client.messages.create(params);
    } catch (err) {
      if (err instanceof Anthropic.RateLimitError) {
        const retryAfter = Number(err.headers?.['retry-after'] || 2 ** attempt);
        const jitter = Math.random() * 1000;
        console.log(`Rate limited. Retry in ${retryAfter}s (attempt ${attempt + 1})`);
        await new Promise(r => setTimeout(r, retryAfter * 1000 + jitter));
      } else {
        throw err;
      }
    }
  }
  throw new Error('Exceeded max retries');
}

Throughput Optimization

| Strategy | Impact | |----------|--------| | Use Message Batches API | Bypasses rate limits entirely (async, 24h SLA) | | Use prompt caching | Cached tokens don't count toward input TPM | | Use smaller models for simple tasks | Lower token counts = more requests per minute | | Pre-count tokens with countTokens | Avoid wasted requests that will fail | | Queue and batch requests | Smooth out bursts |

Token Counting

// Count before sending — avoid burning RPM on requests that'll fail
const count = await client.messages.countTokens({
  model: 'claude-sonnet-4-20250514',
  messages,
  system: systemPrompt,
});
console.log(`This request will use ${count.input_tokens} input tokens`);

Python

import anthropic
import time

client = anthropic.Anthropic(max_retries=5)

# Or manual handling:
try:
    message = client.messages.create(...)
except anthropic.RateLimitError as e:
    retry_after = float(e.response.headers.get("retry-after", 5))
    time.sleep(retry_after)

Output

Rate limit tier identified from response headers
SDK configured with appropriate maxRetries setting
Custom backoff implemented with jitter for high-throughput use cases
Throughput optimized using batches, caching, or model selection

Error Handling

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

Examples

See Rate Limit Tiers table, Response Headers section, Built-In SDK Retries, Custom Backoff implementation, and Throughput Optimization strategies above.

Resources

Next Steps

See clade-cost-tuning for cost optimization strategies.

Prerequisites

Completed clade-install-auth
Understanding of HTTP response headers
Familiarity with exponential backoff patterns

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.

Agent Skills: Anthropic Rate Limits

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