Concurrent Agent Workers Collectively Exceed API Rate Limit
Symptom
- Single worker: zero 429 errors. Ten workers: constant 429 storms
- All workers retry at the same time after 429, creating synchronized spikes
- Rate limit errors increase super-linearly with number of workers
- Retry-after header is 60 seconds but all 10 workers wake up simultaneously
- Adding jitter to one worker doesn’t help when 9 others spike at the same time
Root Cause
Per-worker rate limiting doesn’t account for aggregate throughput. Each worker limits itself to N requests/second, but N workers × N requests/second = N² aggregate rate. Additionally, synchronized retry logic causes thundering herd: all workers wait the same duration, then all hammer the API simultaneously.
Fix
Option 1: Shared token bucket across all workers
import asyncio
import time
class SharedTokenBucket:
"""
Single rate limiter shared by all concurrent workers.
Enforces aggregate throughput limit regardless of worker count.
"""
def __init__(self, rate: float, capacity: float = None):
"""
rate: tokens per second (= max requests per second across all workers)
capacity: burst capacity (defaults to rate * 2)
"""
self.rate = rate
self.capacity = capacity or rate * 2
self.tokens = self.capacity
self.last_refill = time.monotonic()
self._lock = asyncio.Lock()
async def acquire(self, tokens: float = 1.0):
"""Wait until token is available. Blocks until rate allows."""
async with self._lock:
while True:
now = time.monotonic()
elapsed = now - self.last_refill
self.tokens = min(self.capacity, self.tokens + elapsed * self.rate)
self.last_refill = now
if self.tokens >= tokens:
self.tokens -= tokens
return
# Calculate wait time for next token
deficit = tokens - self.tokens
wait = deficit / self.rate
await asyncio.sleep(wait)
# One shared bucket for all workers:
# 50 requests/minute = 50/60 ≈ 0.833 per second
api_bucket = SharedTokenBucket(rate=50 / 60, capacity=5)
async def worker(worker_id: int, tasks: list):
for task in tasks:
await api_bucket.acquire() # All workers draw from same bucket
result = await call_api(task)
process(result)
# Launch 10 workers — they collectively stay within 50 req/min
await asyncio.gather(*[worker(i, tasks[i]) for i in range(10)])
Option 2: Centralized request queue with rate-controlled dispatcher
import asyncio
from collections import deque
class RateLimitedDispatcher:
"""
Single dispatcher serializes all API calls with rate control.
Workers submit tasks; dispatcher sends at controlled rate.
"""
def __init__(self, requests_per_second: float):
self.interval = 1.0 / requests_per_second
self.queue: asyncio.Queue = asyncio.Queue()
self._running = False
async def submit(self, coro) -> any:
"""Submit a coroutine and wait for its result"""
future = asyncio.get_event_loop().create_future()
await self.queue.put((coro, future))
return await future
async def run(self):
"""Dispatch one request per interval"""
self._running = True
last_sent = 0.0
while self._running:
coro, future = await self.queue.get()
now = asyncio.get_event_loop().time()
wait = self.interval - (now - last_sent)
if wait > 0:
await asyncio.sleep(wait)
try:
result = await coro
future.set_result(result)
except Exception as e:
future.set_exception(e)
finally:
last_sent = asyncio.get_event_loop().time()
self.queue.task_done()
def stop(self):
self._running = False
# Usage:
dispatcher = RateLimitedDispatcher(requests_per_second=0.8) # 48 req/min
async def main():
dispatch_task = asyncio.create_task(dispatcher.run())
# Workers submit through dispatcher — rate is enforced globally
async def worker(item):
return await dispatcher.submit(call_api(item))
results = await asyncio.gather(*[worker(item) for item in items])
dispatcher.stop()
await dispatch_task
return results
Option 3: Jittered retry with per-worker random offset
import asyncio
import random
async def call_with_jittered_retry(
fn,
max_retries: int = 5,
base_delay: float = 1.0,
jitter_range: float = 2.0
) -> any:
"""
Retry with exponential backoff + large random jitter.
Prevents synchronized retry storms across workers.
"""
for attempt in range(max_retries):
try:
return await fn()
except RateLimitError as e:
if attempt == max_retries - 1:
raise
retry_after = getattr(e, "retry_after", None) or base_delay * (2 ** attempt)
# Add large random jitter — workers spread out over jitter_range seconds
# With 10 workers and 5s jitter, they retry spread over 5s instead of simultaneously
jitter = random.uniform(0, jitter_range)
total_wait = retry_after + jitter
print(f"Worker {asyncio.current_task().get_name()}: "
f"Rate limited. Waiting {total_wait:.1f}s (retry_after={retry_after:.0f}s, jitter={jitter:.1f}s)")
await asyncio.sleep(total_wait)
# Each worker calls this — jitter ensures they don't all wake up simultaneously
result = await call_with_jittered_retry(lambda: api_client.complete(prompt))
Option 4: Redis-based distributed rate limiter (multi-process/multi-host)
import redis
import time
class RedisRateLimiter:
"""
Distributed rate limiter using Redis sliding window.
Works across multiple processes, containers, or hosts.
"""
def __init__(self, redis_client: redis.Redis, key: str, limit: int, window_seconds: int):
self.redis = redis_client
self.key = key
self.limit = limit
self.window = window_seconds
def acquire(self, timeout: float = 60.0) -> bool:
"""
Try to acquire a rate limit slot.
Blocks until slot available or timeout exceeded.
"""
deadline = time.time() + timeout
while time.time() < deadline:
now = time.time()
window_start = now - self.window
pipe = self.redis.pipeline()
# Sliding window: count requests in last window_seconds
pipe.zremrangebyscore(self.key, 0, window_start)
pipe.zcard(self.key)
pipe.zadd(self.key, {str(now): now})
pipe.expire(self.key, self.window * 2)
_, count, _, _ = pipe.execute()
if count < self.limit:
return True # Slot acquired
# Wait a fraction of the window before retrying
jitter = random.uniform(0.1, 0.5)
time.sleep(jitter)
return False # Timed out
limiter = RedisRateLimiter(
redis_client=redis.Redis(),
key="api:rate:anthropic",
limit=50, # 50 requests
window_seconds=60 # per 60 seconds
)
# In each worker (any process, any host):
if limiter.acquire():
result = call_api(task)
else:
raise RuntimeError("Could not acquire rate limit slot — API at capacity")
Option 5: Adaptive rate limiting based on 429 feedback
import asyncio
import time
class AdaptiveRateLimiter:
"""
Dynamically adjust request rate based on 429 response feedback.
Backs off when rate limited, gradually recovers when successful.
"""
def __init__(self, initial_rps: float = 1.0, min_rps: float = 0.1, max_rps: float = 5.0):
self.current_rps = initial_rps
self.min_rps = min_rps
self.max_rps = max_rps
self._lock = asyncio.Lock()
self._last_success = time.monotonic()
async def on_success(self):
"""Gradually increase rate after sustained success"""
async with self._lock:
# Slow recovery: increase by 10% per successful call
if time.monotonic() - self._last_success > 5.0:
self.current_rps = min(self.max_rps, self.current_rps * 1.1)
print(f"Rate increased to {self.current_rps:.2f} rps")
self._last_success = time.monotonic()
async def on_rate_limited(self, retry_after: float = None):
"""Immediately halve the rate on 429"""
async with self._lock:
self.current_rps = max(self.min_rps, self.current_rps * 0.5)
print(f"Rate limited! Reduced to {self.current_rps:.2f} rps")
wait = retry_after or (1.0 / self.current_rps)
await asyncio.sleep(wait)
async def acquire(self):
await asyncio.sleep(1.0 / self.current_rps)
limiter = AdaptiveRateLimiter(initial_rps=0.8)
async def resilient_api_call(payload: dict) -> dict:
while True:
await limiter.acquire()
try:
result = await call_api(payload)
await limiter.on_success()
return result
except RateLimitError as e:
await limiter.on_rate_limited(getattr(e, "retry_after", None))
Rate Limit Strategies Comparison
| Strategy | Scope | Overhead | Best for |
|---|---|---|---|
| Per-worker token bucket | Single worker | Minimal | 1-2 workers, same process |
| Shared token bucket | All workers, one process | Low | Multi-worker single process |
| Central dispatcher queue | All workers, one process | Medium | Bursty workloads |
| Redis sliding window | Cross-process / cross-host | Medium | Distributed agents |
| Adaptive rate limiter | Any scope | Low | Unknown rate limits |
Expected Token Savings
429 storm from 10 workers + exponential retry: ~20,000 wasted tokens Shared bucket eliminates 429s entirely: 0 wasted
Environment
- Multi-worker agent pipelines; especially batch processing with asyncio.gather or thread pools
- Source: direct experience; concurrent workers sharing a rate-limited API is a near-universal scaling problem
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