Agent Spawns Too Many Workers and Runs Out of Memory — OOM Kill
Symptom
- Process killed with
KilledorMemoryErrorunder heavy load asyncio.gather(*[process(t) for t in tasks])with 10,000 tasks causes OOM- Memory grows linearly with number of tasks before any complete
ThreadPoolExecutororProcessPoolExecutorspawns thousands of threads- System swap usage spikes, then OOM killer terminates the process
Root Cause
Unbounded concurrency: creating one coroutine/thread/process per task with no limit. Even lightweight asyncio coroutines use memory (typically 1–10KB each). 10,000 coroutines = 10–100MB just for the coroutines. Add task data and you exhaust available memory before any work completes.
Fix
Option 1: Semaphore to cap concurrent asyncio tasks
import asyncio
async def process_all_tasks(tasks: list, max_concurrent: int = 50) -> list:
"""Process tasks with bounded concurrency"""
semaphore = asyncio.Semaphore(max_concurrent)
async def process_one(task):
async with semaphore: # Only max_concurrent run at once
return await process_task(task)
# Creates coroutines for all tasks but only runs max_concurrent at a time
return await asyncio.gather(*[process_one(t) for t in tasks])
# Usage
results = await process_all_tasks(ten_thousand_tasks, max_concurrent=20)
Option 2: asyncio.Queue as a worker pool
import asyncio
async def worker_pool(tasks: list, num_workers: int = 10) -> list:
"""Fixed-size worker pool — never exceeds num_workers concurrent tasks"""
queue = asyncio.Queue()
results = {}
# Fill queue
for i, task in enumerate(tasks):
await queue.put((i, task))
async def worker():
while True:
try:
idx, task = queue.get_nowait()
except asyncio.QueueEmpty:
return
try:
results[idx] = await process_task(task)
except Exception as e:
results[idx] = {"error": str(e)}
finally:
queue.task_done()
# Start exactly num_workers workers — no more spawned
workers = [asyncio.create_task(worker()) for _ in range(num_workers)]
await asyncio.gather(*workers)
return [results[i] for i in range(len(tasks))]
Option 3: Process in batches to limit memory
import asyncio
from typing import AsyncIterator
async def process_in_batches(
tasks: list,
batch_size: int = 100,
max_concurrent: int = 10
) -> list:
"""Process tasks in batches — only batch_size tasks in memory at once"""
semaphore = asyncio.Semaphore(max_concurrent)
all_results = []
for i in range(0, len(tasks), batch_size):
batch = tasks[i:i + batch_size]
print(f"Processing batch {i//batch_size + 1}: tasks {i}–{i+len(batch)}")
async def process_one(task):
async with semaphore:
return await process_task(task)
batch_results = await asyncio.gather(*[process_one(t) for t in batch])
all_results.extend(batch_results)
# Optional: small pause between batches to let GC run
await asyncio.sleep(0.1)
return all_results
Option 4: ThreadPoolExecutor with bounded size
from concurrent.futures import ThreadPoolExecutor, as_completed
def process_all_sync(tasks: list, max_workers: int = 10) -> list:
"""Thread pool with hard cap — never spawns more than max_workers threads"""
results = [None] * len(tasks)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Submit all tasks — executor queues excess work
future_to_idx = {
executor.submit(process_task_sync, task): i
for i, task in enumerate(tasks)
}
for future in as_completed(future_to_idx):
idx = future_to_idx[future]
try:
results[idx] = future.result()
except Exception as e:
results[idx] = {"error": str(e)}
print(f"Task {idx} failed: {e}")
return results
Option 5: Monitor memory and back off
import asyncio, psutil, os
async def memory_aware_processing(tasks: list, max_memory_pct: float = 80.0) -> list:
"""Slow down when memory usage is high"""
semaphore = asyncio.Semaphore(20)
results = []
async def process_one(task):
# Check memory before each task
mem = psutil.virtual_memory()
if mem.percent > max_memory_pct:
print(f"Memory at {mem.percent:.0f}% — pausing for GC")
await asyncio.sleep(2)
async with semaphore:
return await process_task(task)
return await asyncio.gather(*[process_one(t) for t in tasks])
Option 6: Generator-based processing for huge task sets
import asyncio
from typing import AsyncGenerator
async def task_generator(all_tasks: list) -> AsyncGenerator:
"""Yield tasks one at a time — entire list never in memory"""
for task in all_tasks:
yield task
async def process_streaming(task_gen, max_concurrent: int = 20) -> list:
"""Process from generator — O(1) memory for task queue"""
semaphore = asyncio.Semaphore(max_concurrent)
results = []
async def process_one(task):
async with semaphore:
return await process_task(task)
pending = set()
async for task in task_gen:
# Limit pending tasks
while len(pending) >= max_concurrent * 2:
done, pending = await asyncio.wait(pending, return_when=asyncio.FIRST_COMPLETED)
results.extend(f.result() for f in done)
pending.add(asyncio.create_task(process_one(task)))
if pending:
done, _ = await asyncio.wait(pending)
results.extend(f.result() for f in done)
return results
Concurrency Sizing Guide
| Task type | Recommended max_concurrent |
|---|---|
| HTTP API calls (external) | 10–50 |
| Anthropic API calls | Rate limit ÷ avg response time |
| Database queries | 5–20 (match connection pool size) |
| File I/O | 20–100 |
| CPU-bound tasks | os.cpu_count() |
| Mixed I/O + CPU | 20–50 |
Memory per Worker Type
| Worker type | Memory per worker |
|---|---|
| asyncio coroutine | 1–10KB |
| Thread | 1–8MB (stack) |
| Process | 20–100MB+ |
| Docker container | 50MB–2GB+ |
Expected Token Savings
OOM kill + restart + re-processing all work: ~30,000 tokens Semaphore cap prevents it: 0 wasted
Environment
- High-volume agents processing large task batches
- Source: direct experience with production OOM incidents in agent pipelines
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