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Agent Spawns Too Many Threads — Thread Pool Exhaustion and Resource Starvation

Symptom

• OSError: [Errno 11] Resource temporarily unavailable when spawning threads
• RuntimeError: can't start new thread under load
• Memory grows linearly with concurrent sessions (each thread uses ~8MB stack)
• High CPU overhead from context switching between hundreds of threads
• Agent handles 10 sessions fine but fails at 50 or 100
• Thread count visible in ps or profiler grows unbounded
• Throughput decreases as concurrency increases (opposite of expected)

Root Cause

threading.Thread per request is the wrong model for I/O-bound agents. Python threads are OS threads — each uses 8–16MB of stack memory and requires kernel scheduling. I/O-bound work (API calls, database queries, file reads) spends 90%+ of its time waiting, not running, so more threads don’t increase throughput after a point. The Python GIL prevents true parallel CPU execution anyway. The fix is to replace unbounded thread-per-request with a bounded ThreadPoolExecutor for blocking code, or better, switch to asyncio for all I/O-bound work.

Fix

Option 1: Bounded ThreadPoolExecutor — cap threads at a fixed maximum

import concurrent.futures
import threading
import logging
from typing import Any, Callable

logger = logging.getLogger(__name__)

# WRONG — unbounded thread creation:
def BAD_handle_requests(requests: list[dict]) -> list[Any]:
    results = []
    threads = []
    for req in requests:
        t = threading.Thread(target=lambda r: results.append(process(r)), args=(req,))
        t.start()
        threads.append(t)
    for t in threads:
        t.join()
    return results
# At 500 requests: 500 threads × 8MB = 4GB memory, thousands of context switches

# RIGHT — fixed thread pool:
_POOL = concurrent.futures.ThreadPoolExecutor(
    max_workers=20,              # Hard cap: 20 threads, ever
    thread_name_prefix="agent-worker"
)

def handle_requests_bounded(requests: list[dict]) -> list[Any]:
    """
    Process requests with a bounded thread pool.
    At max_workers=20, excess requests wait in queue instead of creating new threads.
    """
    futures = [_POOL.submit(process, req) for req in requests]
    results = []
    for future in concurrent.futures.as_completed(futures, timeout=60.0):
        try:
            results.append(future.result())
        except Exception as exc:
            logger.error(f"Request failed: {exc}")
            results.append(None)
    return results

def process(request: dict) -> Any:
    """Actual processing logic — runs in a thread pool worker."""
    import anthropic
    client = anthropic.Anthropic()
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=512,
        messages=[{"role": "user", "content": request["message"]}]
    )
    return response.content[0].text

# Choosing max_workers:
# I/O-bound (API calls): max_workers = 10–50 (threads spend time waiting)
# CPU-bound: max_workers = os.cpu_count() (threads actually run)
# Mixed: max_workers = 2 × os.cpu_count()
import os
RECOMMENDED_MAX_WORKERS = min(32, (os.cpu_count() or 4) * 5)

Option 2: Migrate to asyncio — eliminate threads for I/O-bound work

import asyncio
import anthropic
import logging
from typing import Any

logger = logging.getLogger(__name__)

# asyncio handles thousands of concurrent connections with a single thread.
# No thread creation overhead, no GIL contention, minimal memory per "task".

async def process_request(request: dict, semaphore: asyncio.Semaphore) -> Any:
    """
    Async processing — yields control during API calls so other tasks run.
    semaphore limits concurrent Claude calls without blocking threads.
    """
    async with semaphore:
        client = anthropic.AsyncAnthropic()
        response = await client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=512,
            messages=[{"role": "user", "content": request["message"]}]
        )
        return response.content[0].text

async def handle_requests_async(requests: list[dict], max_concurrent: int = 20) -> list[Any]:
    """
    Process all requests concurrently with a semaphore-bounded concurrency.
    500 requests → 20 at a time, but only 1 thread total.
    Memory: ~10KB per asyncio task (vs 8MB per thread).
    """
    semaphore = asyncio.Semaphore(max_concurrent)
    tasks = [process_request(req, semaphore) for req in requests]
    results = await asyncio.gather(*tasks, return_exceptions=True)

    for i, result in enumerate(results):
        if isinstance(result, Exception):
            logger.error(f"Request {i} failed: {result}")
            results[i] = None

    return results

# Run from sync context:
def handle_requests(requests: list[dict]) -> list[Any]:
    return asyncio.run(handle_requests_async(requests, max_concurrent=20))

# asyncio concurrency capacity comparison:
# threading.Thread: 100 threads → ~800MB stack memory
# asyncio tasks:    100 tasks   → ~1MB memory
# asyncio tasks:    10,000 tasks → ~100MB memory (10,000× more than threads at same memory)

Option 3: Thread pool per resource type — separate pools with independent limits

import concurrent.futures
import os
import logging
from typing import Any, Callable, TypeVar

logger = logging.getLogger(__name__)
T = TypeVar("T")

class ResourceSeparatedPools:
    """
    Separate thread pools per resource type.
    Prevents one slow resource from starving all others.
    """

    def __init__(self):
        cpu_count = os.cpu_count() or 4

        # LLM API calls — I/O bound, can have many concurrent:
        self.llm_pool = concurrent.futures.ThreadPoolExecutor(
            max_workers=15,
            thread_name_prefix="llm"
        )

        # Database queries — I/O bound but often have connection limits:
        self.db_pool = concurrent.futures.ThreadPoolExecutor(
            max_workers=10,
            thread_name_prefix="db"
        )

        # CPU-bound work (embeddings, text processing):
        self.cpu_pool = concurrent.futures.ProcessPoolExecutor(
            max_workers=cpu_count,  # One process per CPU core
        )

        # File I/O — usually fast but can block:
        self.io_pool = concurrent.futures.ThreadPoolExecutor(
            max_workers=5,
            thread_name_prefix="io"
        )

    def submit_llm(self, fn: Callable[..., T], *args, **kwargs) -> concurrent.futures.Future:
        return self.llm_pool.submit(fn, *args, **kwargs)

    def submit_db(self, fn: Callable[..., T], *args, **kwargs) -> concurrent.futures.Future:
        return self.db_pool.submit(fn, *args, **kwargs)

    def submit_cpu(self, fn: Callable[..., T], *args, **kwargs) -> concurrent.futures.Future:
        return self.cpu_pool.submit(fn, *args, **kwargs)

    def shutdown(self, wait: bool = True):
        self.llm_pool.shutdown(wait=wait)
        self.db_pool.shutdown(wait=wait)
        self.cpu_pool.shutdown(wait=wait)
        self.io_pool.shutdown(wait=wait)

pools = ResourceSeparatedPools()

def full_pipeline(question: str) -> str:
    """
    Pipeline using separate pools per resource type.
    A slow DB query doesn't block LLM calls.
    """
    import anthropic

    # Step 1: Fetch context from DB (db_pool)
    def fetch_context():
        import sqlite3
        conn = sqlite3.connect("/app/knowledge.db")
        rows = conn.execute("SELECT content FROM docs WHERE topic LIKE ?", (f"%{question}%",)).fetchall()
        conn.close()
        return "\n".join(r[0] for r in rows[:3])

    context_future = pools.submit_db(fetch_context)

    # Step 2: Wait for context, then call LLM (llm_pool)
    context = context_future.result(timeout=10.0)

    def call_llm():
        client = anthropic.Anthropic()
        response = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=1024,
            system=f"Context:\n{context}" if context else "",
            messages=[{"role": "user", "content": question}]
        )
        return response.content[0].text

    llm_future = pools.submit_llm(call_llm)
    return llm_future.result(timeout=30.0)

Option 4: Thread pool monitoring — detect and alert on pool exhaustion

import concurrent.futures
import threading
import time
import logging
from dataclasses import dataclass, field
from typing import Callable, Any

logger = logging.getLogger(__name__)

@dataclass
class PoolStats:
    submitted: int = 0
    completed: int = 0
    failed: int = 0
    rejected: int = 0
    max_queue_depth: int = 0
    current_queue_depth: int = 0

class MonitoredThreadPool:
    """
    ThreadPoolExecutor with monitoring, stats, and backpressure.
    Rejects new work when queue grows too deep instead of unbounded growth.
    """

    def __init__(
        self,
        max_workers: int = 20,
        max_queue_depth: int = 200,   # Reject new work beyond this
        thread_name_prefix: str = "agent"
    ):
        self._pool = concurrent.futures.ThreadPoolExecutor(
            max_workers=max_workers,
            thread_name_prefix=thread_name_prefix
        )
        self._max_queue = max_queue_depth
        self._stats = PoolStats()
        self._lock = threading.Lock()
        self._active_count = 0
        self._max_workers = max_workers

    @property
    def stats(self) -> PoolStats:
        return self._stats

    @property
    def utilization(self) -> float:
        """Fraction of workers currently busy."""
        return self._active_count / self._max_workers

    def submit(self, fn: Callable, *args, **kwargs) -> concurrent.futures.Future | None:
        """
        Submit work. Returns None if pool is overloaded (backpressure).
        Caller should handle None gracefully (queue externally or return 503).
        """
        with self._lock:
            queue_depth = self._stats.submitted - self._stats.completed - self._stats.failed
            self._stats.max_queue_depth = max(self._stats.max_queue_depth, queue_depth)
            self._stats.current_queue_depth = queue_depth

            if queue_depth >= self._max_queue:
                self._stats.rejected += 1
                logger.warning(
                    f"Pool overloaded: queue={queue_depth}/{self._max_queue}, "
                    f"workers={self._active_count}/{self._max_workers}. Rejecting request."
                )
                return None

            self._stats.submitted += 1
            self._active_count = min(self._active_count + 1, self._max_workers)

        def wrapped():
            try:
                result = fn(*args, **kwargs)
                with self._lock:
                    self._stats.completed += 1
                    self._active_count = max(0, self._active_count - 1)
                return result
            except Exception as exc:
                with self._lock:
                    self._stats.failed += 1
                    self._active_count = max(0, self._active_count - 1)
                raise

        return self._pool.submit(wrapped)

    def log_stats(self):
        s = self._stats
        logger.info(
            f"Pool stats: submitted={s.submitted}, completed={s.completed}, "
            f"failed={s.failed}, rejected={s.rejected}, "
            f"queue={s.current_queue_depth}/{self._max_queue}, "
            f"utilization={self.utilization:.0%}"
        )
        if self.utilization > 0.85:
            logger.warning("Thread pool utilization > 85% — consider increasing max_workers or reducing load")

monitored_pool = MonitoredThreadPool(max_workers=20, max_queue_depth=100)

Option 5: Work stealing queue — balance load across workers

import queue
import threading
import time
import logging
from typing import Any, Callable
from dataclasses import dataclass, field

logger = logging.getLogger(__name__)

@dataclass
class WorkItem:
    fn: Callable
    args: tuple
    kwargs: dict
    result_future: "concurrent.futures.Future"
    submitted_at: float = field(default_factory=time.monotonic)

class BoundedWorkerPool:
    """
    Worker pool with bounded queue and work-stealing.
    Workers share a single queue — naturally load-balances without overhead.
    Workers exit when idle, freeing system resources.
    """

    def __init__(
        self,
        min_workers: int = 2,
        max_workers: int = 20,
        queue_size: int = 500,
        idle_timeout: float = 30.0  # Workers exit after 30s idle
    ):
        self._min_workers = min_workers
        self._max_workers = max_workers
        self._idle_timeout = idle_timeout
        self._queue: queue.Queue = queue.Queue(maxsize=queue_size)
        self._workers: set[threading.Thread] = set()
        self._lock = threading.Lock()
        self._shutdown = False

        # Start minimum workers:
        for _ in range(min_workers):
            self._start_worker()

    def _start_worker(self):
        t = threading.Thread(target=self._worker_loop, daemon=True)
        t.start()
        self._workers.add(t)

    def _worker_loop(self):
        """Worker loop — exits after idle_timeout with no work."""
        while not self._shutdown:
            try:
                item: WorkItem = self._queue.get(timeout=self._idle_timeout)
            except queue.Empty:
                # Idle timeout — exit if above min_workers
                with self._lock:
                    if len(self._workers) > self._min_workers:
                        self._workers.discard(threading.current_thread())
                        return
                continue

            try:
                result = item.fn(*item.args, **item.kwargs)
                if not item.result_future.done():
                    item.result_future.set_result(result)
            except Exception as exc:
                if not item.result_future.done():
                    item.result_future.set_exception(exc)
            finally:
                self._queue.task_done()

    def submit(self, fn: Callable, *args, **kwargs) -> "concurrent.futures.Future":
        import concurrent.futures
        future = concurrent.futures.Future()
        item = WorkItem(fn=fn, args=args, kwargs=kwargs, result_future=future)

        # Scale up workers if needed (up to max):
        with self._lock:
            if self._queue.qsize() > 0 and len(self._workers) < self._max_workers:
                self._start_worker()
                logger.debug(f"Scaled up: {len(self._workers)}/{self._max_workers} workers")

        try:
            self._queue.put_nowait(item)
        except queue.Full:
            future.set_exception(RuntimeError("Worker pool queue full — request rejected"))

        return future

    def shutdown(self):
        self._shutdown = True
        for _ in self._workers:
            try:
                self._queue.put_nowait(None)
            except queue.Full:
                pass

pool = BoundedWorkerPool(min_workers=2, max_workers=20, queue_size=200)

Option 6: asyncio + run_in_executor bridge — mix sync and async cleanly

import asyncio
import concurrent.futures
import anthropic
import logging
from typing import Any

logger = logging.getLogger(__name__)

# A shared thread pool for legacy blocking code:
_BLOCKING_POOL = concurrent.futures.ThreadPoolExecutor(
    max_workers=10,
    thread_name_prefix="blocking"
)

async def run_blocking(fn, *args, **kwargs) -> Any:
    """Run a blocking function in the thread pool without creating a new thread."""
    loop = asyncio.get_event_loop()
    return await loop.run_in_executor(_BLOCKING_POOL, lambda: fn(*args, **kwargs))

async def process_session(session_id: str, message: str) -> str:
    """
    Fully async session processing.
    Blocking DB calls go through run_blocking — no new threads created per session.
    """
    # Blocking DB call — uses thread pool, not a new thread:
    def fetch_history():
        import sqlite3
        conn = sqlite3.connect("/app/sessions.db")
        rows = conn.execute(
            "SELECT role, content FROM messages WHERE session_id=? ORDER BY id DESC LIMIT 10",
            (session_id,)
        ).fetchall()
        conn.close()
        return [{"role": r[0], "content": r[1]} for r in reversed(rows)]

    history = await run_blocking(fetch_history)

    # Async LLM call — no thread at all:
    client = anthropic.AsyncAnthropic()
    response = await client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=history + [{"role": "user", "content": message}]
    )
    reply = response.content[0].text

    # Blocking DB write — uses thread pool:
    def save_messages():
        import sqlite3
        conn = sqlite3.connect("/app/sessions.db")
        conn.execute("INSERT INTO messages VALUES (?, 'user', ?)", (session_id, message))
        conn.execute("INSERT INTO messages VALUES (?, 'assistant', ?)", (session_id, reply))
        conn.commit()
        conn.close()

    await run_blocking(save_messages)
    return reply

async def handle_concurrent_sessions(sessions: list[dict]) -> list[str]:
    """
    1000 concurrent sessions → 1 event loop thread + 10 DB threads.
    vs. naive approach: 1000 OS threads × 8MB = 8GB memory.
    """
    semaphore = asyncio.Semaphore(50)  # Max 50 concurrent LLM calls

    async def bounded_process(session):
        async with semaphore:
            return await process_session(session["id"], session["message"])

    return await asyncio.gather(*[bounded_process(s) for s in sessions])

Threading Model Comparison

Approach	Max Concurrency	Memory per Unit	GIL Limited	Best For
threading.Thread per request	~100–500 (OS limit)	~8MB per thread	Yes	Legacy blocking code only
ThreadPoolExecutor(max_workers=N)	N (bounded)	~8MB per active thread	Yes	Bounded blocking I/O
asyncio + AsyncAnthropic	10,000+	~10KB per task	No (for I/O)	All I/O-bound work
asyncio + run_in_executor	Mixed	Per thread pool	Partial	Mix of blocking + async
ProcessPoolExecutor	CPU count	~30MB per process	No	CPU-bound work

Recommended max_workers by Workload

Workload	max_workers Formula	Typical Value
Pure LLM API calls (async preferred)	10–20 if using threads	15
Mixed I/O (DB + API)	2–4 × cpu_count	16–32
CPU-bound (embedding, processing)	cpu_count	4–8
File I/O	4–8	5

Expected Token Savings

N/A — thread exhaustion blocks all API calls, so there are no token savings to measure. Thread-safe bounded pool: agents serve 10× more concurrent users at the same memory footprint.

Environment

• Any Python agent serving multiple concurrent users; thread exhaustion is most common when: migrating from single-user to multi-user deployment, adding background tasks, or integrating legacy blocking libraries into an async agent; the symptom appears suddenly at a specific concurrency threshold (the thread limit) rather than gradually
• Source: direct experience; RuntimeError: can't start new thread is the most common production failure for agents that passed load testing at low concurrency but were deployed without a bounded thread pool

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