fix: use per-thread persistent event loops in worker threads

Replace asyncio.run() with thread-local persistent event loops for
worker threads (e.g., delegate_task's ThreadPoolExecutor). asyncio.run()
creates and closes a fresh loop on every call, leaving cached
httpx/AsyncOpenAI clients bound to a dead loop — causing 'Event loop is
closed' errors during GC when parallel subagents clean up connections.

The fix mirrors the main thread's _get_tool_loop() pattern but uses
threading.local() so each worker thread gets its own long-lived loop,
avoiding both cross-thread contention and the create-destroy lifecycle.

Added 4 regression tests covering worker loop persistence, reuse,
per-thread isolation, and separation from the main thread's loop.
This commit is contained in:
emozilla
2026-03-20 15:41:06 -04:00
parent aafe86d81a
commit ab6abc2c13
2 changed files with 130 additions and 7 deletions

View File

@@ -39,6 +39,7 @@ logger = logging.getLogger(__name__)
_tool_loop = None # persistent loop for the main (CLI) thread
_tool_loop_lock = threading.Lock()
_worker_thread_local = threading.local() # per-worker-thread persistent loops
def _get_tool_loop():
@@ -56,6 +57,28 @@ def _get_tool_loop():
return _tool_loop
def _get_worker_loop():
"""Return a persistent event loop for the current worker thread.
Each worker thread (e.g., delegate_task's ThreadPoolExecutor threads)
gets its own long-lived loop stored in thread-local storage. This
prevents the "Event loop is closed" errors that occurred when
asyncio.run() was used per-call: asyncio.run() creates a loop, runs
the coroutine, then *closes* the loop — but cached httpx/AsyncOpenAI
clients remain bound to that now-dead loop and raise RuntimeError
during garbage collection or subsequent use.
By keeping the loop alive for the thread's lifetime, cached clients
stay valid and their cleanup runs on a live loop.
"""
loop = getattr(_worker_thread_local, 'loop', None)
if loop is None or loop.is_closed():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
_worker_thread_local.loop = loop
return loop
def _run_async(coro):
"""Run an async coroutine from a sync context.
@@ -68,9 +91,10 @@ def _run_async(coro):
loop so that cached async clients (httpx / AsyncOpenAI) remain bound
to a live loop and don't trigger "Event loop is closed" on GC.
When called from a worker thread (parallel tool execution), we detect
that we're NOT on the main thread and use asyncio.run() with a fresh
loop to avoid contention on the shared persistent loop.
When called from a worker thread (parallel tool execution), we use a
per-thread persistent loop to avoid both contention with the main
thread's shared loop AND the "Event loop is closed" errors caused by
asyncio.run()'s create-and-destroy lifecycle.
This is the single source of truth for sync->async bridging in tool
handlers. The RL paths (agent_loop.py, tool_context.py) also provide
@@ -89,11 +113,14 @@ def _run_async(coro):
future = pool.submit(asyncio.run, coro)
return future.result(timeout=300)
# If we're on a worker thread (e.g., parallel tool execution),
# use asyncio.run() with its own loop to avoid contending with the
# shared persistent loop from another parallel worker.
# If we're on a worker thread (e.g., parallel tool execution in
# delegate_task), use a per-thread persistent loop. This avoids
# contention with the main thread's shared loop while keeping cached
# httpx/AsyncOpenAI clients bound to a live loop for the thread's
# lifetime — preventing "Event loop is closed" on GC cleanup.
if threading.current_thread() is not threading.main_thread():
return asyncio.run(coro)
worker_loop = _get_worker_loop()
return worker_loop.run_until_complete(coro)
tool_loop = _get_tool_loop()
return tool_loop.run_until_complete(coro)

View File

@@ -84,6 +84,102 @@ class TestRunAsyncLoopLifecycle:
assert not loop.is_closed(), "Loop closed before second call"
class TestRunAsyncWorkerThread:
"""Verify worker threads get persistent per-thread loops (delegate_task fix)."""
def test_worker_thread_loop_not_closed(self):
"""A worker thread's loop must stay open after _run_async returns,
so cached httpx/AsyncOpenAI clients don't crash on GC."""
from concurrent.futures import ThreadPoolExecutor
from model_tools import _run_async
def _run_on_worker():
loop = _run_async(_get_current_loop())
still_open = not loop.is_closed()
return loop, still_open
with ThreadPoolExecutor(max_workers=1) as pool:
loop, still_open = pool.submit(_run_on_worker).result()
assert still_open, (
"Worker thread's event loop was closed after _run_async — "
"cached async clients will crash with 'Event loop is closed'"
)
def test_worker_thread_reuses_loop_across_calls(self):
"""Multiple _run_async calls on the same worker thread should
reuse the same persistent loop (not create-and-destroy each time)."""
from concurrent.futures import ThreadPoolExecutor
from model_tools import _run_async
def _run_twice_on_worker():
loop1 = _run_async(_get_current_loop())
loop2 = _run_async(_get_current_loop())
return loop1, loop2
with ThreadPoolExecutor(max_workers=1) as pool:
loop1, loop2 = pool.submit(_run_twice_on_worker).result()
assert loop1 is loop2, (
"Worker thread created different loops for consecutive calls — "
"cached clients from the first call would be orphaned"
)
assert not loop1.is_closed()
def test_parallel_workers_get_separate_loops(self):
"""Different worker threads must get their own loops to avoid
contention (the original reason for the worker-thread branch)."""
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from model_tools import _run_async
barrier = threading.Barrier(3, timeout=5)
def _get_loop_id():
# Use a barrier to force all 3 threads to be alive simultaneously,
# ensuring the ThreadPoolExecutor actually uses 3 distinct threads.
loop = _run_async(_get_current_loop())
barrier.wait()
return id(loop), not loop.is_closed(), threading.current_thread().ident
with ThreadPoolExecutor(max_workers=3) as pool:
futures = [pool.submit(_get_loop_id) for _ in range(3)]
results = [f.result() for f in as_completed(futures)]
loop_ids = {r[0] for r in results}
thread_ids = {r[2] for r in results}
all_open = all(r[1] for r in results)
assert all_open, "At least one worker thread's loop was closed"
# The barrier guarantees 3 distinct threads were used
assert len(thread_ids) == 3, f"Expected 3 threads, got {len(thread_ids)}"
# Each thread should have its own loop
assert len(loop_ids) == 3, (
f"Expected 3 distinct loops for 3 parallel workers, "
f"got {len(loop_ids)} — workers may be contending on a shared loop"
)
def test_worker_loop_separate_from_main_loop(self):
"""Worker thread loops must be different from the main thread's
persistent loop to avoid cross-thread contention."""
from concurrent.futures import ThreadPoolExecutor
from model_tools import _run_async, _get_tool_loop
main_loop = _get_tool_loop()
def _get_worker_loop_id():
loop = _run_async(_get_current_loop())
return id(loop)
with ThreadPoolExecutor(max_workers=1) as pool:
worker_loop_id = pool.submit(_get_worker_loop_id).result()
assert worker_loop_id != id(main_loop), (
"Worker thread used the main thread's loop — this would cause "
"cross-thread contention on the event loop"
)
class TestRunAsyncWithRunningLoop:
"""When a loop is already running, _run_async falls back to a thread."""