Two Agents Deadlock Waiting for Each Other — Circular Dependency
Symptom
- Two agents both show as “waiting” indefinitely
- System hangs with no errors — just silence
- Agent A is blocked on
await agent_b.get_result() - Agent B is blocked on
await agent_a.get_result() - Logs show both agents waiting, neither making progress
- Restarting fixes it temporarily but deadlock recurs
Root Cause
Circular resource dependency: A needs B’s output, B needs A’s output, both wait indefinitely. Deadlock requires four conditions (Coffman): mutual exclusion, hold-and-wait, no preemption, circular wait. Eliminating any one prevents deadlock.
Fix
Option 1: Break circular dependency by reordering
# DEADLOCK: A waits for B, B waits for A
async def agent_a():
b_result = await agent_b.get_result() # Waits for B
return process(b_result)
async def agent_b():
a_result = await agent_a.get_result() # Waits for A — DEADLOCK
return process(a_result)
# FIX: identify what B actually needs from A
# Often, B doesn't need A's full result — just an initial value
async def fixed_workflow():
# Step 1: A produces initial data (no B dependency)
initial_data = await agent_a.phase_one()
# Step 2: B uses initial data (no A dependency now)
b_result = await agent_b.run(initial_data)
# Step 3: A completes using B's output
final_result = await agent_a.phase_two(b_result)
return final_result
Option 2: Timeout to detect and break deadlock
import asyncio
async def agent_with_timeout(agent_fn, timeout_seconds: float = 30.0, name: str = "agent"):
try:
return await asyncio.wait_for(agent_fn(), timeout=timeout_seconds)
except asyncio.TimeoutError:
raise RuntimeError(
f"Agent '{name}' timed out after {timeout_seconds}s. "
f"Possible deadlock — check for circular dependencies."
)
async def safe_multi_agent_run():
try:
results = await asyncio.gather(
agent_with_timeout(agent_a, timeout=30, name="agent_a"),
agent_with_timeout(agent_b, timeout=30, name="agent_b"),
)
return results
except RuntimeError as e:
print(f"Deadlock detected: {e}")
# Cancel all pending tasks
for task in asyncio.all_tasks():
if not task.done():
task.cancel()
raise
Option 3: Resource ordering to prevent circular wait
import asyncio
# Assign numeric IDs to resources; always acquire in order
RESOURCE_ORDER = {
"database": 1,
"cache": 2,
"file_system": 3,
"external_api": 4,
}
class OrderedResourceManager:
def __init__(self):
self._locks = {name: asyncio.Lock() for name in RESOURCE_ORDER}
async def acquire_resources(self, resource_names: list[str]):
"""Always acquire locks in canonical order — prevents circular wait"""
ordered = sorted(resource_names, key=lambda r: RESOURCE_ORDER[r])
acquired = []
try:
for resource in ordered:
await self._locks[resource].acquire()
acquired.append(resource)
except:
# Release all acquired locks on failure
for resource in acquired:
self._locks[resource].release()
raise
return acquired
def release_resources(self, resource_names: list[str]):
for resource in resource_names:
self._locks[resource].release()
manager = OrderedResourceManager()
async def agent_a():
# Acquires database then cache (order: 1, 2)
resources = await manager.acquire_resources(["database", "cache"])
try:
return await do_work()
finally:
manager.release_resources(resources)
async def agent_b():
# Also acquires database then cache (same order — no deadlock)
resources = await manager.acquire_resources(["cache", "database"]) # Reordered to 1, 2
try:
return await do_work()
finally:
manager.release_resources(resources)
Option 4: Message-passing instead of direct calls
import asyncio
class AgentMessageBus:
"""Agents communicate via messages — no direct blocking calls"""
def __init__(self):
self._queues = {}
def register(self, agent_id: str) -> asyncio.Queue:
self._queues[agent_id] = asyncio.Queue()
return self._queues[agent_id]
async def send(self, to: str, message: dict):
await self._queues[to].put(message)
async def receive(self, agent_id: str, timeout: float = 30.0) -> dict:
return await asyncio.wait_for(
self._queues[agent_id].get(),
timeout=timeout
)
bus = AgentMessageBus()
async def agent_a(bus):
queue = bus.register("agent_a")
# Send request to B (non-blocking)
await bus.send("agent_b", {"from": "agent_a", "request": "process_data"})
# Wait for response with timeout
response = await bus.receive("agent_a", timeout=30)
return response["result"]
async def agent_b(bus):
queue = bus.register("agent_b")
# Process requests as they arrive — no calls to A
while True:
message = await bus.receive("agent_b", timeout=60)
result = await process(message["request"])
await bus.send(message["from"], {"result": result})
Option 5: Deadlock detection via dependency graph
from collections import defaultdict
class DependencyGraph:
"""Track what each agent is waiting for, detect cycles"""
def __init__(self):
self.waiting_for = {} # agent_id -> agent_id it's waiting on
def add_wait(self, waiter: str, waiting_for: str):
self.waiting_for[waiter] = waiting_for
if self.has_cycle():
self.waiting_for.pop(waiter)
raise RuntimeError(
f"Deadlock detected: {waiter} → {waiting_for} creates a cycle. "
f"Current dependencies: {self.waiting_for}"
)
def remove_wait(self, waiter: str):
self.waiting_for.pop(waiter, None)
def has_cycle(self) -> bool:
visited = set()
for start in self.waiting_for:
current = start
path = set()
while current in self.waiting_for:
if current in path:
return True # Cycle detected
path.add(current)
current = self.waiting_for[current]
return False
dep_graph = DependencyGraph()
async def wait_for_agent(my_id: str, target_id: str, target_fn):
dep_graph.add_wait(my_id, target_id)
try:
return await target_fn()
finally:
dep_graph.remove_wait(my_id)
Deadlock Prevention Checklist
| Coffman Condition | How to break it |
|---|---|
| Mutual exclusion | Use lock-free data structures where possible |
| Hold-and-wait | Acquire all locks at once or release before requesting more |
| No preemption | Use timeouts — allow lock acquisition to fail |
| Circular wait | Always acquire resources in canonical order |
Expected Token Savings
Debugging silent deadlock without detection: ~15,000 tokens (hours of investigation) Timeout + dependency tracking: detects within seconds
Environment
- Multi-agent orchestration systems; most common with shared resources
- Source: direct experience, computer science concurrency fundamentals
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