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Agent Hangs Forever When Tool Call Times Out — No Timeout Handling

Symptom

  • Agent stops responding mid-task — no output, no error
  • Tool call to an external API hangs for 10+ minutes with no timeout
  • asyncio.gather() never completes because one coroutine is blocked
  • Agent process stays alive but does nothing — CPU at 0%, no log output
  • Thread pool exhausted because all threads are waiting on stuck I/O
  • Kubernetes liveness probe eventually kills the pod after 5 minutes of silence

Root Cause

HTTP clients, database drivers, and subprocess calls have no timeout by default — or very long ones (300s+). When the remote service is slow, overloaded, or drops the connection silently, the call blocks indefinitely. requests.get(url) with no timeout parameter will wait forever. asyncio coroutines without asyncio.wait_for() wrappers block the event loop. Without explicit timeouts at every I/O boundary, a single slow tool can freeze the entire agent.

Fix

Option 1: Always set timeouts on HTTP clients

import httpx
import asyncio

# WRONG — hangs forever if server is slow
# response = requests.get("https://api.example.com/data")

# RIGHT — explicit connect + read timeout
async def call_api_with_timeout(url: str, payload: dict) -> dict:
    """
    All HTTP calls must have explicit timeouts.
    connect: time to establish TCP connection
    read: time to receive response body
    write: time to send request body
    pool: time to acquire connection from pool
    """
    timeout = httpx.Timeout(
        connect=5.0,    # 5s to connect
        read=30.0,      # 30s to read response
        write=10.0,     # 10s to write request
        pool=5.0        # 5s to get connection from pool
    )

    async with httpx.AsyncClient(timeout=timeout) as client:
        try:
            response = await client.post(url, json=payload)
            response.raise_for_status()
            return response.json()
        except httpx.TimeoutException as e:
            raise TimeoutError(f"API call to {url} timed out: {e}") from e
        except httpx.HTTPStatusError as e:
            raise RuntimeError(f"API error {e.response.status_code}: {e.response.text}") from e

# Global default timeout for all calls in the agent:
DEFAULT_TIMEOUT = httpx.Timeout(connect=5.0, read=60.0, write=10.0, pool=5.0)

async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT) as client:
    # All requests through this client use the default timeout
    response = await client.get("https://slow-api.example.com/data")

Option 2: asyncio.wait_for — timeout any coroutine

import asyncio
from typing import TypeVar, Callable, Awaitable

T = TypeVar("T")

async def with_timeout(
    coro: Awaitable[T],
    timeout_seconds: float,
    operation_name: str = "operation"
) -> T:
    """
    Wrap any coroutine with a timeout.
    Raises TimeoutError with a clear message if it exceeds the limit.
    """
    try:
        return await asyncio.wait_for(coro, timeout=timeout_seconds)
    except asyncio.TimeoutError:
        raise TimeoutError(
            f"{operation_name} timed out after {timeout_seconds}s. "
            f"The remote service may be unavailable."
        )

# Usage — apply to any tool call:
async def run_agent_tool(tool_name: str, params: dict) -> dict:
    match tool_name:
        case "search_web":
            result = await with_timeout(
                search_web(params["query"]),
                timeout_seconds=15.0,
                operation_name="web search"
            )
        case "run_code":
            result = await with_timeout(
                execute_code(params["code"]),
                timeout_seconds=30.0,
                operation_name="code execution"
            )
        case "fetch_document":
            result = await with_timeout(
                fetch_document(params["url"]),
                timeout_seconds=20.0,
                operation_name="document fetch"
            )
        case _:
            raise ValueError(f"Unknown tool: {tool_name}")
    return result

# For multiple tools in parallel — all with individual timeouts:
async def run_tools_parallel(tool_calls: list[dict]) -> list[dict]:
    tasks = [
        with_timeout(
            run_single_tool(tc),
            timeout_seconds=30.0,
            operation_name=tc["name"]
        )
        for tc in tool_calls
    ]
    # gather with return_exceptions=True — one timeout doesn't cancel others
    results = await asyncio.gather(*tasks, return_exceptions=True)
    return [
        {"error": str(r)} if isinstance(r, Exception) else r
        for r in results
    ]

Option 3: Timeout decorator for tool functions

import asyncio
import functools
import time
from typing import Callable

def timeout(seconds: float, error_message: str = None):
    """
    Decorator that adds a timeout to any async function.
    Use on all tool implementation functions.
    """
    def decorator(func: Callable):
        @functools.wraps(func)
        async def wrapper(*args, **kwargs):
            msg = error_message or f"{func.__name__} timed out after {seconds}s"
            try:
                return await asyncio.wait_for(func(*args, **kwargs), timeout=seconds)
            except asyncio.TimeoutError:
                raise TimeoutError(msg)
        return wrapper
    return decorator

# Apply to every tool:
@timeout(15.0, "Web search took too long — search engine may be slow")
async def search_web(query: str) -> list[dict]:
    async with httpx.AsyncClient(timeout=10.0) as client:
        response = await client.get("https://search-api.example.com/search", params={"q": query})
        return response.json()["results"]

@timeout(30.0, "Code execution timed out — infinite loop or resource limit reached")
async def execute_code(code: str) -> str:
    proc = await asyncio.create_subprocess_exec(
        "python3", "-c", code,
        stdout=asyncio.subprocess.PIPE,
        stderr=asyncio.subprocess.PIPE
    )
    stdout, stderr = await proc.communicate()
    return stdout.decode() or stderr.decode()

@timeout(10.0, "Database query timed out — query may need optimization")
async def query_database(sql: str, params: tuple) -> list[dict]:
    async with db_pool.acquire() as conn:
        rows = await conn.fetch(sql, *params)
        return [dict(row) for row in rows]

Option 4: Watchdog — detect and recover from agent hangs

import asyncio
import time
import signal
import threading

class AgentWatchdog:
    """
    External watchdog that monitors agent activity.
    If no progress is made within the timeout window, raises an alert or restarts.
    """

    def __init__(self, timeout_seconds: float = 120.0):
        self.timeout = timeout_seconds
        self._last_activity = time.monotonic()
        self._running = False
        self._thread: threading.Thread | None = None

    def heartbeat(self, activity: str = ""):
        """Call this regularly to signal the agent is making progress"""
        self._last_activity = time.monotonic()
        if activity:
            print(f"[watchdog] Activity: {activity}")

    def start(self):
        self._running = True
        self._thread = threading.Thread(target=self._watch, daemon=True)
        self._thread.start()
        print(f"[watchdog] Started — will alert if no activity for {self.timeout}s")

    def stop(self):
        self._running = False

    def _watch(self):
        while self._running:
            time.sleep(10)
            elapsed = time.monotonic() - self._last_activity
            if elapsed > self.timeout:
                print(
                    f"[watchdog] ALERT: No agent activity for {elapsed:.0f}s "
                    f"(timeout: {self.timeout}s). Agent may be hung."
                )
                self._on_hang_detected(elapsed)

    def _on_hang_detected(self, elapsed: float):
        """Override to customize hang response"""
        # Option 1: Log and alert
        import logging
        logging.critical(f"Agent hang detected after {elapsed:.0f}s of inactivity")
        # Option 2: Send SIGTERM to trigger graceful restart
        # signal.raise_signal(signal.SIGTERM)
        # Option 3: Raise in main thread via thread-safe flag
        self._hang_detected = True

watchdog = AgentWatchdog(timeout_seconds=120.0)

async def agent_loop(messages: list[dict]) -> str:
    watchdog.start()
    try:
        while True:
            watchdog.heartbeat("calling model")
            response = await call_model(messages)

            if not response.tool_calls:
                return response.content

            for tool_call in response.tool_calls:
                watchdog.heartbeat(f"running tool: {tool_call.name}")
                result = await run_agent_tool(tool_call.name, tool_call.params)
                messages.append({"role": "tool", "content": str(result)})
    finally:
        watchdog.stop()

Option 5: Circuit breaker — stop calling timed-out services

import time
from enum import Enum
from dataclasses import dataclass, field

class CircuitState(Enum):
    CLOSED = "closed"       # Normal — calls go through
    OPEN = "open"           # Tripped — calls fail fast
    HALF_OPEN = "half_open" # Testing — allow one call through

@dataclass
class CircuitBreaker:
    """
    Circuit breaker for external tool calls.
    After N timeouts, stops calling the service for a cooldown period.
    """
    name: str
    failure_threshold: int = 3       # Trips after 3 failures
    recovery_timeout: float = 60.0   # Retry after 60 seconds
    call_timeout: float = 30.0       # Timeout per call

    _state: CircuitState = field(default=CircuitState.CLOSED, init=False)
    _failure_count: int = field(default=0, init=False)
    _last_failure_time: float = field(default=0.0, init=False)

    async def call(self, coro) -> any:
        if self._state == CircuitState.OPEN:
            if time.monotonic() - self._last_failure_time > self.recovery_timeout:
                self._state = CircuitState.HALF_OPEN
                print(f"[circuit:{self.name}] Half-open — testing recovery")
            else:
                remaining = self.recovery_timeout - (time.monotonic() - self._last_failure_time)
                raise RuntimeError(
                    f"Circuit breaker OPEN for {self.name}. "
                    f"Retry in {remaining:.0f}s (service was timing out)."
                )

        try:
            result = await asyncio.wait_for(coro, timeout=self.call_timeout)
            # Success — reset
            if self._state == CircuitState.HALF_OPEN:
                print(f"[circuit:{self.name}] Recovery successful — circuit closed")
            self._state = CircuitState.CLOSED
            self._failure_count = 0
            return result

        except (asyncio.TimeoutError, TimeoutError, Exception) as e:
            self._failure_count += 1
            self._last_failure_time = time.monotonic()

            if self._failure_count >= self.failure_threshold:
                self._state = CircuitState.OPEN
                print(
                    f"[circuit:{self.name}] OPEN after {self._failure_count} failures. "
                    f"Will retry in {self.recovery_timeout:.0f}s."
                )
            raise

# One breaker per external service:
search_breaker = CircuitBreaker("web_search", failure_threshold=3, recovery_timeout=60.0)
db_breaker = CircuitBreaker("database", failure_threshold=5, recovery_timeout=30.0)

async def search_with_circuit_breaker(query: str) -> list[dict]:
    return await search_breaker.call(search_web(query))

Option 6: Per-tool timeout configuration

from dataclasses import dataclass

@dataclass
class ToolConfig:
    """Centralized timeout configuration for all tools"""
    name: str
    timeout_seconds: float
    retry_on_timeout: bool = True
    max_retries: int = 2

TOOL_TIMEOUTS: dict[str, ToolConfig] = {
    "search_web":       ToolConfig("search_web",       timeout_seconds=15.0),
    "fetch_document":   ToolConfig("fetch_document",   timeout_seconds=20.0),
    "run_code":         ToolConfig("run_code",         timeout_seconds=60.0, max_retries=1),
    "query_database":   ToolConfig("query_database",   timeout_seconds=10.0),
    "send_email":       ToolConfig("send_email",       timeout_seconds=30.0, retry_on_timeout=False),
    "call_llm":         ToolConfig("call_llm",         timeout_seconds=120.0),
    "read_file":        ToolConfig("read_file",        timeout_seconds=5.0),
    "list_files":       ToolConfig("list_files",       timeout_seconds=5.0),
}

async def execute_tool_with_config(tool_name: str, params: dict) -> dict:
    config = TOOL_TIMEOUTS.get(tool_name)
    if not config:
        raise ValueError(f"No timeout config for tool: {tool_name}")

    for attempt in range(config.max_retries + 1):
        try:
            return await asyncio.wait_for(
                dispatch_tool(tool_name, params),
                timeout=config.timeout_seconds
            )
        except asyncio.TimeoutError:
            if not config.retry_on_timeout or attempt >= config.max_retries:
                raise TimeoutError(
                    f"Tool '{tool_name}' timed out after {config.timeout_seconds}s "
                    f"(attempt {attempt + 1}/{config.max_retries + 1})"
                )
            wait = 2 ** attempt
            print(f"Tool '{tool_name}' timed out — retrying in {wait}s...")
            await asyncio.sleep(wait)

Timeout Values by Tool Type

Tool Type Recommended Timeout Notes
DNS / TCP connect 3–5s Should be near-instant on healthy network
REST API (simple) 10–30s Add jitter if many concurrent callers
Web scrape / fetch 20–60s Some pages are slow
LLM API call 60–180s Long outputs take time
Code execution 30–120s Depends on task complexity
Database query 5–30s Simple queries: 5s; complex: 30s
File I/O (local) 1–5s Slow only if disk is thrashing
Subprocess Task-dependent Always set; never leave at None

Expected Token Savings

Agent hangs for 10 minutes → user kills process → restarts → re-explains task: ~20,000 tokens Timeout after 30s → clear error → retry or escalate: minimal wasted tokens

Environment

  • Any agent making external I/O calls; critical for agents with tool use, web search, code execution, or database access
  • Source: direct experience; missing timeouts are the most common cause of silent agent hangs in production

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