Concurrency on ErrorVault — Developer Error Code Dictionary

Fix clw-scheduler-limit-exceeded: OpenClaw Scheduler Task Limit Reached

Mon, 10 Aug 2026 03:08:04 +0800

1. Symptoms

When the OpenClaw job scheduler encounters the clw-scheduler-limit-exceeded error, you will observe the following symptoms in your system:

Console Output:

[ERROR] clw-scheduler-limit-exceeded: Maximum concurrent tasks (150) reached
   at Scheduler.schedule() line 342
   at JobQueue.process() line 128
   at TaskDispatcher.dispatch() line 67

Log File Evidence:

2025-03-15 09:42:31.415 [SCHEDULER] WARN  - Scheduler capacity at 100%
2025-03-15 09:42:31.416 [SCHEDULER] ERROR - clw-scheduler-limit-exceeded
2025-03-15 09:42:31.417 [SCHEDULER] INFO  - 150/150 tasks active, 23 queued
2025-03-15 09:42:31.418 [SCHEDULER] DEBUG - Rejected job: pipeline-9823 (priority: LOW)

Behavioral Indicators:

Fix OSError-too-many-open-files: Resolve 'Too many open files' error in Python applications

Wed, 08 Jul 2026 11:17:00 +0800

The OSError: [Errno 24] Too many open files is a common issue encountered in Python applications, particularly those dealing with extensive I/O operations, network connections, or high concurrency. This error indicates that the process has exceeded the operating system’s limit on the number of file descriptors (FDs) it can have open simultaneously. Understanding and addressing this error is crucial for maintaining the stability and performance of your Python applications.

1. Symptoms: Clear description of indicators and shell output.

When your Python application encounters the “Too many open files” error, it typically manifests as a sudden failure of operations that require opening new file descriptors. This can include attempting to open a new file, establish a network socket connection, or even list directory contents. The application might crash, become unresponsive, or simply fail specific tasks.

Fix TimeoutError: Resolving network and asynchronous operation timeouts in Python

Fri, 03 Jul 2026 14:34:00 +0800

TimeoutError in Python is a crucial exception that signals an operation has exceeded its allocated time limit. This error is particularly prevalent in scenarios involving network communication, file I/O, or asynchronous programming where operations might block or take an indeterminate amount of time. Properly understanding and handling TimeoutError is essential for building robust and responsive applications, preventing indefinite hangs, and ensuring graceful degradation when external services or long-running tasks become unresponsive.