This article discusses the inefficiency of polling in OpenClaw agents and introduces a plugin that moves event detection outside the agent loop, resulting in significant reductions in source calls and token usage.
I think many OpenClaw setups are paying an invisible cost. The agent keeps waking up just to ask, "Did anything happen yet?" Heartbeats and cron are clearly useful. However, if the workflow is waiting for a future event, polling can feel off pretty quickly. Here are some examples: \- Check my inbox every 10 minutes \- See if a customer replied \- Watch for a GitHub issue or PR update \- Check whether a lead responded \- Monitor a Discord or Slack channel \- Remind me if something changed The usual tradeoff is frustrating: Poll frequently and you waste tokens or source calls finding nothing. Poll less often and the agent reacts too late. Move the polling loop to a cheaper model, and the routing or filtering quality might decline. What bothered me is that the expensive agent loop has two jobs: 1. Figuring out whether anything important happened 2. Doing the actual work after something important happened These tasks should probably be separate. The agent should handle the event instead of constantly checking if it exists. To address this, I created a small OpenClaw plugin that moves the waiting and filtering outside the agent loop. The agent registers what it's waiting for and is only activated when something matches. Plugin: [https://github.com/qordinate-ai/watchline-openclaw-plugin](https://github.com/qordinate-ai/watchline-openclaw-plugin) I also created a small benchmark for the email version of this: GitHub: [https://github.com/qordinate-ai/watchbench](https://github.com/qordinate-ai/watchbench) Dataset: [https://huggingface.co/datasets/watchline/watchbench-email-v0](https://huggingface.co/datasets/watchline/watchbench-email-v0) In the current test of 50 incoming emails with five watches, moving filtering out of the polling loop resulted in: \- **68.2% fewer source calls** \- **91.0% fewer downstream agent tokens** compared to polling I'm not claiming this is a universal solution. It's a small benchmark, and email is just one source. If the agent is waiting for something, don't keep waking it up to check. Let a cheaper event or filter layer decide when there's something worth continuing for. I'm curious how others are handling this today. Are you mainly using cron or heartbeat? A cheap model router? Custom scripts? Or just accepting the token and latency tradeoff?
A user reports that Android charging notifications repeatedly woke their OpenClaw Astra agent, resulting in 127.8M recorded tokens in a single day — though ~98% were cached input tokens, not billed at the full rate. The post seeks advice on debouncing system-notification events or isolating background events to smaller contexts.
The article critiques current browser AI agents for inefficiency due to repeatedly parsing and reasoning about the same websites, and proposes a model where agents reuse proven interaction paths to reduce token consumption and improve speed.
An OpenClaw agent with a heartbeat was consuming 50 million tokens daily due to a bloated session and a bug that kept it running despite being disabled. The author shares how to identify and fix the issue by clearing the session and configuring heartbeat settings.
The author discusses the limitations of managing AI agent workflows via chat interfaces like Telegram with OpenClaw, advocating for dedicated dashboards and standardized UIs. They highlight emerging tools like Paperclip and Multica that aim to solve agent management issues.
A 13-week recap of using OpenClaw as a daily AI agent on a Raspberry Pi, highlighting strengths like cron-based automation and memory curation, and pain points like model config issues and subagent orchestration.