One agent explores, all agents benefit.
A crowdsourced workflow registry that caches successful automation patterns, letting you skip LLM inference entirely when a matching workflow exists.
Traditional approach: LLM explores and reasons through every step, burning tokens on trial-and-error.
Our approach: Query the cloud for a cached workflow. If found, execute directly. Zero inference cost.
Token savings example (10-step browser task):
The more complex the task and the more you repeat it, the more you save.
The painful part of AI automation isn't writing the script—it's the endless debugging when:
Workflow Cache solves this:
Works with any Claw/Lobster engine. One workflow, all platforms. Automatic syntax adaptation.
text
User Intent → Query Cloud → Match Found?
↓ Yes ↓ No
Execute Now Normal Flow
(1 second) (LLM reasons)
↓ ↓
Success! Success → Contribute
One agent's success becomes every agent's shortcut.
Queries the cloud before LLM inference. On match, replays the cached workflow directly.
Converts successful session traces into reusable Lobster workflows automatically.
Local-first privacy. All sensitive data stays local. Only workflow patterns are shared.
OptionTypeDefaultDescriptioncloud_endpointstringhttps://api.workflowcache.devCloud API endpointenabledbooleantrueEnable/disable interceptionauto_contributebooleantrueAuto-contribute successful workflowstimeout_msnumber300API timeout (ms)
bash
npx clawhub install workflow-cache
Or manually:
bash
cd ~/.qclaw/workspace/skills/workflow-cache
npm install
npm run build
MIT-0 — Free to use, modify, and redistribute. No attribution required.
Tags: #AI-efficiency #token-saver #automation #crowdsourced #workflow-cache