Launching today
ClawPane
One API. LLM routing for cost, task-fit, latency per request
39 followers
One API. LLM routing for cost, task-fit, latency per request
39 followers
ClawPane plugs into OpenClaw as a model provider and automatically routes every agent request to the best model — cheapest, fastest, or highest quality. No model names in your agent config. 20–45% cost reduction. Zero config changes to your agents.
Payment Required
Launch Team / Built With
ClawPane
Hey PH! 👋 I’m Apostolos, the person behind ClawPane.
I built ClawPane after my first day on @OpenClawturned into a $50 bill from “simple testing” (Claude was the main culprit). OpenClaw is great at agent orchestration, but I kept running into the same gap: agents need a smart way to choose which model should handle this prompt without me hand-tuning every step.
ClawPane is an extension to OpenClaw that plugs in as a single custom provider. You set priorities (cost, speed, quality, carbon), and ClawPane automatically routes each request to the best-fit model. Your agents don’t change. Your prompts don’t change. Only the model selection does—per request.
Why ClawPane (instead of “just OpenClaw”)? OpenClaw orchestrates agents and tools; it doesn’t try to optimize model choice across providers and prompts. ClawPane adds that missing layer: per-prompt model selection, enforcement, and visibility—built specifically to fit OpenClaw’s provider pattern and drop in fast.
Here’s what you get:
Automatic routing per request: stop overpaying on easy prompts, reserve strong models for hard ones
Multiple router profiles: cost-first support agents, quality-first coding agents, latency-first realtime flows
Automatic fallbacks: keep running when a provider degrades or goes down
Real-time cost + latency per request: see exactly what each agent call costs and how long it took
Who it’s for: anyone building agents on OpenClaw who’s tired of manual model picking, brittle defaults, and surprise bills—especially with mixed workloads and production reliability requirements.
What models are you using with OpenClaw today—and where does model choice hurt the most?