Agent Budget Router
Deterministic local-first routing by privacy, quality, risk and marginal cost. The public acquisition layer.
✓
Policy routingChoose by rules, not guesswork.
✓
Local-firstNo prompt transmission in the MVP.
Try the open-source router today. We are validating a broader control plane for selective context loading, project continuity and subscription ROI.
The free layer solves basic routing. The paid layer is designed around the problems that appear once you use several AI tools seriously: quota juggling, broken context, tool overload, hidden cost and governance.
Deterministic local-first routing by privacy, quality, risk and marginal cost. The public acquisition layer.
Planned coordination of GPT, Claude, Gemini and local models through one task, context and cost policy.
Proposed selective loading of the MCP tools, skills, files and instructions a task needs.
These prices are intentionally visible before billing is enabled. We are testing willingness to pay, not pretending the checkout is live.
Services validate urgent willingness to pay and expose the exact workflows worth turning into product features.
We map your current GPT / Claude / Gemini / local stack, define routing rules, reduce duplicate context, and give you an operating blueprint.
Map tool permissions, suspicious execution paths, prompt/tool exposure and high-risk integrations before an agent touches production data.
The goal is to sit one layer earlier: decide what context to load, which model should work, when to escalate, and whether the outcome justified the spend.
| Problem | Free router | Control Plane Pro | Security Audit |
|---|---|---|---|
| Which model should do this task? | ✓ rules | ✓ smart + quota-aware | — |
| Too many MCP/tools in context | — | ✓ Context Optimizer | ✓ exposure review |
| Context breaks between AI tools | — | ✓ shared handoff | — |
| Do my subscriptions pay back? | basic | ✓ ROI ledger | — |
| What can this agent access? | — | policy view | ✓ deep review |
Each page explains one operating problem and routes back into the same willingness-to-pay test.
Know whether ChatGPT, Claude, Gemini and local models are paying back.
Read use case →Keep context intact when work moves between Claude, Codex and Gemini.
Read use case →Load only the tools and skills the current task needs.
Read use case →Not yet. Planned pricing is shown as a transparent demand test while the payment platform is under review. Early-access requests influence build priority and launch order.
Agent Budget Router v0.1.0 is public on GitHub and provides deterministic local-first routing. The paid Control Plane is the product being validated next.
That is not the goal. Those tools are strong in gateway and observability workflows. YJS AI Labs is focusing on orchestration before execution: context selection, model choice, quota awareness, handoff and ROI.
Yes. Local/cloud hybrid routing is part of the product thesis. The objective is to keep sensitive or low-value work local when policy and capability allow it.
The first users are expected to be AI builders and power users. GitHub requests give us structured demand evidence without waiting for the billing stack. Do not post secrets or confidential project data.
Tell us your AI stack, biggest pain and what would be worth paying for.