Overview
Frontier self-improvement for your AI Agent — we build and manage your platform's data moat with a plug-and-play continuous learning agent.
ultrarouter helps teams turn the corrections and outcomes their product already generates into tested improvements — and keep that learning when they change models. It is five capabilities on one ledger and one gate, reached through two parallel entry actions. You choose the job; the products sit behind it.
Two ways in
Migration and Flywheel are parallel offers. Start with either — neither is a prerequisite for the other.
- Plan a model migration — available today. Find the replacement that meets your quality bar on your application, with the failures and the rollout plan in front of you. Built on the paired Bench comparison, the qualified evaluators and the auto-rollback canary. Start from the migrate guides. A guided one-screen migration workspace is planned.
- Build your data flywheel — planned (Phase 10). Your reviewers already show what good looks like; put those corrections to work and see whether the next version performs better. The outcome ledger, calibration and the first learned-context lever are being built — this is planned capability, labeled as such, not a shipped feature.
The ownership promise
"We handle your data moat" means connecting real outcomes to tested improvements
and preserving customer-owned learning across model changes. Your data stays
tenant-isolated and fully exportable, and raw items never cross a workspace. We
process authorized workspace content to train and evaluate models exclusively for
your workspace; we never use it to train shared models or another customer's models.
The customer-facing training switch (on by default under a training agreement) and the
train_allowed enforcement are built; what is not built yet is a governed trainer, so
no customer-data training run executes yet — see
the training-ready export. A
ledger of outcomes is a starting point, not proof on its own — it becomes valuable
when its evidence helps the next decision. We do not sell a valuation multiple, a
moat certificate, or a guarantee of improvement.
Five capabilities, one customer journey
The first question is your job, not which products to configure. These are the capabilities behind the two jobs above.
| Capability | What it is | Availability |
|---|---|---|
| Router | One OpenAI-compatible URL for 17 providers: BYOK, fallbacks, spend caps, streaming and tools. | Available today |
| Evals | Evaluators generated from your traffic, proved to catch defects, gating every change from PR to canary to rollback. | Available today |
| Scoring | Which model holds your quality bar at what cost per successful task — paired, signed and refusable. | Available today |
| Flywheel | The outcome ledger: what happened next to every answer, learned into rules and examples, behind a holdout, training-ready. | Planned — Phase 10 |
| Context | Deterministic context assembly with a manifest: modules, contracts, budgets and SOPs that load lazily. | Planned — Phase 10 |
Planned capabilities are labeled throughout. Any improvement claim we publish carries its scope, sample size and confidence interval, or it does not appear.
What is a drop-in proxy?
The available capabilities are OpenAI/OpenRouter-compatible: point your existing
OpenAI or OpenRouter SDK at the gateway base URL, keep your messages, and you are
done. POST /v1/chat/completions (streaming + non-streaming), POST /v1/embeddings,
GET /v1/models — the official openai client works unmodified; only the base URL
and key change. Compression is a safety taxonomy (off
by default), routing is pinned, multi-offer or auto, and
the savings ledger computes honest, procurement-defensible
numbers.
Start here
- Quickstart — make your first call in five minutes.
- For agents — the machine-first quickstart (env, curl, TS, Python, error taxonomy, rate limits).
- Concepts — call types, routing, compression, evals, savings.
- API reference — every endpoint, generated from the spec.
- Migrate — from OpenAI direct or OpenRouter.
Built for agents
The whole site is machine-ingestible. Add the documentation MCP server so your coding agent can search the docs, read pages, fetch the OpenAPI spec, and get runnable request examples. It is not on a package registry yet: build it in your ultrarouter checkout and register it from there. MCP servers for Claude Code and Codex has the Codex form, the installer and the authenticated management MCP.
pnpm --filter "@ultrarouter/docs-mcp..." build
claude mcp add --scope user ultrarouter-docs -- node "$PWD/packages/docs-mcp/dist/bin.js"Every page is also available as clean markdown at <url>.md, and the whole corpus
is at /llms-full.txt (agent quickstart first). See
For agents for the full list of surfaces.
Status
The gateway, dashboard, SDKs (TypeScript + Python), compression, evals, canary
auto-routing, and savings accounting are all real and documented here — that is the
Router, Evals and Scoring surface. Flywheel is a preview that runs on the local
stack, and Context is a planned Phase-10 capability; each is labeled wherever it
appears. The hosted gateway (https://api.ultrarouter.io) is invite-only for now:
invited accounts sign in at https://app.ultrarouter.io and bring their own provider
keys. Learning is not on the hosted service yet, and there is no hosted MCP endpoint.
You can also run the stack locally (base URL http://localhost:8080) or self-host.
Anything not-yet-shipped is marked plainly.