TlabRouter
A self-updating AI model discovery and routing platform that searches for providers with free access, verifies the claims, keeps a normalized registry, and routes across models that are actually usable.
→ candidate providers
→ free quota claims
→ model catalog
verify()
✓ authoritative source
✓ usable endpoint
provider A · ready
provider B · quota
provider C · offline
→ best usable route
The problem
Lists of “free AI APIs” go stale quickly. Providers change quotas, models disappear, signup conditions change, and a search result is not the same thing as a routable endpoint.
TlabRouter treats discovery and verification as separate systems: the web can suggest candidates, but only verified data becomes routable.
Design principles
Search creates candidates. Verification decides what enters the registry.
Recurring quota, trial credit and genuinely free access are classified separately.
Provider credentials should remain on the user’s machine whenever possible.
The router aims to expose an OpenAI-compatible local API where feasible.
Private while the core gets boring.
The repository is intentionally private during early MVP development. Once the discovery, verification and routing boundaries are stable enough, the public story can be much cleaner.