# I run a 'radar' that finds free LLM endpoints and auto-adopts the good ones — behind a five-part gate so it can't adopt junk > Source: > Published: 2026-09-16 08:00:03+00:00 Free LLM endpoints appear and vanish constantly — a provider opens a generous tier, a new open model shows up on someone's gateway, a promo runs for a month. Chasing them by hand is a losing game. So I built a small radar ([model-radar](https://github.com/casareanderson/model-radar)) that watches for free models and, when one is genuinely good enough, **adopts it automatically** into my agent stack — but only after it clears a gate strict enough that it has never once adopted something that embarrassed me. The auto-adoption is the flashy bit. The *gate* is the bit worth your time, because "just use the free model" is advice that will quietly wreck an agent pipeline. The appeal is obvious: a lot of agent work — summarise, triage, draft, classify — doesn't need a frontier model, and paying frontier prices for it is silly. A free tier that's "good enough" for the boring 90% is real money saved. The catch is that "free model" and "works in an agent" are almost unrelated properties. A model can score beautifully on a leaderboard and be **useless for agentic work**, because agent work needs things benchmarks don't measure: So a radar that adopted models on *quality scores* would be a machine for introducing silent failures. The whole design problem is: **how do you let a process adopt something automatically without letting it adopt something bad?** The radar can *find* candidates freely. It can *promote* one only by passing every stage of a fixed, deterministic gate — and if any stage fails, it's rejected, full stop. No score, no averaging, no "it's mostly fine." The gate checks, in order, things a leaderboard never will: Only a model that clears all five gets adopted. Everything is **measured on real tasks and written down**, so adoption is a reproducible decision I can audit later, The principle underneath is the same one my whole estate runs on: **let the automated part gather and propose freely; let it *act* only through a gate that a bad candidate cannot argue its way past.** A guardrail written as a suggestion in a prompt is decoration. A guardrail written as a pass/fail function is a wall. The failure I designed the whole thing to avoid is the one that gets everyone else: a cheaper model quietly swapped in that *looks* like it's working — emits calls, returns fluent text — while ignoring tool results or truncating context, producing confident garbage that no error ever flags. The gate exists specifically to catch the model that fails *silently*, because that's the only kind of failure that survives long enough to hurt you. [model-radar is on GitHub](https://github.com/casareanderson/model-radar). Even if you never auto-adopt anything, the transferable idea is the gate: **before you route real work to a free model, test it on the thing agents actually need — can it tool-call and use the result, does it keep your whole prompt, is it stable — and make that test a hard pass/fail, not a score.** Cheap intelligence is worth *Notes from someone who wanted a coffee-a-month agent bill and refused to pay for it in silent failures, so built a radar that adopts free models automatically and a gate strict enough to trust it.* 🤖 *Drafted with AI assistance from my own homelab notes, logs and repos, then reviewed and edited before publishing.*