The compaction threshold is max(context_length * threshold_percent,
MINIMUM_CONTEXT_LENGTH=64000). The floor prevents premature compression on
large models, but degenerates at small windows: a model at exactly 64000
ctx gets max(32000, 64000) = 64000 — a threshold equal to the ENTIRE
window. should_compress() can then never fire, because the provider
rejects the request before usage reaches 100%. Auto-compression silently
never triggers for any model whose context_length <= MINIMUM /
threshold_percent (e.g. 64K-per-slot local models).
Centralize the calc in _compute_threshold_tokens(). When the floor would
meet or exceed the context window, trigger at 85% of the window
(_MIN_CTX_TRIGGER_RATIO) — high enough that a minimum-context model uses
most of its budget before compacting (compacting at the 50% percentage
would waste half the small window), but below 100% so compaction actually
fires before the provider rejects the request. This mirrors the existing
gpt-5.5/Codex 85% autoraise rationale. Large-context behavior (floor at
64000) is unchanged; both call sites (__init__ and update_model) use the
shared helper.
Co-authored-by: soynchux <soynchuux@gmail.com>
Co-authored-by: LeonSGP43 <154585401+LeonSGP43@users.noreply.github.com>
Co-authored-by: Tranquil-Flow <tranquil_flow@protonmail.com>
