context_breakdown._chars_to_tokens and native_compaction._approx_tokens did raw
chars//4, under-counting CJK/Cyrillic by 2-4x next to the conversation slice
that already used estimate_tokens_rough — the /context pie chart mixed two
estimators. Both now call the canonical. The four private `= 4` ratio
constants import one CHARS_PER_TOKEN from agent/model_metadata.py. Estimates
only feed UI and budgets; no prompt or message bytes change.
The usage anchor (real usage.prompt_tokens + delta estimate of what was appended since)
identified the priced transcript by id() of the last message, so it was None on EVERY
gateway turn (history is re-read from the DB each turn) and in every fresh process
(--resume, desktop per-turn serve). Those are exactly the surfaces where the bytes/4
estimate then fired local compression against payloads the provider priced far under
threshold (#99421, #104462).
- agent/usage_anchor.py owns the anchor: content fingerprint instead of id(), persisted on
the session row (model_config._usage_anchor) via set_usage_anchor(), restored on the first
resumed turn while the durable transcript still matches, cleared with the row on
compaction / codex-native rewrite / session reset.
- Callers repointed from model_metadata (the compat table follows).
Design and persistence slot from #99585 by @686f6c61; re-authored against the Sep 2026
layout (the branch predates the model_metadata / agent_init split).
On reasoning models a long tool loop replays the current turn's thinking +
scaffolding on every request, so the LAST request's prompt_tokens can exceed
the durable transcript by hundreds of K — all of which evaporates at the turn
boundary. The status bar and /context breakdown rendered that raw figure, so
users watched 'context' jump (e.g.) 850K -> 600K across a turn boundary and
read it as a broken compaction.
- conversation_loop: capture a turn-base usage anchor from the turn's FIRST
provider response (api_call_count == 1), where replay is minimal.
- anchored_context_tokens: new charge_stale_thinking kwarg forwarded to the
delta estimate (stale reasoning excluded on all but the newest assistant
message).
- cli status snapshot + context_breakdown: prefer the turn-base anchored
figure; fall back to last-response anchor / raw last_prompt_tokens.
- All _usage_anchor invalidation sites also clear _turn_base_usage_anchor.
Display-only: compression trigger math keeps using real last-request usage
(the inflated request is what actually risks the window mid-loop).
Every provider response carries usage.prompt_tokens — exact ground truth
for the full request (system prompt + tool schemas + history). Context-size
checks now anchor on the last main-loop response's usage and estimate only
the messages appended since, instead of re-estimating the whole history
with chars/4 heuristics and flat 1500-token image costs. The estimate error
window shrinks from the entire conversation to one turn and self-corrects
at every response.
- agent/model_metadata.py: capture_usage_anchor() / anchored_context_tokens()
with a structural base-message identity check that fails closed on any
transcript rewrite.
- agent/conversation_loop.py: anchor captured at the single main-loop usage
site (MoA uses pre-fold aggregator usage; advisor/aux calls never anchor);
pre-API pressure check prefers the anchor.
- agent/turn_context.py: preflight compression estimate prefers the anchor.
- agent/context_breakdown.py: /context display prefers the anchor.
- Invalidation: compaction rewrite (conversation_compression), codex native
compaction (codex_runtime), session reset/switch (run_agent), plus the
fail-closed structural check for splices/micro-compaction.
- Usage-less responses keep the previous anchor; no anchor -> pure
estimation fallback (first request of a session).
Extends the cherry-picked /context command (PR #52184) and prompt-size
attribution helpers (PR #66656) into one visual context view across
surfaces, and absorbs the per-component budget-visibility goal of the
/tokens proposal (PR #48470):
- agent/context_breakdown.py: pure renderers over the existing payload —
a 5x20 glyph block grid (1 cell ~= 1% of the model window), an
'Estimated usage by category' table with free space, and expanded
per-skill / per-toolset listings via compute_context_details(), which
reuses the prompt-size attribution mechanism (skills index-line bytes +
registry tool->toolset map) converted to the same chars/4 heuristic.
- cli.py: /context [all] renders grid + category table (+ expanded
listings) from the live agent and in-memory conversation history.
- gateway/slash_commands.py: /context appends the plain-text category
table (no grid — monospace not guaranteed on messaging platforms);
/context all adds the expanded listings. Fail-open: breakdown errors
never break the gauge.
- hermes_cli/commands.py: /context gains the 'all' subcommand; /version
demoted to /hermes version on Slack to keep the 50-slash cap.
- tests: renderer unit tests against synthetic payloads, registry test,
gateway /context + /context all + failure-degradation handler tests.
- docs: slash-commands reference + CLI guide entries.
Read-only and locally computed: no provider calls, no prompt-cache impact.
Co-authored-by: RemyFevry <29257684+RemyFevry@users.noreply.github.com>
Co-authored-by: joelbrilliant <joelbrilliant1@gmail.com>
Co-authored-by: CharlesMcquade <6466275+CharlesMcquade@users.noreply.github.com>
Let users click the status bar context indicator to see how tokens are
split across system prompt, tools, rules, skills, MCP, and conversation.
Co-authored-by: Cursor <cursoragent@cursor.com>
teknium1