smart-compaction

name

smart-compaction

description

Manage duoduo's idle auto-compact for channel sessions — enable/disable per conversation, read the measured compaction stats, and retune thresholds with the break-even formula when the owner changes model/backend or asks about idle-session cost. Trigger when the owner mentions 自动压缩/smart compaction/压缩策略, asks why a session was compacted (a compact notice appeared), switches a session to a metered or short-cache-TTL backend, or asks to protect a conversation from compaction. Also use proactively right after the FIRST auto-compaction of a session you manage — that event carries the calibration measurement.

Smart Compaction — idle auto-compact management

What it is (one paragraph)

Channel sessions accumulate context; after an idle gap longer than the prompt-cache TTL, the next message re-writes the whole context into cache at a premium. The daemon ships a deterministic sweeper: when a LIVE session has been idle auto_compact_idle_minutes and its context exceeds auto_compact_min_context_tokens, it silently runs /compact in-band. It is off by default everywhere; you (the agent) are the policy layer that enables and tunes it per conversation.

The knobs (per instance, per kind)

duoduo session config <session|alias> get            # merged view + per-key source + measured stats
duoduo session config <session|alias> set auto_compact_idle_minutes=50 auto_compact_min_context_tokens=100000
duoduo session config <session|alias> unset auto_compact_idle_minutes   # back to kind default
duoduo session config --kind <kind> set ...          # kind-wide default (ONLY when the user explicitly says "kind"/"所有会话")

• Recommended starting values (signed off for subscription/1h-TTL backends): idle_minutes=50 (fires inside the 1h cache TTL → the compaction read is 10× cheaper), min_context_tokens=100000 (≈2× the measured post-compact floor).
• OFF = set auto_compact_idle_minutes=0. This RETAINS the calibrated threshold — the on/off switch and the calibration are separate fields (thermostat rule: switching off doesn't forget the set-point). A fresh start is an explicit unset.
• Scope discipline: default to the CURRENT instance. Touch --kind only when the user explicitly widens scope.
• set/unset take effect on the session's NEXT drain or the next 60s sweep — no restart. get never mutates anything.

Reading the stats — never mental-arithmetic what the kernel precomputed

session config get prints the session's compact_stats after each compaction (any origin — manual, auto, or the model's own reactive compact):

field	meaning
post_total	measured S_total: the floor compaction cannot go below (prompt chain + summary)
history_post	the compacted history size — this is the summary's OUTPUT cost (formula's O)
p_estimate	fixed prompt-chain overhead (compaction can never reclaim this)
suggested_min_context_tokens	kernel-computed 2 × post_total — your default retune target
g_gt1h_since_prev_compact	cold (>1h-gap) returns between the last two compactions — the measured G
g_5m1h_since_prev_compact	mid-gap returns — becomes cold too on 5-minute-TTL backends
idle_ms	how long the session had been idle when the auto fire happened (absent for manual)
threshold_at_fire	the min_context_tokens in effect when the auto fire triggered (absent for manual/reactive)

The compact notice (kernel → you)

On a smart-compaction-enabled session (and only there — sessions with the feature off get no injection), the first real turn after ANY compaction carries a <smart-compact-notice> block:

• data line — compacted <ts>: ctx <pre>→<post>, history <pre>→<post>. post is the measured floor; no need to query stats for the basics.
• mismatch line (only when mis-calibrated) — threshold <T> < suggested <S> — re-calibrate via smart-compaction skill. This is your retune signal: apply guardrail 4 immediately. It also means the kernel FUSE is now armed: auto-compact refuses to fire again while the threshold sits at or below the measured floor (a fire there can never get under the threshold — a pure no-gain loop), so retuning is what re-arms auto-compaction.
• transcript line — the full pre-compact transcript path on disk. Compaction summarizes your context but deletes nothing: for lost RECENT detail, spawn a cheap subagent (haiku/sonnet) to search that file — do not read it whole into your freshly-slimmed context. memory/dossiers lag hours behind live chat; they are the long-term layer, not the recent-recall layer.

The break-even algorithm (only for the judgment the arithmetic can't make)

C* = (5·O + 1.25·G·S) / (1.25·G − w)

• S ← compact_stats.post_total (measured, this session — never assume)
• O ← the summarization OUTPUT size, i.e. the tokens the compact summary itself costs: compact_stats.history_post (the compacted history — ~4K typical). Do NOT use the history SHRINK (history_pre − history_post, which can be ~50K); that is what was removed, not what the summary costs, and using it inflates C* by an order of magnitude.
• G ← g_gt1h_since_prev_compact (add g_5m1h too when the backend TTL is 5-minute-class). These are kernel-measured from the ledger — read them.
• w ← trigger warmth: 0.1 if the backend cache TTL exceeds idle_minutes (subscription = 1h TTL), else 1.0 (metered API default, Bedrock, Vertex)

Domain: the formula is only valid when 1.25·G > w. When 1.25·G ≤ w the denominator is zero or negative — there are too few cold returns per cycle for compaction to pay for itself at any context size; that region is governed by guardrail 2 (raise the threshold or disable), not by C*.

Sessions with context above C* profit from compaction; below it, compaction loses money. TTL is a fact about the backend you must know or ask — it is deliberately NOT in the kernel.

G is a feedback variable, not a fixed input. The threshold you set determines how often the session compacts, which sets the length of each compaction cycle, which changes how many cold returns fall inside it — i.e. the very G you will measure next time. Treat each retune as one step of a loop: set → observe the next compact_stats (new G) → re-evaluate. Don't over-fit to a single cycle's G; watch the trend across a few compactions.

Guardrails (hard rules)

1. Never set the threshold below 1.2 × measured post_total — below the physical floor, compaction is meaningless churn plus fidelity loss.
2. Metered / short-TTL backends: keep 100000 only if measured G ≥ 2. For G≈1 sessions (one heavy task, one overnight return, re-fattened next day) the cold break-even is ≈305K — raise the threshold there or disable.
3. Retune = act-then-inform: apply the change, then tell the owner in ONE line what changed and why (e.g. "已把这个会话的压缩阈值从 100K 调到 76K — 实测压缩地板是 38K"). Never silently, never with a wall of text.
4. First auto-compaction IS the calibration: when the first compact notice arrives, read suggested_min_context_tokens and retune the instance to it. No pre-calibration ritual is needed for new kinds.
5. When the owner says a conversation is precious ("这个会话的上下文别动"), set auto_compact_idle_minutes=0 on that instance and confirm.
6. After any compaction, if you are unsure of an earlier detail: for recent specifics, have a cheap subagent search the transcript path the notice gives you; for long-term knowledge, consult memory/dossiers. Never reconstruct from the summary alone.
7. Configure a kind AFTER installing its channel. --kind <kind> on a kind whose channel is not yet installed CREATES the kind config file; a later channel install then SKIPS its seed (the file already exists), so the channel comes up without its shipped defaults. Prefer instance-scope (config <session>) for anything but a deliberate kind-wide rollout.

Cache-less compatible backends

If the session runs on an endpoint with no prompt caching at all, every turn pays full input price on the whole context — compaction helps MORE there, not less. Same knobs, same formula with w=1 and cache premiums read as plain input cost.