Jev Compaction

Two opencode plugins that shrink context by deleting what is provably stale and truncating what is probably done with — never by summarizing.

• ./server — the pruner, a server plugin that runs before every model request.
• ./tui — a sidebar widget showing how much context it removed.

Runs entirely locally by default. No API key, no per-request cost.

Why this looks the way it does

v0.1 asked a hosted model a judgement per tool call ("should this still be in the history?"). That failed in a specific, instructive way: it deleted a short file of hard constraints, and the scores were mushy. Two independent measurements agreed on the cause — with a noul-style primitive (calibrated P(true)), factual questions are reliable and judgement questions are not (0.996 on an explicit fact versus 0.003–0.28 on judgements).

It was also expensive. A 25k-token state resent on every request, roughly 1,000 times a day, cost about $1/day — against a saving measured at $0.0001 on a model whose cached input is $0.003/M. The economics were upside down.

v0.3 asks only facts, computes the ones it can exactly, and treats the model as a narrow refinement rather than the decision-maker.

How it decides

reason	how	action
referenced	the target string (path, command) appears in later prose — exact search	keep
superseded	a later call with the same tool and target — exact	drop
error-resolved	this call errored, a later call to the same target succeeded — exact	drop
small-result	under SMALL_RESULT_CHARS, not worth touching	keep
model-unreferenced	large, unmentioned, not superseded; local model says nothing quotes it	truncate
model-referenced	as above, but the model says something does	keep
inconclusive	the question could not be answered (no backend, timeout, low confidence)	truncate

Deletion requires deterministic evidence. A model answer can only ever cause a truncation, which keeps a bounded head plus a [laya-compaction truncated …; re-run the tool if needed] note, so the model can recover by re-running. Nothing is ever deleted on a probabilistic answer.

Install

opencode plugin opencode-jev-compaction --global

Then run the local backend:

# once
uv venv -p 3.12 ~/laya-server/.venv
uv pip install -p ~/laya-server/.venv laya-mlx

# run (first start downloads a few hundred MB of weights)
~/laya-server/.venv/bin/python ~/opencode-jev-compaction/scripts/laya-server.py

laya-server.py is a thin transport: Laya already returns the Jev response shape, so it exists only so the plugin can speak HTTP to a local process. It binds 127.0.0.1:8000 and serializes inference (MLX is not reliably reentrant).

To keep it running across logins, wrap that command in a launchd agent or run it under tmux. Startup takes a few seconds plus the one-time download.

Configure

Variable	Default	Purpose
LAYA_BASE_URL	http://127.0.0.1:8000/v1/systemone	Backend endpoint. A hosted Jev endpoint works too.
LAYA_COMPACTION	on	0 disables everything.
LAYA_COMPACTION_THRESHOLD	60000	Estimated context tokens before it engages.
LAYA_PRESERVE_RECENT	6	Newest messages never touched, minimum 1.
LAYA_SMALL_RESULT_CHARS	600	Results this size or smaller are left alone.
LAYA_TRUNCATE_HEAD	300	Characters kept when a result is truncated.
LAYA_EXCERPT_CHARS / LAYA_AFTER_CHARS	400 / 1000	What the model sees. Keep these small: Laya's sequence budget is 512 tokens.
LAYA_REFERENCED_HIGH	0.7	Probability of "quotes" needed to keep.
LAYA_TIMEOUT_MS / LAYA_CONCURRENCY	8000 / 4	Per-question timeout, parallel questions.
LAYA_MAX_QUESTIONS	40	Cap on model questions per prune.
LAYA_DAILY_REQUEST_CAP	400	Requests per day, per process.
LAYA_DEBUG	off	1 appends a trace to ~/.local/share/opencode/laya-compaction.log.

Degraded mode

If the backend is unreachable, times out, or answers below LAYA_REFERENCED_HIGH, the model-* rows simply do not apply: the deterministic reasons still fire and every residual becomes inconclusive → truncate. The plugin is fully functional without any model, and LAYA_COMPACTION=0 turns it off entirely.

Metrics and reporting

~/.local/share/opencode/laya-compaction.json (totals), -ledger.jsonl (one line per run that changed something, with the reason breakdown and re-run counts), -usage.json.

npm run report

Reports engagement, the reason breakdown, whether decisions are good (re-run rate), pruned vs unpruned sessions, before/after the install boundary, and the subagent cost share measured directly. It refuses to print a quality verdict when the installed version does not record re-runs, so a 0 cannot be misread as "nothing was undone".

Measured caveats

Recorded here because they are the reason the design is conservative:

• noul cannot answer a statement about text. Against this same local model, a statement and its own negation both scored ~0.95. As a two-option choice with explicit criteria, the same cases separate cleanly (0.75–0.99 on a real quote, 0.80–0.91 on unrelated text). That is why the code uses choice and says not to change it back.
• The model sees only about 1000 characters of what came after — Laya's sequence budget is 512 tokens. Remote references are found by the exact string search, not by the model.
• Accuracy is model- and task-specific. Independent comparison found the hosted Jev model ahead of open-weight Laya on ambiguous inputs (78% vs 57% on 40 tickets), and confidently wrong on a multi-intent case. That is a small sample: treat it as directional.
• Correlation, not causation, in the cohort tables. A session is only pruned once it is large, so the pruned cohort is longer by construction.

Not affiliated

Not built by, endorsed by, or affiliated with the opencode team, TypeSafe, or Convai Innovations. Names are used only to describe what this plugs into.

License

MIT. The original strategy was adapted from fast-jev-compaction (MIT) — see NOTICE. v0.3 diverges from it substantially: the decision core is now deterministic, and the model is an optional local refinement.