dsh-sieve

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sieve / dsh-sieve

Your agent navigates countless decisions across long loops, yet most tokens pay for dead logs and stale history. sieve is a precision mesh inside the execution loop: cutting through context inflation and delivering only the critical context that decides the next step.

Context engineering, token efficiency and long-task supervision plugins for DeepSeek Harness (DSH)

−36% request payload (offline replay, 100 trajectories) · −21% main model input tokens (live model, 80 runs) · ~0.3 s per judgment · 0 DSH core patches

How each number was measured, and what it does not show: offline replay · live model results. The live runs predate the current Jev / Laya setup.

A real session in DSH Web: while the agent reads code and runs tests, the Sieve page on the right counts up the estimated input token reduction and the characters taken out of the context.

sieve is a high-performance DSH plugin that filters tool output, forgets stale context, and discloses skills strictly on demand. Every raw output is archived before rewriting and remains one read away. Powered by the same judge engine, a suite of harness modules supervises long tasks to eliminate false completions and infinite loops. [!IMPORTANT] sieve needs a judge model: Jev (hosted, needs an API key) or Laya (local, macOS on Apple Silicon). It never borrows the session's own model. Without one, the token-saving decisions change nothing and the harness modules run on their rules alone. Set one up. Why sieve

Your agent navigates hundreds of critical decisions across complex tool-calling loops.

Yet one glance at its real context ledger reveals a grim reality: actual user prompts and core code represent only a tiny fraction of the payload. The overwhelming bulk is pure context inflation: The tool output tsunami: A test runner spews 8,000 characters. The model only needs the single failing assertion, yet repeatedly pays for 200+ lines of passing tests on every subsequent turn. Ghostly stale history: A file read thirty steps ago—long since superseded—rides along as a ghost passenger in every request. Bloated skill catalogs: Hundreds of skill descriptions loaded at session start when 97% are never touched, permanently diluting the model's attention.

This is not pocket change—it is a critical signal-to-noise crisis.

Input tokens snowball quadratically with every turn. As the context window chokes on dead logs, the model's attention dilutes, hallucinations increase, and tasks stall out.