deepseek-harness/packages/llm/token-meter

@deepseek-ai/dsh-token-meter

English | 中文

Replay-aware token measurement through the singleton ctx.tokenMeter service. It advances one isolated fold per session from the durable log, so compaction and other pressure-sensitive plugins can share accounting without depending on CompactService.

Configuration

The estimator has no settings. It intentionally uses one fixed heuristic: four characters per token plus structural overhead for roles, blocks, and request-envelope fields. Any key is rejected; model capacity belongs to the adapter that owns an exact provider/model route and is available through ctx.llm.resolveModelInfo().context.

Measurement contract

ctx.tokenMeter directly exposes two operations:

• measure(session, requestHeader?) returns request pressure and the current priced surface at one consumed-log revision.
• estimateMessage(message) prices one message with the fixed heuristic.

measure() synchronizes once and returns one detached, deeply immutable snapshot. totalTokens is request-and-response pressure, while surfaceTokens is the surface-only heuristic total and equals the sum of nodes[].tokens. A requestHeader override affects pressure fields only; the surface fields still describe the current session. Every call clones the positional nodes, so measurement is O(surface).

The fold tracks full request-header snapshots, step boundaries, surface appends and replacements, successful assistant messages, provider usage, and the chunk seqs cited by each assistant message. Provider usage is reused only when the latest successful call's canonical request envelope matches the measured envelope and its total is no lower than that call's full heuristic anchor; a later success replaces the earlier anchor. Otherwise the complete current envelope and surface are estimated. Surface changes remain signed relative to a matching anchor, including negative deltas after shrinking replacements.

Usage accounting sums disjoint input, cache-read, cache-write, and output buckets; reasoning is not added again. Every successful call records an assistant anchor, including content-less calls. An explicit empty sourceEventSeqs list means a known empty provider stream, while an absent legacy list conservatively treats the durable assistant output as provider output.

Session projections

When the composition provides ctx.sessionProjections, token-meter registers three units through an optional child fiber.

tokenUsage carries the complete durable log's uncachedInputTokens, outputTokens, cacheReadTokens, and cacheWriteTokens. Usage chunks are counted even when a request later fails; a final assistant-message usage for the same (turn, step) replaces that sample instead of double-counting it. Reasoning remains an output subdivision. The single last-sample slot relies on a session-log ordering property: once a later step reports usage, a legal log never reports usage for an earlier step again.

contextPressure carries optional pressureTokens — the newest provider-reported prompt size, summing uncached input plus cache reads and writes — optional projectedTokens, and optional contextWindow from the newest request/context record. Both figures stay absent until a provider reports usage; capacity stays absent for a route whose adapter advertises none. Output is excluded, so pressureTokens holds still while a turn streams and steps forward when the next request reports its usage.

projectedTokens is what the NEXT request's prompt would cost: the sample plus the heuristic repricing of everything the surface gained or lost since it was taken, clamped at zero and folded through the same surface-fold.ts the measurement service replays. Only the delta is estimated, so the figure stays anchored to the provider while reacting the moment content lands — or a compaction shadows a span. That last case is why the field exists: compaction summarizes through a direct ctx.llm.stream() call and appends no usage of its own, so pressureTokens alone reports the pre-compaction prompt until an entire further turn completes. Occupancy displays read projectedTokens.

contextBreakdown carries heuristic systemTokens, toolsTokens, and messageTokens — the context's composition rather than its provider-billed size. The envelope figures reprice last-wins on every request/header; the message figure replays surface-fold.ts — the same positional fold measure() runs — so it equals measure().surfaceTokens at every event boundary and compaction shrinks it the way it shrinks the next request. All three figures use the measurement service's fixed heuristic and are estimates: they will not sum to projectedTokens, whose provider anchor carries exactly the error — CJK text and JSON schemas underprice badly at four characters per token — that the composition rows still contain. Present them as an approximate composition, never as a total.

All three units use the standard projection baseline, live frame, higher-seq-wins store, and JSON checkpoint paths. Unloading token-meter removes all three keys. A composition without the projection seam keeps the measurement service's existing behavior.

Context occupancy is an approximation, by design

The occupancy fields are independent last-wins records and are not one atomic observation of a single request. Switching models pairs the fresh capacity with the previous route's sample until the next request reports usage, and pressureTokens describes the last request rather than the surface as it stands right now — projectedTokens carries that sample forward over the surface's movement, but its anchor is still the older request.

This is deliberate. An occupancy percentage is a user-facing reference figure, not a billing record or a gating input — nothing in the harness makes decisions from it, and compaction reads measure() instead. A UI computes occupancy by dividing measured pressure by the separately resolved capacity for the selected model.

The Agent Note records the rejected atomic-pair comparison. Consumers that need an exact same-boundary figure should call measure() at their own request boundary rather than read this projection.

Composition

- name: '@deepseek-ai/dsh-token-meter'
- name: '@deepseek-ai/dsh-compact-basic'

Both plugins have usable defaults. The meter remains independent of model routing and optional compaction. A deployment configures capacity on its LLM adapter and compaction policy on dsh-compact-basic.

Model Experience

Indirectly, through consumers such as dsh-compact-basic; the service itself adds no prompt, message, schema, tool, or model call.

KV Cache effect

No direct invalidation; the named consumer owns any request-prefix changes.

Known Limitations and Deferred Work

• The fixed heuristic is approximate — content without reusable provider usage is priced by character count plus structural overhead, not an exact provider tokenizer or request serializer.
• Every measurement clones the current surface — coherent immutable snapshots make reads O(surface), including below-threshold pressure checks.
• Provider usage is only reusable for an identical canonical envelope — prompt, prefix, tools, provider, model, or call-config changes deliberately fall back to full heuristic estimation.
• Missing legacy source seqs are handled conservatively — assistant messages without sourceEventSeqs cannot distinguish provider output from listener rewrites, so the fold avoids claiming a known empty or exact chunk stream.