deepseek-harness/packages/llm/token-meter

@deepseek-ai/dsh-token-meter

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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, including the obsolete global contextWindow; model capacity belongs to the adapter that owns an exact provider/model route and is available through ctx.llm.resolveModelContext().

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 assistant-chunk provenance. 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 provenance list means a known empty provider stream, while absent legacy provenance conservatively treats the durable assistant output as provider output.

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.
• Legacy provenance is conservative — assistant messages without sourceEventSeqs cannot distinguish provider output from listener rewrites, so the fold avoids claiming a known empty or exact chunk stream.