deepseek-harness/packages/llm

dsh-llm

Provider-neutral LLM vocabulary and abstract service. This package defines the canonical language spoken by the agent loop, session logs, and every plugin.

Service: LlmService (ctx key: llm)

An adapter registry plus streaming / non-streaming call surfaces. Both call surfaces are interceptable via waterfall events.

Public API

• ctx.llm.registerAdapter(models: string[], adapter: LlmAdapter): () => void Register an adapter for the given model names. Disposed with the calling fiber.
• ctx.llm.models(): string[] — model names with a registered adapter.
• ctx.llm.stream(options: GenerateOptions): AsyncIterable<StreamChunk> Stream one model call as raw chunks (token-level deltas).
• ctx.llm.streamBlocks(options: GenerateOptions): AsyncIterable<ContentBlock> Stream as completed content blocks (convenience view).
• ctx.llm.generate(options: GenerateOptions): Promise<GenerateResult> One model call, fully assembled.

Events

Event	Mode	Purpose
llm/stream	waterfall	Intercept/wrap every streaming model call (retry, caching, routing)
llm/generate	waterfall	Intercept/wrap every non-streaming model call
llm/adapter-change	emit	An adapter was registered or unregistered

Extension points

• Subclass LlmAdapter and call ctx.llm.registerAdapter(models, adapter) to add a new model provider.
• Wrap llm/stream or llm/generate via ctx.on() waterfall listeners for caching, retry, logging, rate-limiting, etc.

Content-block vocabulary (types.ts)

Messages are arrays of typed content blocks: text, reasoning, tool-call, tool-result, image. The union is derived from the merge-extensible ContentBlockMap, so plugins can add block types via declaration merging.

Streaming is a raw chunk protocol (block-start, text-delta, reasoning-delta, tool-call-delta, block-end, usage, finish). BlockAssembler is the single shared implementation that assembles chunks into blocks/messages.

Classes

• LlmAdapter — abstract base class for provider adapters. The only required method is stream().
• BlockAssembler — incrementally assembles raw chunks into complete content blocks and an assistant message. Used by the agent loop (raw chunks for replay
  • assembled for history) and by streamBlocks()/generate().
• HarnessError — base class for the harness error taxonomy: a stable code string (distinct from the human message) plus cause chaining. Lives here, in the leaf package every other imports, so a single base is shared without a new dependency edge. Per-package errors (LlmError, ToolArgsError, InvariantError, …) extend it. isHarnessError(value) narrows at seams.
• LlmError — extends HarnessError; code string (NO_ADAPTER, DUPLICATE_ADAPTER, and adapter codes like AUTH/RATE_LIMIT) plus an optional numeric status when the failure came from a non-2xx provider response.

Real adapters

Two adapters implement LlmAdapter against this vocabulary, deliberately built on different internals to keep the contract honest (see the twin LLM adapters): @deepseek-ai/dsh-llm-deepseek (hand-rolled fetch/SSE) and @deepseek-ai/dsh-llm-pi-ai (via @earendil-works/pi-ai). The pair pinned down the StreamChunk conventions now documented in types.ts (usage before finish, raw-string tool arguments, the two sanctioned error paths).