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Tianyi Cui 30cd67b8a1 simplify(llm): drop unconsumed adapter-change event and assembled call surfaces

The LLM service exposed three call surfaces (stream/streamBlocks/generate) but
the only production consumer — the agent loop — uses stream() exclusively,
feeding raw chunks through its own BlockAssembler for replay fidelity. Drop the
speculative convenience surfaces and the registry-change event that no listener
consumed, leaving stream() as the single model-call contract for both
production and tests.

- Remove LlmService.streamBlocks() and generate(), the llm/generate waterfall,
  and GenerateResult.
- Remove the llm/adapter-change event (declaration + emits) and the
  listener-throw rollback ordering that existed only to protect it; keep the
  HMR rollback disposer.
- Remove BlockAssembler.flushReady()/flushRemaining()/result() and the flushed
  cursor — the streaming-flush slice existed only for streamBlocks().
- Adapter tests drive a stream()+BlockAssembler helper (tests/assemble.ts)
  instead of generate(), exercising the same path production uses.
- Land the AGENTS.md "RFCs are proposals, not golden truth" principle and move
  both RFCs proposed -> implemented.

Implements:
- docs/rfc/implemented/simplification/2026-06-20-drop-unconsumed-llm-adapter-change-event.md
- docs/rfc/implemented/simplification/2026-06-20-drop-unconsumed-llm-assembled-surfaces.md

2026-06-21 01:27:41 +08:00

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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 a single streaming call surface, interceptable via a waterfall event.

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). Consumers assemble the chunks into blocks/messages with BlockAssembler.

Events

Event	Mode	Purpose
`llm/stream`	waterfall	Intercept/wrap every streaming model call (retry, caching, routing)

Extension points

Subclass LlmAdapter and call ctx.llm.registerAdapter(models, adapter) to add a new model provider.
Wrap llm/stream 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. The agent loop feeds it raw chunks (logging them for replay) while reading the assembled blocks/message for history.
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).

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