deepseek-harness/packages/llm/llm-deepseek

@deepseek-ai/dsh-llm-deepseek

DeepSeek chat-completions adapter for the harness LLM seam: hand-rolled fetch + SSE translation from the official wire format (source of truth: the API docs — guides/thinking_mode, guides/tool_calls, api/create-chat-completion) into the StreamChunk protocol.

A second, independent implementation of the same seam exists in @deepseek-ai/dsh-llm-pi-ai (library-backed). Same Config shape — pick one per context (registering both for the same model names throws by design).

Config

- id: llm-deepseek
  name: '@deepseek-ai/dsh-llm-deepseek'
  config:
    apiKey: !!js process.env.DEEPSEEK_API_KEY    # or rely on the env fallback
    baseURL: !!js process.env.DEEPSEEK_BASE_URL  # default: https://api.deepseek.com
    models: [deepseek-v4-flash, deepseek-v4-pro] # one adapter, registered for each name
    thinking: enabled        # optional; provider default is enabled
    reasoningEffort: high    # optional; high | max — omitted ⇒ not sent

models lists every model name this one adapter instance serves: the adapter registers itself for each (the harness model name IS the wire model string), so a generate/stream call routes to it whenever options.model is any of them. Registering a second adapter for a name already taken throws LlmError('DUPLICATE_ADAPTER') (the LLM service enforces one adapter per model, all-or-nothing).

reasoningEffort is omitted by default — when unset, the reasoning_effort wire field is not sent and the server applies its own default for the model. The only accepted values are high and max (DeepSeek's official effort levels). It is meaningful only with thinking enabled (the provider default).

thinking/reasoningEffort are adapter-level request defaults serialized as the official top-level thinking: {type} / reasoning_effort wire fields. They live in adapter config (not GenerateOptions) to keep the core vocabulary provider-neutral.

App attribution

Every request carries the shared attribution header from dsh-llm's attributionHeaders() - the mandatory User-Agent baseline identifying the harness (see dsh-llm § App attribution). Direct DeepSeek requests and OpenAI-compatible gateway requests get no provider-specific app-attribution headers under this adapter contract; OpenRouter app attribution is deferred to a future explicit OpenRouter adapter or mode.

Wire-format notes (verified live + against the official docs)

• Streaming only (stream_options.include_usage always on). usage may arrive attached to the finish chunk or as a trailing usage-only chunk — the translator defers both to [DONE], so usage always precedes finish and nothing follows finish.
• The first thinking-mode chunk carries reasoning_content: "" — handled (no spurious reasoning block).
• Reasoning passback rule: on assistant turns that carried tool calls, reasoning_content is serialized back in history (required by the API in thinking mode); on tool-call-free turns it is dropped (ignored anyway — saves tokens).
• Cache accounting: cacheReadTokens ← prompt_cache_hit_tokens / prompt_tokens_details.cached_tokens; DeepSeek reports no cache-write metric.

Errors

Non-2xx responses throw LlmError with stable codes: AUTH (401/403), RATE_LIMIT (429), INVALID_REQUEST (400), SERVER (5xx), HTTP_<status> otherwise. Protocol violations throw STREAM_CLOSED (no [DONE]) or MALFORMED_RESPONSE (bad JSON payload). Unknown wire finish_reasons (e.g. content_filter, insufficient_system_resource) become finish {kind: 'error', code: <REASON>} chunks.

Testing

Unit suites run against a local node:http mock SSE server (no network). Real-API coverage lives in tests/adapter.e2e.ts (pnpm run test:e2e, key-gated): V4 Flash + V4 Pro across thinking enabled/disabled and both official effort levels, including the thinking+tools round trip with reasoning passback.

Model Experience

DeepSeek request

What the model sees: The selected DeepSeek model receives the harness system prompt, message history, tool schemas, stop sequences, and call config without adapter-authored prompt prose. On a prior assistant turn with tool calls, its reasoning content is passed back as required; reasoning from tool-call-free turns is omitted.

Token effect: Provider tokenization governs exact input. Conditional reasoning passback increases tool-round-trip context, while dropping other reasoning avoids paying those tokens again; cache-read usage is reported when available.

DeepSeek response

What the model sees: Reasoning, text, and raw-string tool arguments are translated into harness chunks for the loop to log and assemble.

Token effect: Generated tokens follow provider thinking and effort settings plus the request's maxTokens; only loop-retained blocks affect later input.

Known Limitations and Deferred Work

• tool_choice is not mapped — not part of the core vocabulary (MVP cut, shared with the pi-ai twin).
• Requests use raw fetch, not @cordisjs/plugin-http — no shared proxy/interception configuration; adoption is deferred until a second adapter wants it (TODO(http)).
• Serialization flattens user and tool-result content to text blocks — plugin-added block types are skipped, and empty tool output crosses the wire as the literal (no output).