dsh-llm-ai

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A models.dev-cataloged multi-provider LLM adapter for the DeepSeek Harness: every provider and model fact comes from the community-maintained models.dev api.json registry, and the request runtime is a harness-owned openai-completions streaming implementation (direct fetch + SSE) with no pi-ai dependency. It replaces dsh-llm-pi-ai on the same ctx.llm seam.

• Protocol support: v1 serves openai-completions only (the overwhelming majority of OpenAI-compatible endpoints); anthropic-messages and other protocols are v2 work.
• Verification status: 205+ unit tests at per-file 100% coverage; deployed as a complete replacement on DSH Desktop 2.0.3 (the dsh 0.1.1-rc.2 family) and in daily use.
• Version baseline: developed against @deepseek-ai/*@next (the 0.1.1-rc.2 line); peers are compatible with the same generation.

Install (npm)

Published as the unscoped package dsh-llm-ai (the @deepseek-ai/dsh-llm-ai name is the harness monorepo's integrated twin). With the official CLI:

dsh plugin --profile <name> add dsh-llm-ai

One command installs and mounts: the CLI forwards to pnpm add in the profile directory, sees this package's dsh.bundle.patch declaration, appends it to the dsh.profile.bundles layer stack, and the profile boot merges the bundled patch — which disables the bundled llm-pi-ai mount and inserts llm-ai (the two adapters cannot coexist: the configurable-provider directory keys provider ids globally and both declaring the same catalog id fails DUPLICATE_DIRECTORY at load).

Notes:

• Migrating from a manual mount: remove the old llm-ai insert and llm-pi-ai disable lines from the profile's own cordis.patch.yml to avoid a duplicate entry id.
• Bundled-UI builds (DSH Desktop / web-app releases predating the llm-ai migration) hardcode the llm-pi-ai namespace in their Models page — apply the single-string alias documented under Deployment, step 4, to the installed copy at <profile>/node_modules/dsh-llm-ai/lib/index.js.
• Provider profiles live in the llm-ai: settings section (see the configuration reference); credential references need no migration.

Features

• models.dev catalog: api.json is fetched once at plugin load and cached to disk under the DSH home (storages/models-dev-cache.json); offline boots serve the last good snapshot; a fetch failure is loud only when nothing is cached. catalogUrl / catalogCachePath override the endpoint and cache location.
• openai-completions runtime: streaming SSE (eventsource-parser), text, tool calls with raw-string arguments, tiered reasoning, image input with oldest-first offload under maxRequestImageBytes, usage and cache-hit accounting, an idle watchdog (streamIdleTimeoutMs), and exactly one provider request per stream() call.
• Stable error codes: AUTH / QUOTA / RATE_LIMIT / CONTEXT_WINDOW_EXCEEDED / INVALID_REQUEST / SERVER / HTTP_<n> / TRANSPORT / TIMEOUT / ABORTED / STREAM_CLOSED / MALFORMED_RESPONSE / EMPTY_RESPONSE (retryable classification).
• Compat switches: maxTokensField / supportsDeveloperRole / thinkingFormat (openai | deepseek | openrouter), resolved per field model → route → protocol default; unknown and valueless keys are refused listing the offered set — nothing is silently dropped.
• Reasoning declarations: reasoningEfforts maps each selectable level to its wire spelling; off is tri-state (absent = not offered; declared without a value = send the disabled spelling; declared with a value = send it); an undeclared level is refused before any network I/O. Without a declaration, a registry model's offered levels come from models.dev reasoning_options effort values (each level spelled as itself; none → valueless off); toggle, empty, absent, or non-canonical-only options keep the protocol default set (off/low/medium/high); a profile declaration still reshapes either.
• Dynamic configuration: the providers dict merges with the user settings section per provider, effective on the next request without a restart; dormant mounting (zero routes with no providers); atomic re-registration when the route set changes.
• Credentials: configuration stores apiKeyEnv references only; each request resolves them through the credentials seam, then the trusted environment; format checks (INVALID_CREDENTIAL) and empty references (MISSING_CREDENTIAL) name the route and every configuration entry point and never any part of the key.
• Endpoint interrogation: GET /models discovery for hand-declared gateways (a 4 MiB received-bytes ceiling, a typed draft key winning over stored references, and the DISCOVERY_* error family).
• Configurable-provider directory: declares all 203 models.dev providers to configuration surfaces (including families it cannot yet serve, with honest metadata).

Configuration reference

Cordis composition entry:

- id: llm-ai
  name: '@deepseek-ai/dsh-llm-ai'
  # Omitting config mounts dormant (zero routes); a settings section can
  # activate routes at any time.
  config:
    catalogUrl: https://models.dev/api.json      # optional: self-hosted mirror
    catalogCachePath: /path/to/cache.json        # optional: cache location
    providers:
      openai:                    # Catalog route: endpoint, protocol, and models
        apiKeyEnv: OPENAI_API_KEY    # all inherited from models.dev.
      zai-coding-cn:             # Hand-declared route: api + baseURL + a
        apiKeyEnv: ZAI_CODING_CN_API_KEY   # non-empty models list are required.
        api: openai-completions
        baseURL: https://open.bigmodel.cn/api/coding/paas/v4
        models:
          - { id: glm-5.3, contextWindow: 1000000, maxTokens: 131072 }

Provider profile fields: apiKeyEnv (credential reference), displayName, api (openai-completions only in v1), baseURL, models (replaces the route's catalog; unset fields default from the registry entry of the same id), modelOverrides (reshapes individual models while the rest of the catalog keeps serving), compat (the three switches), reasoning (the deployment default level), retryPolicy (omission = normal mode with five retries), headers, defaultContextWindow / defaultMaxTokens / defaultInput (fallbacks for configured entries that state no capacity), streamIdleTimeoutMs (five-minute default), maxRequestImageBytes (20 MiB default).

Catalog resolution notes: a models.dev model with no context window is refused rather than guessed; a modelOverrides key naming a model the catalog does not describe is refused; timeoutMs is gone (it named pi-ai runtime behavior) and configuring it fails with migration directions.

DSH Desktop deployment (the complete procedure)

The following is the verified deployment path (executed on DSH Desktop 2.0.3 / dsh 0.1.1-rc.2). The desktop loads plugins through the pnpm mini-workspace at ~/.dsh/profiles/desktop/ — the .app bundle itself is never modified.

1. Build a self-sufficient install directory

mkdir -p ~/Downloads/project/github/dsh-llm-ai-app
# Take the build outputs from the harness repo (tsc lib/types + bundled runtime)
cp -R <harness>/packages/llm/llm-ai/lib ~/Downloads/project/github/dsh-llm-ai-app/

The install directory's package.json declares the plugin plus every peer as a real dependency from npm's @next dist-tag (self-sufficient, the vibe-island pattern):

{
  "name": "@deepseek-ai/dsh-llm-ai",
  "version": "0.1.1-rc.2",
  "type": "module",
  "main": "lib/index.js",
  "exports": {
    ".": { "types": "./lib/types/index.d.ts", "default": "./lib/index.js" },
    "./invariant": { "types": "./lib/types/invariant.d.ts", "default": "./lib/invariant.js" }
  },
  "dependencies": {
    "@deepseek-ai/cordis": "next",
    "@deepseek-ai/dsh-attachment": "next",
    "@deepseek-ai/dsh-credentials": "next",
    "@deepseek-ai/dsh-home-paths": "next",
    "@deepseek-ai/dsh-launch-environment": "next",
    "@deepseek-ai/dsh-llm": "next",
    "@deepseek-ai/dsh-settings": "next",
    "@deepseek-ai/dsh-timeout": "next",
    "@deepseek-ai/schemastery": "next",
    "eventsource-parser": "^3.1.1"
  }
}

cd ~/Downloads/project/github/dsh-llm-ai-app && pnpm install
node --input-type=module -e "const m = await import('./lib/index.js'); console.log(typeof m.apply)"   # smoke test: function

2. Wire it into the profile workspace

Add to the dependencies of ~/.dsh/profiles/desktop/package.json:

"@deepseek-ai/dsh-llm-ai": "link:/Users/<you>/Downloads/project/github/dsh-llm-ai-app"

cd ~/.dsh/profiles/desktop && pnpm install

3. Composition patch

Append to ~/.dsh/profiles/desktop/cordis.patch.yml (keep any managed blocks already there, such as vibe-island):

- id: llm-pi-ai
  disabled: true
- insert:
    - id: llm-ai
      name: '@deepseek-ai/dsh-llm-ai'

4. Namespace compatibility (for the desktop's bundled UI)

The key pitfall: the Models page bundled with DSH Desktop (an upstream rc.2 build) hardcodes the "llm-pi-ai" namespace — the add-card's enable gate, protocol choices, form layout, and write target all recognize that name alone. Once llm-ai mounts, the page degrades to "other fields live in settings.yaml" hints.

The fix is a single-string patch on the plugin copy so it registers under llm-pi-ai (the settings section, directory entries, and discovery registration all flow from this one constant):

// dsh-llm-ai-app/lib/index.js — the only occurrence in the file
- const NS = settingsNamespace("llm-ai");
+ const NS = settingsNamespace("llm-pi-ai");

Diagnostic message prefixes (llm-ai: provider "...") need no change. When an upstream desktop release natively knows llm-ai, restore this string and rename the settings section to return to the canonical namespace.

5. settings.yaml migration

In ~/.dsh/settings.yaml, migrate the previous llm-pi-ai: section to the curated routes (anthropic-messages routes must be removed — validation resolves the whole section, so one unserviceable profile refuses all of it). See the configuration reference above for a six-route example. Credential references (apiKeyEnv → environment variables / ~/.dsh/.credentials.yaml) need no migration at all.

Zero-downtime switch: until the app restarts, the still-running pi-ai plugin keeps reading the old section — migrate with both sections present, restart, and delete the inert old section afterwards.

6. Pre-seed the models.dev cache (optional, recommended)

curl -s https://models.dev/api.json -o ~/.dsh/storages/models-dev-cache.json

This guarantees an offline-capable first boot; the plugin still tries a fresh fetch on every load and falls back to the cache on failure.

7. Restart and verify

Fully quit (⌘Q) and reopen DSH Desktop. Expected: the six routes are live, the model picker works, the Models page renders full editable cards (key / endpoint / protocol / model list), the add-provider card is usable, and the protocol dropdown offers openai-completions only.

Troubleshooting

Full rollback: delete the three patch entries from cordis.patch.yml → copy settings.yaml.bak-llm-ai-swap back over settings.yaml → restart. The install directory and profile link can stay (unreferenced means inert).

Known limitations (v1)

• openai-completions only: the anthropic / google / bedrock / vertex / OAuth-only families stay visible in the directory but unserviceable, and naming them under api is refused; anthropic-messages is planned for v2.
• No replay envelope: cross-provider history converts provider-neutrally (no new session-log structure; logs recorded by pi-ai still load).
• The settings layer can add or override routes, never remove routes declared in the composition base (cordis.yml).
• One wire protocol per route: a mixed-protocol provider splits across two route keys.
• tool_choice and stop sequences are unsupported (an MVP cut shared with both predecessors).

Development

src/
  index.ts       plugin apply: catalog load, dormant/atomic registration, the
                 settings section, directory and discovery registration
  adapter.ts     LlmAiAdapter: stream(), per-call snapshot freeze, timeout and
                 abort handling, error classification
  catalog.ts     profile → route/model resolution (models/modelOverrides/
                 compat/reasoning)
  config.ts      the schemastery Config schema and resolveProfiles
  modelsdev.ts   the api.json loader (fetch/cache/offline snapshot, injectable
                 fetchImpl)
  serialize.ts   request serialization, reasoning dispatch, image serialization
                 and offload
  sse.ts         eventsource-parser framing, the [DONE] sentinel, comment
                 watchdog pulses
  translate.ts   wire events → StreamChunk translation (usage precedes finish)
  discovery.ts   GET /models endpoint interrogation
  provider.ts    the protocol table (openai-completions only) and withheld
                 families
  types.ts       the wire vocabulary

pnpm install
npx tsc --noEmit        # typecheck
npx vitest run          # the whole suite (204+, no network)
npx vitest run --coverage  # per-file 100% coverage gate
pnpm run build          # tsdown: lib/ runtime bundle + declarations

The complete decision record lives in the deployment source repository under .scratch/llm-ai/ (the spec plus twelve ticket resolutions); the harness monorepo's packages/llm/llm-ai is the integrated twin (with repo gates and doc regeneration). This directory is the origin for publication and standalone development.

License

MIT (following the upstream DeepSeek Harness).