agnes-ai-for-dsh

agnes-ai-for-dsh

Agnes AI integration for DeepSeek Harness — an LLM provider with multimodal support, image/video generation tools, and a settings panel.

中文说明

What it provides

Piece	Description
Provider	agnes-ai, registered with the llm-pi-ai adapter (OpenAI-compatible Chat Completions)
Chat models	agnes-2.5-flash, agnes-3.0-flash, agnes-2.5-pro, agnes-2.5-pro-beta — all accept image input
Tools	agnes_image_generation, agnes_video_generation, agnes_record_generation
Settings panel	An Agnes AI section in Settings listing the live configuration and the model catalogue

Install

dsh plugin install agnes-ai-for-dsh

Or add it to a profile's package.json and reinstall:

{
  "dependencies": {
    "agnes-ai-for-dsh": "git+https://github.com/wangyemen/agnes-ai-for-dsh.git"
  }
}

Then add the bundle to dsh.profile.bundles and restart DSH.

Configure

The provider is pre-declared by cordis.patch.yml, so the only required step is the API key:

1. Open Settings → Models.
2. Find Agnes AI and enter your API key.
3. The key is stored by DSH's credential system under AGNES_AI_API_KEY.

The default endpoint is the China region:

https://api.agnes-ai.cn/v1

To use the Global endpoint, change baseURL to https://apihub.agnes-ai.com/v1 either in cordis.patch.yml or directly in Settings → Models.

Usage

Once enabled, the two media tools are available to the model:

• agnes_image_generation — text-to-image, image-to-image and multi-image editing. Parameters: model, prompt, size (1K–4K), optional ratio, optional image (input URLs), optional return_base64.
• agnes_video_generation — asynchronous video generation with polling. Parameters: model, prompt, optional seconds, mode, size, aspect_ratio, and the mode-specific media fields.

Video generation is asynchronous: the tool creates a task and then polls GET /agnesapi?video_id=…&model_name=… until it settles. agnes-video-2.5-flash accepts only 720P; agnes-video-2.5 accepts 720P, 1080P, 1K and 2K.

Notes for maintainers

Tool schemas

The host's defineTool does not accept raw JSON Schema — it requires a property-map DSL whose entries may carry their own required: true, and it compiles that into object-rooted JSON Schema itself.

That DSL is not valid JSON Schema. If it ever reaches a provider unconverted, the provider rejects the entire request (WorkBuddy returns HTTP 400, code 11129, "工具定义不合规"), so every model reply in the session fails — not merely the one tool. This is not hypothetical: it is exactly what happened when a bare require() threw inside an ESM module and a silent identity fallback registered the raw DSL.

The defence is in src/tools.ts:

• compileParameters() compiles the DSL here, so the plugin does not depend on the host helper for correctness.
• assertValidCompiledSchema() / assertAllToolSchemas() verify the compiled form at load and throw on the invalid shape, so a mistake stops the plugin instead of poisoning every request.
• Registration re-checks the parameters defineTool actually produced and refuses to register if the raw DSL survived.

verify.mjs exercises all of this against the real defineTool.

Build

npm install
npm run build

lib/ is committed, because the package installs from git and the consuming profile does not run build scripts. Rebuild before committing any src/ change, or the stale lib/ is what ships.

License

MIT