/penguin-sdk

name: penguin-sdk
description: Build AI apps on the Penguin Harness SDK — self-contained projects, the createSession/run streaming loop with thinking and image messages, and a complete RAG recipe that ingests documents into a knowledge base and answers with citations behind a web UI.
short_description: Build AI and RAG apps on the Penguin Harness SDK.
short_description_zh: 基于 Penguin SDK 构建 AI 与 RAG 应用。
version: 19
updated: 2026-08-06T00:00:00Z

Penguin Harness SDK

`@prismshadow/penguin-core` is the TypeScript SDK this agent itself runs on. Use it to build your own AI apps:

• An **Agent** loads its state (prompts, tools, skills) from `<root>/<project_id>/agents/<agent_id>/`. Creating an Agent whose directory is empty initializes it with defaults.
• A **Session** is one conversation of an Agent inside a **Workspace** directory.
• `session.run()` executes one task and streams every step (thinking, text, tool calls) as OmniMessages.

To have an agent perform a task, use the `run_subagent` tool — the SDK is for building applications, not for invoking agents.

Before you start

If the user's message only invokes this skill (e.g. "use penguin-sdk skill") without a concrete app to build, ask the user what they want to build. But when the request names a concrete goal — even a single sentence like "build a RAG app that answers questions about these docs" — do **not** ask follow-up questions: build it end to end with the defaults in this skill (self-contained workspace project, project default model, BM25 retrieval, web UI styled per the web-design skill) and list the assumptions you made in your final reply.

Project location

Create the app in the current workspace directory by default (the `CWD` value from your Environment section), as a self-contained project — do not place it under `<app_data_dir>` (PenguinHarness's app data root) or depend on any path outside the project folder. When creating the app's agent, the data root defaults **under the working directory (CWD)** too: point `createAgent({ root })` at a directory inside the project, resolved from the source file so it stays relative:

const agent = await createAgent({ root: path.join(import.meta.dirname, "penguin_data") });

With every reference relative to the project, the user can move or copy the folder anywhere and it still runs.

Keys and the data root — check before you build

**The app's Penguin data root must live inside the CWD workspace — never `~/.penguin`.** Point `createAgent({ root })` and every `penguin config ... --root <dir>` at a directory under the current working directory (e.g. `./penguin_data`); the global `~/.penguin` belongs to the person running Penguin and must never hold — or lend — the app's config or keys.

**Credential first, code second** — a finished app that cannot answer is a failed delivery discovered too late. Before writing any code:

env | grep -oE "(DEEPSEEK|OPENAI|ANTHROPIC|GEMINI)_API_KEY" || echo none

**Only two sources count as a usable credential**: a vault-injected environment variable (the check above; vault keys also appear in your Vault Keys section), or a key already configured in the app's own data root (`penguin config model list --root <data_dir>`). Keys in the global `~/.penguin` or any other `.penguin` directory do **not** count — a bare `penguin config model list` (no `--root`) reads the global store, because the CLI defaults to the global root unless `--root` is given, so a key showing up there proves nothing for the app and must never be used or copied.

If neither counted source yields a key, **stop immediately and ask the user to configure one — do not start building, and do not burn turns re-checking in a loop**: have them open this agent's settings via the **gear icon** on its card (left side, Agents page) and add a model API key (e.g. `DEEPSEEK_API_KEY`) in the **key vault** tab — vault values reach your shell environment on the next task. One clear check, then hand back to the user. Build only after a credential is confirmed, or after clearly agreeing with the user to build now and verify later. Model ids to offer the user come from the penguin CLI catalog (`penguin config model add --help`) and the agenthub-models skill's id table.

Setup

npm install @prismshadow/penguin-core tsx

If the package is not on your npm registry (it is developed in the PenguinHarness monorepo and may not be published), develop inside a checkout of the PenguinHarness repo instead: add your app as a workspace package under `packages/`, depend on `"@prismshadow/penguin-core": "workspace:*"`, then `pnpm install && pnpm build` at the repo root. Tell the user which route you took.

Configure a model for the app's data root, in this order — stop at the first that works:

1. `penguin config model add --root <data_dir> --provider <group> --model-id <id> --api-key <key> [--base-url <url>] [--client-type openai] --set-default` — prefer `--client-type openai --base-url <endpoint>` (works with any OpenAI-compatible endpoint; exact ids in the agenthub-models skill). `--provider` is required: a model is always the `(provider, model_id)` pair and the group is never inferred from the id (`custom` for an endpoint outside the built-in groups). 2. Environment variables cover the **credential only** (`DEEPSEEK_API_KEY`, `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, …) — model selection still comes from the project config, whose preset default is `deepseek-v4-flash`. Env-only setup therefore works out of the box only with `DEEPSEEK_API_KEY`; for another vendor either run the CLI command above or pass a configured `{ provider, modelId }` pair to `createSession`.

Keep model API keys **project-local**: configure them with the penguin CLI into the app's own data root under the working directory, so the project stays self-contained and movable. When building an AI app, **always pass `--root <data_dir>` pointing at the app's data directory inside the current working dir