Pi Runtime Extensions

What does this agent do, and when should you use it?

Pi is a monorepo of shared TypeScript packages for Pi runtime applications, published ESM-only under the @amaster.ai npm scope. It splits into two layers: @amaster.ai/pi-shared and @amaster.ai/pi-storage supply runtime contracts and persistence adapters, while the remaining packages are extensions that each register themselves through a ./extension subpath entry point and are loaded on demand by a host application. Extensions cover IM workspaces (Lark/Feishu, WeCom, DingTalk), browser and desktop automation, persistent memory, Langfuse/OpenTelemetry telemetry, cron scheduling, image and video generation, web access, and security policy. Hosts must supply HTTP routing, authentication, model runtime setup, deployment configuration, and product UI; the README documents two loading models, pi2dsh and dsh-pi-host. Runtime requirements are Node.js >=24 and pnpm 10.18.3, licensed Apache-2.0.

Each extension exports an entry point at its ./extension subpath; hosts import it and pass it to the Pi runtime during setup, which registers LLM-callable tools. On the core side, loadPiSettings reads configuration and createRuntimeStorage creates storage, with JsonRuntimeStorage plus MySQL/Prisma adapters persisting sessions, transcripts, events, memory, artifacts, subagents, and scheduled tasks. pi-browser-use wraps chrome-devtools-mcp behind browser_-prefixed tools, pi-computer-use provides cross-platform desktop automation, and pi-web-access handles web search, URL content extraction, and image search. pi-memory injects curated MEMORY.md and USER.md content into the system prompt as a refreshed snapshot, pi-security enforces resource-aware policies and tool authorization, pi-task-scheduler manages cron-based tasks, pi-telemetry exports through createLangfuseExporter and createOtelExporter, and pi-goal derives a goal from the conversation and iterates until it is met. For workspaces, pi-lark calls lark-cli, pi-wecom calls wecom-cli, and pi-dingtalk calls the dws CLI for calendars, docs, sheets, mail, and approvals. pi-image-gen targets OpenAI gpt-image, Google Nano Banana, Alibaba Qwen-Image, OpenRouter, and custom providers, while pi-video-gen performs lossless clip concat and mixed image/video timelines with bundled LGPL/GPL FFmpeg runtimes.

1. A developer building a host app who wants session, event, artifact, and scheduled-task persistence while keeping their own HTTP routing and front end installs pi-shared plus pi-storage.
2. A team wiring a Pi agent into a Feishu/Lark office workflow needs calendar, docs, drive, sheets, Base, tasks, and IM skills, provided by pi-lark through lark-cli.
3. An ops or engineering group wants an agent to search the web, extract page content, and drive a browser, combining pi-web-access and pi-browser-use in one flow.
4. An assistant scenario needing durable personalization uses pi-memory to inject MEMORY.md/USER.md into the system prompt instead of restating context each turn.
5. A compliance-sensitive team requires per-resource authorization before tool execution and adds the pi-security policy engine and tool authorization.
6. A content team switches between image providers via pi-image-gen and assembles clips, subtitles, TTS, and BGM into a finished video with pi-video-gen.

How do you install or deploy this agent?

Requirements are Node.js >=24 and pnpm 10.18.3; the README recommends Corepack:

corepack enable
corepack install -g [email protected]
pnpm install

Full local check: pnpm run pr-check. Common commands: pnpm build, pnpm typecheck, pnpm test. pi-storage ships a Prisma schema at packages/storage/prisma/schema.prisma; run pnpm --filter @amaster.ai/pi-storage prisma:generate if needed, though the root build and typecheck scripts generate the Prisma client before compiling. As a consumer, install only what you need:

pnpm add @amaster.ai/pi-shared @amaster.ai/pi-storage

The README does not document credential setup for the IM, image, or video providers; consult each package README.

How do you use this agent?

Import from root entry points or focused subpaths, for example:

import { createRuntimeStorage } from "@amaster.ai/pi-storage";
import { JsonRuntimeStorage } from "@amaster.ai/pi-storage/json";
import { loadPiSettings } from "@amaster.ai/pi-shared/settings";
import { createLangfuseExporter } from "@amaster.ai/pi-telemetry/langfuse";
import { createOtelExporter } from "@amaster.ai/pi-telemetry/otel";
import memoryExtension from "@amaster.ai/pi-memory/extension";

Extension packages register themselves through their ./extension subpath entry point; the host imports them and passes them to the Pi runtime during setup. Two host loading models are documented: pi2dsh discovers explicitly installed Pi packages in a DSH profile, while dsh-pi-host loads package names configured in its extensions list. Hosts must supply HTTP routing, authentication, model runtime, and deployment config; the README shows no runnable minimal example.

What are this agent's strengths and limitations?

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

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