
local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
A curated, one-click installable configuration pack for the Pi coding agent, featuring 17 plugins, 18 global skills, and 2 MCP servers to enhance multi-agent collaboration, token efficiency, and browser automation.
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MIT
2026-07-25
It turns Pi into a powerful multi-agent coding assistant with AST-level code intelligence, persistent memory, and a full browser automation suite, all provided as a copyable, documented template.
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17 plugins · 18 global skills · 2 MCP servers · one-line installer
一份可直接复刻的 Pi 配置,整理成教程形式分享。包含 17 个插件、18 个全局 Skill、2 个 MCP server,配一键安装脚本。
仓库里的每样东西都经过实际使用筛选,目标是把 Pi 打造成一个能多代理协作、能省 token、能跑浏览器的全能终端编码代理。
Pi(npm 包 @earendil-works/pi-coding-agent)是一个开源的终端 AI 编码代理。你给它一个任务,它在你本地的项目里读写文件、跑命令、调用工具来完成任务。
如果你还没装 Pi,先:
npm install -g @earendil-works/pi-coding-agent
然后跑 pi 进入交互界面。本仓库的所有插件都假设你已经有一个能正常工作的 Pi。
理解架构有助于你挑选和编写插件。Pi 是一个插件驱动的代理循环,核心分层:
┌─────────────────────────────────────────────────────┐
│ TUI / RPC / Print ← 用户交互层(终端界面 / API) │
├─────────────────────────────────────────────────────┤
│ Agent Loop ← 代理循环:收 prompt → 调模型 │
│ → 执行工具 → 回传结果 │
├─────────────────────────────────────────────────────┤
│ Providers / Models ← 多模型抽象:OpenAI / Anthropic │
│ / 本地 / 自定义 provider │
├─────────────────────────────────────────────────────┤
│ Tools ← 模型可调用的工具:bash/read/ │
│ edit/web_search/mcp/自定义 │
├─────────────────────────────────────────────────────┤
│ Extensions ← TypeScript 模块,能订阅事件、 │
│ 注册工具/命令/快捷键/主题 │
├─────────────────────────────────────────────────────┤
│ Skills / Prompts ← Markdown 形式的「过程知识」, │
│ 按需注入到模型上下文 │
├─────────────────────────────────────────────────────┤
│ Themes ← JSON 配色方案 │
├─────────────────────────────────────────────────────┤
│ Packages ← 上面这些资源的分发单位 │
│
My Pi coding agent setup: 17 plugins + 18 global skills + 2 MCP servers, with a one-line installer and tutorial README.
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