knowledge-agent-template

vercel-labsMIT活跃维护

基于文件系统和知识库的 AI Agent 模板,让智能体随时读取你的知识库内容,保持回答最新,适合快速搭建聊天机器人。

Open source file-system and knowledge based agent template. Build AI agents that stay up to date with your knowledge base

暂无版本1,079star140fork同步于 2026-11-07 07:33 UTC

怎么上手

由 README 提炼,执行前请确认
git clone https://github.com/vercel-labs/knowledge-agent-template.git
cd knowledge-agent-template
bun install
cp apps/app/.env.example apps/app/.env
bun run dev
硬件要求按 README 说明

README 未说明本地硬件要求;运行依赖 Vercel Sandbox 作为隔离执行环境,模型通过 Vercel AI SDK 接入

下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

基于文件系统的知识库 AI Agent 模板,用 grep/find/cat 检索,无需向量库

基于文件系统和知识库的 AI Agent 模板,让智能体随时读取你的知识库内容,保持回答最新,适合快速搭建聊天机器人。

核心能力

  • 在隔离沙箱内用 grep、find、cat 检索知识源
  • 无需 embedding 模型与向量数据库,结果确定可解释
  • 同一 Agent 可部署为网页聊天、GitHub Bot、Discord Bot
  • 内置管理后台,含用量统计、错误日志与源同步控制
  • 按问题复杂度自动路由到便宜或强力的模型

容易踩的坑

需先配置 BETTER_AUTH_SECRET、GITHUB_CLIENT_ID、GITHUB_CLIENT_SECRET 环境变量才能启动;本地开发需自备 AI_GATEWAY_API_KEY,Vercel 上才自动用 OIDC;知识源需在 /admin 后台添加并手动触发同步后才可检索

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

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    README 原文

    未翻译

    Open source file-system and knowledge based agent template. Build AI agents that stay up to date with your knowledge base — grep, find, and cat across your sources, no embeddings, no vector DB. Plug any source (GitHub repos, YouTube transcripts, custom APIs) and deploy as a chat app, a GitHub bot, a Discord bot, or all at once.

    Features

    File-Based Search — No Embeddings Needed

    No vector database. No chunking pipeline. No embedding model. Agents use grep, find, and cat inside isolated sandboxes to search across all your sources. Results are deterministic, explainable, and instant. Zero infrastructure overhead.

    Multi-Platform Bots — One Agent, Everywhere

    Write your agent once, deploy it on the web chat, GitHub Issues, Discord - and soon Slack, Linear, and more. Powered by pluggable adapters via the Chat SDK. Adding a new platform is a single adapter file.

    Built-in Admin Panel

    Full admin interface out of the box: usage stats, error logs, user management, source configuration, and content sync controls. No need for external dashboards or third-party monitoring.

    AI-Powered Admin Agent

    Ask your app about itself. "What errors happened in the last 24 hours?", "Show token usage by model", "Which endpoints are slowest?" — the admin agent has access to internal tools like query_stats, query_errors, run_sql, and chart to answer operational questions in natural language.

    Smart Complexity Router

    Every incoming question is classified by complexity (trivial → complex) and routed to the right model. Simple questions go to fast, cheap models. Hard questions go to powerful ones. Cost optimization happens automatically, no manual rules to maintain.

    Real-Time Tool Visualization

    The chat UI shows what the agent is doing in real time: which files it's reading, which commands it's running, and how long each step takes. No black box.

    Shareable Conversations

    Share any conversation with a single click. Generates a public read-only link with full metadata — author, title, and the complete exchange.

    Shared Sandbox Pool

    Sandboxes are pooled across users and conversations. When a chat starts, it connects to an already-running sandbox instead of creating a new one — startup in under 100ms. If none is available, a pre-built snapshot spins one up in 1–3s. Because a sandbox is shared, agents reach it through a parsed command policy that permits only read-only commands — nothing that can write a file or spawn a process — so multiple agents search the same up-to-date content without duplicating resources or affecting each other.

    Architecture

    ┌─────────────────────────────────────────────────────────────────┐
    │                        Your AI Application                      │
    │                     (Discord bot, GitHub bot, etc.)             │
    └─────────────────────────────────┬───────────────────────────────┘
                                      │
                                      ▼
    ┌─────────────────────────────────────────────────────────────────┐
    │                          @savoir/sdk                            │
    │              AI SDK compatible tools (bash, bash_batch)         │
    └─────────────────────────────────┬───────────────────────────────┘
                                      │ API calls
                                      ▼
    ┌─────────────────────────────────────────────────────────────────┐
    │                         apps/app                                │
    │                    (Unified Nuxt Application)                   │
    │  ┌─────────────┐  ┌──────────────┐  ┌────────────────────────┐  │
    │  │   Sandbox   │  │   Content    │  │   Vercel Workflows     │  │
    │  │   Manager   │  │     Sync     │  │   (scheduled sync)     │  │
    │  └──────┬──────┘  └──────┬───────┘  └────────────────────────┘  │
    └─────────┼────────────────┼──────────────────────────────────────┘
              │                │
              ▼                ▼
       ┌────────────┐   ┌─────────────┐
       │   Vercel   │   │   GitHub    │
       │   Sandbox  │   │  Snapshot   │
       │            │   │    Repo     │
       └────────────┘   └─────────────┘
    

    Packages

    PackageDescription
    @savoir/sdkAI SDK compatible tools for agents
    @savoir/agentAgent core: router, prompts, tools, types
    apps/appUnified Nuxt app (chat UI + API + bots)

    Quick Start

    Using the SDK

    import { generateText } from 'ai'
    import { createSavoir } from '@savoir/sdk'
    
    const savoir = createSavoir({
      apiUrl: process.env.SAVOIR_API_URL!,
      apiKey: process.env.SAVOIR_API_KEY,
    })
    
    const { text } = await generateText({
      model: yourModel, // any AI SDK compatible model
      tools: savoir.tools, // bash and bash_batch tools
      maxSteps: 10,
      prompt: 'How do I configure authentication?',
    })
    
    console.log(text)
    

    Deploy to Vercel

    Deploy with Vercel

    Self-hosting

    # Clone the repository
    git clone https://github.com/vercel-labs/knowledge-agent-template.git
    cd knowledge-agent-template
    
    # Install dependencies
    bun install
    
    # Configure environment variables
    cp apps/app/.env.example apps/app/.env
    # Edit .env with your configuration
    
    # Start the app
    bun run dev
    

    Required environment variables:

    # Authentication
    BETTER_AUTH_SECRET=your-secret        # Secret for signing sessions/tokens
    GITHUB_CLIENT_ID=...                  # GitHub OAuth app client ID
    GITHUB_CLIENT_SECRET=...              # GitHub OAuth app client secret
    
    # AI (optional — only needed for local dev, Vercel uses OIDC automatically)
    # AI_GATEWAY_API_KEY=...              # Vercel AI Gateway API key
    
    # Sandbox
    # NUXT_GITHUB_SNAPSHOT_REPO=org/repo  # Optional default (can be set from admin sandbox UI)
    # NUXT_GITHUB_TOKEN=ghp_...           # Optional fallback override (GitHub App is the default path)
    
    

    See ENVIRONMENT.md for the full list of environment variables.

    Customization

    Knowledge Agent Template is designed as a reusable template. See the Customization Guide for how to:

    • Rename your instance (name, icon, description)
    • Add content sources (GitHub repos, YouTube channels, custom)
    • Add custom AI tools
    • Add bot adapters (Slack, Linear, etc.)
    • Customize AI prompts
    • Theme the UI
    • Deploy to production

    AI-assisted customization: The project includes local skills in .agents/skills/ (add-tool, add-source, add-bot-adapter, rename-project). You can delegate these tasks to an AI agent — e.g. "Follow the rename-project skill to rename this to MyDocs" — instead of doing them manually.

    Configuration

    Sources are managed through the admin interface at /admin. You can add GitHub repositories and YouTube channels as knowledge sources, then trigger a sync from the UI.

    Sources can also be listed programmatically via the SDK (savoir.client.getSources()).

    See SOURCES.md for detailed source configuration options.

    How It Works

    For the full technical deep-dive, see Architecture.

    1. Sources in Database: Sources are stored in SQLite via NuxtHub, managed through the admin interface
    2. Content Aggregation: Sources (GitHub repos, YouTube transcripts, custom APIs, etc.) are synced to a snapshot repository via Vercel Workflow
    3. Sandbox Creation: When an agent needs to search, the API creates/recovers a Vercel Sandbox with the snapshot repo cloned
    4. File-based Search: The SDK bash and bash_batch tools execute grep/find/cat commands in the sandbox to search and read content
    5. AI Integration: Tools are compatible with the Vercel AI SDK for seamless integration with any LLM

    Bots

    Knowledge Agent Template includes built-in bot integrations powered by the Vercel Chat SDK:

    • GitHub Bot: Responds to mentions in GitHub issues and PRs. Uses a GitHub App for authentication and webhooks.
    • Discord Bot: Responds to mentions and continues conversations in threads. Uses the Discord API.

    Both bots use the same AI agent and knowledge base as the chat interface. Want to add your own? See Adding a Bot Adapter.

    Development

    # Install dependencies
    bun install
    
    # Start the app in dev mode
    bun run dev
    
    # Build all packages
    bun run build
    
    # Run tests
    bun run test
    
    # Lint and fix
    bun run lint:fix
    

    Built With

    Contributing

    See CONTRIBUTING.md for how to get involved. To report a security vulnerability, see SECURITY.md.

    License

    MIT