接入你的智能体
从发布者处安装。不是 PlugMCP 端点。
远程 URL 还是本机命令？ 两种安装方式的区别
来自发布者
Your AI writes code using knowledge from its training data. Your project uses libraries released after that cutoff. The result: hallucinated APIs, deprecated patterns, and code that doesn't compile.
Your team's decisions — use PaymentIntents, not Charges; always wrap API responses; never useEffect for data fetching — exist nowhere in the AI's training data. The code it generates is idiomatic React or idiomatic Node — just not idiomatic .
Detects what your AI doesn't know about your project and fixes it. Post-training gap detection, runtime context injection via MCP.
When we asked Claude to write code for botid (published 2026-03-03, post-training):
"I can't verify the botid package's actual API. I won't output code for a package whose API I can't verify. Guessing function names and signatures would likely give you broken code."
BookLib detects every post-training API in your codebase and resolves the gaps automatically. Real output: booklib analyze on vercel/ai-chatbot — 82 dependencies, 274 post-training APIs across 158 files.
Without BookLib — AI uses AI SDK v5 patterns from training data:
import { OpenAIStream, StreamingTextResponse } from 'ai'; // removed in v6
import OpenAI from 'openai';
export async function POST(req: Request) {
const response = await new OpenAI().chat.completions.create({
model: 'gpt-4', stream: true, messages,
});
return new StreamingTextResponse(OpenAIStream(response));
}
With BookLib — AI gets v6 docs injected at runtime:
import { streamText, convertToModelMessages,
createUIMessageStreamResponse } from 'ai';
import { openai } from '@ai-sdk/openai';
export async function POST(req: Request) {
const result = streamText({
model: openai('gpt-4o'),
messages: convertToModelMessages(messages),
});
return createUIMessageStreamResponse({
stream: result.toUIMessageStream(),
});
}
Requires Node.js >= 18.
npm install -g @booklib/core
booklib init
The wizard detects your stack, configures MCP for your AI tools, and builds the knowledge index. Then see what your AI doesn't know:
booklib analyze
Website and skill browser at booklib-ai.github.io/booklib.
Most context tools wait for your AI to ask. BookLib detects gaps before coding starts and injects corrections as code is written.
Scans your dependencies across npm, PyPI, Maven, Crates.io, RubyGems, Go modules, Packagist, Pub, Swift, and NuGet. Checks publish dates against the model's training cutoff, then cross-references with your source code to find the exact files and APIs affected.
For each gap, BookLib fetches current documentation:
booklib connect commandPreToolUse and PostToolUse hooks inject context as your AI writes code:
Your team's decisions live nowhere in public docs. BookLib auto-detects project documentation — specs, ADRs, architecture docs — and indexes them alongside your team decisions.
booklib capture --title "use PaymentIntents not Charges" --type decision
booklib connect notion database <db-id>
booklib connect github discussions org/repo
booklib init detects your AI tools and configures MCP automatically.
10 tools via MCP, 14 total with instruction-file support. See AGENTS.md for per-tool setup.
Setup
Daily use
Knowledge
Sources
Run booklib --help --all for the full list.
Everything runs locally by default. Embeddings via HuggingFace Transformers (CoreML on macOS, CPU elsewhere), vector search via Vectra, lexical search via BM25, all persisted in .booklib/. Optional cloud modes (Ollama, Anthropic, OpenAI) for AI-powered reasoning.
BookLib complements code context tools:
See CONTRIBUTING.md for the full guide.
Detects what your AI doesn't know about your project and fixes it. Post-training gap detection, runtime context injection via MCP.
工具
暂无工具列表。远程服务器在首次健康检查后会获得工具列表。
常见问题
在本页复制适用于 Cursor、Claude Desktop、Claude Code 或 VS Code 的 stdio 配置。它使用发布者的安装方式，而不是 PlugMCP 代理。
此记录未列出必需的环境变量。
Booklib 通过 stdio 在本地运行。
上次检查时间：2026-10-28。 Detects what your AI doesn't know about your project and fixes it. Post-training gap detection, runtime context injection via MCP.