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The Problem

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 yours.

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."

The Fix

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(),
  });
}

Getting Started

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.


How It Works

Most context tools wait for your AI to ask. BookLib detects gaps before coding starts and injects corrections as code is written.

1. Detect Knowledge Gaps

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.

2. Resolve Automatically

For each gap, BookLib fetches current documentation:

  1. Context7 — instant, version-specific library docs

  2. GitHub — releases, wiki, and discussions

  3. Manual — suggests the right booklib connect command

3. Protect at Runtime

PreToolUse and PostToolUse hooks inject context as your AI writes code:

  • Runtime injection — 3-10 lines of relevant knowledge inserted before each edit, powered by a pre-computed context map

  • Import checking — flags unknown APIs not in the index (11 languages)

  • Contradiction detection — warns when code violates team decisions in real-time

4. Capture Team Knowledge

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

Features

Feature

Details

Gap Detection

10 package ecosystems, cross-referenced with source code

Runtime Injection

Pre/PostToolUse hooks deliver context as AI writes code

Context Map

Maps knowledge to code scopes via imports, terms, file patterns

Auto-Resolution

Context7 + GitHub + web connectors fetch current docs

Processing Modes

Fast (BM25), Local (Ollama), Cloud AI — choose in wizard

Import Checking

Flags unknown APIs in JS/TS, Python, Go, Rust, Java, Kotlin, Ruby, PHP, C#, Swift, Dart

Decision Checking

Detects when code contradicts captured team rules

Knowledge Graph

Nodes, typed edges, auto-linking, BFS traversal

Source Connectors

GitHub, Notion, Context7, local files, web docs, SDD specs (.specify, .planning, .kiro)

Source Detection

Auto-detects 12 content types: OpenAPI, ADRs, Gherkin, project docs, and more

Hybrid Search

BM25 + vector search + Reciprocal Rank Fusion + cross-encoder reranking

23 Expert Skills

Distilled from Effective Java, Clean Code, DDD, and 20 more canonical books


Works With

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.


CLI Reference

Setup

Command

Description

booklib init

Guided setup — detects stack, configures MCP, builds index

booklib index

Rebuild the search index

booklib doctor

Health check for skills and config

Daily use

Command

Description

booklib gaps

Find post-training dependencies

booklib resolve-gaps

Auto-fix gaps via Context7 and GitHub

booklib analyze

Show affected files and post-training APIs

booklib search "<query>"

Search skills and knowledge

Knowledge

Command

Description

booklib capture --title "<t>"

Save a team decision or insight

booklib check-imports <file>

Flag unknown APIs

booklib check-decisions <file>

Check code against team rules

Sources

Command

Description

booklib connect <path>

Index local documentation

booklib connect github releases <repo>

Index GitHub changelogs

booklib connect notion database <id>

Index Notion pages

booklib sources

List connected sources

Run booklib --help --all for the full list.


Architecture

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:

Layer

Tool

What it knows

Documentation

Context7

Current library APIs

Code structure

lsp-mcp

Functions, types, call graphs

Knowledge

BookLib

Post-training gaps, team decisions, expert principles


Contributing

See CONTRIBUTING.md for the full guide.

MIT License | Issues | Ko-fi | Docs

A
license - permissive license
-
quality - not tested
C
maintenance

Maintenance

Maintainers
4dResponse time
1dRelease cycle
27Releases (12mo)
Issues opened vs closed

Resources

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If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/booklib-ai/booklib'

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