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Promptbook: Turn your company's scattered knowledge into AI ready books

Package Exports

  • @promptbook/color
  • @promptbook/color/esm/index.es.js
  • @promptbook/color/umd/index.umd.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@promptbook/color) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

✨ Promptbook: AI Agents

Turn your company's scattered knowledge into AI ready Books

[NPM Version of ![Promptbook logo - cube with letters P and B](./design/logo-h1.png) Promptbook](https://www.npmjs.com/package/promptbook) [Quality of package ![Promptbook logo - cube with letters P and B](./design/logo-h1.png) Promptbook](https://packagequality.com/#?package=promptbook) Known Vulnerabilities 🧪 Test Books 🧪 Test build 🧪 Lint 🧪 Spell check 🧪 Test types Issues

🌟 New Features

  • 🚀 GPT-5 Support - Now includes OpenAI's most advanced language model with unprecedented reasoning capabilities and 200K context window
  • 💡 VS Code support for .book files with syntax highlighting and IntelliSense
  • 🐳 Official Docker image (hejny/promptbook) for seamless containerized usage
  • 🔥 Native support for OpenAI o3-mini, GPT-4 and other leading LLMs
  • 🔍 DeepSeek integration for advanced knowledge search
⚠ Warning: This is a pre-release version of the library. It is not yet ready for production use. Please look at latest stable release.

📦 Package @promptbook/color

To install this package, run:

# Install entire promptbook ecosystem
npm i ptbk

# Install just this package to save space
npm install @promptbook/color

🎨 Core Features

Color Creation and Parsing

Create colors from various formats including hex, RGB, HSL, and CSS color names:

import { Color } from '@promptbook/color';

// From hex values
const blue = Color.fromHex('#009edd');
const shortHex = Color.fromHex('#09d');

// From RGB values
const red = Color.fromValues(255, 0, 0);
const transparentRed = Color.fromValues(255, 0, 0, 128); // 50% alpha

// From CSS color names
const navy = Color.get('midnightblue');

// From various string formats
const color1 = Color.fromString('#ff0000');
const color2 = Color.fromString('rgb(255, 0, 0)');
const color3 = Color.fromString('red');

Color Manipulation

Transform colors using various operators:

import { Color, darken, lighten, grayscale, negative } from '@promptbook/color';

const originalColor = Color.fromHex('#009edd');

// Darken and lighten colors
const darker = darken(originalColor, 0.2);
const lighter = lighten(originalColor, 0.3);

// Convert to grayscale
const gray = grayscale(originalColor);

// Create negative/inverted color
const inverted = negative(originalColor);

// Adjust alpha channel
const transparent = withAlpha(originalColor, 0.5);

Color Analysis

Analyze color properties and relationships:

import { colorDistance, colorLuminance, colorHue, colorSaturation, areColorsEqual } from '@promptbook/color';

const color1 = Color.fromHex('#ff0000');
const color2 = Color.fromHex('#00ff00');

// Calculate distance between colors
const distance = colorDistance(color1, color2);

// Get color properties
const luminance = colorLuminance(color1);
const hue = colorHue(color1);
const saturation = colorSaturation(color1);

// Compare colors
const areEqual = areColorsEqual(color1, color2);

Color Mixing and Blending

Mix colors and find optimal color combinations:

import { mixColors, mixWithColor, nearest, furthest, textColor } from '@promptbook/color';

const red = Color.fromHex('#ff0000');
const blue = Color.fromHex('#0000ff');

// Mix two colors
const purple = mixColors(red, blue, 0.5); // 50/50 mix

// Mix with a specific color
const tinted = mixWithColor(red, Color.fromHex('#ffffff'), 0.2); // Add 20% white

// Find nearest color from a palette
const palette = [Color.get('red'), Color.get('green'), Color.get('blue')];
const closest = nearest(Color.fromHex('#ff3333'), palette);

// Find furthest color for maximum contrast
const contrast = furthest(red, palette);

// Get optimal text color for readability
const textColorForBackground = textColor(Color.fromHex('#333333'));

Random Colors and Utilities

Generate random colors and work with color collections:

import { $randomColor, CSS_COLORS, colorToDataUrl } from '@promptbook/color';

// Generate random color
const randomColor = $randomColor();

// Access CSS color constants
const allCssColors = CSS_COLORS;
const specificColor = CSS_COLORS.midnightblue;

// Convert color to data URL (1x1 pixel image)
const dataUrl = colorToDataUrl(Color.fromHex('#ff0000'));

🔧 Color Class API

The Color class provides a comprehensive interface for working with individual colors:

import { Color } from '@promptbook/color';

const color = Color.fromHex('#009edd');

// Access color channels
console.log(color.red); // 0-255
console.log(color.green); // 0-255
console.log(color.blue); // 0-255
console.log(color.alpha); // 0-255

// Shorthand properties
console.log(color.r, color.g, color.b, color.a);

// Alpha-related properties
console.log(color.opacity); // Same as alpha
console.log(color.transparency); // 255 - alpha

// Convert to different formats
console.log(color.toHex()); // "#009edd"
console.log(color.toRgb()); // "rgb(0, 158, 221)"
console.log(color.toString()); // Same as toHex()

// Clone color
const cloned = color.clone();

🎯 Type Safety

All functions are fully typed with TypeScript, providing excellent IDE support and compile-time safety:

import type { ColorTransformer } from '@promptbook/color';

// ColorTransformer type for functions that transform colors
const myTransformer: ColorTransformer = (color: Color) => {
    return darken(color, 0.1);
};

🌈 CSS Color Support

The library includes comprehensive support for CSS color names:

import { CSS_COLORS, Color } from '@promptbook/color';

// All standard CSS colors are available
const colors = [
    Color.get('red'),
    Color.get('midnightblue'),
    Color.get('lightcoral'),
    Color.get('darkslategray'),
    // ... and many more
];

🔄 Fluent API with Take Chain

Many functions return colors wrapped in a "take chain" for fluent operations:

import { Color } from '@promptbook/color';

const result = Color.fromHex('#ff0000')
    .then((color) => darken(color, 0.2))
    .then((color) => withAlpha(color, 0.8))
    .then((color) => color.toHex());

💡 This package is designed to work seamlessly with other Promptbook packages and can be used independently for color manipulation tasks in any JavaScript/TypeScript project.

Rest of the documentation is common for entire promptbook ecosystem:

📖 The Book Whitepaper

For most business applications nowadays, the biggest challenge isn't about the raw capabilities of AI models. Large language models like GPT-5 or Claude-4.1 are extremely capable.

The main challenge is to narrow it down, constrain it, set the proper context, rules, knowledge, and personality. There are a lot of tools which can do exactly this. On one side, there are no-code platforms which can launch your agent in seconds. On the other side, there are heavy frameworks like Langchain or Semantic Kernel, which can give you deep control.

Promptbook takes the best from both worlds. You are defining your AI behavior by simple books, which are very explicit. They are automatically enforced, but they are very easy to understand, very easy to write, and very reliable and portable.

Paul Smith & Associés Book

Aspects of great AI agent

We have created a language called Book, which allows you to write AI agents in their native language and create your own AI persona. Book provides a guide to define all the traits and commitments.

You can look at it as prompting (or writing a system message), but decorated by commitments.

Persona commitment

Personas define the character of your AI persona, its role, and how it should interact with users. It sets the tone and style of communication.

Paul Smith & Associés Book

Knowledge commitment

Knowledge Commitment allows you to provide specific information, facts, or context that the AI should be aware of when responding.

This can include domain-specific knowledge, company policies, or any other relevant information.

Promptbook Engine will automatically enforce this knowledge during interactions. When the knowledge is short enough, it will be included in the prompt. When it is too long, it will be stored in vector databases and RAG retrieved when needed. But you don't need to care about it.

Paul Smith & Associés Book

Rule commitment

Rules will enforce specific behaviors or constraints on the AI's responses. This can include ethical guidelines, communication styles, or any other rules you want the AI to follow.

Depending on rule strictness, Promptbook will either propagate it to the prompt or use other techniques, like adversary agent, to enforce it.

Paul Smith & Associés Book

Action commitment

Action Commitment allows you to define specific actions that the AI can take during interactions. This can include things like posting on a social media platform, sending emails, creating calendar events, or interacting with your internal systems.

Paul Smith & Associés Book

Read more about the language

Where to use your AI agent in book

Books can be useful in various applications and scenarios. Here are some examples:

Chat apps:

Create your own chat shopping assistant and place it in your eShop. You will be able to answer customer questions, help them find products, and provide personalized recommendations. Everything is tightly controlled by the book you have written.

Reply Agent:

Create your own AI agent, which will look at your emails and reply to them. It can even create drafts for you to review before sending.

Coding Agent:

Do you love Vibecoding, but the AI code is not always aligned with your coding style and architecture, rules, security, etc.? Create your own coding agent to help enforce your specific coding standards and practices.

This can be integrated to almost any Vibecoding platform, like GitHub Copilot, Amazon CodeWhisperer, Cursor, Cline, Kilocode, Roocode,...

They will work the same as you are used to, but with your specific rules written in book.

Internal Expertise

Do you have an app written in TypeScript, Python, C#, Java, or any other language, and you are integrating the AI.

You can avoid struggle with choosing the best model, its settings like temperature, max tokens, etc., by writing a book agent and using it as your AI expertise.

Doesn't matter if you do automations, data analysis, customer support, sentiment analysis, classification, or any other task. Your AI agent will be tailored to your specific needs and requirements.

Even works in no-code platforms!

How to create your AI agent in book

Now you want to use it. There are several ways how to write your first book:

From scratch with help from Paul

We have written ai asistant in book who can help you with writing your first book.

Your AI twin

Copy your own behavior, personality, and knowledge into book and create your AI twin. It can help you with your work, personal life, or any other task.

AI persona workpool

Or you can pick from our library of pre-written books for various roles and tasks. You can find books for customer support, coding, marketing, sales, HR, legal, and many other roles.

🚀 Get started

Take a look at the simple starter kit with books integrated into the Hello World sample applications:

💜 The Promptbook Project

Promptbook project is ecosystem of multiple projects and tools, following is a list of most important pieces of the project:

Project About
Book language Book is a human-understandable markup language for writing AI applications such as chatbots, knowledge bases, agents, avarars, translators, automations and more.
There is also a plugin for VSCode to support .book file extension
Promptbook Engine Promptbook engine can run applications written in Book language. It is released as multiple NPM packages and Docker HUB
Promptbook Studio Promptbook.studio is a web-based editor and runner for book applications. It is still in the experimental MVP stage.

Hello world examples:

🌐 Community & Social Media

Join our growing community of developers and users:

Platform Description
💬 Discord Join our active developer community for discussions and support
🗣️ GitHub Discussions Technical discussions, feature requests, and community Q&A
👔 LinkedIn Professional updates and industry insights
📱 Facebook General announcements and community engagement
🔗 ptbk.io Official landing page with project information

🖼️ Product & Brand Channels

Promptbook.studio

📸 Instagram @promptbook.studio Visual updates, UI showcases, and design inspiration

📚 Documentation

See detailed guides and API reference in the docs or online.

🔒 Security

For information on reporting security vulnerabilities, see our Security Policy.

📦 Packages (for developers)

This library is divided into several packages, all are published from single monorepo. You can install all of them at once:

npm i ptbk

Or you can install them separately:

⭐ Marked packages are worth to try first

📚 Dictionary

The following glossary is used to clarify certain concepts:

General LLM / AI terms

  • Prompt drift is a phenomenon where the AI model starts to generate outputs that are not aligned with the original prompt. This can happen due to the model's training data, the prompt's wording, or the model's architecture.
  • Pipeline, workflow scenario or chain is a sequence of tasks that are executed in a specific order. In the context of AI, a pipeline can refer to a sequence of AI models that are used to process data.
  • Fine-tuning is a process where a pre-trained AI model is further trained on a specific dataset to improve its performance on a specific task.
  • Zero-shot learning is a machine learning paradigm where a model is trained to perform a task without any labeled examples. Instead, the model is provided with a description of the task and is expected to generate the correct output.
  • Few-shot learning is a machine learning paradigm where a model is trained to perform a task with only a few labeled examples. This is in contrast to traditional machine learning, where models are trained on large datasets.
  • Meta-learning is a machine learning paradigm where a model is trained on a variety of tasks and is able to learn new tasks with minimal additional training. This is achieved by learning a set of meta-parameters that can be quickly adapted to new tasks.
  • Retrieval-augmented generation is a machine learning paradigm where a model generates text by retrieving relevant information from a large database of text. This approach combines the benefits of generative models and retrieval models.
  • Longtail refers to non-common or rare events, items, or entities that are not well-represented in the training data of machine learning models. Longtail items are often challenging for models to predict accurately.

Note: This section is not a complete dictionary, more list of general AI / LLM terms that has connection with Promptbook

💯 Core concepts

Advanced concepts

Data & Knowledge Management Pipeline Control
Language & Output Control Advanced Generation

🔍 View more concepts

🚂 Promptbook Engine

Schema of Promptbook Engine

➕➖ When to use Promptbook?

➕ When to use

  • When you are writing app that generates complex things via LLM - like websites, articles, presentations, code, stories, songs,...
  • When you want to separate code from text prompts
  • When you want to describe complex prompt pipelines and don't want to do it in the code
  • When you want to orchestrate multiple prompts together
  • When you want to reuse parts of prompts in multiple places
  • When you want to version your prompts and test multiple versions
  • When you want to log the execution of prompts and backtrace the issues

See more

➖ When not to use

  • When you have already implemented single simple prompt and it works fine for your job
  • When OpenAI Assistant (GPTs) is enough for you
  • When you need streaming (this may be implemented in the future, see discussion).
  • When you need to use something other than JavaScript or TypeScript (other languages are on the way, see the discussion)
  • When your main focus is on something other than text - like images, audio, video, spreadsheets (other media types may be added in the future, see discussion)
  • When you need to use recursion (see the discussion)

See more

🐜 Known issues

🧼 Intentionally not implemented features

❔ FAQ

If you have a question start a discussion, open an issue or write me an email.

📅 Changelog

See CHANGELOG.md

📜 License

This project is licensed under BUSL 1.1.

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

You can also ⭐ star the project, follow us on GitHub or various other social networks.We are open to pull requests, feedback, and suggestions.

🆘 Support & Community

Need help with Book language? We're here for you!

We welcome contributions and feedback to make Book language better for everyone!