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@promptbook/utils

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

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✨ Promptbook: AI Agents

Turn your company's scattered knowledge into AI ready Books

NPM Version of Promptbook logo Promptbook Quality of package Promptbook logo Promptbook Known Vulnerabilities 🧪 Test Books 🧪 Test build 🧪 Lint 🧪 Spell check 🧪 Test types Issues

🌟 New Features

  • Gemini 3 Support
⚠ 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/utils

To install this package, run:

# Install entire promptbook ecosystem
npm i ptbk

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

Comprehensive utility functions for text processing, validation, normalization, and LLM input/output handling in the Promptbook ecosystem.

🎯 Purpose and Motivation

The utils package provides a rich collection of utility functions that are essential for working with LLM inputs and outputs. It handles common tasks like text normalization, parameter templating, validation, and postprocessing, eliminating the need to implement these utilities from scratch in every promptbook application.

🔧 High-Level Functionality

This package offers utilities across multiple domains:

  • Text Processing: Counting, splitting, and analyzing text content
  • Template System: Secure parameter substitution and prompt formatting
  • Normalization: Converting text to various naming conventions and formats
  • Validation: Comprehensive validation for URLs, emails, file paths, and more
  • Serialization: JSON handling, deep cloning, and object manipulation
  • Environment Detection: Runtime environment identification utilities
  • Format Parsing: Support for CSV, JSON, XML validation and parsing

✨ Key Features

  • 🔒 Secure Templating - Prompt injection protection with template functions
  • 📊 Text Analysis - Count words, sentences, paragraphs, pages, and characters
  • 🔄 Case Conversion - Support for kebab-case, camelCase, PascalCase, SCREAMING_CASE
  • ✅ Comprehensive Validation - Email, URL, file path, UUID, and format validators
  • 🧹 Text Cleaning - Remove emojis, quotes, diacritics, and normalize whitespace
  • 📦 Serialization Tools - Deep cloning, JSON export, and serialization checking
  • 🌐 Environment Aware - Detect browser, Node.js, Jest, and Web Worker environments
  • 🎯 LLM Optimized - Functions specifically designed for LLM input/output processing

Simple templating

The prompt template tag function helps format prompt strings for LLM interactions. It handles string interpolation and maintains consistent formatting for multiline strings and lists and also handles a security to avoid prompt injection.

import { prompt } from '@promptbook/utils';

const promptString = prompt`
    Correct the following sentence:

    > ${unsecureUserInput}
`;

The prompt name could be overloaded by multiple things in your code. If you want to use the promptTemplate which is alias for prompt:

import { promptTemplate } from '@promptbook/utils';

const promptString = promptTemplate`
    Correct the following sentence:

    > ${unsecureUserInput}
`;

Advanced templating

There is a function templateParameters which is used to replace the parameters in given template optimized to LLM prompt templates.

import { templateParameters } from '@promptbook/utils';

templateParameters('Hello, {name}!', { name: 'world' }); // 'Hello, world!'

And also multiline templates with blockquotes

import { templateParameters, spaceTrim } from '@promptbook/utils';

templateParameters(
    spaceTrim(`
        Hello, {name}!

        > {answer}
    `),
    {
        name: 'world',
        answer: spaceTrim(`
            I'm fine,
            thank you!

            And you?
        `),
    },
);

// Hello, world!
//
// > I'm fine,
// > thank you!
// >
// > And you?

Counting

These functions are useful to count stats about the input/output in human-like terms not tokens and bytes, you can use countCharacters, countLines, countPages, countParagraphs, countSentences, countWords

import { countWords } from '@promptbook/utils';

console.log(countWords('Hello, world!')); // 2

Splitting

Splitting functions are similar to counting but they return the split parts of the input/output, you can use splitIntoCharacters, splitIntoLines, splitIntoPages, splitIntoParagraphs, splitIntoSentences, splitIntoWords

import { splitIntoWords } from '@promptbook/utils';

console.log(splitIntoWords('Hello, world!')); // ['Hello', 'world']

Normalization

Normalization functions are used to put the string into a normalized form, you can use kebab-case PascalCase SCREAMING_CASE snake_case kebab-case

import { normalizeTo } from '@promptbook/utils';

console.log(normalizeTo['kebab-case']('Hello, world!')); // 'hello-world'
  • There are more normalization functions like capitalize, decapitalize, removeDiacritics,...
  • These can be also used as postprocessing functions in the POSTPROCESS command in promptbook

Postprocessing

Sometimes you need to postprocess the output of the LLM model, every postprocessing function that is available through POSTPROCESS command in promptbook is exported from @promptbook/utils. You can use:

Very often you will use unwrapResult, which is used to extract the result you need from output with some additional information:

import { unwrapResult } from '@promptbook/utils';

unwrapResult('Best greeting for the user is "Hi Pavol!"'); // 'Hi Pavol!'

📦 Exported Entities

Version Information

  • BOOK_LANGUAGE_VERSION - Current book language version
  • PROMPTBOOK_ENGINE_VERSION - Current engine version

Configuration Constants

  • VALUE_STRINGS - Standard value strings
  • SMALL_NUMBER - Small number constant

Visualization

  • renderPromptbookMermaid - Render promptbook as Mermaid diagram

Error Handling

  • deserializeError - Deserialize error objects
  • serializeError - Serialize error objects

Async Utilities

  • forEachAsync - Async forEach implementation

Format Validation

  • isValidCsvString - Validate CSV string format
  • isValidJsonString - Validate JSON string format
  • jsonParse - Safe JSON parsing
  • isValidXmlString - Validate XML string format

Template Functions

  • prompt - Template tag for secure prompt formatting
  • promptTemplate - Alias for prompt template tag

Environment Detection

  • $getCurrentDate - Get current date (side effect)
  • $isRunningInBrowser - Check if running in browser
  • $isRunningInJest - Check if running in Jest
  • $isRunningInNode - Check if running in Node.js
  • $isRunningInWebWorker - Check if running in Web Worker

Text Counting and Analysis

  • CHARACTERS_PER_STANDARD_LINE - Characters per standard line constant
  • LINES_PER_STANDARD_PAGE - Lines per standard page constant
  • countCharacters - Count characters in text
  • countLines - Count lines in text
  • countPages - Count pages in text
  • countParagraphs - Count paragraphs in text
  • splitIntoSentences - Split text into sentences
  • countSentences - Count sentences in text
  • countWords - Count words in text
  • CountUtils - Utility object with all counting functions

Text Normalization

  • capitalize - Capitalize first letter
  • decapitalize - Decapitalize first letter
  • DIACRITIC_VARIANTS_LETTERS - Diacritic variants mapping
  • string_keyword - Keyword string type (type)
  • Keywords - Keywords type (type)
  • isValidKeyword - Validate keyword format
  • nameToUriPart - Convert name to URI part
  • nameToUriParts - Convert name to URI parts
  • string_kebab_case - Kebab case string type (type)
  • normalizeToKebabCase - Convert to kebab-case
  • string_camelCase - Camel case string type (type)
  • normalizeTo_camelCase - Convert to camelCase
  • string_PascalCase - Pascal case string type (type)
  • normalizeTo_PascalCase - Convert to PascalCase
  • string_SCREAMING_CASE - Screaming case string type (type)
  • normalizeTo_SCREAMING_CASE - Convert to SCREAMING_CASE
  • normalizeTo_snake_case - Convert to snake_case
  • normalizeWhitespaces - Normalize whitespace characters
  • orderJson - Order JSON object properties
  • parseKeywords - Parse keywords from input
  • parseKeywordsFromString - Parse keywords from string
  • removeDiacritics - Remove diacritic marks
  • searchKeywords - Search within keywords
  • suffixUrl - Add suffix to URL
  • titleToName - Convert title to name format

Text Organization

  • spaceTrim - Trim spaces while preserving structure

Parameter Processing

  • extractParameterNames - Extract parameter names from template
  • numberToString - Convert number to string
  • templateParameters - Replace template parameters
  • valueToString - Convert value to string

Parsing Utilities

  • parseNumber - Parse number from string

Text Processing

  • removeEmojis - Remove emoji characters
  • removeQuotes - Remove quote characters

Serialization

  • $deepFreeze - Deep freeze object (side effect)
  • checkSerializableAsJson - Check if serializable as JSON
  • clonePipeline - Clone pipeline object
  • deepClone - Deep clone object
  • exportJson - Export object as JSON
  • isSerializableAsJson - Check if object is JSON serializable
  • jsonStringsToJsons - Convert JSON strings to objects

Set Operations

  • difference - Set difference operation
  • intersection - Set intersection operation
  • union - Set union operation

Code Processing

  • trimCodeBlock - Trim code block formatting
  • trimEndOfCodeBlock - Trim end of code block
  • unwrapResult - Extract result from wrapped output

Validation

  • isValidEmail - Validate email address format
  • isRootPath - Check if path is root path
  • isValidFilePath - Validate file path format
  • isValidJavascriptName - Validate JavaScript identifier
  • isValidPromptbookVersion - Validate promptbook version
  • isValidSemanticVersion - Validate semantic version
  • isHostnameOnPrivateNetwork - Check if hostname is on private network
  • isUrlOnPrivateNetwork - Check if URL is on private network
  • isValidPipelineUrl - Validate pipeline URL format
  • isValidUrl - Validate URL format
  • isValidUuid - Validate UUID format

💡 This package provides utility functions for promptbook applications. For the core functionality, see @promptbook/core or install all packages with npm i ptbk

Rest of the documentation is common for entire promptbook ecosystem:

📖 The Book Whitepaper

Nowadays, the biggest challenge for most business applications isn't the raw capabilities of AI models. Large language models such as GPT-5.2 and Claude-4.5 are incredibly capable.

The main challenge lies in managing the context, providing rules and knowledge, and narrowing the personality.

In Promptbook, you can define your context using simple Books that are very explicit, easy to understand and write, reliable, and highly portable.

Paul Smith

PERSONA You are a company lawyer.
Your job is to provide legal advice and support to the company and its employees.
RULE You are knowledgeable, professional, and detail-oriented.
TEAM You are part of the legal team of Paul Smith & Associés, you discuss with {Emily White}, the head of the compliance department. {George Brown} is expert in corporate law and {Sophia Black} is expert in labor law.

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.

Commitments are special syntax elements that define contracts between you and the AI agent. They are transformed by Promptbook Engine into low-level parameters like which model to use, its temperature, system message, RAG index, MCP servers, and many other parameters. For some commitments (for example RULE commitment) Promptbook Engine can even create adversary agents and extra checks to enforce the rules.

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

PERSONA You are a company lawyer.

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

PERSONA You are a company lawyer.
Your job is to provide legal advice and support to the company and its employees.
You are knowledgeable, professional, and detail-oriented.

KNOWLEDGE https://company.com/company-policies.pdf
KNOWLEDGE https://company.com/internal-documents/employee-handbook.docx

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

PERSONA You are a company lawyer.
Your job is to provide legal advice and support to the company and its employees.
You are knowledgeable, professional, and detail-oriented.

RULE Always ensure compliance with laws and regulations.
RULE Never provide legal advice outside your area of expertise.
RULE Never provide legal advice about criminal law.
KNOWLEDGE https://company.com/company-policies.pdf
KNOWLEDGE https://company.com/internal-documents/employee-handbook.docx

Team commitment

Team commitment allows you to define the team structure and advisory fellow members the AI can consult with. This allows the AI to simulate collaboration and consultation with other experts, enhancing the quality of its responses.

Paul Smith & Associés

PERSONA You are a company lawyer.
Your job is to provide legal advice and support to the company and its employees.
You are knowledgeable, professional, and detail-oriented.

RULE Always ensure compliance with laws and regulations.
RULE Never provide legal advice outside your area of expertise.
RULE Never provide legal advice about criminal law.
KNOWLEDGE https://company.com/company-policies.pdf
KNOWLEDGE https://company.com/internal-documents/employee-handbook.docx
TEAM You are part of the legal team of Paul Smith & Associés, you discuss with {Emily White}, the head of the compliance department. {George Brown} is expert in corporate law and {Sophia Black} is expert in labor law.

Promptbook Ecosystem

!!!@@@

Promptbook Server

!!!@@@

Promptbook Engine

!!!@@@

💜 The Promptbook Project

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

ProjectAbout
Agents Server Place where you "AI agents live". It allows to create, manage, deploy, and interact with AI agents created in Book language.
Book language Human-friendly, high-level language that abstracts away low-level details of AI. It allows to focus on personality, behavior, knowledge, and rules of AI agents rather than on models, parameters, and prompt engineering. There is also a plugin for VSCode to support .book file extension
Promptbook Engine Promptbook engine can run AI agents based on Book language. It is released as multiple NPM packages and Promptbook Agent Server as Docker Package Agent Server is based on Promptbook Engine.

🌐 Community & Social Media

Join our growing community of developers and users:

PlatformDescription
💬 DiscordJoin our active developer community for discussions and support
🗣️ GitHub DiscussionsTechnical discussions, feature requests, and community Q&A
👔 LinkedInProfessional updates and industry insights
📱 FacebookGeneral announcements and community engagement
🔗 ptbk.ioOfficial landing page with project information

🖼️ Product & Brand Channels

Promptbook.studio

📸 Instagram @promptbook.studioVisual 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 ManagementPipeline Control
Language & Output ControlAdvanced 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!

Keywords

ai
ai-agents
ai-application-framework
ai-assistant
ai-automation
ai-development

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Package last updated on 06 Jan 2026

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