Promptbook: Turn your company's scattered knowledge into AI ready books
npm install @promptbook/vercelTurn your company's scattered knowledge into AI ready Books
](https://www.npmjs.com/package/promptbook)
](https://packagequality.com/#?package=promptbook)
- 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.
@promptbook/vercel- Promptbooks are divided into several packages, all are published from single monorepo.
- This package @promptbook/vercel is one part of the promptbook ecosystem.
To install this package, run:
``bashInstall entire promptbook ecosystem
npm i ptbk
is adapter for all Vercel AI SDK model providers.🧡 Usage
`typescript
import { createOpenAI } from '@ai-sdk/openai';
import { usageToHuman } from '@promptbook/core';
import type { Prompt } from '@promptbook/types';
import { createExecutionToolsFromVercelProvider } from '@promptbook/vercel';
import colors from 'colors';
import * as dotenv from 'dotenv';dotenv.config({ path: '.env' });
const openaiVercelProvider = createOpenAI({
apiKey: process.env.OPENAI_API_KEY,
});
const openaiPromptbookExecutionTools = createExecutionToolsFromVercelProvider({
title: 'OpenAI',
vercelProvider: openaiVercelProvider,
availableModels: [],
additionalChatSettings: {
// ...
},
});
/**/
const chatPrompt = {
title: 'Joke',
parameters: {},
content:
Write a joke,
modelRequirements: {
modelVariant: 'CHAT',
modelName: 'gpt-3.5-turbo',
systemMessage: 'You are an assistant who only speaks in rhymes.',
temperature: 1.5,
},
} as const satisfies Prompt;
const chatPromptResult = await openaiPromptbookExecutionTools.callChatModel!(chatPrompt);
console.info({ chatPromptResult });
console.info(colors.cyan(usageToHuman(chatPromptResult.usage)));
console.info(colors.bgBlue(' User: ') + colors.blue(chatPrompt.content));
console.info(colors.bgGreen(' Chat: ') + colors.green(chatPromptResult.content));
/**/
`
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See the other model integrations:
- OpenAI
- Anthropic Claude
- Google Gemini
- Vercel
- Azure OpenAI
---
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.
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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
commitment)_ Promptbook Engine can even create adversary agents and extra checks to enforce the rules.####
Persona commitmentPersonas 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 commitmentKnowledge 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 commitmentRules 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 commitmentTeam 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.
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!!!@@@
#### 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:
Project
About
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.
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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
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#### 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:
`bash
npm i ptbk
`Or you can install them separately:
> ⭐ Marked packages are worth to try first
- ⭐ ptbk - Bundle of all packages, when you want to install everything and you don't care about the size
- promptbook - Same as
- ⭐🧙♂️ @promptbook/wizard - Wizard to just run the books in node without any struggle
- @promptbook/core - Core of the library, it contains the main logic for promptbooks
- @promptbook/node - Core of the library for Node.js environment
- @promptbook/browser - Core of the library for browser environment
- ⭐ @promptbook/utils - Utility functions used in the library but also useful for individual use in preprocessing and postprocessing LLM inputs and outputs
- @promptbook/markdown-utils - Utility functions used for processing markdown
- _(Not finished)_ @promptbook/wizard - Wizard for creating+running promptbooks in single line
- @promptbook/javascript - Execution tools for javascript inside promptbooks
- @promptbook/openai - Execution tools for OpenAI API, wrapper around OpenAI SDK
- @promptbook/anthropic-claude - Execution tools for Anthropic Claude API, wrapper around Anthropic Claude SDK
- @promptbook/vercel - Adapter for Vercel functionalities
- @promptbook/google - Integration with Google's Gemini API
- @promptbook/deepseek - Integration with DeepSeek API
- @promptbook/ollama - Integration with Ollama API
- @promptbook/azure-openai - Execution tools for Azure OpenAI API- @promptbook/fake-llm - Mocked execution tools for testing the library and saving the tokens
- @promptbook/remote-client - Remote client for remote execution of promptbooks
- @promptbook/remote-server - Remote server for remote execution of promptbooks
- @promptbook/pdf - Read knowledge from
documents
- @promptbook/documents - Integration of Markitdown by Microsoft
- @promptbook/documents - Read knowledge from documents like .docx, .odt,…
- @promptbook/legacy-documents - Read knowledge from legacy documents like .doc, .rtf,…
- @promptbook/website-crawler - Crawl knowledge from the web
- @promptbook/editable - Editable book as native javascript object with imperative object API
- @promptbook/templates - Useful templates and examples of books which can be used as a starting point
- @promptbook/types - Just typescript types used in the library
- @promptbook/color - Color manipulation library
- ⭐ @promptbook/cli - Command line interface utilities for promptbooks
- 🐋 Docker image - Promptbook server
📚 Dictionary
The following glossary is used to clarify certain concepts:
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- 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_
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- 📚 Collection of pipelines
- 📯 Pipeline
- 🙇♂️ Tasks and pipeline sections
- 🤼 Personas
- ⭕ Parameters
- 🚀 Pipeline execution
- 🧪 Expectations - Define what outputs should look like and how they're validated
- ✂️ Postprocessing - How outputs are refined after generation
- 🔣 Words not tokens - The human-friendly way to think about text generation
- ☯ Separation of concerns - How Book language organizes different aspects of AI workflows
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Data & Knowledge Management
Pipeline Control
- 📚 Knowledge (RAG) - Retrieve and use external information
- 📽 Media handling - Working with images, audio, video, spreadsheets
- 🔴 Anomaly detection - Identifying unusual patterns or outputs
- 🌏 Remote server - Executing workflows on remote infrastructure
- 🃏 Jokers (conditions) - Adding conditional logic to workflows
- 🔳 Metaprompting - Creating prompts that generate other prompts
Language & Output Control
Advanced Generation
- 🌏 Linguistically typed languages - Type systems for natural language
- 🌍 Auto-Translations - Automatic multilingual support
- 👮 Agent adversary expectations - Safety and control mechanisms
- 🔙 Expectation-aware generation - Outputs that meet defined criteria
- ⏳ Just-in-time fine-tuning - Dynamic model adaptation
🚂 Promptbook Engine
➕➖ When to use Promptbook?
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- 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
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- 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)_
🐜 Known issues
- 🤸♂️ Iterations not working yet
- ⤵️ Imports not working yet
🧼 Intentionally not implemented features
- ➿ No recursion
- 🏳 There are no types, just strings
❔ FAQ
If you have a question start a discussion, open an issue or write me an email.
- ❔ Why not just use the OpenAI SDK / Anthropic Claude SDK / ...?
- ❔ How is it different from the OpenAI
s GPTs?See CHANGELOG.md
This project is licensed under BUSL 1.1.
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.
Need help with Book language? We're here for you!
- 💬 Join our Discord community for real-time support
- 📝 Browse our GitHub discussions for FAQs and community knowledge
- 🐛 Report issues for bugs or feature requests
- 📚 Visit ptbk.io for more resources and documentation
- 📧 Contact us directly through the channels listed in our signpost
We welcome contributions and feedback to make Book language better for everyone!