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@promptbook/openai

Promptbook: Run AI apps in plain human language across multiple models and platforms

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✨ Promptbook: AI apps in plain Language

Write AI applications using plain human language across multiple models and platforms.

NPM Version of Promptbook logo - cube with letters P and B Promptbook Quality of package Promptbook logo - cube with letters P and B Promptbook Known Vulnerabilities Build Status Coverage Status Issues

🌟 New Features

  • 💡 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

📦 Package @promptbook/openai

To install this package, run:

# Install entire promptbook ecosystem
npm i ptbk

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

@promptbook/openai integrates OpenAI's API with Promptbook. It allows to execute Promptbooks with OpenAI GPT models.

🧡 Usage

import { createPipelineExecutor } from '@promptbook/core';
import {
    createCollectionFromDirectory,
    $provideExecutionToolsForNode,
    $provideFilesystemForNode,
    $provideScrapersForNode,
    $provideScriptingForNode,
} from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/javascript';
import { OpenAiExecutionTools } from '@promptbook/openai';

// 🛠 Prepare the tools that will be used to compile and run your books
// Note: Here you can allow or deny some LLM providers, such as not providing DeepSeek for privacy reasons
const fs = $provideFilesystemForNode();
const llm = new OpenAiExecutionTools(
    //            <- TODO: [🧱] Implement in a functional (not new Class) way
    {
        isVerbose: true,
        apiKey: process.env.OPENAI_API_KEY,
    },
);
const executables = await $provideExecutablesForNode();
const tools = {
    llm,
    fs,
    scrapers: await $provideScrapersForNode({ fs, llm, executables }),
    script: await $provideScriptingForNode({}),
};

// ▶ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./books', tools);

// ▶ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.book`);

// ▶ Create executor - the function that will execute the Pipeline
const pipelineExecutor = createPipelineExecutor({ pipeline, tools });

// ▶ Prepare input parameters
const inputParameters = { word: 'cat' };

// 🚀▶ Execute the Pipeline
const result = await pipelineExecutor(inputParameters).asPromise({ isCrashedOnError: true });

// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);

🤺 Usage with OpenAI's Assistants (GPTs)

TODO: Write a guide how to use OpenAI's Assistants with Promptbook

🧙‍♂️ Wizard

Run books without any settings, boilerplate or struggle in Node.js:

import { wizard } from '@promptbook/wizard';

const {
    outputParameters: { joke },
} = await wizard.execute(`https://github.com/webgptorg/book/blob/main/books/templates/generic.book`, {
    topic: 'Prague',
});

console.info(joke);

🧙‍♂️ Connect to LLM providers automatically

You can just use $provideExecutionToolsForNode function to create all required tools from environment variables like ANTHROPIC_CLAUDE_API_KEY and OPENAI_API_KEY automatically.

import { createPipelineExecutor, createCollectionFromDirectory } from '@promptbook/core';
import { JavascriptExecutionTools } from '@promptbook/javascript';
import { $provideExecutionToolsForNode } from '@promptbook/node';
import { $provideFilesystemForNode } from '@promptbook/node';

// 🛠 Prepare the tools that will be used to compile and run your books
// Note: Here you can allow or deny some LLM providers, such as not providing DeepSeek for privacy reasons
const tools = await $provideExecutionToolsForNode();

// ▶ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./books', tools);

// ▶ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.book`);

// ▶ Create executor - the function that will execute the Pipeline
const pipelineExecutor = createPipelineExecutor({ pipeline, tools });

// ▶ Prepare input parameters
const inputParameters = { word: 'dog' };

// 🚀▶ Execute the Pipeline
const result = await pipelineExecutor(inputParameters).asPromise({ isCrashedOnError: true });

// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);

💕 Usage of multiple LLM providers

You can use multiple LLM providers in one Promptbook execution. The best model will be chosen automatically according to the prompt and the model's capabilities.

import { createPipelineExecutor } from '@promptbook/core';
import {
    createCollectionFromDirectory,
    $provideExecutionToolsForNode,
    $provideFilesystemForNode,
} from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/javascript';
import { OpenAiExecutionTools } from '@promptbook/openai';
import { AnthropicClaudeExecutionTools } from '@promptbook/anthropic-claude';
import { AzureOpenAiExecutionTools } from '@promptbook/azure-openai';

// ▶ Prepare multiple tools
const fs = $provideFilesystemForNode();
const llm = [
    // Note: You can use multiple LLM providers in one Promptbook execution.
    //       The best model will be chosen automatically according to the prompt and the model's capabilities.
    new OpenAiExecutionTools(
        //            <- TODO: [🧱] Implement in a functional (not new Class) way
        {
            apiKey: process.env.OPENAI_API_KEY,
        },
    ),
    new AnthropicClaudeExecutionTools(
        //            <- TODO: [🧱] Implement in a functional (not new Class) way
        {
            apiKey: process.env.ANTHROPIC_CLAUDE_API_KEY,
        },
    ),
    new AzureOpenAiExecutionTools(
        //            <- TODO: [🧱] Implement in a functional (not new Class) way
        {
            resourceName: process.env.AZUREOPENAI_RESOURCE_NAME,
            deploymentName: process.env.AZUREOPENAI_DEPLOYMENT_NAME
            apiKey: process.env.AZUREOPENAI_API_KEY,
        },
    ),
];
const executables = await $provideExecutablesForNode();
const tools = {
    llm,
    fs,
    scrapers: await $provideScrapersForNode({ fs, llm, executables }),
    script: await $provideScriptingForNode({}),
};

// ▶ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./books', tools);

// ▶ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.book`);

// ▶ Create executor - the function that will execute the Pipeline
const pipelineExecutor = createPipelineExecutor({ pipeline, tools });

// ▶ Prepare input parameters
const inputParameters = { word: 'dog' };

// 🚀▶ Execute the Pipeline
const result = await pipelineExecutor(inputParameters).asPromise({ isCrashedOnError: true });

// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);

💙 Integration with other models

See the other model integrations:

🤖 Using Promptbook as an OpenAI-compatible model

You can use Promptbook books as if they were OpenAI models by using the OpenAI-compatible endpoint. This allows you to use the standard OpenAI SDK with Promptbook books.

First, start the Promptbook server:

import { startRemoteServer } from '@promptbook/remote-server';

// Start the server
await startRemoteServer({
    port: 3000,
    collection: await createCollectionFromDirectory('./books'),
    isAnonymousModeAllowed: true,
    isApplicationModeAllowed: true,
});

Then use the standard OpenAI SDK with the server URL:

import OpenAI from 'openai';

// Create OpenAI client pointing to your Promptbook server
const openai = new OpenAI({
    baseURL: 'http://localhost:3000', // Your Promptbook server URL
    apiKey: 'not-needed', // API key is not needed for Promptbook
});

// Use any Promptbook book as a model
const response = await openai.chat.completions.create({
    model: 'https://promptbook.studio/my-collection/write-article.book', // Book URL as model name
    messages: [
        {
            role: 'user',
            content: 'Write a short story about a cat',
        },
    ],
});

console.log(response.choices[0].message.content);

This allows you to:

  • Use Promptbook books with any OpenAI-compatible client
  • Integrate Promptbook into existing OpenAI-based applications
  • Use Promptbook books as models in other AI frameworks

Rest of the documentation is common for entire promptbook ecosystem:

🤍 The Book Abstract

It's time for a paradigm shift! The future of software is written in plain English, French, or Latin.

During the computer revolution, we have seen multiple generations of computer languages, from the physical rewiring of the vacuum tubes through low-level machine code to the high-level languages like Python or JavaScript. And now, we're on the edge of the next revolution!

It's a revolution of writing software in plain human language that is understandable and executable by both humans and machines – and it's going to change everything!

The incredible growth in power of microprocessors and the Moore's Law have been the driving force behind the ever-more powerful languages, and it's been an amazing journey! Similarly, the large language models (like GPT or Claude) are the next big thing in language technology, and they're set to transform the way we interact with computers.

This shift will happen whether we're ready or not. Our mission is to make it excellent, not just good.

Join us in this journey!

🚀 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:

ProjectAbout
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:

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

📘 Book Language Blueprint

A concise, Markdown-based DSL for crafting AI workflows and automations.

Introduction

Book is a Markdown-based language that simplifies the creation of AI applications, workflows, and automations. With human-readable commands, you can define inputs, outputs, personas, knowledge sources, and actions—without needing model-specific details.

Example

# 🌟 My First Book

-   BOOK VERSION 1.0.0
-   URL https://promptbook.studio/hello.book
-   INPUT PARAMETER {topic}
-   OUTPUT PARAMETER {article}

# Write an Article

-   PERSONA Jane, marketing specialist with prior experience in tech and AI writing
-   KNOWLEDGE https://wikipedia.org/
-   KNOWLEDGE ./journalist-ethics.pdf
-   EXPECT MIN 1 Sentence
-   EXPECT MAX 5 Pages

> Write an article about {topic}

→ {article}

Each part of the book defines one of three circles:

1. What: Workflows, Tasks and Parameters

What work needs to be done. Each book defines a workflow (scenario or pipeline), which is one or more tasks. Each workflow has a fixed input and output. For example, you have a book that generates an article from a topic. Once it generates an article about AI, once about marketing, once about cooking. The workflow (= your AI program) is the same, only the input and output change.

Related commands:

2. Who: Personas

Who does the work. Each task is performed by a persona. A persona is a description of your virtual employee. It is a higher abstraction than the model, tokens, temperature, top-k, top-p and other model parameters.

You can describe what you want in human language like Jane, creative writer with a sense of sharp humour instead of gpt-4-2024-13-31, temperature 1.2, top-k 40, STOP token ".\n",....

Personas can have access to different knowledge, tools and actions. They can also consult their work with other personas or user, if allowed.

Related commands:

3. How: Knowledge, Instruments and Actions

The resources used by the personas are used to do the work.

Related commands:

  • KNOWLEDGE of documents, websites, and other resources
  • INSTRUMENT for real-time data like time, location, weather, stock prices, searching the internet, calculations, etc.
  • ACTION for actions like sending emails, creating files, ending a workflow, etc.

General Principles

Book language is based on markdown. It is subset of markdown. It is designed to be easy to read and write. It is designed to be understandable by both humans and machines and without specific knowledge of the language.

The file has a .book extension and uses UTF-8 encoding without BOM.

Books have two variants: flat — just a prompt without structure, and full — with tasks, commands, and prompts.

As it is source code, it can leverage all the features of version control systems like git and does not suffer from the problems of binary formats, proprietary formats, or no-code solutions.

But unlike programming languages, it is designed to be understandable by non-programmers and non-technical people.

📚 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 17 Jun 2025

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