✨ Promptbook: AI Agents

Turn your company's scattered knowledge into AI ready Books

](https://www.npmjs.com/package/promptbook)
](https://packagequality.com/#?package=promptbook)

🌟 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/core

- Promptbooks are divided into several packages, all are published from single monorepo.
- This package @promptbook/core is one part of the promptbook ecosystem.

To install this package, run:

``bash


Install entire promptbook ecosystem

npm i ptbk

Install just this package to save space

npm install @promptbook/core
`

The core package contains the fundamental logic and infrastructure for Promptbook. It provides the essential building blocks for creating, parsing, validating, and executing promptbooks, along with comprehensive error handling, LLM provider integrations, and execution utilities.

🎯 Purpose and Motivation

The core package serves as the foundation of the Promptbook ecosystem. It abstracts away the complexity of working with different LLM providers, provides a unified interface for prompt execution, and handles all the intricate details of pipeline management, parameter validation, and result processing.

🔧 High-Level Functionality

This package orchestrates the entire promptbook execution lifecycle:

- Pipeline Management: Parse, validate, and compile promptbook definitions
- Execution Engine: Create and manage pipeline executors with comprehensive error handling
- LLM Integration: Unified interface for multiple LLM providers (OpenAI, Anthropic, Google, etc.)
- Parameter Processing: Template parameter substitution and validation
- Knowledge Management: Handle knowledge sources and scraping
- Storage Abstraction: Flexible storage backends for caching and persistence
- Format Support: Parse and validate various data formats (JSON, CSV, XML)

✨ Key Features

- 🚀 Universal Pipeline Executor - Execute promptbooks with any supported LLM provider
- 🔄 Multi-Provider Support - Seamlessly switch between OpenAI, Anthropic, Google, and other providers
- 📊 Comprehensive Validation - Validate promptbooks, parameters, and execution results
- 🎯 Expectation Checking - Built-in validation for output format, length, and content expectations
- 🧠 Knowledge Integration - Scrape and process knowledge from various sources
- 💾 Flexible Storage - Memory, filesystem, and custom storage backends
- 🔧 Error Handling - Detailed error types for debugging and monitoring
- 📈 Usage Tracking - Monitor token usage, costs, and performance metrics
- 🎨 Format Parsers - Support for JSON, CSV, XML, and text formats
- 🔀 Pipeline Migration - Upgrade and migrate pipeline definitions

📦 Exported Entities

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

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- createAgentModelRequirements - Create model requirements for agents
- parseAgentSource - Parse agent source code
- isValidBook - Validate book format
- validateBook - Comprehensive book validation
- DEFAULT_BOOK - Default book template

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- createEmptyAgentModelRequirements - Create empty model requirements
- createBasicAgentModelRequirements - Create basic model requirements
- NotYetImplementedCommitmentDefinition - Placeholder for future commitments
- getCommitmentDefinition - Get specific commitment definition
- getAllCommitmentDefinitions - Get all available commitment definitions
- getAllCommitmentTypes - Get all commitment types
- isCommitmentSupported - Check if commitment is supported

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- pipelineCollectionToJson - Convert collection to JSON
- createPipelineCollectionFromJson - Create collection from JSON data
- createPipelineCollectionFromPromise - Create collection from async source
- createPipelineCollectionFromUrl - Create collection from URL
- createPipelineSubcollection - Create filtered subcollection

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- NAME - Project name
- ADMIN_EMAIL - Administrator email
- ADMIN_GITHUB_NAME - GitHub username
- CLAIM - Project claim/tagline
- DEFAULT_BOOK_TITLE - Default book title
- DEFAULT_TASK_TITLE - Default task title
- DEFAULT_PROMPT_TASK_TITLE - Default prompt task title
- DEFAULT_BOOK_OUTPUT_PARAMETER_NAME - Default output parameter name
- DEFAULT_MAX_FILE_SIZE - Maximum file size limit
- BIG_DATASET_TRESHOLD - Threshold for large datasets
- FAILED_VALUE_PLACEHOLDER - Placeholder for failed values
- PENDING_VALUE_PLACEHOLDER - Placeholder for pending values
- MAX_FILENAME_LENGTH - Maximum filename length
- DEFAULT_INTERMEDIATE_FILES_STRATEGY - Strategy for intermediate files
- DEFAULT_MAX_PARALLEL_COUNT - Maximum parallel executions
- DEFAULT_MAX_EXECUTION_ATTEMPTS - Maximum execution attempts
- DEFAULT_MAX_KNOWLEDGE_SOURCES_SCRAPING_DEPTH - Knowledge scraping depth limit
- DEFAULT_MAX_KNOWLEDGE_SOURCES_SCRAPING_TOTAL - Knowledge scraping total limit
- DEFAULT_BOOKS_DIRNAME - Default books directory name
- DEFAULT_DOWNLOAD_CACHE_DIRNAME - Default download cache directory
- DEFAULT_EXECUTION_CACHE_DIRNAME - Default execution cache directory
- DEFAULT_SCRAPE_CACHE_DIRNAME - Default scrape cache directory
- CLI_APP_ID - CLI application identifier
- PLAYGROUND_APP_ID - Playground application identifier
- DEFAULT_PIPELINE_COLLECTION_BASE_FILENAME - Default collection filename
- DEFAULT_REMOTE_SERVER_URL - Default remote server URL
- DEFAULT_CSV_SETTINGS - Default CSV parsing settings
- DEFAULT_IS_VERBOSE - Default verbosity setting
- SET_IS_VERBOSE - Verbosity setter
- DEFAULT_IS_AUTO_INSTALLED - Default auto-install setting
- DEFAULT_TASK_SIMULATED_DURATION_MS - Default task simulation duration
- DEFAULT_GET_PIPELINE_COLLECTION_FUNCTION_NAME - Default collection function name
- DEFAULT_MAX_REQUESTS_PER_MINUTE - Rate limiting configuration
- API_REQUEST_TIMEOUT - API request timeout
- PROMPTBOOK_LOGO_URL - Official logo URL

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- MODEL_TRUST_LEVELS - Trust levels for different models
- MODEL_ORDERS - Ordering preferences for models
- ORDER_OF_PIPELINE_JSON - JSON property ordering
- RESERVED_PARAMETER_NAMES - Reserved parameter names

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- compilePipeline - Compile pipeline from source
- parsePipeline - Parse pipeline definition
- pipelineJsonToString - Convert pipeline JSON to string
- prettifyPipelineString - Format pipeline string
- extractParameterNamesFromTask - Extract parameter names
- validatePipeline - Validate pipeline structure

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- CallbackInterfaceTools - Callback-based interface tools
- CallbackInterfaceToolsOptions - Options for callback tools (type)

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- BoilerplateError - Base error class
- PROMPTBOOK_ERRORS - All error types registry
- AbstractFormatError - Abstract format validation error
- AuthenticationError - Authentication failure error
- CollectionError - Collection-related error
- EnvironmentMismatchError - Environment compatibility error
- ExpectError - Expectation validation error
- KnowledgeScrapeError - Knowledge scraping error
- LimitReachedError - Resource limit error
- MissingToolsError - Missing tools error
- NotFoundError - Resource not found error
- NotYetImplementedError - Feature not implemented error
- ParseError - Parsing error
- PipelineExecutionError - Pipeline execution error
- PipelineLogicError - Pipeline logic error
- PipelineUrlError - Pipeline URL error
- PromptbookFetchError - Fetch operation error
- UnexpectedError - Unexpected error
- WrappedError - Wrapped error container

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- createPipelineExecutor - Create pipeline executor
- computeCosineSimilarity - Compute cosine similarity for embeddings
- embeddingVectorToString - Convert embedding vector to string
- executionReportJsonToString - Convert execution report to string
- ExecutionReportStringOptions - Report formatting options (type)
- ExecutionReportStringOptionsDefaults - Default report options

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- addUsage - Add usage metrics
- isPassingExpectations - Check if expectations are met
- ZERO_VALUE - Zero usage value constant
- UNCERTAIN_ZERO_VALUE - Uncertain zero value constant
- ZERO_USAGE - Zero usage object
- UNCERTAIN_USAGE - Uncertain usage object
- usageToHuman - Convert usage to human-readable format
- usageToWorktime - Convert usage to work time estimate

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- CsvFormatError - CSV format error
- CsvFormatParser - CSV format parser
- MANDATORY_CSV_SETTINGS - Required CSV settings
- TextFormatParser - Text format parser

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- BoilerplateFormfactorDefinition - Boilerplate form factor
- ChatbotFormfactorDefinition - Chatbot form factor
- CompletionFormfactorDefinition - Completion form factor
- GeneratorFormfactorDefinition - Generator form factor
- GenericFormfactorDefinition - Generic form factor
- ImageGeneratorFormfactorDefinition - Image generator form factor
- FORMFACTOR_DEFINITIONS - All form factor definitions
- MatcherFormfactorDefinition - Matcher form factor
- SheetsFormfactorDefinition - Sheets form factor
- TranslatorFormfactorDefinition - Translator form factor

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- filterModels - Filter available models
- $llmToolsMetadataRegister - LLM tools metadata registry
- $llmToolsRegister - LLM tools registry
- createLlmToolsFromConfiguration - Create tools from config
- cacheLlmTools - Cache LLM tools
- countUsage - Count total usage
- limitTotalUsage - Limit total usage
- joinLlmExecutionTools - Join multiple LLM tools
- MultipleLlmExecutionTools - Multiple LLM tools container

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- _AnthropicClaudeMetadataRegistration - Anthropic Claude registration
- _AzureOpenAiMetadataRegistration - Azure OpenAI registration
- _DeepseekMetadataRegistration - Deepseek registration
- _GoogleMetadataRegistration - Google registration
- _OllamaMetadataRegistration - Ollama registration
- _OpenAiMetadataRegistration - OpenAI registration
- _OpenAiAssistantMetadataRegistration - OpenAI Assistant registration
- _OpenAiCompatibleMetadataRegistration - OpenAI Compatible registration

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- migratePipeline - Migrate pipeline to newer version
- preparePersona - Prepare persona for execution
- book - Book notation utilities
- isValidPipelineString - Validate pipeline string
- GENERIC_PIPELINE_INTERFACE - Generic pipeline interface
- getPipelineInterface - Get pipeline interface
- isPipelineImplementingInterface - Check interface implementation
- isPipelineInterfacesEqual - Compare pipeline interfaces
- EXPECTATION_UNITS - Units for expectations
- validatePipelineString - Validate pipeline string format

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- isPipelinePrepared - Check if pipeline is prepared
- preparePipeline - Prepare pipeline for execution
- unpreparePipeline - Unprepare pipeline

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- identificationToPromptbookToken - Convert ID to token
- promptbookTokenToIdentification - Convert token to ID

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- _BoilerplateScraperMetadataRegistration - Boilerplate scraper registration
- prepareKnowledgePieces - Prepare knowledge pieces
- $scrapersMetadataRegister - Scrapers metadata registry
- $scrapersRegister - Scrapers registry
- makeKnowledgeSourceHandler - Create knowledge source handler
- promptbookFetch - Fetch with promptbook context
- _LegacyDocumentScraperMetadataRegistration - Legacy document scraper
- _DocumentScraperMetadataRegistration - Document scraper registration
- _MarkdownScraperMetadataRegistration - Markdown scraper registration
- _MarkitdownScraperMetadataRegistration - Markitdown scraper registration
- _PdfScraperMetadataRegistration - PDF scraper registration
- _WebsiteScraperMetadataRegistration - Website scraper registration

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- BlackholeStorage - Blackhole storage (discards data)
- MemoryStorage - In-memory storage
- PrefixStorage - Prefixed storage wrapper

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- MODEL_VARIANTS - Available model variants
- NonTaskSectionTypes - Non-task section types
- SectionTypes - All section types
- TaskTypes - Task types

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- REMOTE_SERVER_URLS - Remote server URLs

> 💡 This package does not make sense on its own, look at all promptbook packages or just install all by 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.

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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 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.

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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 ptbk
- ⭐🧙‍♂️ @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 .pdf 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

🔍 View more concepts

🚂 Promptbook Engine

!Schema of 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

See more

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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)_

See more

🐜 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 OpenAIs GPTs?
- ❔ How is it different from the Langchain?
- ❔ How is it different from the DSPy?
- ❔ How is it different from _anything_?
- ❔ Is Promptbook using RAG _(Retrieval-Augmented Generation)_?
- ❔ Is Promptbook using function calling?

📅 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!

- 💬 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!