Promptbook lets you create persistent AI agents that work on real goals for your company. The Agents Server is the heart of the project — a place where your AI agents live, remember context, collaborate in teams, and get things done.
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 define your agents using simple Books — a human-readable language that is explicit, easy to understand and write, reliable, and highly portable. You then deploy them to the Agents Server, where they run persistently and work toward their goals.
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Paul Smith |
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.
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.
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Paul Smith & Associés |
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.
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Paul Smith & Associés |
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.
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Paul Smith & Associés |
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.
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Paul Smith & Associés |
Promptbook is an ecosystem of tools centered around the Agents Server — a production-ready platform for running persistent AI agents.
The Agents Server is the primary way to use Promptbook. It is a web application where your AI agents live and work. You can create agents, give them knowledge and rules using the Book language, organize them into teams, and let them work on goals persistently. The Agents Server provides a UI for managing agents, an API for integrating them into your applications, and can be self-hosted via Docker or deployed on Vercel.
The Promptbook Engine is the open-source core that powers everything. It parses the Book language, applies commitments, manages LLM provider integrations, and executes agents. The Agents Server is built on top of the Engine. If you need to embed agent capabilities directly into your own application, you can use the Engine as a standalone TypeScript/JavaScript library via NPM packages.
Promptbook project is an ecosystem centered around the Agents Server — a platform for creating, deploying, and running persistent AI agents. Following is a list of the most important pieces of the project:
| Project | About |
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| ⭐ Agents Server | The primary way to use Promptbook. A production-ready platform where your AI agents live — create, manage, deploy, and interact with persistent agents that work on goals. Available as a hosted service or self-hosted via Docker. |
| 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
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| Promptbook Engine | The open-source core that powers the Agents Server. Can also be used as a standalone TypeScript/JavaScript library to embed agent capabilities into your own applications. Released as multiple NPM packages. |
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 |
| Professional updates and industry insights | |
| General announcements and community engagement | |
| 🔗 ptbk.io | Official landing page with project information |
| 📸 Instagram @promptbook.studio | Visual updates, UI showcases, and design inspiration |
The following glossary is used to clarify certain concepts:
- 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
- 📚 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
| Data & Knowledge Management | Pipeline Control |
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| Language & Output Control | Advanced Generation |
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This project is licensed under BUSL 1.1 (Business Source License 1.1).
The BUSL 1.1 license allows:
- Free use for research, evaluation, and development purposes
- Contributions to the project under the same license
- A clear path to commercialization
For commercial use, please contact us for licensing options. The license automatically converts to an open-source license after the change date specified in the license file.
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!