@promptbook/azure-openai weekly downloads statistics

🐙 Promptbook: Invisible AI Agents

Create persistent AI agents that turn your company's scattered knowledge into action - powered by the Agents Server

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

🚀 Quick deploy

Run the standalone VPS installer _(for example on DigitalOcean)_ only on a fresh server. Interactive mode asks for the installation values:

Non-interactive mode takes defaults from command-line options. Use --env live, --env preview, --env production, or --env lts to pick the installed environment; the default is preview. When --openai-api-key is provided with the default OpenAI Codex runner, the installer installs and configures Codex automatically; other runner authentication can be configured later from the UI or SSH:

⚠ 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/azure-openai

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

To install this package, run:

@promptbook/azure-openai integrates Azure OpenAI API with Promptbook. It allows to execute Promptbooks with Azure OpenAI GPT models.

Note: This is similar to @promptbook/openai but more useful for Enterprise customers who use Azure OpenAI to ensure strict data privacy and compliance.

🧡 Usage

🧙‍♂️ Wizard

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

🧙‍♂️ 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.

💕 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.

💙 Integration with other models

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

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 Claude Mythos, GPT-5.6 or Kimi K2.7 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.

Lawyer

GOAL You are a company lawyer. Your job is to provide legal advice and support to the company and its employees. Respond to incoming legal inquiries via email and keep the company website updated with the latest legal policies.

RULE You are knowledgeable, professional, and detail-oriented. 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.

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. Your job is to provide legal advice and support to the company and its employees.

Goal commitment

Goals define what the agent should actively work toward. Unlike a chatbot that only responds when asked, an agent with goals takes initiative and works on tasks persistently on the Agents Server.

Company Lawyer

GOAL Your job is to provide legal advice and support to the company and its employees. GOAL Respond to incoming legal inquiries via email within 24 hours. GOAL Keep the company website updated with the latest legal policies and compliance information.

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…

Description from the npm listing.