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IAToolkit

Project description

IAToolkit

The Open-Source Framework for Building Real-World AI Assistants on Your Private Data

Website | Full Documentation | Quickstart Guide

IAToolkit Demo


✨ Why IAToolkit?

IAToolkit is more than a collection of utilities — it is a structured foundation for building real-world, enterprise-grade AI assistants that run inside your own environment, understand your data, and respect your business rules.

Whether you’re:

  • building a production chatbot for your company, or
  • learning how serious AI applications are architected,

IAToolkit gives you a clean, opinionated architecture:

  • a shared Core with Interfaces & Chat
  • an Intelligence Layer that orchestrates prompts, tools, RAG, and rules
  • Connectors & Tools to talk to SQL, documents, and APIs
  • a Data Access Layer based on SQLAlchemy
  • and a multi-tenant design where each Company defines its own configuration, context, and tools.

The goal is simple: help you move quickly from “cool demo” to assistant that actually works in the real world.


🧱 Architecture in a Nutshell

At the heart of IAToolkit is a structured internal architecture:

  • Interfaces & Chat
    Handle HTTP/JSON/HTML, sessions, and the conversational flow between users, the server, and the LLM.

  • Intelligence Layer
    The core of the system. Interprets user intent, reads each Company’s configuration, and orchestrates SQL queries, document retrieval, prompts, tools, and RAG. This is where real-world behavior lives.

  • Connectors & Tools Layer
    Bridges the intelligence with your systems. Provides access to SQL databases, internal documents, APIs, and custom Python tools so the assistant can execute workflows, not just answer questions.

  • Data Access Layer
    Uses SQLAlchemy to offer structured and predictable access to the internal database, making it safe to grow from one Company to many.

  • Company Modules
    Each Company has its own company.yaml, context, prompts, tools, and branding, forming a clean boundary within a shared IAToolkit Core.

If you want a deeper explanation of the design decisions behind this, see the
🏛️ Foundation Article.


🔌 Connect to Anything

Build AI assistants that truly understand your business.

  • Connect to SQL databases (PostgreSQL, MySQL, SQLite)
  • Query structured data using natural language
  • Perform semantic search on PDFs, DOCX, TXT, XLSX
  • Use IAToolkit as a full RAG engine out-of-the-box
  • Combine database queries, document retrieval, and tools in a single answer

Your assistant isn’t limited to the chat history — it can see real numbers, real entities, and real documents.


🏢 Multi-Tenant by Design

IAToolkit is built for scenarios where you serve more than one “domain”:

  • SaaS products serving multiple customers
  • Agencies or consultancies building assistants for several clients
  • Large enterprises with multiple business units

Each Company is a logical tenant, defined by:

  • a company.yaml configuration file (data sources, LLM choices, tools, roles, branding)
  • contextual resources (schemas, prompts, documents, examples)
  • optional Python tools that the LLM can call (SQL helpers, API calls, custom business actions)

This gives you:

  • Clear isolation between tenants
  • Clean separation for multi-client deployments
  • A straightforward path to scale from 1 to 100+ customers, without rewriting your core

🧠 Built for Real-World Systems

IAToolkit is designed with production in mind — reliable, maintainable, and adaptable:

  • Swap between OpenAI (GPT), Google Gemini, or future LLM providers
  • Keep a clean separation between UI, business logic, and LLM orchestration
  • Use an Intelligence Layer to organize prompts, tools, and RAG in a consistent way
  • Integrated authentication and session handling
  • Detailed logging of prompts, tool calls, and token usage
  • Runs anywhere: local machine, Docker, cloud, serverless

You can start small on a laptop and grow into a full-scale internal assistant without changing frameworks.


🚀 Getting Started in 3 Minutes

Get your first AI assistant running locally by following our “Hello World” example.

Our Quickstart Guide walks you through:

  • creating and activating a virtual environment
  • configuring your .env file with API keys and basic settings
  • launching the application and talking to your first Company

➡️ Start the Quickstart Guide


📚 Documentation

The documentation is designed to grow with you — from basic setup to extending the framework with your own Companies, tools, and workflows.

Guide Description
🚀 Quickstart Guide The fastest way to install, configure, and run IAToolkit for the first time.
⚙️ Companies & Components A deep dive into the company.yaml file, the core of all configuration.
💻 Programming Guide Understand the internal architecture, services, and design patterns to extend the framework.
☁️ Deployment Guide Learn how to deploy your IAToolkit application to a production environment.
🗃️ Database Guide An overview of the core database schema used by the IAToolkit framework itself.
🏛️ Foundation Article The “why” behind IAToolkit’s architecture for enterprise-grade assistants.
🗓️ Implementation Plan A 3-month mini-project plan to deploy a real AI assistant integrated with corporate data.

➡️ Explore all documentation


🤝 Contributing

We welcome contributions of all kinds — new features, bug fixes, documentation improvements, or ideas for better developer experience.

Please read our Contributing Guide to get started.


📄 License

IAToolkit is open-source software licensed under the MIT License.

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