> ## Documentation Index
> Fetch the complete documentation index at: https://io-migration.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> A modular framework for orchestrating LLMs, tools, memory, and user interaction.

Welcome to the IO Intelligence Agent Framework — our foundational architecture for building modular, production-grade AI agents. This framework guides how we design, orchestrate, and scale intelligent systems across various use cases.

> [!NOTE]
>
> This framework is currently used internally and shared here to help developers understand our AI agent design philosophy.

## Overview

This framework brings together the key layers of the modern AI stack and provides a structured approach to building agents that can:

* Retrieve relevant context and knowledge
* Reason and make decisions
* Use external tools and APIs
* Interact with users through intuitive UIs

It is designed for real-world use cases and is actively used in production.

## Architecture

![](https://mintcdn.com/io-migration/gNTVwsExumjDr_tJ/images/docs/f95f974e1e75c3beff44de80b413e384b3e710125ce8f319f1a3d04cb603d966-Orchestration2.jpeg?fit=max\&auto=format\&n=gNTVwsExumjDr_tJ\&q=85\&s=a526bb4a7ef6639a5906c2bf947eef92)

At the core of the framework is an **orchestration layer**, powered by **Langchain** and **LlamaIndex**. It acts as the central hub, coordinating data flow and interactions between the following components:

* **Prompt Engineering** Tools like **Langsmith** and **Promptsmith** help design structured prompts and prompt chains that drive agent behavior.
* **Frontend / UI** Built with platforms like **Vercel AI** and **Stramship**, allowing users to interact with agents seamlessly.
* **AI Tools / Agents** We integrate with services such as **VertexAI** and **Postman** to enable action-taking, API execution, and task automation.
* **Vector Databases** **Pinecone** and **Deviate** provide long-term, searchable memory via retrieval-augmented generation (RAG).
* **LLMs** Models from **OpenAI** and **Snowflake** power the core reasoning and natural language understanding behind the agents.

## Why Orchestration Matters

AI agents often need to perform multiple tasks in a coordinated flow:

1. Query memory from a vector database
2. Call a tool or external API
3. Interpret results with an LLM
4. Return structured output to the user

The orchestration layer ensures all these components work together smoothly and reliably.

## Use Cases

The IO Intelligence Agent Framework powers a wide variety of real-world applications, including:

* Autonomous customer support and helpdesk agents
* Internal copilots for devops, analytics, and operations
* Document Q/\&A and knowledge assistants
* Task agents that combine memory, reasoning, and API execution

## Built With

| Layer | Technologies Used |
| - | - |
| Orchestration | Langchain, LlamaIndex |
| Prompt Engineering | Langsmith, Promptsmith |
| Frontend | Vercel AI, Stramship |
| Tools | VertexAI, Postman |
| Vector DBs | Pinecone, Deviate |
| LLMs | OpenAI, Snowflake |

***

## Get Involved

This framework is open source and available on [GitHub repository](https://github.com/ionet-official/iointel). If you’re building agent-based systems or are exploring LLM orchestration, we invite you to explore, fork, and build with it.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.