Developer Guide

New to AI-Q? This page walks you through the documentation in the order that will get you productive fastest.

1. Install

Set up Python, install dependencies with uv, and configure your environment variables (primarily NVIDIA_API_KEY).

Read: Installation

2. Run the Agent

Launch the CLI and submit your first research query. This gives you a working mental model of what the system does before you look at how it works.

Read: Quick Start

3. Understand the Architecture

Learn the two-path design — an intent classifier routes queries to either the fast shallow researcher or the multi-phase deep researcher — and how data flows through the system.

Read: Architecture Overview then Data Flow

4. Explore Individual Agents

Each agent has its own page covering state models, configuration, prompt templates, and internal flow diagrams.

• Intent Classifier — Query routing
• Shallow Researcher — Fast, bounded tool-calling
• Deep Researcher — Multi-phase subagent workflow
• Clarifier — Human-in-the-loop before deep research

5. Customize and Extend

Once you understand the agents, learn how to tailor the system to your needs:

• Swap LLMs — Use different models for different roles
• Enable or disable tools — Configure which data sources agents can access
• Edit prompts — Modify agent behavior through Jinja2 templates
• Add a new tool — Integrate a new search API or data source
• Configuration reference — Full YAML config guide

6. Deploy

Move from local development to Docker Compose.

Read: Docker Compose then Production