AI Agents From Scratch demystifies AI agents by providing a hands-on, from-scratch approach using local LLMs and Node.js. It targets developers and researchers who want to understand the underlying mechanisms of agents (LLM, tools, memory, ReAct patterns) before leveraging production frameworks, offering a path to deeper comprehension and wiser framework utilization.

How It Works

This project demystifies AI agents by guiding users through building them from first principles using local LLMs via node-llama-cpp. It breaks down agent functionality into core components: LLM interaction, system prompts for specialization, function calling for tool use, memory for state persistence, and reasoning patterns like ReAct. This modular, step-by-step approach allows for a fundamental understanding of how agents operate before abstracting complexity with frameworks.

Quick Start & Requirements

• Installation: npm install
• Prerequisites: Node.js 18+, at least 8GB RAM (16GB recommended). Users must download LLM models (GGUF format) and place them in the ./models/ directory.
• Running Examples: node intro/intro.js, node simple-agent/simple-agent.js, node react-agent/react-agent.js (and others as per the learning path).
• Documentation: Detailed explanations are provided in CODE.md and CONCEPT.md files within each example directory.

Highlighted Details

• Progressive learning path from basic LLM interaction to complex ReAct agents.
• Includes a PromptDebugger utility to inspect LLM inputs.
• Clearly defines AI Agent components: LLM + System Prompt + Tools + Memory + Reasoning Pattern.
• Explains the evolution of agent capabilities through distinct examples.

Maintenance & Community

The project is presented as an educational resource with an open invitation for contributions, including documentation improvements, bug fixes, and sharing user projects. No specific community channels (like Discord/Slack) or formal maintenance structure are detailed.

Licensing & Compatibility

The project is licensed under an "Educational resource - use and modify as needed for learning" license. This implies it is primarily for personal learning and experimentation, with potential restrictions on commercial use or integration into proprietary systems without further clarification.

Limitations & Caveats

This repository focuses on foundational understanding and requires manual setup of local LLMs and models. It is not a production-ready framework but a pedagogical tool. Users need to manage model downloads and ensure sufficient hardware resources. The educational license may impose restrictions on commercial application.