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AI Agents From Scratch

Learn to build AI agents locally without frameworks. Understand what happens under the hood before using production frameworks.

Purpose

This repository teaches you to build AI agents from first principles using local LLMs and node-llama-cpp. By working through these examples, you'll understand:

• How LLMs work at a fundamental level
• What agents really are (LLM + tools + patterns)
• How different agent architectures function
• Why frameworks make certain design choices

Philosophy: Learn by building. Understand deeply, then use frameworks wisely.

Getting Started

Prerequisites

• Node.js 18+
• At least 8GB RAM (16GB recommended)
• Download models and place in ./models/ folder, details in DOWNLOAD.md

Installation

npm install

Run Examples

node intro/intro.js
node simple-agent/simple-agent.js
node react-agent/react-agent.js

Learning Path

Follow these examples in order to build understanding progressively:

1. Introduction - Basic LLM Interaction

intro/ | Code Explanation | Concepts

What you'll learn:

• Loading and running a local LLM
• Basic prompt/response cycle

Key concepts: Model loading, context, inference pipeline, token generation

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2. Translation - System Prompts & Specialization

translation/ | Code Explanation | Concepts

What you'll learn:

• Using system prompts to specialize agents
• Output format control
• Role-based behavior
• Chat wrappers for different models

Key concepts: System prompts, agent specialization, behavioral constraints, prompt engineering

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3. Think - Reasoning & Problem Solving

think/ | Code Explanation | Concepts

What you'll learn:

• Configuring LLMs for logical reasoning
• Complex quantitative problems
• Limitations of pure LLM reasoning
• When to use external tools

Key concepts: Reasoning agents, problem decomposition, cognitive tasks, reasoning limitations

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4. Batch - Parallel Processing

batch/ | Code Explanation | Concepts

What you'll learn:

• Processing multiple requests concurrently
• Context sequences for parallelism
• GPU batch processing
• Performance optimization

Key concepts: Parallel execution, sequences, batch size, throughput optimization

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5. Coding - Streaming & Response Control

coding/ | Code Explanation | Concepts

What you'll learn:

• Real-time streaming responses
• Token limits and budget management
• Progressive output display
• User experience optimization

Key concepts: Streaming, token-by-token generation, response control, real-time feedback

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6. Simple Agent - Function Calling (Tools)

simple-agent/ | Code Explanation | Concepts

What you'll learn:

• Function calling / tool use fundamentals
• Defining tools the LLM can use
• JSON Schema for parameters
• How LLMs decide when to use tools

Key concepts: Function calling, tool definitions, agent decision making, action-taking

This is where text generation becomes agency!

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7. Simple Agent with Memory - Persistent State

simple-agent-with-memory/ | Code Explanation | Concepts

What you'll learn:

• Persisting information across sessions
• Long-term memory management
• Facts and preferences storage
• Memory retrieval strategies

Key concepts: Persistent memory, state management, memory systems, context augmentation

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8. ReAct Agent - Reasoning + Acting

react-agent/ | Code Explanation | Concepts

What you'll learn:

• ReAct pattern (Reason → Act → Observe)
• Iterative problem solving
• Step-by-step tool use
• Self-correction loops

Key concepts: ReAct pattern, iterative reasoning, observation-action cycles, multi-step agents

This is the foundation of modern agent frameworks!

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Documentation Structure

Each example folder contains:

• <name>.js - The working code example
• CODE.md - Step-by-step code explanation
• Line-by-line breakdowns
• What each part does
• How it works
• CONCEPT.md - High-level concepts
• Why it matters for agents
• Architectural patterns
• Real-world applications
• Simple diagrams

Core Concepts

What is an AI Agent?

AI Agent = LLM + System Prompt + Tools + Memory + Reasoning Pattern
           ─┬─   ──────┬──────   ──┬──   ──┬───   ────────┬────────
            │           │           │       │              │
         Brain      Identity    Hands   State         Strategy

Evolution of Capabilities

1. intro          → Basic LLM usage
2. translation    → Specialized behavior (system prompts)
3. think          → Reasoning ability
4. batch          → Parallel processing
5. coding         → Streaming & control
6. simple-agent   → Tool use (function calling)
7. memory-agent   → Persistent state
8. react-agent    → Strategic reasoning + tool use

Architecture Patterns

Simple Agent (Steps 1-5)

User → LLM → Response

Tool-Using Agent (Step 6)

User → LLM ⟷ Tools → Response

Memory Agent (Step 7)

User → LLM ⟷ Tools → Response
       ↕
     Memory

ReAct Agent (Step 8)

User → LLM → Think → Act → Observe
       ↑      ↓      ↓      ↓
       └──────┴──────┴──────┘
           Iterate until solved

️ Helper Utilities

PromptDebugger

helper/prompt-debugger.js

Utility for debugging prompts sent to the LLM. Shows exactly what the model sees, including:

• System prompts
• Function definitions
• Conversation history
• Context state

Usage example in simple-agent/simple-agent.js

️ Project Structure

ai-agents/
├── README.md                          ← You are here
├── intro/
│   ├── intro.js
│   ├── CODE.md
│   └── CONCEPT.md
├── translation/
│   ├── translation.js
│   ├── CODE.md
│   └── CONCEPT.md
├── think/
│   ├── think.js
│   ├── CODE.md
│   └── CONCEPT.md
├── batch/
│   ├── batch.js
│   ├── CODE.md
│   └── CONCEPT.md
├── coding/
│   ├── coding.js
│   ├── CODE.md
│   └── CONCEPT.md
├── simple-agent/
│   ├── simple-agent.js
│   ├── CODE.md
│   └── CONCEPT.md
├── simple-agent-with-memory/
│   ├── simple-agent-with-memory.js
│   ├── memory-manager.js
│   ├── CODE.md
│   └── CONCEPT.md
├── react-agent/
│   ├── react-agent.js
│   ├── CODE.md
│   └── CONCEPT.md
├── helper/
│   └── prompt-debugger.js
├── models/                             ← Place your GGUF models here
└── logs/                               ← Debug outputs

Key Takeaways

By the end of this repository, you'll understand:

1. LLMs are stateless: Context must be managed explicitly
2. System prompts shape behavior: Same model, different roles
3. Function calling enables agency: Tools transform text generators into agents
4. Memory is essential: Agents need to remember across sessions
5. Reasoning patterns matter: ReAct > simple prompting for complex tasks
6. Performance matters: Parallel processing, streaming, token limits
7. Debugging is crucial: PromptDebugger shows what the model actually sees

What frameworks give you:

Now that you understand the fundamentals, frameworks like LangChain, CrewAI, or AutoGPT provide:

• Pre-built reasoning patterns
• Tool libraries
• Memory management
• Multi-agent orchestration
• Production-ready error handling
• Observability and monitoring

You'll use them better because you know what they're doing under the hood.

Additional Resources

• node-llama-cpp: GitHub
• Model Hub: Hugging Face
• GGUF Format: Quantized models for local inference

Contributing

This is a learning resource. Feel free to:

• Suggest improvements to documentation
• Add more example patterns
• Fix bugs or unclear explanations
• Share what you built!

License

Educational resource - use and modify as needed for learning.

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Built with ❤️ for people who want to truly understand AI agents

Start with intro/ and work your way through. Each example builds on the previous one. Read both CODE.md and CONCEPT.md for full understanding.

Happy learning!