Ai Agents From Scratch
Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of fun
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About
Read the full interactive version: This repository is part of AI Agents From Scratch - a hands-on learning series where we build AI agents step by step, explain every design decision, and visualize what’s happening under the hood. 👉 https://agentsfromscratch.com If you prefer long-form explanations, diagrams, and conceptual deep dives, start there - then come back here to explore the code.
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.
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Learn AI product development fundamentals with local LLMs. Covers prompt engineering, structured output, multi-step reasoning, API design, and frontend integration through 10 comprehensive lessons with visual diagrams.
AI Agents from Scratch in Python
Next Phase: Build LangChain & LangGraph Concepts From Scratch
After mastering the fundamentals, the next stage of this project walks you through re-implementing the core parts of LangChain and LangGraph in plain JavaScript using local models. This is not about building a new framework, it’s about understanding how frameworks work.
Phase 1: Agent Fundamentals - From LLMs to ReAct
Prerequisites
- Node.js 18+
- At least 8GB RAM (16GB recommended)
- Download models and place in
./models/folder, details in DOWNLOAD.md
Installation
npm installRun Examples
node intro/intro.js
node simple-agent/simple-agent.js
node react-agent/react-agent.jsLearning Path
Follow these examples in order to build understanding progressively:
1. Introduction - Basic LLM Interaction
intro/ | Code | 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
2. (Optional) OpenAI Intro - Using Proprietary Models
openai-intro/ | Code | Code Explanation | Concepts
Don't lose this
Three weeks from now, you'll want Ai Agents From Scratch again. Will you remember where to find it?
Save it to your library and the next time you need Ai Agents From Scratch, it’s one tap away — from any AI app you use. Group it into a bench with the rest of the team for that kind of task and you can pull the whole stack at once.
⚡ Pro tip for geeks: add a-gnt 🤵🏻♂️ as a custom connector in Claude or a custom GPT in ChatGPT — one click and your library is right there in the chat. Or, if you’re in an editor, install the a-gnt MCP server and say “use my [bench name]” in Claude Code, Cursor, VS Code, or Windsurf.
a-gnt's Take
Our honest review
Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of fun. Best for anyone looking to make their AI assistant more capable in design & media. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.
Tips for getting started
Tap "Get" above, pick your AI app, and follow the steps. Most installs take under 30 seconds.
What's New
Imported from GitHub
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