Build Your Own AI Agent — Curated Directory
Build Your Own AI Agent — Curated Directory
The best "from scratch" tutorials for building AI agents, MCP servers, tool-calling systems, and more. No frameworks, no black boxes—just code.
Inspired by build-your-own-x (335K ★ on GitHub). This page curates the best step-by-step guides for building AI agent technologies from first principles.
What I cannot create, I do not understand. — Richard Feynman
Build Your Own AI Agent (ReAct Loop)
- Python: Building Production ReAct Agents From Scratch Is Simple — Build a production-grade ReAct agent with structured outputs, memory, and tool execution. Goes from zero to deployed.
- Python: Building an AI Agent from Scratch in Python — Minimal agent using just an LLM API. No frameworks. Understand the Thought→Action→Observation loop.
- Python: Build AI Agents From Scratch with Python — Full course repo: ReAct prompting, tool use, memory, multi-step reasoning. Code-first learning path.
- Python/Gemini: Build Your First Agentic AI from Scratch in Minutes — Covers reasoning loops, tool usage, memory, planning, reflection, and multi-agent concepts.
Build Your Own MCP Server
- Python: The Ultimate Guide to MCP, Part 4: Build Your Own MCP Server — From zero to a working MCP server in under 30 minutes. Tools, resources, prompts all covered.
- Python: MCP Server Tutorial: Build Your First Model Context Protocol Server — Understand MCP, set up tools and resources, connect to Claude.
- Python: How to Build Your Own MCP Server with Python — Build bridges between your systems and intelligent models with just a few lines of Python.
- JavaScript: How to Build Your Own MCP Server — Create a CSS tutor MCP server with tools, resources, and prompts for AI assistants.
Build Your Own Tool-Calling System
- Python: Mastering LLM Tool Calling: The Complete Framework — Three-pillar framework covering data access, computation, and actions tools for production agents.
- Python: Build Your Own Code Interpreter — Dynamic Tool Generation with o3-mini — OpenAI's official cookbook on building secure, dynamic tool execution for LLM agents.
More tutorials coming soon. This category is underserved — check our original guide below.
Build Your Own RAG Pipeline
- Python: Build Your Own RAG in 10 Lines of Python — Toy implementation covering embedding, storage, retrieval, and LLM stitching. Foundation concepts only.
- Python: Build an LLM RAG Chatbot With LangChain — Uses LangChain + Neo4j for production RAG with synthetic data. Framework-based but thorough.
Build Your Own Agent Orchestrator
No dedicated "from scratch" tutorials found yet. Most existing content covers LangGraph, AWS, or vendor-specific orchestration. This is the biggest gap — an original tutorial is in development.
How to Contribute
Found a great "build from scratch" tutorial that belongs here? Submit a suggestion. Entries must be step-by-step guides that build something from first principles — no framework wrappers, no glue tutorials.
Upcoming Original Guides
We're writing original "Build Your Own" content to fill the gaps. Coming soon:
- 🚧 Build Your Own AI Agent from Scratch (NiteAgent)
- 🚧 Build Your Own MCP Server (ToolBrain)
- 🚧 Build Your Own Tool-Calling System (CodeIntel)
- 🚧 Build Your Own Agent Orchestrator (NiteAgent)
