Learn Claude Code Complete Guide 2026: The Free Learning Site That Builds Your Own AI Agent from Scratch in 20 Sessions

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"Claude Code-like AI coding agents — how are they actually built?" "I want to learn agent internals at the code level..."

The perfect free learning site for you is Learn Claude Code (learn.shareai.run).

This site takes you from 0 to 1, building a nano Claude Code-like agent while adding one mechanism at a time. From a simple agent loop to a full multi-agent harness, you learn across 20 progressive sessions.

All the code is readable right in the browser, and each session comes with a real implementation of 102–1,708 lines. It supports Japanese, English, and Chinese.

In this article, I'll break down the whole picture of Learn Claude Code — the learning path, every session, and how to get started.

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What You'll Learn in This Article

• What Learn Claude Code is (and what you can learn)
• The core pattern: the agent loop
• The 20-session learning path and what each covers
• The 5 architecture layers
• How to study effectively

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What Is Learn Claude Code?

Learn Claude Code is a free learning site for building a Claude Code-like AI agent from scratch.

Key features

• 20 progressive sessions: learn in order, from a simple loop to a complete multi-agent harness
• Real code included: each session ships with a Python implementation of 102–1,708 lines
• 1 session = 1 mechanism: add one feature at a time (tool execution, permissions, memory, etc.)
• 3 languages: Japanese, English, Chinese
• Free: no signup needed — read it straight from your browser

Site URL: https://learn.shareai.run/

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The Core Pattern: The Loop Every AI Agent Shares

The first thing Learn Claude Code teaches is the basic loop shared by all AI coding agents.

Call the model, run tools, return results. In production, policy, permission, and lifecycle layers sit on top of this.

while True:
    response = client.messages.create(messages=messages, tools=tools)
    if response.stop_reason != "tool_use":
        break
    for tool_call in response.content:
        result = execute_tool(tool_call.name, tool_call.input)
        messages.append(result)

This simple loop is the foundation of every agent. The site's approach is to add one mechanism at a time on top of it — permissions, hooks, memory, subagents, and more.

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The Learning Path: All 20 Sessions

Learn Claude Code consists of 20 sessions, s01 through s20.

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The 5 Architecture Layers

The 20 sessions are grouped into 5 orthogonal concerns (architecture layers).

This grouping lets you understand the whole agent from five perspectives: tool execution, planning, memory, concurrency, and multi-agent.

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How to Study

1. Get the big picture on the homepage

Open the homepage first and review the core pattern and the overall learning path.

2. Work through s01 in order

Click "Start learning" and go through s01: The Agent Loop first — following the order is the recommended approach.

• Each session adds exactly one mechanism on top of the previous one
• The code grows gradually from 102 lines to 1,708 lines
• Reading the code carefully as you go deepens understanding

3. Or dive into just the layer you care about

If you already have some knowledge, reading only the sessions you need by architecture layer is also effective.

• Interested in multi-agent → s12, s15–s20
• Want to learn memory → s08, s09
• Starting from the basics → s01–s04

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Summary

Learn Claude Code is a free learning site where you build an AI coding agent from zero, at the code level.

• 20 progressive sessions: from a simple loop to a complete multi-agent harness
• 1 session = 1 topic: build understanding by adding one mechanism at a time
• Real code included: Python implementations of 102–1,708 lines per session
• 5 architecture layers: Tools / Planning / Memory / Concurrency / Multi-Agent
• 3 languages, completely free: https://learn.shareai.run/

For anyone who wants to "understand how AI agents actually work, in code," this is the ideal learning material. Start your first step toward building your own agent with the agent loop in s01.