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Guide · Open source

How to use learn-claude-code: clone, read and build an agent harness

Updated · written with AI from the project's own documentation and checked against our catalogue data

learn-claude-code is an MIT-licensed, Python-based teaching repository from shareAI-lab that walks through building the "harness" around an AI model: the tools, context management, and permission rules that let a model act as a coding agent. It is modeled loosely on Claude Code and is meant to show how such a system is put together from scratch. This guide explains what the project is, how to get the code, and how to read it so you come away understanding the design, not just the files.

learn-claude-codeshareAI-lab/learn-claude-code · ★ 79.7k · MIT

Quick start

  1. 1Confirm Python is installed. The only prerequisite named in the source is Python. Make sure you can run Python on your machine before continuing.
  2. 2Clone the repository. Run the git clone command for https://github.com/shareAI-lab/learn-claude-code to create a local copy.
  3. 3Read the README first. The README explains the core argument that the model supplies agency and the harness supplies the environment. Read it before opening code.
  4. 4Map the harness components. Identify where tools, context management, and permissions are defined so you can see how the pieces connect.
  5. 5Compare with Claude Code concepts. Use the README's list of Claude Code features to check which ones the code covers and which it does not.
  6. 6Apply the patterns. Reuse the atomic tool design and permission boundaries in your own agent project.

What learn-claude-code teaches

The repository's central idea is that agency comes from the model, while a working agent product needs both the model and a harness. The README puts it simply: the model is the driver and the harness is the vehicle. The project teaches you how to build the vehicle.

The README contrasts this with drag-and-drop workflow builders and prompt-chain libraries, which it argues are procedural pipelines rather than true agents. This is a strong opinion, so read it as the author's position rather than a neutral comparison.

  • Harness = Tools + Knowledge + Observation + Action Interfaces + Permissions, according to the README.
  • The model decides and reasons; the harness executes and provides context.
  • The design patterns are presented as generalizing beyond coding to other domains.
Harness = Tools + Knowledge + Observation + Action Interfaces + Permissions

Get the code and check prerequisites

The install method is clone-and-build. The only prerequisite named in the source is Python. To get a local copy, clone the repository from GitHub.

The README is the authority on any further setup steps. The source excerpt used for this page does not list build or run commands, so check the repository's README for those.

  • Prerequisite: Python.
  • Licence: MIT.
  • Homepage: https://learn.shareai.run
git clone https://github.com/shareAI-lab/learn-claude-code

Read the harness components in order

The README lists the parts that make up a Claude Code-style harness: one agent loop plus tools, on-demand skill loading, context compaction, subagent spawning, a task system with dependency graphs, async mailbox team coordination, task-bound worktrees for parallel edits, permission governance, hooks, memory persistence, and MCP external capability routing.

A practical way to study the project is to take one component at a time and ask what problem it solves: how the tools are defined, how old history is shortened, and where approval for risky actions is enforced.

  • Tools: file read, write, edit, glob, grep, shell (bash) and browser, as listed in the README.
  • Context: compaction shortens older history; subagents keep focused work in a separate message list.
  • Permissions: sandbox file access, approval for destructive operations, and trust boundaries.

Apply the patterns to your own agent tooling

The README's guidance for harness engineers is to implement atomic, composable tools with clear descriptions, load knowledge on demand rather than upfront, and separate permissions from the model's reasoning.

Use the repository as a reference for these patterns, then adapt them to your own domain and codebase. The README notes that the patterns are not limited to coding.

  • Keep each tool to one action the agent can take.
  • Load domain knowledge when needed, not all at once.
  • Put sandboxing and approval steps in the harness, not in prompts.

Limits to keep in mind

learn-claude-code is a teaching project, not a finished coding assistant. If you need an assistant to edit your repository today, a product built for that purpose is a better fit.

The material assumes you can read Python and understand agent concepts. The README also argues against workflow-builder style platforms, so weigh its framing alongside other sources.

  • Not a ready-to-use coding assistant.
  • Assumes comfort reading Python.
  • Opinionated; not a neutral comparison of frameworks.

What people use it for

  • Studying how a Claude Code-style agent loop, tools, and permissions fit together
  • Learning the design of a coding agent as a hands-on Python project
  • Building your own agent tooling with clear harness patterns
  • Teaching a course or workshop on LLM agents versus scripted workflows

Frequently asked questions

How do I install learn-claude-code?

The install method is clone-and-build. Run git clone https://github.com/shareAI-lab/learn-claude-code, then follow the README for any further setup. The source only lists Python as the prerequisite.

Is learn-claude-code free to use?

The repository is licensed under MIT, as listed in the source. Check the LICENSE file in the repository for the full terms.

What language is learn-claude-code written in?

It is written in Python. You should be comfortable reading Python to get value from the material.

Is learn-claude-code a ready-to-use coding assistant?

No. The project is a teaching repository that shows how a harness is built. It is not a finished product for editing code.

How does learn-claude-code compare to Aider or LangChain?

The source lists LangChain, AutoGPT, OpenHands and Aider as comparable projects, but it does not compare them in detail. learn-claude-code focuses on teaching harness design, while those tools are built for use. Compare them on their own READMEs.

Who should not use learn-claude-code?

Non-programmers and people who want an immediately usable assistant should choose something else. The project assumes Python skills and agent concepts.

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Our verdict

learn-claude-code suits developers, engineers and students who want to understand how coding agents like Claude Code are structured, and who are comfortable with Python. It is not a finished coding assistant, and its README takes a strong stance against workflow-builder platforms, so treat that framing as opinion. If you need a tool to use today, pick a product built for that purpose.

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