A 20-lesson tutorial project teaching developers how to build the operational environment (harness) around an LLM. The project is run locally via source code or Python and requires a paid Anthropic API key for inference.
Project overview
The curriculum addresses the conceptual gap between basic prompt-chaining and actual agent capabilities by progressively building harness mechanisms step-by-step.
Project type
AI Agent · AI Coding · Learning Resources
Use cases
Coding & Development · Learning & Education
Deployment
Refer to project documentation
License
MIT
Best for
Developers and educators who need a structured, conceptual approach to understanding and building agent environments step-by-step.
Key capabilities
A tutorial track consisting of 20 chapters. Each chapter adds one harness mechanism to an agent loop, starting from basic tool use and advancing to comprehensive agent assembly.
Every chapter includes a complete narrative README, translations, and a standalone runnable code.py implementation.
Limitations and risks
The project intentionally simplifies or omits certain production-level mechanisms to maintain a clear learning path.
Running the tutorial code requires a paid Anthropic API key, meaning inference relies on an external service and data is sent outside the local environment.
Getting started
Setup requires cloning the repository, installing dependencies via pip, and configuring a .env file with an ANTHROPIC_API_KEY. Users can then run the first chapter via python s01_agent_loop/code.py. The setup difficulty is rated medium.
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GitHub project description: Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1
README: This repository is a 0-to-1 harness engineering learning project: it teaches how to build the working environment around an agent model.
README: The word "agent" has been hijacked by an entire prompt-plumbing industry.
README: What they produce are Rube Goldberg machines -- over-engineered, brittle, procedural rule pipelines with an LLM wedged in as a glorified text-completion node. That is not an agent.
README: This repository teaches you how to build the vehicle.