The open-source repository bojieli/ai-agent-book contains the Chinese book
A fast-growing star count can tell you that many people noticed a repository. It cannot tell you that you learned the material.
Here is a simple way to use the book as a lab manual instead of another saved link.
For every chapter you study, create one record:
chapter: 1
concept: tool-using agent loop
example_path: <repository path>
command: <exact command>
expected_output: <one observable result>
actual_output: <fill after running>
failure_changed: <one input you changed>
what_i_learned: <one sentence>
Do not mark a chapter complete because you read it. Mark it complete when you can run one example, predict one output, and explain one failure.
Pin a commit, create the environment described by the repository, and run the smallest example for the chapter. Record Python version, dependencies, command, and output.
Change one bounded input. For an agent loop, remove a tool, return malformed JSON, or reduce the step limit to one. Predict the failure before running it.
Write the lifecycle without framework names:
input -> model decision -> validation -> tool execution -> observation -> stop
If you cannot identify where the failed input was rejected—or should have been rejected—repeat the experiment.
After three chapters, rebuild one example without copying the framework glue. A useful beginner target is a deterministic two-tool loop:
add(a, b)
accepts two numbers;weather(city)
returns a fixed fixture;Expected evidence includes one successful call, one schema failure, and one step-limit failure.
I have not executed every example in the repository. Its contents and setup instructions can change, so pin the commit you study and follow the current README. This workflow measures your reproducibility and understanding; it does not rate the book's completeness.
A star can put a book on your radar. Only an experiment puts the concept in your hands.