This week we noticed a long technical article circulating in the Chinese developer community: a developer wrote a local code agent called MyCodeAgent in 14,000 lines of Python, without using a development framework like LangChain, and laid bare in the source code the three-layer core logic that is normally hidden away. Our judgment: the black box of AI coding tools is being opened.
What This Is
The core of the article is breaking down an "AI Code Agent" into a three-stage pipeline.
The first stage is the CLI entry (command-line interface), responsible for parsing arguments and deciding the run mode—whether to run once and exit, or stay open like a chat session waiting for you to type. The second stage is dependency assembly, stringing components together in a fixed order: Config → LLM (large model) → Tools → Agent. Get the order wrong and everything downstream breaks.
The third stage is the ReAct main loop (a "think-then-act" loop): the model first reads the user's input, then judges whether to call external tools (read files, execute commands), feeds the results back into the context, and lets the model continue to judge—until the model believes it can output an answer or is forcibly interrupted.
One easy-to-miss but critical detail: in interactive mode it uses prompt_toolkit (a CLI input-handling library) to read keyboard input, with the up/down arrows navigating input history—the same stack Claude Code uses. Products that look magical and "AI" on the surface rely heavily on battle-tested Python CLI libraries underneath.
Industry View
Supportive voices argue that this "framework-free" code gives enterprise IT departments, for the first time, the ability to audit what AI tools are actually doing—before, with LangChain, when something broke you could only guess; now every layer can be paused with breakpoints and debugged.
But opposing opinions are equally sharp: a senior architect commented in our reader group, "Reading the code doesn't mean knowing how to run it well." The "completion-gate check" (verifying whether the model is truly ready to output an answer) inside the ReAct loop is extremely subtle—the model saying "I'm done" and actually being done are frequently two different things, and exposing the code does not automatically improve reliability. Another risk: exposing internal logic pushes products into homogeneous competition—everyone ends up using the prompt_toolkit + rich (terminal-output beautification library) combo, so the contest devolves into raw model cost and latency.
Impact on Regular People
For enterprise IT: CTOs and procurement teams evaluating AI coding tools now have a reference point—they can ask vendors "how does your completion-gate check work," instead of just listening to sales pitches.
For individual professionals: non-technical managers don't need to learn the code in the short term, but they do need to recognize a basic fact: today's AI coding assistants are essentially "scripts that loop-call large models," not some form of mysterious intelligence. Don't mythologize them in budgeting and workflow planning.
For consumer market: the emergence of open-source "anatomy" versions means pricing pressure on comparable commercial products will grow—subscription tools like Cursor and Claude Code will find it increasingly hard to justify further price hikes next year.