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It's a great point and everyone should know it: the core of a coding agent is really simple, it's a loop with tool calling. Having said that, I think if you're

It's a great point and everyone should know it: the core of a coding agent is really simple, it's a loop with tool calling. Having said that, I think if you're going to write an article like this and call it "The Emperor Has No Clothes: How to Code Claude Code in 200 Lines of Code", you should at least include a reference to Thorsten Ball's excellent article from wayyy back in April 2025 entitled "How to Build an Agent, or: The Emperor Has No Clothes" (https://ampcode.com/how-to-build-an-agent https://ampcode.com/how-to-build-an-agent)! That was (as far as I know) the first of these articles making the point that the core of a coding agent is actually quite simple (and all the deep complexity is in the LLM). Reading it was a light-bulb moment for me. FWIW, I agree with other commenters here that you do need quite a bit of additional scaffolding (like TODOs and much more) to make modern agents work well. And Claude Code itself is a fairly complex piece of software with a lot of settings, hooks, plugins, UI features, etc. Although I would add that once you have a minimal coding agent loop in place, you can get it to bootstrap its own code and add those things! That is a fun and slightly weird thing to try. (By the way, the "January 2025" date on this article is clearly a typo for 2026, as Claude Code didn't exist a year ago and it includes use of the claude-sonnet-4-20250514 model from May.) Edit: and if you're interested in diving deeper into what Claude Code itself is doing under the hood, a good tool to understand it is "claude-trace" (https://github.com/badlogic/lemmy/tree/main/apps/claude-trace https://github.com/badlogic/lemmy/tree/main/apps/claude-trac...). You can use it to see the whole dance with tool calls and the LLM: every call out to the LLM and the LLM's responses, the LLM's tool call invocations and the responses from the agent to the LLM when tools run, etc. When Claude Skills came out I used this to confirm my guess about how they worked (they're a tool call with all the short skill descriptions stuffed into the tool description base prompt). Reading the base prompt is also interesting. (Among other things, they explicitly tell it not to use emoji, which tracks as when I wrote my own agent it was indeed very emoji-prone.)

KellyCriterion 9mo ago
can you show us the >>core of a coding agent which is, according to your words, >>really simple and would you mind sharing a URL so I could check it out then?
- libraryofbabel 9mo ago
  It's in TFA or in the https://ampcode.com/how-to-build-an-agent https://ampcode.com/how-to-build-an-agent article I linked? Or is that not what you're looking for?
  - KellyCriterion 9mo ago
    Sorry, sounded like "your version", instead of the one listed :-)
aszen 9mo ago
The most imp part is editing code, to do that reliably Claude models are trained on their own str replace tool schema I think. Models find it hard to modify existing code, they also can't just rewrite whole files bcz that's expensive and doesn't scale.
- libraryofbabel 9mo ago
  Yeah, there is definitely some RLVR training going on for the Claude LLMs to get them good at some of the specific tool calls used in Claude Code, I expect. Having said that, the string replacement tool schema for file edits is not very complicated at all (you can see it in the tool call schema Claude Code sends to the LLM), so you could easily use that in your own 200-300 line agent if you wanted to make sure you're playing to the LLM's strengths.
  - aszen 9mo ago
    Yeah that's one example, but I suspect they train the model on entire sequences of tool calls, so unless you prompt the model exactly as them you won't get the same results. There's a reason they won the agent race, their models are trained to use their own tools.
    - libraryofbabel 9mo ago
      Agree, the RLVR tasks are probably long series of tool calls at this point doing complex tasks in some simulated dev environment. That said, I think it's hard to say how much of a difference it really makes in terms of making Claude Code specifically better than other coding agents using the same LLM (versus just making the LLM better for all coding agents using roughly similar tools). There is probably some difference, but you'd need to run a lot of benchmarks to find out.
      - aszen 9mo ago
        Agreed it probably contributes to the model improving for all agents but crucially it is verifiably better against their own agent. So they get a good feedback loop to improve both
bredren 9mo ago
I've been exploring the internals of Claude Code and Codex via the transcripts they generate locally (these serve as the only record of your interactions with the products)[1]. Given the stance of the article, just the transcript formats reveals what might be a surprisingly complex system once you dig in. For Claude Code, beyond the basic user/assistant loop, there's uuid/parentUuid threading for conversation chains, queue-operation records for handling messages sent during tool execution, file-history-snapshots at every file modification, and subagent sidechains (agent-*.jsonl files) when the Task tool spawns parallel workers. So "200 lines" captures the concept but not the production reality of what is involved. It is particularly notable that Codex has yet to ship queuing, as that product is getting plenty of attention and still highly capable. I have been building Contextify (https://contextify.sh https://contextify.sh), a macOS app that monitors Claude Code and Codex CLI transcripts in real-time and provides a CLI and skill called Total Recall to query your entire conversational history across both providers. I'm about to release a Linux version and would love any feedback. [1] With the exception of Claude Code Web, which does expose "sessions" or shared transcripts between local and hosted execution environments.
- Johnny_Bonk 9mo ago
  This is very interesting, especially if you could then use an llm across that search to figure out what has and maybe has not been completed, and then reinject those findings into a new Claude code session
  - handfuloflight 9mo ago
    For that you'd be better off having the LLM write TODO stubs in the codebase and search for that. In fact, most of the recent models just do this, even without prompting.
  - bredren 9mo ago
    I haven't written the entry yet but it is pretty incredible what you can get when letting a frontier model RAG your complete CLI convo history. You can find out not just what you did and did not do but why. It is possible to identify unexpectedly incomplete work streams, build a histogram of the times of day you get most irritated with the AI, etc. I think it is very cool and I have a major release coming. I'd be very appreciative of any feedback.
alansaber 9mo ago
Ah I just assumed it was the same article reposted
misternugget 9mo ago
Hey! Thorsten Ball here. Thanks for the shout-out. I was quite confused when someone sent me this article: same "Emperor has no clothes", same "it's only x hundred lines", implements the same tools, it even uses the same ASCII colors when printing you/assistant/tool. Then I saw the "January 2025" in the title and got even more confused. So, thanks for your comment and answering all the questions I had just now about "wait, did I wake up in a parallel universe where I didn't write the post but someone else did?"
- justanotherprof 9mo ago
  Many thanks for your article, it was one of the true "aha" moments for me in 2025! It is a shame that your work is apparently being appropriated without attribution to sell an online course...
- libraryofbabel 9mo ago
  Hi! Thanks again for writing that first Emperor Has No Clothes blog post; like I said, it really inspired me and made everything click early on when I was first dipping my toes into the world of agents. Whenever I teach this stuff to other engineers, I often think back to that moment of realizing exactly how the exchange between LLM, tool call requests, tool functions, and agent code works and I try to get that across as the central takeaway. These days I usually add diagrams to get really clear on what happens on which side of the model api. I do wonder whether the path here was: 1) You wrote the article in April 2025 2) The next generation of LLMs trained on your article 3) The author of TFA had a similar idea, and heavily used LLMs to help write the code and the article, including asking the LLM to think of a catchy title. And guess what title the LLM comes up with? There are also less charitable interpretations, of course. But I'd rather assume this was honestly, if sloppily, done.
justanotherprof 9mo ago
I am glad you pointed out Thorsten Ball's truly excellent article: I was about to add a comment to that effect!

KellyCriterion 9mo ago

can you show us the >>core of a coding agent which is, according to your words, >>really simple and would you mind sharing a URL so I could check it out then?

libraryofbabel 9mo ago
It's in TFA or in the https://ampcode.com/how-to-build-an-agent https://ampcode.com/how-to-build-an-agent article I linked? Or is that not what you're looking for?
- KellyCriterion 9mo ago
  Sorry, sounded like "your version", instead of the one listed :-)

aszen 9mo ago

The most imp part is editing code, to do that reliably Claude models are trained on their own str replace tool schema I think. Models find it hard to modify existing code, they also can't just rewrite whole files bcz that's expensive and doesn't scale.

libraryofbabel 9mo ago
Yeah, there is definitely some RLVR training going on for the Claude LLMs to get them good at some of the specific tool calls used in Claude Code, I expect. Having said that, the string replacement tool schema for file edits is not very complicated at all (you can see it in the tool call schema Claude Code sends to the LLM), so you could easily use that in your own 200-300 line agent if you wanted to make sure you're playing to the LLM's strengths.
- aszen 9mo ago
  Yeah that's one example, but I suspect they train the model on entire sequences of tool calls, so unless you prompt the model exactly as them you won't get the same results. There's a reason they won the agent race, their models are trained to use their own tools.
  - libraryofbabel 9mo ago
    Agree, the RLVR tasks are probably long series of tool calls at this point doing complex tasks in some simulated dev environment. That said, I think it's hard to say how much of a difference it really makes in terms of making Claude Code specifically better than other coding agents using the same LLM (versus just making the LLM better for all coding agents using roughly similar tools). There is probably some difference, but you'd need to run a lot of benchmarks to find out.
    - aszen 9mo ago
      Agreed it probably contributes to the model improving for all agents but crucially it is verifiably better against their own agent. So they get a good feedback loop to improve both

bredren 9mo ago

I've been exploring the internals of Claude Code and Codex via the transcripts they generate locally (these serve as the only record of your interactions with the products)[1]. Given the stance of the article, just the transcript formats reveals what might be a surprisingly complex system once you dig in. For Claude Code, beyond the basic user/assistant loop, there's uuid/parentUuid threading for conversation chains, queue-operation records for handling messages sent during tool execution, file-history-snapshots at every file modification, and subagent sidechains (agent-*.jsonl files) when the Task tool spawns parallel workers. So "200 lines" captures the concept but not the production reality of what is involved. It is particularly notable that Codex has yet to ship queuing, as that product is getting plenty of attention and still highly capable. I have been building Contextify (https://contextify.sh https://contextify.sh), a macOS app that monitors Claude Code and Codex CLI transcripts in real-time and provides a CLI and skill called Total Recall to query your entire conversational history across both providers. I'm about to release a Linux version and would love any feedback. [1] With the exception of Claude Code Web, which does expose "sessions" or shared transcripts between local and hosted execution environments.

Johnny_Bonk 9mo ago
This is very interesting, especially if you could then use an llm across that search to figure out what has and maybe has not been completed, and then reinject those findings into a new Claude code session
- handfuloflight 9mo ago
  For that you'd be better off having the LLM write TODO stubs in the codebase and search for that. In fact, most of the recent models just do this, even without prompting.
- bredren 9mo ago
  I haven't written the entry yet but it is pretty incredible what you can get when letting a frontier model RAG your complete CLI convo history. You can find out not just what you did and did not do but why. It is possible to identify unexpectedly incomplete work streams, build a histogram of the times of day you get most irritated with the AI, etc. I think it is very cool and I have a major release coming. I'd be very appreciative of any feedback.

alansaber 9mo ago

Ah I just assumed it was the same article reposted

misternugget 9mo ago

Hey! Thorsten Ball here. Thanks for the shout-out. I was quite confused when someone sent me this article: same "Emperor has no clothes", same "it's only x hundred lines", implements the same tools, it even uses the same ASCII colors when printing you/assistant/tool. Then I saw the "January 2025" in the title and got even more confused. So, thanks for your comment and answering all the questions I had just now about "wait, did I wake up in a parallel universe where I didn't write the post but someone else did?"

justanotherprof 9mo ago
Many thanks for your article, it was one of the true "aha" moments for me in 2025! It is a shame that your work is apparently being appropriated without attribution to sell an online course...
libraryofbabel 9mo ago
Hi! Thanks again for writing that first Emperor Has No Clothes blog post; like I said, it really inspired me and made everything click early on when I was first dipping my toes into the world of agents. Whenever I teach this stuff to other engineers, I often think back to that moment of realizing exactly how the exchange between LLM, tool call requests, tool functions, and agent code works and I try to get that across as the central takeaway. These days I usually add diagrams to get really clear on what happens on which side of the model api. I do wonder whether the path here was: 1) You wrote the article in April 2025 2) The next generation of LLMs trained on your article 3) The author of TFA had a similar idea, and heavily used LLMs to help write the code and the article, including asking the LLM to think of a catchy title. And guess what title the LLM comes up with? There are also less charitable interpretations, of course. But I'd rather assume this was honestly, if sloppily, done.

justanotherprof 9mo ago

I am glad you pointed out Thorsten Ball's truly excellent article: I was about to add a comment to that effect!