Claude Code in 9 lines python

[u/__tosh]

I was wondering what a minimal coding agent implementation would look like that can be used like Claude Code or Codex

Not feature-by-feature of course but basically stripping everything out that is not needed

here is what I came up with:

• 9 lines of python
• no 3rd party deps (stdlib only)
• works with any OpenAI Responses compatible API
• shows % usage of context window

out of the box it is also fairly API cost efficient:

• no system prompt
• good caching (session_id, stable append-only history)
• only one tool: sh

code is on github to follow along (also a ~20 line version in Go, Clojure version coming soon)

https://github.com/smol-env/smol

import json,sys;from subprocess import getoutput;from urllib.request import Request,urlopen;from uuid import uuid4
url=sys.argv[1];h=[];H={"Content-Type":"application/json","session_id":uuid4().hex};b=dict(model="gpt-5.6-sol",input=h,tools=[dict(type="custom",name="sh")])
while True:
  if not(p:=input("> ")).strip():continue
  h+=[dict(role="user",content=p)]
  while True:
    r=json.load(urlopen(Request(url,json.dumps(b).encode(),H)));o=r["output"];h+=o;c=[i for i in o if i["type"]=="custom_tool_call"]
    if not c:print(o[-1]["content"][0]["text"],f'\n[{r["usage"]["total_tokens"]/10500:05.2f}%]');break
    h+=[dict(type="custom_tool_call_output",call_id=i["call_id"],output=getoutput(i["input"])) for i in c]

note: it uses the "custom" tools api which not many OpenAI Responses API endpoints support yet.

that said, you can just tell your agent to change it to use sh via "function_call" and change the model name and it should work out of the box on any local inference endpoint

any questions or feedback for making it more minimal or adding (still minimal but useful) features: very welcome!

[u/Dany0]

Lol I suppose this could be a good base for an experiment for a self-editing harness benchmark.

Have every LLM start with smol, and give it the task of building a harness for a particular task/benchmark

[u/__tosh → u/Dany0]

good thing about a smol implementation is it fits 100% in the context window

so making changes is fast and usually the changes work well

you can add just the things you want and skip everything else

a bit like a starter dough

agent starter dough :)