I'm still confused about what this codemode is. Models have been trained to chain bash and other typical unix tools well. They're so good at that to an uncanny level. Why do we want to not utilize this ability? Is it just a permission management issue in case you don't want the model to use shell directly?

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the_mitsuhiko an hour ago | parent | next [-]

We will write about it. The best way to think about it is that codemode solves a different problem than bash in that bash is a way for the agent to run a particular tool: running bash.

Codemode is a way for the LLM to orchestrate harness level tools. The reason this happening now, is because the models by the labs are increasingly trained on this. Codex for instance in responses lite requires codemode to even perform parallel tool calling.

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agentdev001 an hour ago | parent | prev [-]

From my understanding, code mode came about due to some agents not having access to a shell.

	▲	andrewingram 16 minutes ago \| parent \| next [-]
		The value is that rather than an agent chaining together tool calls itself (which means each step sends the result back to the agent for it to analyse and work out what to do next), it writes a script for the harness to execute that chains together all the calls. The major benefits are: * speed - much fewer hops back to the LLM * fewer tokens - intermediate execution steps in the script don't leak into context, only the final result does. * repeatability - if the LLM needs to repeat work, it can reuse a script it wrote last time. If you have a harness that has access to a full shell and knows how to use bash or python, you'll often see it writing little scripts. For setups that don't (ie normal model API requests with tool calls), you can give it an lightweight secure execution environment like just-bash, or quickjs.
	▲	asar 42 minutes ago \| parent \| prev [-]
		thanks! it now clicked for me. so instead of cat its read_file, even if read_file resolves to cat, cat is not always available.