[krick]

I don't quite understand the purpose. Yes, it's clearly stated, but, what do you mean "a reasonable subset of Python code" while "cannot use the standard library"? 99.9% of Python I write for anything ever uses standard library and then some (requests?). What do you expect your LLM-agent to write without that? A pseudo-code sorting algorithm sketch? Why would you even want to run that?

[impulser_]

They plan to use to for "Code Mode" which mean the LLM will use this to run Python code that it writes to run tools instead of having to load the tools up front into the LLM context window.

[DouweM]

(Pydantic AI lead here) We’re implementing Code Mode in https://github.com/pydantic/pydantic-ai/pull/4153 with support for Monty and abstractions to use other runtimes / sandboxes.

The idea is that in “traditional” LLM tool calling, the entire (MCP) tool result is sent back to the LLM, even if it just needs a few fields, or is going to pass the return value into another tool without needing to see the intermediate value. Every step that depends on results from an earlier step also requires a new LLM turn, limiting parallelism and adding a lot of overhead.

With code mode, the LLM can chain tool calls, pull out specific fields, and run entire algorithms using tools with only the necessary parts of the result (or errors) going back to the LLM.

These posts by Cloudflare: https://blog.cloudflare.com/code-mode/ and Anthropic: https://platform.claude.com/docs/en/agents-and-tools/tool-us... explain the concept and its advantages in more detail.

[pama]

I like your effort. Time savings and strict security are real and important. In modern orchestration flows, however, a subagent handles the extra processing of tool results, so the context of the main agent is not poluted.

[notepad0x90]

It's pydantic, they're verifying types and syntax, those don't require the stdlib. Type hints, syntax checks, likely logical issues,etc.. static type checking is good with that, but LLMs can take to the next level where they analyze the intended data flow and find logical bugs, or good syntax and typing but not the intended syntax.

For example, incorrect levels of indentation. Let me use dots instead of space because of HN formatting:

for key,val in mydict.items():

..if key == "operation":

....logging.info("Executing operation %s",val)

..if val == "drop_table":

....self.drop_table()

This uses good syntax, and I the logging part is not in the stdlib, so I assume it would ignore it or replace it with dummy code? That shouldn't prevent it from analyzing that loop and determining that the second if-block was intended to be under the first, and the way it is written now, the key check isn't done.

In other words, if you don't want to do validate proper stdlib/module usage, but proper __Python__ usage, this makes sense. Although I'm speculating on exactly what they're trying to do.

EDIT: I think I my speculation was wrong, it looks like they might have developed this to write code for pydantic-ai: https://github.com/pydantic/pydantic-ai , i'll leave the comment above as-is though, since I think it would still be cool to have that capability in pydantic.