Simon Willison’s Weblog

llm-fragment-symbex. I released a new LLM fragment loader plugin that builds on top of my Symbex project.

Symbex is a CLI tool I wrote that can run against a folder full of Python code and output functions, classes, methods or just their docstrings and signatures, using the Python AST module to parse the code.

llm-fragments-symbex brings that ability directly to LLM. It lets you do things like this:

llm install llm-fragments-symbex
llm -f symbex:path/to/project -s 'Describe this codebase'

I just ran that against my LLM project itself like this:

cd llm
llm -f symbex:. -s 'guess what this code does'

Here's the full output, which starts like this:

This code listing appears to be an index or dump of Python functions, classes, and methods primarily belonging to a codebase related to large language models (LLMs). It covers a broad functionality set related to managing LLMs, embeddings, templates, plugins, logging, and command-line interface (CLI) utilities for interaction with language models. [...]

That page also shows the input generated by the fragment - here's a representative extract:

# from llm.cli import resolve_attachment
def resolve_attachment(value):
    """Resolve an attachment from a string value which could be:
    - "-" for stdin
    - A URL
    - A file path

    Returns an Attachment object.
    Raises AttachmentError if the attachment cannot be resolved."""

# from llm.cli import AttachmentType
class AttachmentType:

    def convert(self, value, param, ctx):

# from llm.cli import resolve_attachment_with_type
def resolve_attachment_with_type(value: str, mimetype: str) -> Attachment:

If your Python code has good docstrings and type annotations, this should hopefully be a shortcut for providing full API documentation to a model without needing to dump in the entire codebase.

The above example used 13,471 input tokens and 781 output tokens, using openai/gpt-4.1-mini. That model is extremely cheap, so the total cost was 0.6638 cents - less than a cent.

The plugin itself was mostly written by o4-mini using the llm-fragments-github plugin to load the simonw/symbex and simonw/llm-hacker-news repositories as example code:

llm \
  -f github:simonw/symbex \
  -f github:simonw/llm-hacker-news \
  -s "Write a new plugin as a single llm_fragments_symbex.py file which
   provides a custom loader which can be used like this:
   llm -f symbex:path/to/folder - it then loads in all of the python
   function signatures with their docstrings from that folder using
   the same trick that symbex uses, effectively the same as running
   symbex . '*' '*.*' --docs --imports -n" \
   -m openai/o4-mini -o reasoning_effort high"

Here's the response. 27,819 input, 2,918 output = 4.344 cents.

In working on this project I identified and fixed a minor cosmetic defect in Symbex itself. Technically this is a breaking change (it changes the output) so I shipped that as Symbex 2.0.