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Agents & MCP

Simon Willison Launches llm-coding-agent Python Library via Claude Code Spec-Driven TDD

July 3, 2026· 4 min read
OKCurated by Oleksandr Kuzmenko, AI Product Engineer·Updated July 3, 2026·Sources cited on every story
AI-assisted · editor-reviewed·How we use AI
Agents & MCP

Simon Willison built and shipped `llm-coding-agent` as a slop-alpha on PyPI, a lightweight command-line agent utilizing the `llm` alpha. It implements file reading, file searching, and shell command execution entirely using Spec-Driven Test-Driven Development.

Impact: Medium

Why it matters

It demonstrates how developer tooling like Claude Code can be used with spec-driven TDD to bootstrap fully functional LLM agents quickly with structured tools.

TL;DR

  • 01The project was built using spec-driven TDD assisted by Claude Code.
  • 02It provides a set of CLI tools to edit, read, list, and search files, and execute shell commands.
  • 03A programmatic Python API is also available to run the coding agent with approval checks.

Key facts

Library version
slop-alpha
Package registry
PyPI
Development methodology
Spec-Driven TDD with Claude Code

Zero-Config CLI Invocation

Using the ultra-fast uv package manager, you can execute the coding agent directly without local installation:

uvx --prerelease=allow --with llm-coding-agent llm code

This command hooks into the default llm config and uses your configured environment API keys (such as OpenAI).

Set of Core Tools

The tool suite implemented in llm-coding-agent covers:

  • CodingTools_edit_file: Performs highly precise, exact-whitespace modifications to target files and outputs a git diff representation.
  • CodingTools_execute_command: Safely runs terminal commands with a standard 120-second timeout window.
  • CodingTools_read_file: Operates like cat -n, accepting offset and line limit parameters to prevent token-burn on massive target logs.

Programmatic Python Interface

For developers looking to integrate autonomous coding behaviors inside custom workflows, the library exports a simple class API:

from llm_coding_agent import CodingAgent
agent = CodingAgent(model="gpt-5.5", root="/workspace", approve=True)
agent.run("Fix the failing test in tests/test_parser.py")

✓ When to use

  • When trying out spec-driven development methodologies or rapid, agentic code drafting via CLI.

✕ When NOT to use

  • When you require a fully mature, production-ready enterprise workflow engine with zero intervention required.

What to do today

  • →Run the library with `uvx` and experiment with test workflows using the `--yolo` flag.
#llm-coding-agent#Claude Code#llm#uv
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