TL;DR
A short tutorial demonstrates that a usable coding assistant can be implemented in roughly 200 lines of Python by wrapping a large language model with three simple filesystem tools. The design relies on an LLM issuing structured tool calls while a local program executes those calls and feeds results back into the conversation loop.
What happened
An explainer walks through building a minimal coding agent that mirrors core behaviors of commercial assistants. The design frames the interaction as a conversational loop: the user speaks, the LLM decides when a tool is needed and emits a single-line, structured tool call, the host program runs the requested tool (read, list or edit files), then returns the tool result to the LLM so it can continue. The author provides compact Python examples: utilities to resolve paths, three tool implementations (read_file, list_files, edit_file), a tool registry generated from function docstrings and signatures, a parsing routine for detecting tool calls, and a thin API wrapper that calls an Anthropic Claude model. An outer loop collects user input and an inner loop repeatedly invokes the LLM and executes any requested tools until the model responds without asking for more actions. The writeup contrasts this minimal agent with production systems that add more robustness and UX features.
Why it matters
- Demonstrates that key functionality of coding assistants can be implemented with a small amount of code rather than opaque infrastructure.
- Clarifies the separation of responsibilities: the LLM reasons and requests actions; local code executes filesystem operations.
- Provides a lightweight template developers can extend with additional tools or policies.
- Highlights how structured tool calls enable deterministic bridging between model outputs and program actions.
Key facts
- The minimal agent centers on three tools: read_file, list_files and edit_file.
- Tool docstrings and function signatures are used to generate the LLM-facing tool descriptions.
- The system prompt instructs the model to issue tool calls in the exact format: 'tool: TOOL_NAME({JSON_ARGS})' on a single line.
- Tool responses are wrapped as 'tool_result(…)' messages and appended to the conversation for the LLM to consume.
- A parser extracts tool invocations by scanning assistant output for lines that start with 'tool:' and parsing compact JSON in parentheses.
- The example code uses an Anthropic client and references model 'claude-sonnet-4-20250514' in its LLM wrapper.
- Edit behavior: an empty old_str creates or overwrites a file; otherwise the tool replaces the first occurrence of old_str with new_str.
- The author estimates the full implementation fits at roughly 200 lines of Python.
- The article notes production systems add error handling, streaming responses and smarter context management beyond this minimal design.
What to watch next
- Production-grade agents typically extend this pattern with better error handling and fallback strategies (stated in the source).
- UX features like streaming model output are a common production enhancement (stated in the source).
- Adoption details, security audits, or specific deployment patterns are not confirmed in the source.
Quick glossary
- LLM: A large language model: a neural network trained to generate text given input prompts.
- Tool call: A structured request emitted by the LLM asking the host program to run a specific function with JSON arguments.
- Tool registry: A mapping in code from tool names to the functions that implement them, used to look up and execute requested actions.
- System prompt: A message provided to the model that defines its role and the available tools and calling conventions.
- Agent loop: The control flow that alternates between sending messages to the LLM and executing any tools the model requests.
Reader FAQ
How many lines of code does the example take?
The author describes the working implementation as about 200 lines of Python.
Which tools are required for the agent to edit a project?
The minimal set in the article is three tools: read_file, list_files and edit_file.
Does the LLM directly modify files?
No — the model emits structured tool calls; a local program runs the tools and performs filesystem changes.
Which model does the example call?
The provided wrapper calls an Anthropic Claude model identified as 'claude-sonnet-4-20250514'.
Is this implementation production-ready?
Not confirmed in the source.
The Emperor Has No Clothes: How to Code Claude Code in 200 Lines of Code January 2025 Today AI coding assistants feel like magic. You describe what you want in…
Sources
- How to code Claude Code in 200 lines of code
- Build a Coding Agent from Scratch: The Complete Python …
- From Chaos to Code: Why You Need to Start Embracing …
Related posts
- Robotopia: A 3D, First-Person, Talking Simulator — original headline
- Pre-Commit Lint Checks Are Vibe Coding’s Kryptonite — Lessons Learned
- AI tools autonomously generate and verify a solution to Erdos Problem #728