TL;DR
An author demonstrates how a simple conversational coding agent can be implemented in roughly 200 lines of Python. The example centers on a looped interaction between an LLM and three simple tools—read, list, and edit files—where the program executes tool calls the model requests.
What happened
The piece breaks down a minimal coding assistant into a clear mental model and a compact implementation. The agent treats the LLM as a conversational controller that requests actions; the host program supplies three tools (read a file, list directory contents, and edit/create files), executes those tool calls, and returns structured results back to the model. Tool behavior is documented via function signatures and docstrings that are presented to the LLM inside a generated system prompt. The implementation includes a parser for single-line 'tool: NAME({…})' calls, a thin wrapper for the LLM API (the example uses an Anthropic client calling a named Claude model), and a read/evaluate/respond loop that chains multiple tool calls until the LLM returns a normal assistant response. The author contrasts this concise demo with production agents, which add robustness and extra capabilities.
Why it matters
- Shows the core interaction pattern underlying coding assistants: the LLM requests actions, the host executes them, and results feed back into the conversation.
- Demonstrates that useful coding agents can be implemented with a small, auditable codebase rather than opaque systems.
- Clarifies how explicit tool descriptions and structured tool calls enable the LLM to reason about file operations.
- Highlights the gap between minimal working examples and production offerings that require more error handling and UX features.
Key facts
- Mental model: user message → LLM outputs structured tool calls → host runs tools → results returned → LLM continues.
- Three core tools in the example: read_file (returns file contents), list_files (returns directory listing), edit_file (creates or replaces text).
- Tool functions return dictionaries so the LLM receives structured results to continue reasoning.
- Tool registry maps tool names to functions; a helper composes docstrings and signatures into a system prompt for the LLM.
- Tool call format expected from the LLM is a single line: 'tool: TOOL_NAME({"arg": "value"})'.
- A parser extracts tool invocations by scanning response lines that start with 'tool:' and parsing compact JSON.
- The LLM wrapper in the example uses an Anthropic client and calls model 'claude-sonnet-4-20250514' with system and messages.
- The runtime loop continues calling the LLM and executing requested tools until the model responds without further tool requests.
- The example is described as roughly 200 lines of Python; production systems add features like better error handling and streaming responses.
What to watch next
- Production agents typically add more tools (e.g., grep, bash, web search) and more robust fallback/error behaviors; the example notes these differences.
- Streaming responses and improved UX are cited as production upgrades beyond the minimal loop demonstrated in the example.
- not confirmed in the source
Quick glossary
- LLM: A large language model: a neural network trained on large text corpora that can generate and reason about language.
- Tool call: A structured request from the model indicating the host program should execute a specific function with JSON arguments.
- Docstring: An inline function description used to explain a tool's behavior; here it helps the LLM decide which tool to use.
- System prompt: A message given to the LLM that defines behavior, available tools, and the expected tool-call format.
- Tool registry: A mapping from tool names to the host program's functions so tool invocations can be looked up and executed.
Reader FAQ
Is this implementation production-ready?
The source presents a compact demo and notes that production tools add error handling, streaming, and additional capabilities.
Does the LLM directly modify files?
No. The LLM emits structured tool calls; the host program performs filesystem operations and returns results to the model.
Which API and model does the example use?
The example code shows an Anthropic client and calls model 'claude-sonnet-4-20250514'.
Is the '200 lines' claim exact?
The author describes the implementation as 'about 200 lines' 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 …
- shareAI-lab/learn-claude-code: How can we build a true AI …
- Building an AI Agent From Scratch Using the Anthropic API
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