Noureddine RAMDI / Building an AI coding assistant in Go: Inside the "how-to-build-a-coding-agent" workshop

Created Sat, 23 May 2026 20:41:14 +0000 Modified Sat, 23 May 2026 20:41:27 +0000
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github-stars go ai coding-agent anthropic workshop event-loop

ghuntley/how-to-build-a-coding-agent

Building an AI coding assistant isn’t just about calling an API — it’s about orchestrating a cycle of reasoning, tool invocation, and iterative refinement until you get a useful answer. The “how-to-build-a-coding-agent” GitHub repository by ghuntley is a hands-on workshop that reveals this core mechanism through progressive Go code examples. Starting from a simple chatbot, it builds up to a full-featured coding agent that can read files, explore directories, run shell commands, edit code, and search projects.

architecture and core features of the coding agent workshop

This repository is structured as a step-by-step Go workshop that constructs an AI coding assistant in six stages. It begins with chat.go, a barebones chatbot that connects to the Anthropic Claude API for simple conversational inference. Each subsequent stage adds a new tool with a specific capability:

  • File reading (read.go)
  • Directory listing (list_files.go)
  • Safe shell command execution (bash_tool.go)
  • File editing (edit_tool.go)
  • Code searching powered by ripgrep (code_search_tool.go)

The architecture centers on an event loop that acts as the agent’s heartbeat. Here’s how it works: user input is sent to Claude for inference, which may include requests to use specific tools. These tool-use requests are dispatched by name from a shared tool registry implemented as Go structs with a generic schema helper (GenerateSchema[T]). The results from tool executions are then fed back into the conversation, and the loop repeats until Claude produces a final textual response.

This pattern mimics the operational flow in commercial coding assistants like Cursor or OpenCode but in a much simpler and transparent form. By progressively adding tools, the code illustrates how to extend the agent’s capabilities without complicating the core loop.

The stack is straightforward: Go 1.24.2+ is required, with no external dependencies besides the Anthropic API. The project includes sample files such as fizzbuzz.js, riddle.txt, and AGENT.md to facilitate immediate experimentation.

modular tool registry and event loop: the technical backbone

What sets this repo apart is its clean, modular approach to tool integration. Each tool is defined as a Go struct paired with a generic schema generator, enabling type-safe and reusable code for tool definition and invocation. This design encourages adding new tools without altering the event loop logic.

The event loop itself is a practical demonstration of how a real coding agent operates under the hood. Instead of a one-shot API call, the loop processes multiple cycles of:

  1. Sending user input or tool results to Claude
  2. Interpreting Claude’s response for tool-use commands
  3. Executing requested tools from the registry
  4. Feeding results back into the conversation

This cycle continues until a final answer is reached. It effectively decouples reasoning (LLM inference) from action (tool execution), enabling a cooperative interplay.

The tradeoff here is simplicity versus production readiness. The code is deliberately educational, favoring clarity over robustness or performance. There’s no concurrency, caching, or retry logic. Tool execution is sequential and synchronous, which is fine for learning but insufficient for production scenarios requiring scalability or fault tolerance.

quick start with the agent examples

prerequisites

  • Go 1.24.2+ or devenv (recommended for easy setup)
  • An Anthropic API key

setting up your environment

Option 1: Recommended (using devenv)

devenv shell  # Loads everything you need

Option 2: Manual setup

# Make sure Go is installed
go mod tidy

add your API key

export ANTHROPIC_API_KEY="your-api-key-here"

running the agent stages

Start from the simplest example:

go run chat.go

Try saying “Hello!” and observe the chatbot’s response. Add --verbose to see detailed logs.

Add file reading capabilities:

go run read.go

Try: “Read fizzbuzz.js”

Explore directories:

go run list_files.go

Try: “List all files in this folder” or “What’s in fizzbuzz.js?”

Run safe terminal commands:

go run bash_tool.go

Try: “Run git status” or “List all .go files using bash”

Edit files programmatically:

go run edit_tool.go

Try: “Create a Python hello world script” or “Add a comment to the top of fizzbuzz.js”

Search code with ripgrep:

go run code_search_tool.go

Try: “Find all function definitions in Go files” or “Search for TODO comments”

Sample files included (fizzbuzz.js, riddle.txt, AGENT.md) help you test these capabilities out of the box.

verdict: a practical workshop for understanding AI coding agents

“how-to-build-a-coding-agent” is a valuable resource for developers wanting a transparent and hands-on explanation of AI coding assistants. It clearly demonstrates the core event loop architecture that distinguishes a true agent from a simple prompt wrapper.

Its modular tool registry pattern shows how to extend agent functionality cleanly. The progressive approach lowers the barrier to understanding complex interactions between language models and external tools.

That said, it’s not a production-ready framework. It lacks concurrency, error handling, security considerations, and scalability features needed for real-world deployment. But it’s not meant to be — its strength lies in education and demystification.

If you want to understand how tools like Cursor or OpenCode might work under the hood, or if you’re exploring how to build your own AI coding assistant from scratch, this repo offers a clear, code-first path. The Go code is simple and idiomatic, making it accessible if you have a basic Go background.

The repo also serves as a good starting point to build upon, if you want to experiment with adding more sophisticated tools, parallelism, or better state management.

In short, “how-to-build-a-coding-agent” is worth exploring for anyone curious about the mechanics of AI coding agents, especially those comfortable with Go who want to see these concepts in action rather than theory.

→ GitHub Repo: ghuntley/how-to-build-a-coding-agent ⭐ 5,598 · Go