A step-by-step workshop that teaches you how to build your own AI-powered coding assistant, starting from a basic chatbot and progressively adding powerful tools like file reading, shell command execution, and code search.
One-Minute Overview#
This is a hands-on workshop that guides you through building your own AI coding assistant project. You'll start with a basic chatbot and progressively build 6 versions of the assistant, each adding new capabilities. This project is aimed at developers who want to understand AI agent architecture without needing AI expertise—just follow the steps to complete.
Core Value: Through progressive learning, master the complete process of building an AI coding assistant.
Quick Start#
Installation Difficulty: Low - The project provides detailed setup instructions, with devenv recommended to simplify environment configuration
# Recommended installation
devenv shell # Load the required environment
# Or manual setup
go mod tidy
Is this suitable for me?
- ✅ Developers learning AI coding assistant architecture: The project offers a clear progression path, perfect for understanding how AI agents work
- ✅ Go developers: Implemented in Go, making the code structure easy to understand
- ❌ Users needing production-ready solutions: This is an educational project, not a ready-to-use product
- ❌ Developers seeking specific feature integrations: The project's primary purpose is education, with limited functionality
Core Capabilities#
1. Build 6 versions of a coding assistant - from simple to complex#
Start with basic chat functionality and add new capabilities with each iteration, ending with a powerful local developer assistant Actual Value: Through progressive learning, understand the design principles of AI agent architecture and tool systems
2. Practical tool system implementation#
Includes core functions like file reading, directory browsing, command execution, file editing, and code search Actual Value: These tools are essential for modern coding assistants, and you can directly apply these implementations to your own projects
3. Clear event loop architecture#
Demonstrates the core workflow of an AI agent: receive user input, send to AI, execute tools, return results Actual Value: Understanding this architecture is crucial for building any AI agent and forms a scalable foundation
4. Using Go language and Anthropic API#
Leverages Go's strong typing and concurrency features combined with Claude's powerful capabilities Actual Value: Go is suitable for building high-performance systems, and this project shows how to integrate modern AI capabilities into traditional programming languages
5. Rich examples and test files#
Includes test files like fizzbuzz.js and riddle.txt for practicing various tool functions Actual Value: No need to prepare a test environment—start experimenting and practicing right away
Tech Stack & Integration#
Development Languages: Go, Nix, Makefile, Shell Main Dependencies: Anthropic API, ripgrep (for code search) Integration Method: Runs as a standalone application
Maintenance Status#
- Development Activity: The project is actively maintained with clear documentation and examples
- Recent Updates: Recently updated with a complete workshop guide
- Community Response: Provides troubleshooting guides for common usage scenarios, showing the project considers real-world use cases
Documentation & Learning Resources#
- Documentation Quality: Comprehensive
- Official Documentation: https://ghuntley.com/agent/
- Example Code: Extensive, including implementations of 6 different versions