Welcome! 👋 This workshop will guide you through building your own AI-powered coding assistant — starting from a basic chatbot, and adding powerful tools like file reading, shell command execution, and code searching.
You don’t need to be an AI expert. Just follow along and build step-by-step!
🌐 Want a detailed overview? Check out the blog post: ghuntley.com/agent
By the end of this workshop, you’ll understand how to:
- ✅ Connect to the Anthropic Claude API
- ✅ Build a simple AI chatbot
- ✅ Add tools like reading files, editing code, and running commands
- ✅ Handle tool requests and errors
- ✅ Build an agent that gets smarter with each step
You’ll build 6 versions of a coding assistant.
Each version adds more features:
- Basic Chat — talk to Claude
- File Reader — read code files
- File Explorer — list files in folders
- Command Runner — run shell commands
- File Editor — modify files
- Code Search — search your codebase with patterns
graph LR
subgraph "Application Progression"
A[chat.go<br/>Basic Chat] --> B[read.go<br/>+ File Reading]
B --> C[list_files.go<br/>+ Directory Listing]
C --> D[bash_tool.go<br/>+ Shell Commands]
D --> E[edit_tool.go<br/>+ File Editing]
E --> F[code_search_tool.go<br/>+ Code Search]
end
subgraph "Tool Capabilities"
G[No Tools] --> H[read_file]
H --> I[read_file<br/>list_files]
I --> J[read_file<br/>list_files<br/>bash]
J --> K[read_file<br/>list_files<br/>bash<br/>edit_file]
K --> L[read_file<br/>list_files<br/>bash<br/>code_search]
end
A -.-> G
B -.-> H
C -.-> I
D -.-> J
E -.-> K
F -.-> L
At the end, you’ll end up with a powerful local developer assistant!
Each agent works like this:
- Waits for your input
- Sends it to Claude
- Claude may respond directly or ask to use a tool
- The agent runs the tool (e.g., read a file)
- Sends the result back to Claude
- Claude gives you the final answer
We call this the event loop — it's like the agent's heartbeat.
graph TB
subgraph "Agent Architecture"
A[Agent] --> B[Anthropic Client]
A --> C[Tool Registry]
A --> D[getUserMessage Function]
A --> E[Verbose Logging]
end
subgraph "Shared Event Loop"
F[Start Chat Session] --> G[Get User Input]
G --> H{Empty Input?}
H -->|Yes| G
H -->|No| I[Add to Conversation]
I --> J[runInference]
J --> K[Claude Response]
K --> L{Tool Use?}
L -->|No| M[Display Text]
L -->|Yes| N[Execute Tools]
N --> O[Collect Results]
O --> P[Send Results to Claude]
P --> J
M --> G
end
subgraph "Tool Execution Loop"
N --> Q[Find Tool by Name]
Q --> R[Execute Tool Function]
R --> S[Capture Result/Error]
S --> T[Add to Tool Results]
T --> U{More Tools?}
U -->|Yes| Q
U -->|No| O
end
- Go 1.24.2+ or devenv (recommended for easy setup)
- An Anthropic API Key
Option 1: Recommended (using devenv)
devenv shell # Loads everything you need
Option 2: Manual setup
# Make sure Go is installed
go mod tidy
export ANTHROPIC_API_KEY="your-api-key-here"
A simple chatbot that talks to Claude.
go run chat.go
- ➡️ Try: “Hello!”
- ➡️ Add
--verbose
to see detailed logs
Now Claude can read files from your computer.
go run read.go
- ➡️ Try: “Read fizzbuzz.js”
Lets Claude look around your directory.
go run list_files.go
- ➡️ Try: “List all files in this folder”
- ➡️ Try: “What’s in fizzbuzz.js?”
Allows Claude to run safe terminal commands.
go run bash_tool.go
- ➡️ Try: “Run git status”
- ➡️ Try: “List all .go files using bash”
Claude can now modify code, create files, and make changes.
go run edit_tool.go
- ➡️ Try: “Create a Python hello world script”
- ➡️ Try: “Add a comment to the top of fizzbuzz.js”
Use pattern search (powered by ripgrep).
go run code_search_tool.go
- ➡️ Try: “Find all function definitions in Go files”
- ➡️ Try: “Search for TODO comments”
fizzbuzz.js
: for file reading and editingriddle.txt
: a fun text file to exploreAGENT.md
: info about the project environment
API key not working?
- Make sure it’s exported:
echo $ANTHROPIC_API_KEY
- Check your quota on Anthropic’s dashboard
Go errors?
- Run
go mod tidy
- Make sure you’re using Go 1.24.2 or later
Tool errors?
- Use
--verbose
for full error logs - Check file paths and permissions
Environment issues?
- Use
devenv shell
to avoid config problems
Tools are like plugins. You define:
- Name (e.g.,
read_file
) - Input Schema (what info it needs)
- Function (what it does)
Example tool definition in Go:
var ToolDefinition = ToolDefinition{
Name: "read_file",
Description: "Reads the contents of a file",
InputSchema: GenerateSchema[ReadFileInput](),
Function: ReadFile,
}
Schema generation uses Go structs — so it’s easy to define and reuse.
Phase | What to Focus On |
---|---|
1 | chat.go : API integration and response handling |
2 | read.go : Tool system, schema generation |
3 | list_files.go : Multiple tools, file system |
4 | bash_tool.go : Shell execution, error capture |
5 | edit_tool.go : File editing, safety checks |
6 | code_search_tool.go : Pattern search, ripgrep |
If you use devenv
, it gives you:
- Go, Node, Python, Rust, .NET
- Git and other dev tools
devenv shell # Load everything
devenv test # Run checks
hello # Greeting script
Once you complete the workshop, try building:
- Custom tools (e.g., API caller, web scraper)
- Tool chains (run tools in a sequence)
- Memory features (remember things across sessions)
- A web UI for your agent
- Integration with other AI models
This workshop helps you:
- Understand agent architecture
- Learn to build smart assistants
- Grow capabilities step-by-step
- Practice using Claude and Go together
Have fun exploring and building your own AI-powered tools! 💻✨
If you have questions or ideas, feel free to fork the repo, open issues, or connect with the community!