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Build Your Own AI Coding Assistant: A Step-by-Step Guide with Claude API

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Build Your Own AI Coding Assistant: A Step-by-Step Workshop

Welcome to this exciting technical workshop where you’ll build your own AI-powered programming assistant from scratch! Whether you’re new to artificial intelligence or have some experience, this workshop will guide you through creating increasingly sophisticated versions of your assistant, culminating in a powerful local development tool.

Imagine having an assistant that understands your programming needs, reads your code files, executes system commands, and even helps modify your code—all built with your own hands. This workshop provides clear guidance and examples for every step of the process.

What You’ll Master in This Workshop

Upon completion, you’ll deeply understand how to:

  • Connect to the Anthropic Claude API and interact with advanced large language models
  • Build fundamental AI chatbot capabilities
  • Add practical tools like file reading, code editing, and command execution
  • Handle tool requests and error scenarios
  • Construct an agent that grows smarter with each iteration

What We’re Building: Six Progressively Enhanced Versions

You’ll build six versions of your programming assistant, each adding new capabilities:

  1. Basic Chat — Simple conversation with Claude
  2. File Reader — Read code file contents
  3. File Explorer — List files in directories
  4. Command Runner — Execute safe Shell commands
  5. File Editor — Modify and create files
  6. Code Searcher — Pattern-based codebase searching
graph LR
    subgraph "Application Evolution Path"
        A[Basic Chat] --> B[Add File Reading]
        B --> C[Add Directory Browsing]
        C --> D[Add Shell Commands]
        D --> E[Add File Editing]
        E --> F[Add Code Search]
    end
    
    subgraph "Tool Capability Growth"
        G[No Tools] --> H[Read Files]
        H --> I[Read Files<br/>Browse Directories]
        I --> J[Read Files<br/>Browse Directories<br/>Execute Commands]
        J --> K[Read Files<br/>Browse Directories<br/>Execute Commands<br/>Edit Files]
        K --> L[Read Files<br/>Browse Directories<br/>Execute Commands<br/>Edit Files<br/>Search Code]
    end
    
    A -.-> G
    B -.-> H
    C -.-> I
    D -.-> J
    E -.-> K
    F -.-> L

After completing these six steps, you’ll have a fully-featured local development assistant that genuinely understands your needs and helps accomplish programming tasks.

Understanding How Intelligent Assistants Work

Each intelligent assistant follows a similar workflow we call the “event loop”—the heartbeat of your assistant.

  1. Waits for your input question or instruction
  2. Sends the input to the Claude model
  3. Claude may respond directly or request to use a tool
  4. The assistant runs the appropriate tool (like reading a file)
  5. Returns the tool execution results to Claude
  6. Claude provides the final answer based on the results
graph TB
    subgraph "Intelligent Assistant Architecture"
        A[Assistant Core] --> B[Anthropic Client]
        A --> C[Tool Registry]
        A --> D[Get User Message 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 History]
        I --> J[Run Inference]
        J --> K[Claude Response]
        K --> L{Tool Use Required?}
        L -->|No| M[Display Text Response]
        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

The beauty of this architecture lies in its extensibility—you can easily add new tools without changing the core logic.

Preparation Before Starting

Environment Requirements

Before beginning, ensure you have:

Configure Your Environment

Option 1: Recommended Using Devenv

devenv shell  # Load all required environments

Option 2: Manual Setup

# Ensure Go is installed
go mod tidy

Set Up API Key

export ANTHROPIC_API_KEY="your-api-key"

After setup, verify with:

echo $ANTHROPIC_API_KEY

If your key displays correctly, the environment variable is properly set.

Starting with the Basics: Building a Chatbot

Step 1: chat.go — Basic Chat Functionality

We begin with a simple chatbot capable of basic conversation with Claude.

Run with:

go run chat.go

Try asking: “Hello!” or “What can you help me with?”

To understand the underlying mechanics, add the --verbose flag for detailed logs:

go run chat.go --verbose

This foundational version, while simple, forms the core of the entire system. It establishes connection with the Claude API and handles basic conversational interactions.

Gradually Adding Practical Tools

Step 2: read.go — File Reading Capability

Now, let’s empower Claude with file reading ability. This feature is particularly useful for code analysis.

Run with:

go run read.go

Try the command: “Read fizzbuzz.js”

You’ll discover Claude can now read specified file contents and respond based on that content. This functionality lays the groundwork for more complex operations.

Step 3: list_files.go — Directory Browsing Functionality

To help Claude better understand your project structure, we add directory browsing capability.

Run with:

go run list_files.go

Try these commands:

  • “List all files in this folder”
  • “What’s inside fizzbuzz.js?”

Your assistant can now not only read individual files but also explore entire directory structures, greatly aiding project architecture comprehension.

Step 4: bash_tool.go — Shell Command Execution

This version adds Shell command execution capability, significantly expanding your assistant’s functional range.

Run with:

go run bash_tool.go

Try these commands:

  • “Run git status”
  • “List all .go files using bash”

Note that while this feature is powerful, use it cautiously and only run commands you trust.

Step 5: edit_tool.go — File Editing Functionality

Now your assistant gains code modification capability! It can create files, edit existing files, and perform various code changes.

Run with:

go run edit_tool.go

Try these commands:

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

This feature is particularly suitable for automating repetitive code modification tasks or quickly creating template files.

Step 6: code_search_tool.go — Code Search Functionality

The final tool adds powerful code search capability using ripgrep for pattern matching.

Run with:

go run code_search_tool.go

Try these commands:

  • “Find all function definitions in Go files”
  • “Search for TODO comments”

This functionality proves extremely useful for quickly locating specific code patterns in large codebases.

Workshop Sample Files

To help test each functionality, the workshop includes several sample files:

  1. fizzbuzz.js: For testing file reading and editing capabilities
  2. riddle.txt: An interesting text file to explore the assistant’s comprehension
  3. AGENT.md: Documentation containing project environment information

These files provide immediate testing materials, allowing you to experience each new feature right away.

Frequently Asked Questions

What if the API key doesn’t work?

  • Ensure proper environment variable setup: echo $ANTHROPIC_API_KEY
  • Check your quota status on the Anthropic console

What about Go language errors?

  • Run go mod tidy to ensure correct dependencies
  • Confirm you’re using Go 1.24.2 or later

Tool execution errors?

  • Use the --verbose flag to view complete error logs
  • Check file paths and permission settings

Environment configuration issues?

  • Use devenv shell to avoid most environment configuration problems

Deep Dive into Tool Mechanics

Tools function like plugin systems for intelligent assistants. Each tool contains three core components:

  • Name: Tool identifier, such as read_file
  • Input Schema: Information structure the tool requires
  • Execution Function: The tool’s specific implementation logic

In Go language, tool definitions look like this:

var ToolDefinition = ToolDefinition{
    Name:        "read_file",
    Description: "Read file contents",
    InputSchema: GenerateSchema[ReadFileInput](),
    Function:    ReadFile,
}

Schema generation utilizes Go’s struct features, making definition and reuse straightforward. This design makes adding new tools intuitive and efficient.

Learning Path Guidance

To maximize learning effectiveness, we recommend progressing through these stages:

Stage Key Focus Areas
Phase 1 chat.go: Master API integration and response handling
Phase 2 read.go: Understand tool systems and schema generation
Phase 3 list_files.go: Learn multi-tool collaboration and filesystem operations
Phase 4 bash_tool.go: Master Shell execution and error capture
Phase 5 edit_tool.go: Understand file editing and safety checks
Phase 6 code_search_tool.go: Learn pattern searching and ripgrep integration

Each stage builds upon the previous one, ensuring you progressively master all concepts.

Developer Environment Optimization (Optional)

If you choose to use devenv, it provides:

  • Multi-language support: Go, Node, Python, Rust, .NET
  • Git and other development tools
  • Consistent environment configuration

Common commands:

devenv shell   # Load complete environment
devenv test    # Run test checks
hello          # Greeting script (example)

Using devenv avoids “it works on my machine” problems, ensuring environment consistency.

Advanced Directions After Completion

After completing this workshop, consider expanding your intelligent assistant:

  • Custom tools: Such as API callers, web crawlers, etc.
  • Tool chains: Enable tools to execute complex tasks sequentially
  • Memory functionality: Remember important information across sessions
  • Web user interface: Create more friendly interaction interfaces
  • Multi-model integration: Connect other AI models and services

These expansion directions can make your assistant more powerful and personalized.

Workshop Content Summary

This practical workshop helps you:

  • Deeply understand intelligent assistant architecture design
  • Learn methodologies for building practical AI assistants
  • Master complex system development through progressive enhancement
  • Practice combining Claude API with Go language applications

Most importantly, you’ve not only learned how to build a programming assistant but also mastered the general pattern of integrating large language models with practical tools. This pattern can apply to various scenarios, from document processing to data analysis, from automation scripts to intelligent decision support.

You now possess the foundational capabilities to build intelligent assistants. Whether for improving personal work efficiency or developing collaborative tools for teams, this knowledge and experience will open new doors.

Begin your building journey! During practice, you’ll discover more interesting application scenarios and optimization directions. If you encounter problems or have new ideas during practice, feel free to share and discuss with the community.

Wishing you abundant收获 in this exploration process as you build truly useful intelligent programming partners!

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