Build Your Own: From Reader to Builder

What You've Learned

Over the last 7 chapters, you've seen the full picture of Claude Code:

These aren't Claude Code-specific ideas — they're the building blocks of any AI Agent system.

50 Lines of Code: A Minimal Agent

Here's a working minimal AI Agent. It implements the core loop from Claude Code — call the model, run tools, loop until done:

import anthropic, subprocess, json

client = anthropic.Anthropic()

tools = [
    {
        "name": "bash",
        "description": "Run a bash command and return stdout",
        "input_schema": {
            "type": "object",
            "properties": {"command": {"type": "string"}},
            "required": ["command"]
        }
    },
    {
        "name": "read_file",
        "description": "Read a file and return its contents",
        "input_schema": {
            "type": "object",
            "properties": {"path": {"type": "string"}},
            "required": ["path"]
        }
    }
]

def run_tool(name, args):
    if name == "bash":
        r = subprocess.run(args["command"], shell=True,
                          capture_output=True, text=True, timeout=30)
        return r.stdout + r.stderr
    if name == "read_file":
        return open(args["path"]).read()
    return "Unknown tool"

def agent(task):
    messages = [{"role": "user", "content": task}]

    while True:
        resp = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=4096,
            tools=tools,
            messages=messages,
        )
        # Collect assistant response
        messages.append({"role": "assistant", "content": resp.content})

        # No more tool calls -> done
        if resp.stop_reason == "end_turn":
            return [b.text for b in resp.content if hasattr(b, "text")]

        # Execute tool calls and feed results back
        results = []
        for block in resp.content:
            if block.type == "tool_use":
                output = run_tool(block.name, block.input)
                results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": output
                })
        messages.append({"role": "user", "content": results})

# Try it
for line in agent("List the files in the current directory"):
    print(line)

This IS Claude Code's core

Look at that while True loop — it's the skeleton of Claude Code's 40,000+ lines of code. The difference is just more tools, permission checks, UI, and context management.

You can write the Agent core in less time than it takes to finish a cup of coffee.

Which Model Should You Use?

You don't have to use Claude. Any model that supports tool use / function calling works:

Approach	Models	Cost	Quality
Anthropic API	Claude Sonnet / Opus	Per-token pricing	Best for tool use
OpenAI API	GPT-4o / o3	Per-token pricing	Very good
Local models	Qwen, DeepSeek, Llama	Free (needs GPU)	Usable, weaker tool calling
API aggregators	Various platforms	Budget pricing	Depends on underlying model

You don't need to run models locally. Most people call cloud APIs. Local models still lag behind in tool use accuracy.

How Is This Different from Existing Tools?

	Claude Code	Cursor	Cline	Build Your Own
Who drives	AI drives, you supervise	You drive, AI assists	Flexible	You decide
Models	Claude only	Multi-model	Any model	Any model
Interface	Terminal	IDE	IDE extension	You design it
Customization	Moderate	Limited	Moderate	Total freedom
Data privacy	Cloud	Cloud	You control	You control
Cost	From $20/mo	From $20/mo	API costs only	API costs only

Building your own isn't about making "a better Cursor." It's about:

1. Learning — Understanding Agent architecture is the hottest skill in tech right now
2. Specialization — Build tools focused on your domain (data analysis, DevOps, docs, etc.)
3. Control — Own your data flow and costs completely
4. Product — Turn it into a product serving a specific audience

Three Levels of Project Ideas

Beginner: Personal CLI Assistant

Expand the 50-line code above:

• Add file editing tools (Write, Edit)
• Add basic permission confirmation before bash commands
• Add conversation history save/restore
• Use Rich (Python) or Ink (Node.js) for a polished terminal UI

Intermediate: Domain-Specific Agent

Pick a domain you know well and build a specialized tool:

• Data Analysis Agent: tools for CSV reading, SQL execution, chart generation
• DevOps Agent: tools for service health checks, log reading, service restarts
• Documentation Agent: tools for Markdown parsing, TOC generation, link checking
• Code Review Agent: tools for reading PR diffs, checking conventions, generating reviews

The key is tool design — what tools you give the Agent defines what it can do.

Advanced: Agent Platform

Bigger ideas:

• MCP Tool Marketplace — A discovery and distribution platform for MCP servers
• Agent Debugger — Visualize an Agent's execution trace for debugging
• Multi-Agent Framework — Inspired by Claude Code's Task tool and Teams system

Suggested Learning Path

Phase	Goal	Resources
Week 1	Get the minimal Agent running	The 50-line code in this chapter
Week 2	Add 3-5 tools + basic permissions	Reference: tools/ directory in source
Week 3	Add streaming output + context compaction	Reference: services/compacting/ in source
Week 4	Build a domain-specific Agent MVP	Your own idea

Further Resources

Official

• Anthropic Engineering Blog: Best Practices
• Claude Code Documentation

Community Analysis

• Inside Claude Code's Architecture — Deep English architecture analysis
• Architecture & Internals Guide — Most comprehensive architecture doc
• Claude Code Reverse Engineering — Runtime behavior analysis
• Claude Code Source Analysis — Chapter-by-chapter series

Hands-On Tutorials

• Build Your Own in Python (250 Lines)
• Claude Agent SDK TUI
• OpenCode — Open-source terminal AI Agent

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One Last Thing

Claude Code's architecture teaches us a counterintuitive lesson: when the AI model is powerful enough, the best framework is no framework. A while loop + good tools + good prompts is all you need for a production-grade AI Agent.

Now it's your turn.